Cellular Consequences of Genetic variation

Mapping the yeast structural interactome with AlphaFold3: an open call for collaboration

noreply@blogger.com (Pedro Beltrao) — Wed, 04 Mar 2026 10:24:00 +0000

We are excited to announce the early-stage release of our S. cerevisiae structural interactome mapping project. Using AlphaFold3 (AF3), we are systematically predicting the protein-protein interactions and their 3D structures across the yeast proteome.

We are currently about 25–30% of the way through the project. Rather than waiting until the end, we are releasing our data and current benchmarks early. We want to avoid duplicate work in the community, provide researchers with immediate access to high-confidence structural models, and put out an open call for collaborators to help us finish this project.

Predicting structures for all possible protein pair individually is computationally difficult. To facilitate this, we are using the pooling approach that we recently published together with Horia Todor in Carol Gross' lab at UCSF. By packing randomly sampled proteins into pools of up to 5,000 tokens, we can cover the interactome far more efficiently. Surprisingly, in addition to being more time efficient, the pooling approach also results in more accurate confidence scores, perhaps due to some in silico competition reducing the false positive rate.

Current progress and early access

For our yeast interactome, we are currently limiting our target list to a subset of approximately 4,000 proteins that are expressed under standard conditions. By grouping these, we condensed the interactome down to 300,000 unique pools. We attempted still to further optimize the screen and through our benchmarking, we tested the impact of a reduced number of recycles.We found that dropping from 10 recycles to just 3 recycles cuts the compute time in half without a meaningful loss in predictive power. In our tests against STRING scores, the AUC only shifted marginally from 0.85 (10 recycles) to 0.84 (3 recycles). The prediction is based on a corrected ipTM value described in the mycoplasma paper.

We have so far computed close to 30% of the target set corresponding to prediction scores and structures for over 4 million pairs of yeast proteins. We are making the data available through a docker image found in this github page. The docker image creates also a simple web app to go through the list and select individual files but at the moment this release is going to be most useful for those capable of parsing through large scale datasets.

A call for collaborators

While our optimizations have made this project possible, completing the remaining ~70% of the interactome is still a big challenge and we would certainly welcome collaborations. We are looking for labs or institutions with significant access to high-end compute. We also welcome collaborations focused on the downstream biological analysis of these structures and applying the network to specific biological questions.If you have the compute power to help us process the remaining pools, or if you are interested in diving into the analysis, please reach out. We do ask that researchers refrain from publishing proteome-wide or large-scale data mining studies until our formal publication is released.

AI "peer" review - the impact on scientific publishing

noreply@blogger.com (Pedro Beltrao) — Tue, 18 Nov 2025 16:00:00 +0000

It is the first time, in the second half of this year, that I am not trying to urgently deal with something. So, instead of working on some manuscripts from the lab (sorry!), I took some time to look in more detail at the outputs of two recently announced science AI "assistants" dedicated to scientific publishing. The q.e.d. science peer review system and the Nature Research Assistant tool. This was not a very rigorous or quantitative assessment but instead I had a look at the tool's outputs based on 3 manuscripts from our lab - 2 recent preprints and 1 manuscript that we are still working on.

If you haven't tried it yet, q.e.d. tries to identify and list what are the claims made in a scientific paper and then identify any major or minor gaps in these claims. Visually, it is presented as hierarchical tree with a main message for the whole manuscript, main claims and related (sub) claims. It is refreshing and positive to me that they decided to present this in a way that is different from the standard text peer review format but, in essence, this is very much the type of information obtained in a peer review report. In addition, the section "What's new" also provides a description of what the model believes is the most novel about the work and what might have been done in some way by other studies.

Before getting into more details about the output of q.e.d., I also tried the same 3 manuscripts in the Nature Research Assistant tool. This is clearly more conservative in scope and it provides a series of suggestions, primarily focused on improving the text. The tool does provide a list of identified "overstated claims" which comes closer to the idea of finding gaps in scientific claims/statements as done in q.e.d. science.

How good is the output of these AI assistants

Regarding the output of these tools, I am really impressed by the level of detail of q.e.d. For every gap, it has a written explanation of the identified issues and suggestions for additional work or text changes to mitigate the issue. Many of the identified gaps require quite detailed technical knowledge. In one particular example, the tool found a very non-trivial gap in the null model of a statistical test that required knowledge of proteomics, evolution and bioinformatics. The 3 manuscripts are very computational, which the authors indicate is not an area they have focused during development. One of the manuscripts was flagged as being from a domain knowledge that does not fit their current set of domain areas. Still, I could expect to see many of these comments in a human peer review report. Is there something in these gaps that we never considered before, and that I need to absolutely act on? Not really, but that can be said honestly of a significant fraction of all peer-review comments. I would generally rank these AI generated comments as about average. Not among the most useful peer-review comments but certainly better than many we have received over the years.

The output of Nature's research assistant is much more what you would expect of a tool dedicated to improving the text of a manuscript. I think it is most useful to find parts of the text that could benefit from improved clarity. In the way the information is presented it also promotes the author's revising the sections, deciding to use or not the suggestions from the tool, instead of simply feeding the whole text through an LLM. It is more of an assistant than a replacement for writing. I don't think I would give money to a tool like this over say a general LLM chat bot.

For comparison purposes, I tried to recreate the output of q.e.d. using a standard LLM chatbot (Gemini Pro in this case). I took one of the manuscripts and tried to formulate the prompt in the way to get also a list of claims, gaps and suggested changes. The output was not as good as q.e.d. but some of the gaps were the same although it seemed qualitatively a bit more superficial.

AI "peer" review is here to stay

Whether we want it or not, these tools are now reaching a point where they can be used to identify gaps in a scientific manuscript that could pass as a human (peer) review report. There are many ways these tools can be used and abused. The most positive outcome of this might be that authors take advantage of these as assistants to help improve the clarity of the manuscripts before making them public. The most obvious negative outcome is that these will be used as lazy human reviewing just copy-pasted to satisfy the ever growing need to peer-review our ever growing production of scientific papers every year. Given that these reports can be generated quickly, potentially as part of the submission process, it could well be that a good way to preempt the use of these by human peer-reviewers might be that the journal already provides them to the peer-reviewers as part of the request for assessment. This would already make clear that the editor/journal is aware of the things that an automated report would bring up and avoid having the reviewers simply trying to fake a report. Finally, there is also a likely scenario that editors of scientific journals start to integrate these reports as part of their initial editorial decisions. In particular, from the editorial perspective, these tools might end up serving as biased and lazy assessments of novelty and impact.

As a peer-reviewer, I don't think these automated reports would reduce the level of work I need to do. I still would need to spend the time to read through a paper, consider the methods used and try to figure out if there are issues that the authors might have missed and if the claims and interpretation make sense relative to the data. Having such automated reports might be a useful addition as well as having a list of related published papers.

Perhaps one aspect that is not strongly emphasized in q.e.d. but more obvious in Nature's research assistant and even other tools is the connection of a given manuscript with the broader scientific literature. As scientists, I think it is fair to admit that it is hard to be fully aware of all of the work that has been published in a field. Sometimes the connections between our work and existing literature are less obvious because they can happen through analogy and/or shared methods. Surfacing such connections in the process of writing up a manuscript in an easy way would be particularly useful.

Science and scientific publishing in the age of AI slop

We were already drowning in scientific papers before ChatGPT and co. Now there is growing evidence of papers being produced by AI and quite a lot of buzz around the concept of fully automated AI scientists. So it is unfortunately unavoidable that this is going to translate to an even stronger increase in the number of publications and added pressures to the scientific publishing system. One optimistic take on this is that the added publications will be easy to ignore crap that won't affect our productivity but it is at least likely to result in more added wasted money being spent feeding the already rich publishing industry. Unfortunately, I think this will also hurt attempts to move away from our expensive and inefficient traditional publishing system when scientists worry more about "high-quality" science. The current (bad) proxies for quality (i.e. high impact factor journals) can't be easily changed to something else in an environment where many scientists will rightfully be even more worried about scientific rigor.

Why do we still publish in scientific journals ?

noreply@blogger.com (Pedro Beltrao) — Wed, 23 Jul 2025 07:21:00 +0000

We publish in scientific journals to disclose our discoveries, such that others can build upon them. But we now have preprint servers and we can quickly make our discoveries available to others. So maybe we publish in scientific journals because we value the peer review that is organized by them. However, we also have now journal independent peer review systems, like Review Commons, which allow us to perform peer review on top of preprints, in a way that does not require subsequent submission to a scientific journal. So why do we still publish in scientific journals ?

Once in a while someone online complains about the cost of open access publication fees, the so called article processing charge (APC). Looking at this simplistically, it does seem ridiculous that a journal might ask the authors $5-10k USD to publish a paper when all the work is apparently done by scientists that write and review the articles. Of course, this APC cost is a lot more complicated than this and there is an historical context and background knowledge that is needed to discuss these. In reality, a lot of the cost goes into sustaining the editorial salaries of journals with high rejection rates. I covered this in detail in a previous blog post discussing the costs from EMBO Press. In addition to the editorial salary costs for journals with high rejection rates, we also don't have a free market since we don't pick journals based on price and service quality but on how publishing in certain journals will be perceived by others.

So, for many reasons, the major costs of scientific publishing are not the act of peer review and making knowledge public. If I had to guess, the actual costs publishing a peer-review article with near 0% rejection rate would be below $500USD per paper if done in high volume. The main costs of publishing are primarily the costs linked to the system of filtering scientific publications into tiers of perceived "impact". It was, for a long time, nearly impossible to evolve scientific publishing and I have argued for almost 20 years that we needed to split the publishing process into modular bits that would allow for much more innovation. With the rise of preprints, social media and dedicated peer-review services, I think we now could work towards getting rid of scientific journals. Or at least, we now have a clear direction of focus on what is missing in this potential alternative system - a new reward infrastructure.

The reward infrastructure in science

So why do we still publish in scientific journals ? The reality is that people still want to chase high impact journals. Pretending that we don't is not going to change anything. Despite having tenure and secure funding for my group, I feel that I cannot stop trying to publish in some journals because of what it means for the career of my lab members; for how my peers perceive and evaluate our work; for establishing new collaborations and applying for additional funding. So how are we going to change this and what could the consequences be ?

Unfortunately, there is no incentive for any single individual to change the reward system. At least as of now, this would require a large number of labs within a sub-field to jointly commit to a change in practice, perhaps assisted by some external entity. We could assume that social media, conferences and recommendation engines (Google Scholar) are enough to spread knowledge and that within a specific sub-field it is possible to evaluate each other without the need for journal proxies. I am not sure this is really true but if we accepted this, then a number of labs in a field could commit to no longer publishing in scientific journals. This could be assisted by, at the same time, creating an overlay journal of their field where academic editors would select a subset of peer-reviewed preprints that represent some particularly strong advance in the field.

Unfortunately, this idea is unlikely to work because it relies on collective action by a majority of groups within a field. I don't have better ideas but this is for me the last barrier remaining. We still need to work out how we would pay for the peer-review service but ideas that would help change the reward system in a way that do not require collective action are now what is needed.

What could go wrong if it happened

Despite all that we complain about in our current system of tiered journals, they do aim to improve science. They might not work as intended but they aim to filter science by accuracy and perceived value to others. If we managed to get rid of these things, we could have an even worse problem with the sheer number and quality of scientific outputs. As an almost anecdotal evidence, our group has become at lot worst at working through the revisions of our papers in a timely fashion. If our manuscripts were not out as preprints I think we would be much more in a hurry to do the revisions.

The other important caveat around this is that time and attention is always limiting. There will always be a need to filter and evaluate science by proxies. If we didn't have science journals we might be complaining about how attention in social media is being used a bad proxy for the value of research.

I am truly curious to know how scientific interactions would change without scientific journals. Would people still want to apply to our group, want to collaborate on projects, invite us to conferences if our outputs were essentially peer-reviewed preprints? For my lab members that might read this - don't worry, this is not a declaration of intention.

State of the lab 12 - Becoming an established scientist

noreply@blogger.com (Pedro Beltrao) — Sun, 16 Mar 2025 13:32:00 +0000

This blog post is part of a (nearly) yearly series on running a research group in academia. This post summarizes year 12, the 3rd year after moving to ETH Zurich. In the last blog post I wrote down some of our overall research directions for the first 5 years of the group at ETH and I will wait another year or two before reflecting back on those commitments. This time, I wanted to try to write down some thoughts I have been having about essentially becoming more established in academia. This includes a longer term perception of group turnover, the time and resources needed to achieve research objectives and some activities that go beyond the management of the research group.

Group member turnover cycles

With 12 years of managing a research group, I have gotten used to some of the broader rhythms of turnover of the lab. Our lab is now almost totally renewed with just 1 lab member that came with the lab from EMBL. While this turnover was somewhat enforced by the move from EMBL to ETH, the turnover of lab members is a constant in academia given the short term nature of the lab members’ positions. In our group PhD students have typically stayed for around 4 years and postdoc have typically stayed for up to 5 years. Since there is some degree of clustering of the hires there tends to be some periods of higher turnover. We have had something like 2 to 3 periods where the lab has seen a large change. In the group, I try to hire from diverse backgrounds (e.g. biology, CS and math) and we work with a range of experimental and computational approaches, including for example yeast genetics, proteomics, structural bioinformatics, machine learning, etc. This creates a nice dynamic of group members building up their projects, while at the same time learning about the capabilities of the rest of the lab. The projects are usually meant to be somewhat synergistic, trying to address bigger goals from the individual problems (see past blog post on this). This means we have had windows of around 3 years when things click together before the turnover starts again. We are just around that exciting stage in the cycle and I am really looking forward to making the best of it. I still don’t enjoy what comes next, when the group will inevitably turnover again. I have accepted that it is an opportunity to steer the ship into new directions but sometimes it is disappointing to change the group just around the time it feels like we can take on almost any challenge.

Longer term view of science

One thing that has been on my mind is that I am sometimes weary about the time it can take to achieve a research goal. I am not talking here about an individual research project which tends to take on the order of 2 to 3 years on average. In our group we have tried to address some bigger research goals, such as trying to understand the evolution of protein phosphorylation or the functional relevance of individual phosphosites. These kinds of challenges take multiple independent projects and over 10 years of time to make a meaningful dent on. These days I will look at a potential long term research goal and I will think about the many different types of methods and steps that will be needed and this can distract me from the excitement of figuring those things out. I should say that I am by no means jaded about doing research. I still get such a thrill discussing the day-to-day results with lab members, being at the frontier and trying to figure things out. It is just when I pause to think about the longer term view, either in the past or trying to project into the future that I sometimes wish things could just move faster. I have taken part in a couple of large multi-PI projects that have moved very quickly and from these I can see the temptation of trying to have large labs.

From junior to “established” PI

There is no point in time when a switch happens and someone is no longer considered a junior PI but after 12 years I can safely assume that label no longer applies to me. This has brought some relatively small changes in my job, one simple one being that I no longer think about tenure. For most of my career I was on fixed term positions, including my first group leader position at EMBL which had a time limit of 9 years. I joined ETH 3 years ago on a tenured contract and not having to think about my next job has left me with a tiny post-tenure slump - what am I aiming for ? Related to the previous section, I have considered that I could enjoy overseeing science at a higher level than as a group leader. As one example, I organized an application for a National Centre of Competence in Research (NCCRs) with 19 PIs interested in human genetics in Switzerland. While the application failed, I was really keen and excited to co-direct the center if it had been funded.

Another aspect of my job that has changed somewhat is a higher commitment to activities outside the lab, such as taking part in committees, advisory panels or formal and informal mentorship of junior PIs. I don’t feel particularly overwhelmed by these activities but that might change if I am required to take part in more committees within ETH. Not everything is an additional burden to an already busy job. I have felt that being more visible and connected in international science comes with benefits, including being easier to at least discuss collaborations or having labs interested in joint grant applications.

Scientists that have worked in academia for longer than I have might find some of these things funny and I am certainly curious about what it will feel like reading this 10 years and more from now. In fact, the blog is now a bit over 20 years old with posts starting in my PhD. While I don’t post much these days I aim to continue at least this yearly series while I feel there are some new things to say beyond the progress in our science.

State of the lab 10 and 11 - the first years at ETH Zurich

noreply@blogger.com (Pedro Beltrao) — Mon, 13 Nov 2023 15:09:00 +0000

Yet another lake by a mountain in Switzerland

This blog post is part of a (nearly) yearly series on running a research group in academia. This post summarizes years 10 and 11, the first 2 years after moving to ETH Zurich. It also marks the end of the first decade as a research group leader, which is meaningful only because we have ten fingers and use 10 as a base for counting but I digress. There has been a lot to adapt to in moving to a new country including all the basics of moving, re-building the group and starting teaching. It was a lot easier than the first time around since I didn't have to set up the group from zero. Some people came with me, some stayed at EMBL-EBI with funding that couldn't be moved and generally speaking we could continue several computational related projects without much interruption. If we were primarily lab based then I think the interruption would have been more dramatic. Unexpectedly, there were more periods of high stress than I typically have. There was no particular reason for the stress but just a combination of multiple small things and probably due mostly to the adaptation to a new place. I will cover here some of the biggest things I am having to adapt to and also some of the research directions planned for the first 5 years of the group at ETH. One aspect that I will not cover is networking and getting to know the Swiss research landscape, but I will come to it in a later post.

The Swiss style of leadership

The EMBL, where I was before, has a very top-down leadership. EMBL is funded by different counties that are represented in the EMBL council. There is a director general who is appointed by the council and has a lot of control. Of course, there is a hierarchical support structure with a senior management team, heads of research units and a group of "senior scientists" that support the director in decision making. I am still figuring out ETH but there is a very different feel to it, both in size and style of leadership. EMBL employs around 2000 people while ETH has around 12,000. Organizationally, ETH is divided into 16 departments, and each department is further split into different institutes. For example, I am in the Department of Biology, which has 6 institutes, and I am in the Institute of Molecular Systems Biology (IMSB). As leadership, there is an executive board, including the president of ETH, then the Department heads, and in each department there is the meeting of heads of institute and the professorial conferences (i.e. all votes from professors). At least in the Department of Biology the heads of the institutes and the leadership of the Department are meant to rotate every 2 years. At these levels - institute and department - the leadership feels highly representative with lots and lots (!) of voting. This representative rotational leadership feels very different from EMBL and I think mirrors more broadly a Swiss way of doing things. The obvious consequence of this is that any change requires deep consensus and therefore radical change is less likely but it is too early to say much more.

Teaching at undergraduate level

During 9 years at EMBL I had almost zero teaching duties. I voluntarily taught some classes in the GABBA PhD program in Portugal and not much more. At ETH teaching is now an important part of my job. I am teaching courses in Bioinformatics and Systems Biology, primarily to biology students, which are all very familiar topics and close to my area of research. I don't particularly enjoy the act of teaching, in particular standing in front of 70-100 students and trying to explain things. As an introvert I am more comfortable with 1-on-1 or small group discussions and I get very tired with the interaction of teaching in a classroom setting. I have always said that Biology students should learn more computational skills so at least I have the opportunity now to influence that at ETH. In fact, the biology curriculum was changed right when I was joining to add more bioinformatics and they do have the chance to learn it with multiple lectures that cover bioinformatics and machine learning. Despite it being a mixed bag for me I am privileged in that I have a very low teaching load in topics that I like. Teaching is an area that I feel I could do more for and it could have an impact, in particular if we made it open to anyone. However, it is still something that I find difficult to fully devote to given the research role.

Our research at ETH during the first 5 years

The start of the research group at ETH has been fantastic. There was another big turnover of the group members during the transition, the second major turnover since the group started 11 years ago. I am really happy with the team we have here and having done this sort of turnover before, I can already see the growing potential of many projects that have started here. So the next 2-3 years is going to be about building up these projects and trying to coordinate them such that they interact and feed off each other. We have very generous stable funding as all other tenured prof positions at ETH - so called endowed professorships in the US or positions with core funding for the European researchers. Surprisingly, there is not a lot of oversight on this research funding which is a big difference from EMBL where the units, and their group leaders, are reviewed every 4 years. So I thought I could at least write down our commitment for research over the first 5 years here, in the spirit of disclosing what we are doing with this public research funding.

Human genetics research - mechanisms linking genotype to phenotype

Human genetics is an area that we started working on in the last 3-4 years or so of EMBL. Some of these things are already visible in recently published articles, including some protein-interaction network-based analyses of trait-associated genes. We continue to actively work on this and one direction of focus is to try to build interaction networks that are specific to different tissues or cell types. We are working on a manuscript on this and it is an area to continue to build upon, to be able to study the differences in cell biology of different cells/tissues and how genetic changes manifest differently in these. A second direction of focus here is to study the relation between common and rare variants linked to related traits using networks.

From cells to proteins - we are finishing a project where we are using protein structures to annotate functional residues in proteins to study mechanisms of pathogenicity. One aspect of this that will need further development is expanding on the prediction of structural modelling of protein interactions with other proteins and other molecules. Finally, we are interested in how genetic variation controls protein levels and ideally how to build computational models that can integrate the impact of genetic variation through control of protein levels, interactions, organs and organismal traits, ideally without a black-box modelling approach. All of these things are actively ongoing and I expect to have progress to report in the coming years.

Post-translational regulation - large scale studies of kinase signalling

There are over 100,000 phophosphosites discovered in human proteins and over 20,000 found in budding yeast proteins. We don't have good methods to study the functional role of these phosphosites nor to reconstruct the kinase/phosphatase-substrate signalling network of different cells. About half of the group is continuing to work on these problems and here at ETH we managed to consolidate the computational and experimental parts of our group which used to run in different locations while I was at EMBL. Because we are doing more of the experimental work now, this part of the group had a slower start but things are now moving along very well. Some of the problems that we are working on include the prediction of the biological process regulated by phosphosites; studying the impact of phosphorylation on protein conformational change; experimental methods to map kinase-substrate interactions and large scale mutational studies of PTMs. The thought has crossed my mind to phased-down a bit this area of research, or at least to move more into mammalian systems in our experimental work to make it more complementary to the human genetics side of the lab.

Structural bioinformatics, protein evolution and other

We have been having a lot of fun with AlphaFold2 ! With the current fast pace of change in protein related bioinformatics methods I am sure we will continue to play with these methods as they come. It is not likely that we will do a lot of method development ourselves, it is not our way, but I think we are very good partners for method developers to help make the bridge to applications. Protein structures, protein design and evolution models are all things we will likely be playing around with in the coming years.

20 years of open science or how we haven't radically changed the way we do science online

noreply@blogger.com (Pedro Beltrao) — Wed, 16 Nov 2022 15:40:00 +0000

Around 20 years ago I was a starting PhD student and it was an exciting time for the internet. It was the time of blogs, wikis and a large increase in public participation with more user generated content in what is commonly known as the start of Web 2.0. These were the times of web based online communities such as the now defunct Kuro5hin or the great survivor slashdot.org. I started this blog 19 years ago and I was also "hanging out" in an online community called Nodalpoint. Nodalpoint no longer exists but it was a discussion forum/wiki for bioinformatics with some of these discussions still preserved thanks to the magic of the way back machine.

Around the time of 2002-2006 all of the excitement around Web 2.0 was also infecting academia with many discussions around open science. I know that open science is a vague term that can mean many different things including open access, citizen science, open source and many others. One specific aspect that I want to focus on is the idea of organizing research in a way that is not based on local group structures. In 2005 I wrote a Nodalpoint post on "Virtual collaborative research" which is similar in spirit to open source software development but with a focus on discovery not tool development. Part of this would mean surfacing more of our ongoing research and taking part in research projects that are not organized by traditional research group structures. The idea of being extremely open about ongoing research activities was advocated by others under the term of "open notebook science".

Over the following years I made a few attempts at starting such open research projects with blog posts where I tried to set up tools and ideas where others could take part in (see posts from 2007, 2008 and 2010). The last project idea I tried to propose in such way ended up being one of the major projects from my postdoc and basically one of research lines I am still working on. In the end, none of these attempts really took off as open collaborative research projects. In hindsight, I am not surprised it didn't work. Even within local structures of research institutes and university departments there is so much discussion on incentives for local collaborations. While I think the traditional structures for organizing research do work, as a PhD student and postdoc I was very frustrated by the apparent difficulty of making the most of everyone's expertise. As a group leader I have more capacity to establish collaborations but I still think we aren't using the internet to its full capacity.

So what happened in the decade from 2010 to 2020 ? Blogs and online communities mostly died out and Web2.0 was swallowed by corporations. One major change was the rise of large social networks and the standardization of the stream as way for people to share information and interact. Academia started participating in social networks around the time of Friendfeed (2007-2015) and such participation become mainstream with the popularization of Twitter. I honestly would never have predicted the rise of academic twitter and it is truly a sign of how the geeks have inherited the earth.

The reason I am even thinking about open science these days is that over the past couple of years we have been involved in projects that have illustrated this potential of large collaborations empowered by the internet. I wanted to write this down also to have something to come back to in the future. The first project was a study of phosphorylation changes during SARS-CoV-2 infection. Like many others, when the pandemic sent our research group home, I though about what we could do to help and sent emails to a few people that could be working on the topic. Nevan Krogan, my former postdoc supervisor, was very keen to involve us which lead to several projects including this study of protein phosphorylation. This was probably one of the most exciting projects I have been involved with and included a very spontaneous collaboration among a large international team coordinated by a few people through slack. In this case the network of interactions was provided by Nevan and it was possible because everyone was pushing in the same direction triggered by a catastrophe. I wish everyone could feel the sense of power that I think we felt during this project. There was so much scientific capacity at the disposal of this single project and we could iterate through experiments and data analysis at an incredible pace. It is even hard to express how it felt to be able to just get things done when you had the world experts for what was required to do at every step.

A second even more interesting example was a community effort to study the value of AlphaFold2 in a series of applications. When AlphaFold2 was released, several scientists started sharing their early observations of how AlphaFold2 and predicted structures could be used for different applications. I though all of these examples were really exciting and that we could structure this output into a manuscript. So I just contacted people that were doing this and also asked on social media if anyone else wanted to participate. In the end every contribution to this was quite modular and it was easy to integrate this into a manuscript with a few meetings and a google doc to put things together. Perhaps the less usual thing that happened was receiving actual results through Twitter chat.

I think both of these examples required a trigger - the pandemic and the release of AlphaFold2 - that led to many scientists moving in the same direction. In both of these cases I think we achieved in a few months what would take a single group potentially one to several years to do. Yet, these interactions remain difficult to make. Perhaps simply because we are just too busy with our own research questions or more likely because of the importance of credit and evaluation systems in academia. These days I am actually less in favor of radical sharing of ongoing research, in the spirit of open notebook science. I don't think we have the attention span for it. It would be too difficult to navigate and may lead to more "group think" instead of divergent thinking and ideas. Maybe the simple existence of social networks like twitter are already a good step forward. I certainly get to know more people and what they may be up to via this. Lets see what the next 20 years bring.

Independent evaluation of AlphaFold-Multimer

noreply@blogger.com (Pedro Beltrao) — Tue, 08 Mar 2022 15:02:00 +0000

AlphaFold2 has been widely reported as a fantastic leap forward in the prediction of protein structures from sequence, when sequence has enough homologs to build a reasonable multiple sequence alignment. When AlphaFold2 was released (Jumper et al. 2021) there were several independent reports of how it could also be used for the prediction of structures of protein complexes despite the fact that it was not trained to do so (Bryant et al., 2021; Ko and Lee, 2021; Mirdita et al. 2022). Together with the lab of Arne Elofsson, in work led by David Burke in our group and Patrick Bryant in Arne's group, we have shown that it can be applied in reasonably large scale to predict structures of protein complexes for known human interactions (Burke et al. 2021). There is a lot to investigate still but it is clear that this is an extremely exciting direction of research since that lead to a major advances in the structural analysis of cell biology, evolution, biotechnology, etc.

Soon after these first reports, DeepMind released an AlphaFold version that was re-trained specifically for prediction of structures of protein complex - AlphaFold-Multimer (Evans et al. 2021). Given that they reported an even higher success rate with this specifically trained model we were quite excited to give this a try. David Burke selected a set of 650 pairs of human proteins from the Hu.MAP dataset, known to physically interact and for which the experimental structure has been solved. A structure was predicted using AF v2.1.1 (AF-multimer) using default settings and the model_1_multimer parameter set. A second model was predicted using AF using the model1 monomer parameter set and the FoldDock pipeline. For each model, DockQ scores were produced which reflect the similarity of the predicted structure with the experimental structure with a specific focus on the interaction interface residues. A DockQ score value below 0.23 can be considered essentially an incorrect or random model.

Below we show a direct comparison between the two AlphaFold2 models with the AF2 Multimer showing a very significant improvement based on DockQ scores. Of all predictions tested, there were 51% above DockQ>0.23 with AF2 Multimer and 40%>0.23 with "standard" AlphaFold2. This improvement (+11%) is not as large as that reported by the DeepMind team (+25%) on their own test set. There could be several reasons for the difference but more importantly this would be more than enough to justify using Multimer for the prediction of protein complexes.

However, David quickly realised that there were many examples of clashes at the predicted interface with the AF2 Multimer model. In the figure below we show just an example of this which, despite the high DockQ score (0.85) clearly has several overlapping residues. That is, while the interface region is likely to be correct, the model at the interface has serious errors.

These clashes in predicted structures are quite frequent with 69% of predictions having some clash. The clashes can be quite extreme with several involving a very high fraction of the total length of the protein as shown in the distribution below. Such clashes are essentially not seen in the predictions made with the earlier version of AlphaFold2.

While there may be some cases where the clashes could be minimised, as it stands the models produced by AF-multimer may not be usable for a large fraction of cases. However, these issues are of course easy to spot. DeepMind is in fact aware of this bug since around November and have said they are working on it. From the point of view of predicting the regions of the proteins where the interaction will occur AF-multimer may still be usable as it is and hopefully DeepMind will find a fix for this problem.

A closer look at the costs of EMBO publishing

noreply@blogger.com (Pedro Beltrao) — Wed, 02 Feb 2022 13:03:00 +0000

There has been a lot of discussions on social media about the price that some publishers are coming up for publishing a paper in their journals - the so called article processing charges (APC). With some journals asking for values that are on the order of 10k and many scientists finding these values to be outrageous. Given that journals don't work to produce the research articles and get academics to do the evaluation, how can these journals claim the costs of publishing a paper to be anywhere close to 10k ? While I agree that these are outrageous values, I don't really believe that the price is mostly profit. A good source of information for the costs associated with running a publisher are those that have been disclosed by EMBO Publishing. Before we go into these I need to disclose that I serve on the Publications Advisory Board of EMBO publishing. I don't receive anything from EMBO and this is merely an advisory committee but it has given me some insight into what is a very real attempt from non-profit publisher to come up with an APC that is low and what they could compromise on their current set-up to achieve it.

With that out of the way lets just look at the most recent numbers that EMBO has disclosed which were for 2019 (see here). EMBO has (or had in 2019) 17 professional scientific editors and 6 support staff, that handled a total of 5,766 submissions in 2019. That is on the order of 28 submissions handled per month per editor, 1.3 per working day. I don't know about you but making a call on 1 paper per day plus finding/chasing reviewers is not easy if you try to do it properly, even if you can make some rejections fairly quickly. From these they ended up publishing 472 (8%). This part is not totally transparent, for example maybe some of the submissions included the reviews and news&views articles that were ultimately also published. If that is the case then the total number published would be 681 (12%). It is also not totally clear if the submissions include also revision submissions. Regardless, this shows that the total of EMBO publishing ends up having acceptance rates that are quite low (10-20%). I should stress that I truly don't know the actual number. As we easily see, this rejection rate is really key for the high estimated cost per paper.

The costs that they have disclosed includes ~2,5 million euro for the EMBO Press office, of which around 2 million is listed as salaries and benefits. The number of staff is there as well so you can guestimate the average salary for the 23 staff and you can also look up EMBO editor salary on Glassdoor to get an idea. I truly don't know what the salary is but I guess on average it could be on the order of 4-6k net per month. The other costs include 1,723,639 euro that EMBO Publishing pays to Wiley which in fact does the actual publishing. The majority of this cost is listed as "Wiley publishing services (incl. production, sales and marketing)" (1,281,552 euro). This is certainly a place where costs are not very transparent, at least to me, and where profit to Wiley is included, likely with a decent margin. I certainly don't know enough about finances to figure out but Wiley is claimed to have around 30% of operating profit margin but for the purposes of some later calculations, lets assume that maybe 50% of these costs are profit that could be magically removed (e.g. EMBO sets up their own publishing infrastructure). Finally, EMBO also lists 1,342,374 euro in "surplus" which is re-invested into some publishing related actives like the EMBO Source Data project, other pilots trying to innovate on the publishing side and back to EMBO itself which further supports EMBO program activities (fellowships, etc).

With these numbers then the total cost includes the 4,225,920 of actual cost and the 1,342,374 for EMBO activities (5,568,294 euro total). So if you don't take anything out of this, you would need a price of 11797 euro for each of the 472 paper published in 2019 to finance this. If you exclude the EMBO surplus that would be 8953 per paper and excluding 50% of Wiley costs it would get down to 7127 per paper. Even without anything from Wiley you would only get to 5301 per paper. Of course, you can also argue that the salaries costs could be lower but what can't really be argued is that academic editors can do this for "free" since that is time that most likely is even more expensive and less efficient.

So the 10k APC number certainly contains parts that can be reduced but we are not talking about a 1k per paper cost. For that you would need to change the rejection rates and this is what really starts mattering in the end. If you go to maybe something like 50% acceptance rates which could correspond to something like 2000 papers published in this case, then the APC could be somewhere on the order of 1500-2500 euro. Keep also in mind that submission numbers would tend to decrease over time if the impact factors go down with higher acceptance rates (yes, some people still care about those). Of course, this scales across multiple journals and this is where the big publishers are just taking advantage since the overall acceptance rate across the large portfolio of journals is much higher than 10% and high acceptance rate journals (e.g. Scientific Reports) can cross-subsidise low acceptance rate journals (Nature).

It is important again to keep in mind that all of these prices per paper have been there for decades but were paid via journals subscription charges instead of APCs and therefore they were not transparent and people were not really paying attention. In the end, the discussion for me is not really around the 30% savings we could have by pushing the publishers to lower their prices, but more about how we go about doing the filtering (i.e. target audience) and subjective evaluation of value to science (i.e. impact). Revolutions are not real solutions in academic publishing. If you propose a solution that requires a majority of people to change their habits in the span of 3 years it is dead on arrival.

State of the lab 9 - an informal report on the 9 years of EMBL-EBI

noreply@blogger.com (Pedro Beltrao) — Wed, 19 Jan 2022 15:48:00 +0000

This blog post is part of a yearly series (or close to yearly) on running a research lab in academia. 2021 was the last of 9 years as a group leader at EMBL-EBI, which is the standard time given to group leaders to establish and run their labs at EMBL. For this year's blog post I thought it was a good time to look back at the full 9 years and I am going to (briefly) cover the time at EMBL with some numbers including giving an approximate account of the finances. This is something that I do with the group at the start of every year but it still feels strange to make financial numbers public.

The scientists

A lot has happened during 9 years. Starting with the people, we have had 7 PhD students, 1 of which co-supervised, 13 postdocs and 10 interns/visiting lab members. The total group size was around 10 for the majority of the time which, as a manager, feels about right in what I can do as a direct line manager. It is fair to say that science is a very social activity and working with different people with different personalities, through the good and bad, is really enriching. Not to get all corny but the personal interactions are some of the things that stick with me the most over the time. It is always in those extremes - the "unfairly" rejected paper or unexpected positive response, individual personal and work difficulties that are overcome or sometimes not. Mental well being is an example of such difficulties that across the broader society we are not good at dealing with and that have also not always been easy as a manager.

From these 30 lab members there are 7 that will continue with the group over the next few years: Cristina (senior scientist), Jurgen (postdoc) and Miguel (postdoc) have joined me at ETH and Eirini (PhD student), David (postdoc), Inigo (postdoc) and Danish (postdoc) will remain at EMBL-EBI with funding that cannot be moved. From the PhD students and postdocs that have left all but 2 have left with published papers as first or co-first authors. One PhD student decided not to continue the PhD and one postdoc left after several years without a first author paper. In both cases I feel some blame as the project ended up being difficult and the results were just not very positive.

The publications and science

In total we published 45 original research papers, 3 review articles and 2 news&views over the course of 9 years. This includes only research that was really done after starting the group and also includes 8 preprints that have not yet been published in a journal after peer-review. This is split into 27 papers where I am listed as co-corresponding author and I also think our group played an important role in the final outcome, plus 18 on which our group had some input into. I am showing on the figure the distribution of these papers along the 9 years. The first paper from our group only came at year 3 with the first real significant set of publications coming at year 4 and 5. In regards to the non-tenure track system, even by this crude metric it is easy to see how different it would be if I had to apply to the job market at year 6-7 vs year 8-9. Of course, note that the numbers for 2021 in particular are inflated by preprints that will ultimately be published in a journal most likely in 2022. Another clear trend that feels true to me is the increase of small collaboration efforts where our group just helped out in some modest way. I think this is a reflection of just being more integrated into the local and broader academic networks.

I am not going to go into the scientific outcomes of the 9 years in any detail. I think some of the strongest work we did was on the evolution and functional importance of protein phosphorylation with multiple publications that have built on each other and where I think our contributions move this field forward. There was also a smaller line of research on the genetics of trait variation that I wouldn't consider to be at the cutting edge but it has been fun to work on. In particular it has been interesting to step closer to the fields of human genetics and genetics of human disease where making advances requires the interactions between people with such different ways of viewing science. Just the language barriers between human genetics, cell biology, biochemistry and chemical biology have been fascinating to get into.

The funding

So now something that feels less comfortable or at least less common to discuss - the funding. Before going into any numbers, I should caveat this by saying that these are very rough approximations that of course should not be considered an actual financial statement. These numbers also don't take into account the money spent on the whole infrastructure (administration, grants, IT, etc) but are just the funding spent on research lab members, including my salary, and consumables. With that out of the way, over 9 years we spent approximately 5.7 million euros as broken down per year on the figure. Although we have had a small wet lab running in the last 6 years, I would say that 90% of this was on salaries. Of these around 2.7 million were from external grant funding, plus ~450k from competitive internal postdoc fellowships. This of course just shows how amazing it is to work in a place with core funding. I ended up being very successful early on with 2 million funded in years 2 a 3 and this made me too careless about applying for grants later on which I now consider a real error on my part. I applied in total to 13 external grants with 6 being successful.

So a number that immediately is easy to get but that is probably quite meaningless is the money spent per research paper. We spent a total of ~127k euros per paper or 210k if we only count those where I am listed as co-corresponding. Of course this varies so much per paper really with my very rough estimates on bounds to be something like between 25k to 1 million. Given that we mostly spend the budget on salaries this simply reflects the amount of people time spent on a project.

To new beginnings

This is a somewhat dry recap of the 9 years of EMBL but I thought it would be interesting, at least to me, to have these things written down. Even if these are just numbers, I am curious to see what the next 9-10 years look like. I am sitting in my new office at ETH, just close to two weeks after arriving in Zurich. There is a lot to adapt to, including teaching material that I should be preparing right now. I am curious to see how long it will take me to get into the local academic network and how much the move will impact on our capacity to do work. The lab work is really the part that will take the longest as I don't think we will run any experiment before middle of the year and although we have the budget for an MS instrument that will take even longer to get going. In any case, I am excited about the new beginning here.

A not so bold proposal for the future of scientific publishing

noreply@blogger.com (Pedro Beltrao) — Thu, 10 Jun 2021 15:07:00 +0000

Around 15 years ago I wrote a blog post about how we could open up more of the scientific process. The particular emphasis that I had in mind was to increase the modularity of the process in order to make it easier to change parts of it without needing a revolution. The idea would be that manuscripts would be posted to preprint servers that could accumulate comments and be revised until they are considered suitable for accreditation as a peer review publication. At the time I also though we could even be more extreme and have all of the lab notebooks open to anyone which I no longer consider to be necessarily useful.

Around 15 years have passed and while I was on point with the direction of travel I was very off the mark in terms of how long it would take us to get there. Quite a lot has happened in the last 15 years with the biggest changes being the rise of open access, preprint servers and social media. PLoS One started as a journal that wanted us to do post-publication peer review. It started with peer reviewed focused on accuracy, wanting then to leverage the magic of internet 2.0 to rank articles by how important they were through likes and active commenting by other scientists. The post-publication peer review aspect was a total failure but the journal was an economic success that led to the great PLoS One Clone Wars with consequences that are still being felt today - just go and see how many new journals your favourite publisher opened this year.

The rise of preprint servers has been the real magic for me. We live in each others scientific past by at least 2 years or so. If you sit down and have a science chat with me I can tell you about all of the work that we are doing which won't be public for some 2 years. If I didn't put our group's papers out as preprints you would be waiting at least 6-12 months to know about them. Preprint servers are a time machine, they move everyone forward in time by 12 months and speed up the exchange of ideas as they are being generated around the globe. If you don't post your manuscripts as preprints you are letting others live in the past and you are missing out on increased visibility of your own research.

Preprint servers also serve the crucial need to dissociate the act of making a manuscript public from the process of peer review, certification as a peer-reviewed paper and dissemination. This is important because it allows the whole scientific publishing system to innovate. This is needed because we waste too much money and time on a system that is currently not working to serve the authors or readers efficiently.

So after nearly 15 the updated version of the proposal is almost unchanged:

I no longer think it would be that useful to have lab notebooks freely available to anyone to read. There are parts of research that are too unclear and I suspect that the noise to information ratio would be too high for this to be of value. However, useful datasets that are not yet published could be more readily made available prior to publication. Along these lines, the ideas in the form of funded grant proposals should be disclosed after the funding period has lapsed. As for the flow from manuscript to publication, the main ideas remain and the system already exist to make these more than just ideas. There are already independent peer review systems like Review Commons. Such systems could eventually be paid and could lead to the establishment of professional paid peer reviewers. Such costs would then be deducted from other publishing costs depending on how the accreditation was done. Eventually "traditional" publishing could be replaced by overlay journals, like preLights, whose job would be to identify peer reviewed preprints that are of interest to a certain community.

Social media for me has been the most surprising change in scientific communication. I didn't expect so many scientists to join online discussions via social media. Then again, I didn't foresee the geekification of society. In many ways social media is already acting as a "publishing" system in the sense of distribution. Most of the articles I read today I find through twitter or Google Scholar recommendations. As we are all limited by the attention we can give, I think one day, instead of complaining about how impact factors distort hiring decisions we will be complaining about how social media biases distort what we think is high value science.

So finally, what can you do to move things along if you feel it is important ? If you think we have too many wasteful rounds of peer reviewing across different journals; that the cost of open access publishing is too high or even simply that publicly funded research should be free to read and openly available to mine ? Then the best single thing you can do today is make your manuscripts available via preprint servers.

Lab move to ETH Zurich, the job search and fixed term PI positions

noreply@blogger.com (Pedro Beltrao) — Fri, 21 May 2021 07:29:00 +0000

ETH Zurich (credit)

Next January, after 9 years at the EMBL, I will be joining ETH Zurich as a tenured faculty of the Department of Biology with my research group hosted at the Institute for Molecular and Systems Biology (IMSB). I am really excited about this move and I think the IMSB is a perfect fit for the type of research that we do. We primarily use computational approaches to study the relation between genotype and phenotype with a specific focus on post-translational regulatory systems (more on the EBI website or my GScholar page). IMSB has a long tradition of method development in large scale measurements of biological systems with a current interest in mechanistically explaining trait variation. The smaller experimental component of our group uses yeast genetics which is also a great fit for the groups around including our future neighbours in the Institute of Biochemistry. Research wise the group will remain focused on: studying the evolution and functional importance of post-translational regulation; determining the regulatory networks of a cell, and how they change under different conditions including disease. More broadly we also study the mechanisms that underlie trait variation across individuals of the same species. In terms of methods it will remain primarily computational with around 30% of the group devoted to lab work. The lab will be fully equipped for large scale yeast genetics with the exciting addition of having funding for a MS instrument for the proteomics.

Teaching, scientific integration and group structure

With any move there is always some thoughts about the challenges ahead. Professionally, the types of things on my mind are that I will need to setup the group, integrate myself scientifically and prepare myself for teaching. Setting up the group and integrating myself within the local environment won't be new experiences. I feel I was too slow with both of these things when I first joined EMBL-EBI so I am curious if I will be able to move things along faster this time. Coming from EMBL and the local EBI/Sanger campus I have the impression that ETH is less collaborative but there were clearly many people interested in collaborating just from the small sample I got during interviews. There is an interesting difference in group structure between EMBL and ETH where at ETH a group can have sub-groups with junior PIs that can have varying degrees of independence as per the decision of the more senior PI. Organising a lab in this way will be something new. Finally, I will have to teach at the undergraduate level for the first time. I have always said that students coming out of biology or related topics need to have better training in bioinformatics. While daunting this will be my chance to contribute to this training directly.

The interview process and decisions

For those less familiar with the EMBL, group leaders are hired for a maximal period of 9 years with only a few exceptions (around 10%) that end up having an open-ended contract. We get generous core funding and get to tap into a great scientific network which more than compensates for the lack of tenure. This means that around year 7 your thoughts start moving into the future. At faculty presentations I would often write how many years I had left in the tittle slide as a personal reminder. Towards the end of year 7 I started applying and spent most of year 8 applying and interviewing. The first time I applied for PI positions it was all very unidirectional, with myself looking broadly for possible places. This time it felt more like dating a potential future university/institute with expressions of interests on both sides. One of the issues in going into this is that I didn't really know what my value would be in the market. I knew I had a good CV and would certainly find a job, I just didn't know where I could aim for in terms of seniority and resources. That become clearer only after the first interview and the expression of interest of places I felt were really fantastic.

The second half of 2020 became then about trying to find the best place professionally and personally. I ended up applying to 10 places, interviewed in 8 and received 5 offers. I tried to find a job in my home country (Portugal) but from the two places I was interested one picked another candidate and the other could not make an offer that was not fixed term. The decision ended up being among 3 places with the major differentiation factor being between 2 offers that had less core funding but higher management responsibilities and ETH with incredibly generous core funding and the best scientific fit (but less seniority). Personally the decisions were about staying in the UK or moving to France or Switzerland. There is quite a lot to be said about this choice (safety, adventure, integration, kid friendly, jobs for partner, etc) and in the end we went with Switzerland. While excited I am also anxious about yet another move to what will be my 5th home country, the now almost familiar sense of uprooting and new beginnings. But this is not yet time for goodbyes.

Non-tenure group leader positions (in Europe)

I don't know who invented the fixed term, non tenure track, group leader positions in academia. It may have been EMBL and this model has clearly spread across Europe with many research institutes having some form of junior positions that have a variable number of years (5 to 12) to set up a group and then necessarily need to move on to a different place. EMBL does this because it is funded by many member state countries to train the next generation of "academic leaders" that will lead research groups across the member states. The obvious advantage of hosting these positions is that it keeps the institute forever young if you manage the turnover well. I think these positions can work well if they remain a relatively small proportion of the total PI/faculty positions; there is some level of support to at least kick start the group; and the positions last a sufficient number of years. Having gone through this at EMBL my impression is that 7 years would be the bare minimum and 9-10 years would be ideal. This also depends on the level of support beyond the PI salary. If conditions are not met then it is not worth setting up people for failure with the selfish goal of using the higher turnover to bring in new ideas/methods. Don't give people super postdoc positions for 3-5 years with no funding and no chances of tenure just because you want fresher ideas around. If there is some mechanism for tenure or open ended contract then it should be crystal clear from the start how (un)likely this is and what are the transparent criteria for achieving it.

State of the lab 7 & 8 - The last years at EMBL

noreply@blogger.com (Pedro Beltrao) — Fri, 29 Jan 2021 12:15:00 +0000

This is usually part of a yearly series of posts where I note down thoughts related to managing a research group in academia over the years. This post covers years 7 and 8 and it brings me now to the start of year 9, my last at EMBL. While I usually do one of these posts every year, with all of the craziness of 2020 I ended up skipping one.

Year 7, group turnover

2019 was the year where the group fully turned over all lab members that were with us since the earlier years with 2 postdocs (Haruna Imamura and David Ochoa) and 3 PhD students (David Bradley, Claudia Hernandez-Armenta and Marta Strumillo) leaving. Haruna is now a Research Scientist at the Systems Biology Institute in Japan, David O is a the platform coordinator at Open Targets and Claudia and David B are now doing postdocs. Marta is finding her way through consulting. We were joined by 2 postdocs (David Burke and Miguel Correa) and 2 PhD students (Eirini Petsalaki and Rosana Garrido). This constant turnover of group members is quite difficult to manage both personally and professionally. Year 7 was really the year with largest amount of changes in the group and there is something to be considered about trying to make sure that changes remain gradual. However, it is not always possible to plan for this to happen. While I think that this change in academia is generally positive for science, I do wonder what could be achieved if this was not a requirement (see earlier post).

Managing research focus over the years

Over the last few years, the research in the group had some dispersion in terms of the group research topics. At the start, the group was named "Evolution of cellular interactions" with a primary focus on the evolution and functional relevance of protein phosphorylation. While this remained the central focus there were other areas we worked on including cancer genomics and genetics of human disease and microbial trait diversity. We also have work that is not yet visible on drug mode-of action predictions. This led me to change the group name to "Cellular consequences of genetic variation" which could better serve as umbrella to the different topics. This is, at least in part, a simple reflection of funding opportunities but also a reflection of true movement in my research interests and the environment I have been working in (Genome Campus). On one hand I feel this dispersion is detrimental in that we could do more with a single minded focus, but on the other hand these extensions have not really been the majority of our work and also act as way for the group to explore new directions. My visual reference for this is a cell sending out protrusions in some directions to feel out the environment around. On some of these new areas (e.g. microbial trait diversity) I feel we have done enough, even with a small total investment, to make the work stand on its own.

I have to say that the without explicitly planning for it, the dispersion worked to my advantage when applying for position last year as it allowed me to present the group through slightly different lenses depending on where I was interviewing in. Of course, this is only beneficial if there is sufficient research progress made by the group not to appear superficial or unfocused. I suspect that this movement in research topics is normal but I haven't had many deep conversations with others about how this has happened to them in their research groups. In some cases, the changes in topics for some groups seem more abrupt from the outside but it could be just a perception. I will soon have an opportunity to rethink where we put most of our research efforts and likely cut back on some of these extensions.

Year 8 - A new group, the pandemic and the job market

At the start of last year, I was finally getting comfortable with the idea that the group had changed so much and I was truly excited about the new beginning. Just as the year was starting and I was enjoying this excitement the pandemic hit. As I had described before, we ended up devoting some effort in the group to work on SARS-CoV-2 projects which I think was also good for group morale. However, the changes in working conditions, the effort on the SARS projects and my need to go back to the job market made me less capable of keeping up with some of the projects in the group. While most of the work has kept going there are at least 3 projects/manuscripts that have been neglected simply for my own lack of time/effort. We all know these stories of PIs that let work pile up on their desk and I feel it as a failure although I can rationalise why I really didn't have the time to fully keep up.

Finally, over last year I was fully back on the job market and I am so relieved that this is now over. Since there nothing official that I can announce I will wait to write up in detail what the process was like and compare it to my first attempt to secure a PI position. I can at least say that I will leave EMBL-EBI at the end this year and I will certainly write more about the 9 years of EMBL. I do want to look back to all that has been good (mostly) and bad, make a summary of what I feel were the biggest advances we made, perhaps discuss the finances, and more broadly go over the issues of this lack of tenure for junior PIs now implemented in so many European research institutions.

A year of SARS-CoV-2 research

noreply@blogger.com (Pedro Beltrao) — Fri, 04 Dec 2020 18:12:00 +0000

This post may be premature but I feel like writing down some thoughts about the roller coaster that this year has been. At the start of the year, with the number of reported cases rising in Europe the EMBL and our institute (EMBL-EBI) decided to send everyone home as precautionary measure. As most of our group is computational, this has meant we have been working from home for most of this year. Early on, somewhat frustrated by not being able to help, I emailed a few people that could be working on the virus. Nevan Krogan replied saying our help would be useful and we joined the global effort to contribute to solving this crisis.

Science at science fiction speed

Over the course of 9 months we took part in 4 projects, some of these being the most thrilling science I have ever taken part in. We condensed what would easily be a 3 to 5 years research project into something done in 3-4 months, involving typically 10-20 research groups with a few key people helping to direct the research. We were collecting data, analysing and suggesting new experiments in the span of days with some of the best scientists in the world. Contributing to the direction of this level of resources has been an amazing experience that I wish every scientist could try at least once in their life. These projects were all geared towards studying how SARS-CoV-2 takes control of its target cells to be able to suggest human targeting drugs that could counter the infection. Several of the compounds identified in these studies are in clinical trials for COVID-19 so I feel the projects met their main objective.

While this has been my perspective from working on these specific projects we are all aware of the amazing scientific progress that has been made over the course of this year. I remember seeing the movie Contagion and almost laughing at the unrealistically fast pace of research in the movie. However, SARS-CoV-2 research has in fact happened at an incredibly fast pace that probably matches the movie.

Why don't we do this for disease X?

One discussion point that has come up often is if we can learn from this period to apply it to research into other diseases. Science is an international endeavour but the degree of collaborations for SARS-CoV-2 research has been higher than usual. The effort put into this was also high among the projects I have seen personally but this eventually results in some exhaustion and it is not sustainable. I don't think this is easy to repeat for other diseases without the same external sense of urgency. Most scientists won't just drop what they are working on to fully focus on some other research question. Maybe it is an argument for even higher degree of collaboration, in particular between academia and biotech/pharma. There may be some small increase in productivity of collaborations through the use of online tools like slack and zoom but overall I don't see that the way we do science has been dramatically changed going forward.

The case for higher spending in research

I'm gonna have to science the s**t out of this

Jeremy Farrar has often said that science is our exit strategy for this crisis. From testing, tracking the spread, to treatments and vaccines. It is this single minded effort of so much of the worlds research capacity that will lead to a long lasting solution. This already looks to be within reach with some treatment options, new ways of testing and critically, what appear to be effective vaccines. Soon enough we will be looking back and asking ourselves if there is something we could have done better. As trained scientists our reflex is to pause and think carefully about all the things that could have worked better. Were we efficient ? Did we deal well with the deluge of studies ? Was the peer-review too shallow and quick? It is our instinct to be critical but maybe we should be more vocal about how amazing the response of the scientific community has been. More importantly, this is the time to demand higher funding rates. If society can't see how important science is during a pandemic, when are we going to make our case ? This is the capacity of a research infrastructure that is funded by 1-2% of national budgets, what could humanity achieve if we were to double it ?

Over the last 10 years academic science budgets have been squeezed and a lot has been said about how academic science needs to be more applied and how much we should justify the investment it is being made. This week, DeepMind, a private research institute funded by what is essentially an advertising company (Alphabet/Google) has made headlines with their impressive research into predicting the structure of a protein from its sequence. An advertising company finds the money to invest into what are fundamental biological problems and in the middle of a pandemic that is being solved by a global scientific infrastructure we can't get the EU science budget to increase. We should be ready to make our case over the course of the next months.

PlanS, the cost of publishing, diversity in publishing and unbundling of services

noreply@blogger.com (Pedro Beltrao) — Thu, 30 May 2019 17:32:00 +0000

A few days ago I had another conversation about PlanS with someone involved in a non-profit scientific publisher. I am still sometimes surprised that these publishers have been very much reacting to the changes in the landscape. In hindsight I can understand that the flipping of the revenue model to author fees has been threatened for a long time but always seemed to be moving along slowly. Without going into PlanS at all, the issue for many of the smaller publishers is that they simply cannot survive under an author fee model because their revenue from the subscription would translate to an unacceptable cost per article (given that they reject most articles). These smaller publishers typically use their profit to then fund community activities (e.g. EMBO press). The big publishers will do just fine because they have a structure that captures most articles in *some* journal so their average cost per article would end up being acceptable in a world without subscriptions.

I don’t want to go into the specifics of PlanS at all but I see clearly the perspective of the founders and wider society of wanting to have open access and even reducing the costs of publishing. The publishers have been given quite a lot of time to adapt and maybe some amount of disruption is now needed. One potential outcome of fully flipping the paying model might be that we simply lose the smaller publishers and consequently lose also their community activities if they can’t find alternative ways to fund them. There are enough journals in scientific publishing that, to be honest, I think the disruption will not be large.

Less publishers means less innovation in publishing

What I fear we will lose with the reduction in the number of publishers is the potential to generate new ideas in scientific publishing. Publishers like EMBO press, eLife and others have been a great engine for positive change. Examples include more transparent peer review, protection from scooping, cross-commenting among peer-reviewers, checks on image manipulation, and surfacing the data underlying the figures (see SourceData). While this innovation tends to spread across all publishers it is not rewarded by the market. Scientific publishing does not work within a well-functioning economic market. We submit to the journals that have the highest perceived “impact” and such perceived impact is then self-sustaining. It would take an extraordinary amount of innovation to disrupt leaders in the market. For me, this is a core problem of publishing, the fact that the market is not sensitive to innovation.

To resolve this problem we would have to continue the work to reduce the evaluation of scientists by the journals they publish in. Ideas around alt-metrics have not really moved the needle much. Without any data to support this, my intuition is that the culture has changed somewhat due to people discussing the issue but the change is very slow. I still feel that working on article recommendation engines would be a key part of reducing the “power” of journal brands (see previous post). Surprisingly, preprints and twitter are already working for me in terms of getting reasonable recommendations but peer-review is still a critically important aspect of science.

Potential solutions for small publishers

Going back to the small publishers, one thing that has been on my mind is how they can survive the coming change in revenue model. Several years ago I think the recommendation could have been to just grow and find a way to capture more articles across a scale of perceived impact (previous post). However, there might not be space for other PLOS One clones. An alternative to growing in scale would be to merge with other like-minded publishers. This is probably not achievable in practice but some cooperation is being tested, as for example in the Life Science Alliance journal. Another thought I had was then to try to get the market to appreciate the costs around some of the added value of publishing. This is essentially the often discussed idea of unbundling the services provided by publishers (the Ryanair model?).

Maybe the most concrete example of unbundling of a valuable service could be the checks on non-ethical behavior such as image manipulation or plagiarism. These checks are extremely valuable but right now their costs are not really considered as part of the cost of publishing. Publishers could consider developing a package of such checks, that they use internally, as a service that could be sold to institutions that would like to have their outgoing publications checked. Going forward, some journals could start demanding some certification of ethical checks or funding agencies could also demand such checks to be made on articles resulting from their funded research. Other services could be considered for unbundling in the same way (e.g. peer review) but these checks on non-ethical practices seem the most promising.

(disclosures: I currently serve on the editorial board of Life Science Alliance the Publications Advisory Board for EMBO Press)

Research summary - Predicting phenotypes of individuals based on missense variants and prior knowledge of gene function

noreply@blogger.com (Pedro Beltrao) — Fri, 29 Mar 2019 12:11:00 +0000

I have been meaning to write blog posts summarising different aspects of the work from our group over the past 6 years, putting it into context with other works and describing also some future perspectives. I have just been at the CSHL Network Biology meeting with some interesting talks that prompted me to put some thoughts to words regarding the issue of mapping genotypes to phenotypes, making use of prior cell biology knowledge. Skip to the last section if you just want a more general take and perspective on the problem.

Most of the work of our group over the past 6 years has been related to the study of kinase signalling. One smaller thread of research has been devoted to the relation between genotypes and phenotypes of individuals of the same species. My interest in this comes from the genetic and chemical genetic work in S. cerevisiae that I contributed while a postdoc (in Nevan Krogan’s lab). My introduction to genetics was from studies of gene deletion phenotypes in a single strain (i.e. individual) of a model organism. Going back to the works of Charlie Boone and Brenda Andrews, this research always emphasised that, despite rare, non-additive genetic and environment-gene interactions are numerous and constrained in predictable ways by cell biology. To me, this view of genetics still stands in contrast to genome-wide association studies (GWAS) that emphasise a simpler association model between genomic regions and phenotypes. In the GWAS world-view, genetic interactions are ignored and knowledge of cell biology is most often not considered as prior knowledge for associations (I know I am am exaggerating here).

Predicting phenotypes of individuals from coding variants and gene deletion phenotypes

Over 7 years ago, some studies of strains (i.e. individuals) of S. cerevisiae made available genome and phenotypic traits. Given all that we knew about the genetics and cell biology of S. cerevisiae I thought it would not be crazy to take the genome sequences, predict the impact of the variants on proteins of these strains and then use the protein function information to predict fitness traits. I was brilliantly scooped on these ideas by Rob Jelier (Jelier et al. Nat Genetics 2011) while he was in Ben Lehner’s lab (see previous blog post). Nevertheless, I though this was an interesting direction to explore and when Marco Galardini (group profile, webpage) joined our group as a postdoc he brought his own interests in microbial genotype-to-phenotype associations and which led to a fantastic collaboration with the Typas lab in Heidelberg pursuing this research line.

Marco set out to scale up the initial results from Ben’s lab with an application to E. coli. This entailed finding a large collection of strains from diverse sources, by sending emails to the community begging them to send us their collections. We compiled publicly available genome sequences, sequence some more and performed large scale growth profiling of these strains in different conditions. From the genome sequences, Marco calculated the impact of variants, relative to the reference genome and used variant effect predictors to identify likely deleterious variants. Genomes, phenotypes and variant effect predictions are available online for reuse. For the lab reference strain of E. coli, we had also quantitative data of the growth defects caused by deleting each gene in a large panel of conditions. We then tested the hypothesis that the poor growth of a strain of E. coli (in a given condition) could be predicted from deleterious variants in genes known to be important in that same condition (Galardini et al. eLife 2017). While our growth predictions were significantly related to experimental observations the predictive power was very weak. We discuss the potential reasons in the paper but the most obvious would be errors in the variant effect predictions and differences in the impact of gene deletion phenotypes in different genomic contexts (see below).

Around the same time Omar Wagih (group profile, twitter), a former PhD student, started the construction of a collection of variant effect predictors, expanding on the work that Marco was doing to try to generalise to multiple mechanisms of variant effects and to add predictors for S. cerevisiae and H. sapiens. The result of this effort was the www.mutfunc.com resource (Wagih et al. MSB 2018). Given a set of variants for a genome in one of the 3 species mutfunc will try to say which variants may have an impact on protein stability, protein interactions, conserved regions, PTMs, linear motifs and TF binding sites. There is a lot of work that went into getting all the methods together and a lot of computational time spent on pre-computing the potential consequence of every possible variant. We illustrate in the mutfunc paper some examples of how it can be used.

Modes of failure – variant effect predictions and genetic background dependencies

One of the potential reasons why the growth phenotypes of individual stains may be hard to predict based on loss of function mutations could be that the variant effect predictors are simply not good enough. We have looked at recent data on deep mutational scanning experiments and we know there is a lot of room for improvement. For example, the predictors (e.g. FoldX, SIFT) can get the trends for single variants but really fail for more than one missense variant. We will try to work on this and the increase in mutational scanning experiments will provide a growing set of examples on which to derive better computational methods.

A second potential reason why loss of function of genes may not cause predictable growth defects would be that the gene deletion phenotypes depends on the rest of the genetic background. Even if we were capable of predicting perfectly when a missense variant causes loss of function we can’t really assume that the gene deletion phenotypes will be independent of the other variants in the genome. To test this we have recently measured gene deletion phenotypes in 4 different genetic backgrounds of S. cerevisiae. We observed 16% to 42% deletion phenotypes changing between pairs of strains and described the overall findings in this preprint that is currently under review. This is consistent with other works, including RNAi studies in C. elegans where 20% of 1,400 genes tested had different phenotypes across two backgrounds. Understanding and taking into account these genetic background dependencies is not going to be trivial.

Perspectives and different directions on genotype-to-phenotype mapping

Where do we go from here ? How do make progress in mapping how genotype variants impact on phenotypes ? Of course, one research path that is being actively worked on is the idea that one can perform association studies between genotypes and phenotypes via “intermediate” traits such as gene expression and all other sorts of large scale measurements. The hope is that by jointly analysing such associations there can be a gain in power and mechanistic understanding. Going back to the Network Biology meeting this line of research was represented with a talk by Daifeng Wang describing the PsychENCODE Consortium with data for the adult brain across 1866 individuals with measurements across multiple different omics (Wang et al. Science 2018). My concern with this line of research is that it still focuses on fairly frequent variants and continues not to make full use of prior knowledge of biology. If combinations of rare or individual variants contribute significantly to the variance of phenotypes such association approaches will be inherently limited.

A few talks at the meeting included deep mutational scanning experiments where the focus is mapping (exhaustively) genotype-to-phenotype on much simpler systems, sometimes only a single protein. This included work from Fritz Roth and Ben Lehner labs. For example, Guillaume Diss (now a PI at FMI), described his work in Ben’s lab where they studied the impact of >120,000 pairs of mutations on an protein interaction (Diss & Lehner eLife 2018). Ben’s lab has several other examples where they have look in high detail and these fitness maps for specific functions (e.g. splicing code, tRNA function). From these, one can imagine slowly increasing the system complexity including for example pathway models. This is illustrated in a study of natural variants of the GAL3 gene in yeast (Richard et al. MSB 2018). This path forward is slower than QTL everything but the hope would be that some models will start to generalise well enough to apply them computationally at a larger scale.

Yet another take on this problem was represented by Trey Ideker at the meeting. He covered a lot of ground on his keynote but he showed how we can take the current large scale (unbiased) protein-protein functional association networks to create a hierarchical view of the cellular functions, or a cellular ontology (Dutkowski et al. Nat Biotech 2013 , www.nexontology.org). Then this hierarchical ontology can be used to learn how perturbations of gene functions combine in unexpected ways and at different levels of the hierarchy (Ma et al. Nat Methods 2018). The notion being that higher levels in the hierarchy could represent the true cellular cause of a phenotype. In other words, DNA damage repair deficiency could be underlying cause of a given disease and there are multiple ways by which such deficiency can be caused by mutations. Instead of performing linear associations between DNA variants and the disease, the variants can be interpreted at the level of this hierarchical view of gene function to predict the DNA damage repair deficiency and then associate that deficiency with the phenotype. The advantages of this line of research would be to be able to make use of prior cell biology knowledge and in a framework that explicitly considers genetic interactions and can interpret rare variants.

I think these represent different directions to address the same problem. Although they are all viable, as usual, I don't think they are equally funded and explored.

State of the lab 6 – group turnover and getting back in the job market

noreply@blogger.com (Pedro Beltrao) — Wed, 09 Jan 2019 14:18:00 +0000

This blog post is part of a yearly series and marks the end of the 6th year as a group leader at EMBL-EBI. Continuing on the theme of the last post of this series, 2018 was a year of wrapping up projects. We finished and made available 4 preprints (plus a few collaborations) in 2018 with 4 more manuscripts ready to be submitted early this year. As in 2017 the group continued to work at full potential with most lab members having been in the group for several years. Some of the turnover I was expecting last year was postponed for the current year. This will make 2019 particularly challenging both personally and professionally with 3 postdocs and 3 PhD students leaving. I have had a few conversations about lab turnover with more senior colleagues. Their typical responses have been that while it is hard to imagine how the group can survive when experienced people leave the incoming lab members bring new ideas and are a great opportunity to start new directions. Being an optimist I look forward to this new chapter in the group although it will be certainly sad to say goodbye to so many people.

We often talk about the issues in academia that are not great: the publish-or-perish mentality, chasing the big journals, the job market, etc. Looking back through the last 2 years I really want to make the point of how great it has been to manage this team of scientists. We got to hit that sweet spot where most team members have been in the group for a few years, know each other’s’ capacities and there are synergies in skill sets and projects. With group members doing a mix of computational and experimental work and a knowledge base ranging from structural biology to genetics. It feels like we could aim our collective capacity to almost any problem and we would make progress. I guess this is what is expected but for me it was the first time seeing it build up within the group. I am sure the group will hit that sweet spot again with a different configuration of people and ideas but the next few years will be a period of reconfiguration.

Group leaders at EMBL typical have a maximum of 9 years and I am currently left with 3 years to move to a new position. Although it is still some time, 3 years means I am now making the last set of hires. We will have 2 postdoc and 1 PhD positions open this year and the group size will start to decrease. Besides focusing on the start of the new projects I will be very actively applying for funding with the idea of taking that funding with me when I move. Given the time it takes to interview and have decisions made for academic posts I will start applying this year if I find interesting places that will consider hiring me in a joint position or with a delay in the start time. I aim to move the group in 2021 but could start sooner as a joint appointment which would give me time to start the new group and apply to and/or move funding. The job application period at the end of the postdoc was one of the most stressful in my life so I am not looking forward to doing it again.

Scientifically there is much to write about but instead of trying to summarise what we have finished in 2018 I think it is the right time to write a few separate blog posts with a summary of what we have achieved over the last 6 years. There have been a few separate threads of research that have resulted in multiple manuscripts so I will group them, describe the work, the people that did it all and what I think are some of the open questions that we may work on in the future.

State of the lab 5 – in the flow with 4 years to go

noreply@blogger.com (Pedro Beltrao) — Wed, 10 Jan 2018 18:04:00 +0000

This blog post is part of a yearly series and marks the end of the 5th year as a group leader at EBI. In March we had an external evaluation of all research groups at EMBL-EBI. It was an interesting experience and overall it was judged a great success for EBI. For our group it was also part of the evaluation towards the standard renewal of contract where I got the 4 year extension. Since there is essentially no tenure at EMBL this also means that I have 4 years until I have to find a senior PI position. This is still a long time but it will increasingly be on my mind going forward. I am not particularly worried but I feel like there are many more places now in Europe with fixed term junior group leader positions. The postdoc bubble will turn into the junior PI bubble and we will have another big barrier and competition in the transition between junior and senior positions.

Personally it is almost strange to stay in the same place after 5 years since I have been typically staying 4-5 years in each place during university (Coimbra), PhD (Heidelberg) and postdoc (San Francisco). It looks like I will have to find some other excuse to thin out my pile of papers on the desk instead of simply moving to a new country and trashing everything.

The end of a cycle
Last year was our most productive year so far, as measured by the number of publications. This year is going to top it based on the manuscripts that I should be working on at the moment instead of writing this post (sorry guys). The research in the group is just flowing with more synergies among the group members. Just when everything is working so well is when so many in the group are leaving. Last year our first PhD student finished (Omar, now at DeepGenomics) and two postdocs have left (Romain moved to benevolentAI and Sheriff is now a project leader at EBI). This year there will be even more people potentially leaving. It is going to be a new challenge to try to keep the science going through the turnover. On the other hand, new arrivals signal the start of new projects and are an opportunity to move the group in new directions. Just at the end of the year, we had 3 new members starting: Allistair (PhD student), Inigo (postdoc) and Abel (visiting PhD student). Abel and Inigo will be working on the impact of mutations in protein interactions and control of protein abundance while Allistair will likely work on the evolution of regulatory networks.

Highlight from 2017 – Predicting condition specific phenotypes from genomes
Most of the work in the group is focused on understanding the function and impact of genetic variants on protein post-translational regulation, in particular for phosphorylation and ubiquitin. However, we have been also working more generically on the genotype to phenotype problem. I think these analyses could use more prior knowledge information and we are trying to contribute in this direction.

Part of this work, led by Marco (GScholar, Twitter) and in collaboration with the Typas lab in Heidelberg was finally published at the end of this year. The question we wanted to address was to what extent we can predict condition specific phenotypes of a strain of E. coli based on its genome and what we know from the well-studied E. coli K-12 lab strain. This is inspired by work that Rob Jelier and Ben Lehner did in S. cerevisiae but on larger scale. To set the project up, imagine we know that a given gene X of E. coli is required for growth under high heat. Then, if that gene X is not present or severely mutated in a strain of E. coli, we would expect that this mutated strain should not survive well in high heat. To test this in large scale we assembled a panel of hundreds of strains of E. coli for which we obtained genomes and fitness measurements under many conditions. We modelled the consequence of mutations using different methods and we collected prior knowledge of which genes are supposed to be important for each condition. In the end we could only predict which strains would tend to grow poorly for around 40% of conditions. This level of success may not be surprising since we didn't take into account for example issues like gene expression levels or compensation by new genes. It could be that gene function may be a lot more plastic than currently assumed but to prove this we will need different experiments.

Besides testing the central question expressed above this collection of E. coli strains with associated data will hopefully serve as resource for future studies. Any additional layer of molecular data (e.g. gene expression) or phenotype (e.g. motility) we measure can make use of all of pre-exiting information. We could ask if motility correlates with the growth under several drugs we tested for example. All of the resources for this collection are freely available and of course this would not be possible without the hard work of the scientist that collected the strains to begin with (listed here).

Highlights for the year ahead
We have 3 different projects that are close to completion that relate to the functional relevance of protein phosphorylation. This is probably going to be our biggest contribution of 2018. We continue to work with the cancer related datasets, primarily using these data to study protein post-translational regulation. Not necessarily to better understand cancer but making use of the large genetic and molecular variation that exists in cancer to better understand the regulatory processes of normal cells. Additionally we will have some progress to report on the evolution of protein kinases and potentially the evolution and regulation of ubiquitylation.

Group member profile - Omar Wagih

noreply@blogger.com (Pedro Beltrao) — Fri, 05 Jan 2018 16:34:00 +0000

The latest instalment of this blog post series is by Omar Wagih (@omarwagih, Gscholar) who has just last month successfully defended his PhD. Along with Marco, Omar has been part of the group working on studying how DNA variants relate to phenotypes. He developed the mutfunc resource and the fantastic guess the correlation game.

What was the path the brought you to the group? Where are you from and what did you work on before arriving in the group?
My love of genetics is, in more ways that one biologically ingrained. Growing up in a family of scientists, I was always surrounded by a wealth of information which I instinctually sought to organise. For this, I pursued my undergraduate and masters degree at the University of Toronto, majoring in computational biology and computer science, respectively. Along the way, I was fortunate to work in some of the leading computational biology labs in Canada including those of Gary Bader, Philip Kim, Charlie Boone, Brenda Andrews, Andrew Fraser and Andrew Emili. I worked on a range of projects which ranged from analysing images of genetic screens of yeast to determining the impact of disease mutations on kinase-substrate phosphorylation. These experiences led me to develop an interest in understanding how changes in the genome translate to variability in cellular physiology, and ultimately phenotype, which prompted me to pursue my PhD.

What are you currently working on?
My current project involves working towards a deeper understanding of how changes in the genome propagate to phenotypic variability by predicting which cellular mechanisms are likely to be impacted. For the past several years I have been developing and using computational methodologies to assess the mechanistic impact of natural and disease-causing mutations. I have been applying these to yeast, human and bacteria models in hopes of streamlining hypothesis-driven variant annotation. I have also been utilising these predictions to assess the overall burden these mutations impose on gene function and putting such information towards conducting gene-phenotype associations.

What are some of the areas of research that excite you right now?
I'm intrigued by novel mutagenesis technologies that are allowing us experimentally assess the impact of genetic variants on cellular fitness and function in a massively parallel fashion. Technologies like deep mutational scanning CRISPR are becoming increasingly common in achieving this and their off-target effects are steadily being reduced.

With such massive amounts of mutagenesis data, I'm also interested in how machine learning methodologies such as deep learning can be applied to learn how mutations collectively impinge on cellular function and ultimately phenotype. This would significantly improve the precision of variant impact predictors and, in my opinion, will have crucial roles in shaping the development of novel and personalised drug therapies.

What sort of things do you like outside of the science?
Whether I'm skiing, hiking, camping or exploring the city, or you'll more likely than less find me outdoors. I often partake in sports. During my time in Cambridge, I rowed for my college and was part of the university boxing team.

I have been fascinated by drones for a while and own a DJI Phantom 3, which I often use for aerial filming. I also enjoy landscape and portrait photography, particularly with my 50mm lens. If I still have extra time on my hands, you'll find me implementing silly ideas that come to mind into apps or games. Here are a few I've made: genewords, pubtex, and guess the correlation.

Building rockets in academia - big goals from individual projects

noreply@blogger.com (Pedro Beltrao) — Mon, 26 Jun 2017 17:27:00 +0000

SpaceX just launched and landed another two rockets over the weekend. I don’t get tired of watching those images of re-entry and landing. The precision is mesmerizing and extremely inspiring. Leading a research group in academia I often look at research intensive companies and wonder about the differences and similarities between how research is done in both. I have never worked in such a company environment so these thoughts are certainly from the perspective of academia.

The big goals and peripheral bets

From reading about big tech companies and start-ups I can relate to how they appear to organize their product portfolio into a small number of main goals – their core product(s) – while at the same time experimenting with peripheral goals/products. Tesla started as a car company but may end up being a large battery company with small side of car manufacturing. As another example, most major tech companies are today experimenting with virtual reality. In these experiments, those involved face similar questions about uncertain outcomes and timeliness of their steps as we do in academia. One of the thrills in academia is that leap into the unknown where it is crucial to ask the right question just at the right time. The speed of progress in research can be very uneven with times spent floundering in the dark and times where you just happen to walk in the right direction and find big riches. Sometimes those explorations will lead you to unintended directions, away from your core research, where it might be worth moving additional resources. Aiming in the right direction at the right time is a rare skill that a researcher must have but that we don’t spend enough time training for. Also, the balance between focusing on the core and exploring other areas of interest is difficult to set. In academia it seems easier to obtain funding to keep working on your core than to move to new areas. I wonder how companies deal with these issues. I am extremely thankful to be working in a research institute where I get core funding that, although I have to justify, I get to use to explore ideas outside the core of what we do. Such flexibility could be a bigger part of how research funding gets distributed.

Individualized contributions to group goals

While setting a big goal and exploring peripheral objectives might have a lot in common between academia and companies, there is one aspect of how we work that appears very different. In setting the big overarching questions we have to accommodate the fact that each individual group member will have to stand out. PhD students are working on their theses and postdocs are building the work on which they will stand as future group leaders. Each project has to brilliantly stand on its own while simultaneously fitting together with other group projects, contributing to an even greater goal. As each research project can be an unpredictable grasp in the dark, as a group leader I feel like I have to be build an alluring house of cards. Projecting how several research projects might move forward and create an illusionary image of how they fit together to solve THE big question. Not only will we build the rocket that will save mankind but every single contribution from each team member has to solve an important problem. It is obvious that the overarching goal will have to shift with time as some projects move to their potential unintended outcomes. In the context of being flexible to follow peripheral bets, maintaining the big picture goal may be challenging. I would not be the first to propose more career tracks in academia where professional researchers don’t have to move into management roles to keep working in academic science. It would be interesting to try it out on some research institutions to see the effect it would have on how research agendas would be organized.

Postdoc positions on context dependent cell signalling (wet and/or dry)

noreply@blogger.com (Pedro Beltrao) — Fri, 28 Apr 2017 13:46:00 +0000

Why do some mutations cause cancer in some tissues and not others ? What happens to the cell signalling pathways during differentiation ? Why are some genes essential in some cell types and not others or why are some drugs more effective at killing some cell types than others ?

We think that this is a great time to be asking these questions of how the genetic background or tissue of origin changes cell states. More precisely for us, how this re-wires cell signalling. It has become routine to measure changes in phosphorylation across different conditions, including different cancer types. The Sanger and others are establishing panels of human cell lines that are being profiled with an increasing array of omics technologies with drug sensitivity and CRISPR based gene essentiality information. These panels offer a great opportunity to address these questions.

We want to combine the work we have been doing in studying human signalling with phosphoproteomic data, with variant effect predictors, microscopy based studies of cell signalling and network modelling to address this question of context dependent changes in cell signalling.

To support this research we have 2 postdoc positions available: one would be primarily computational and would involve image analysis and network modelling in collaboration with microscopy groups (see here for project and application); the second would be primarily experimental with a focus on microscopy. The latter would be available via the ESPOD fellowship scheme in collaboration with Leopold Parts group at Sanger (see here for project description and here to apply). The split between computational and experimental is open and wet/dry mixed candidates are encouraged as well to apply to both.

These projects complement existing work in the group using cancer Omics data to study the genetic determinants of changes in protein abundance and phosphorylation and will be in collaboration with work developed by the Petsalaki group at EBI that is also recruiting. Email me if you have any questions/concerns about the positions.

17 years of systems biology

noreply@blogger.com (Pedro Beltrao) — Mon, 10 Apr 2017 10:30:00 +0000

I know that 17 years is not a very round number. It is also fairly arbitrary as I am assuming systems biology started around 2000 (see below). I was last week in Portugal, where every year for the past 8 years I have been teaching a week long course on Systems and Synthetic Biology to the GABBA PhD program. This might be the last year I take part in this course and so I felt it would be a good time to try to put some thoughts in a blog post. This course has been jointly co-organised from the beginning with Silvia Santos and we had several guests throughout the years including Mol Sys Bio editors Thomas Lemberger and Maria Polychronidou and other PIs: Julio Saez-Rodriguez, Andre Brown, Hyun Youk and Paulo Aguiar. Some of what I write below has been certainly influenced by discussions with them. This is not meant as an extensive review so apologies in advance for missing references.

Where did systems biology come from?

It is not contentious to say that systems biology came about in response to the ever narrower view of reductionist approaches in biology. Reductionism is still extremely important and I assume that, as a movement, it was an opposition to the idea that biology was animated by some magical force that could never be comprehended. Since the beginning of the course we have asked students to read the assay “Can a biologist fix a radio?” by Yuri Lazebnik (2002). The article captures well the limitations of reductionist research. The more we know about a system, apoptosis in Yuri's case, the more complex and non-intuitive some observations may seem. Yuri's description of how a biologist would try to understand how a radio works is comical and still very apt today:

We would “remove components one at a time or to use a variation of the method, in which a radio is shot at a close range with metal particles. In the latter case radios that malfunction (have a “phenotype”) are selected to identify the component whose damage causes the phenotype. Although removing some components will have only an attenuating effect, a lucky postdoc will accidentally find a wire whose deficiency will stop the music completely. The jubilant fellow will name the wire Serendipitously Recovered Component (Src) and then find that Src is required because it is the only link between a long extendable object and the rest of the radio.”

One of the driving forces for the advent of systems biology was this limitation, so brilliantly captured by Yuri, that reductionism can fail when we are overwhelmed with large systems of interconnected components.

Around the time that Yuri wrote this article our capacity to make measurements of biological objects was undergoing a revolution we generally call omics today. In 2001 the first drafts of the human genome were published (Lander et al. 2001; Venter et al. 2001). Between 2000 and 2002 we had the first descriptions of large scale protein-protein (Uetz et al. 2000; Ito et al. 2001; Gavin et al. 2002) and genetic interactions mapping (Tong et al. 2001). The capacity to systematically measure all aspects of biology appeared to be within our grasp. The interaction network representation of nodes connected by edges is now an icon in biology, even if not as recognisable as the double helix. This ever increasing capacity to systematically measure biology was, alongside the complexity of highly connected components, the second major driving force for the advent of systems biology.

What is systems biology?

So around 2000 biology was faced with this upcoming flood of data and highly complex nonlinear systems. Reductionism was failing because mental models were insufficient to cope with the information available. The reaction was a call for increased formalism, better ways to see how the sum of the parts really works. Perspectives were written (Kitano 2002) and institutes were born (Institute for SystemsBiology). Within the apparent complexity of biology there might be emergent principles that we were not seeing simply because we were looking too narrowly and could not combine information in a formal way. Whatever the system of interest (e.g. proteins, cells, organisms, ecosystems) there must ways to take information from one level of abstraction (e.g. proteins) and understand the relevant system's features of the abstraction layer above it (e.g. cell behaviours). This comes closest to a definition of systems biology put forward by Tony Hyman (Hyman 2011) but many others have defined it in vaguely similar ways, or maybe in similarly vague ways.

Power laws and the perils of searching for universal principles

When introducing systems biology I have been giving two examples of work that illustrate some of the benefits (network motifs) but also some of the perils (power law networks) of trying to find universal principles in biology. One of these examples was the research on the organisation of biological networks. As soon as different networks were starting to be assembled, such as protein-protein, genetic and metabolic networks, an observation was made that the distribution of interactions per gene/protein is not random (as studied by Paul Erdös). Most proteins have very few interactions while some rare proteins have a disproportional large amount of interactions – dubbed “hubs”. Barabasi and many others had a series of papers describing these non-random distributions, called power-law networks (Jeong et al. 2000), in all sorts of biological networks. Analogies were drawn to other non-biological networks with similar properties and it is not an understatement to say that there was some hype around this. The hope was that by thinking of the common processes that can give rise to such networks (e.g. preferential attachment) we would know, in some deep way, how biology is organised. I will just say that I don’t think this went very far. Modelling biological networks as nodes and edges allowed the application of graph theory approaches to biology, which has indeed been a very useful inheritance from this work. However, we didn't find deep meaning in the analogies drawn between the different biological and man-made networks, although I am sure some will disagree.

Network motifs, buzzers and blinkers

Around the same time, the group of Uri Alon published very influential work describing recurring network motifs in directed networks (Milo et al. 2002; Shen-Orr et al. 2002). For example, in the E. coli transcriptional network they found some regulatory relationships between 3 different genes/operons that occurred more often than expected by chance. One example, illustrated to the right, was named a coherent feedforward loop where an activating signal was sent from an “upstream” element X to a “downstream” element Z both directly and indirectly via an intermediate third element. The observation begs the question of the usefulness of such an arrangement (Mangan and Alon 2003; Kalir et al. 2005). This has been generalised to studying the relation between any set of such directed interactions with specific reaction parameters – defined as the topology - and their potential functions. In a great review Tyson, Chen and Novak summarise some of these ideas of how regulatory networks can act, among other things as “sniffers, buzzers, toggles and blinkers” (Tyson et al. 2003).

These and other similar works showed that, within the complexity of regulatory networks, design principles can be found that encapsulate the core relationships giving rise to a behaviour. Once these rules are known, an observed behaviour will constrain the possible space of topologies that can explain it. This has led researchers to search for missing regulatory interactions that are needed to satisfy such expected constraints. For example, Holt and colleagues searched for a positive feedback that would be expected to exist for the switch-like dissolution of the sister-chromatid cohesion at the start of anaphase (Holt et al. 2008). This mapping between regulatory networks and their function can be applied to any system of interest and at any scale. The same types of regulatory interactions are used for termites building spatially organised mounds and for growing neurons seeking to form connections (as illustrated in a review by Dehmelt and Bastiaens). Different communities of scientists can come together in systems biology meetings and talk in the same language of design principles. This elegance of finding “universal” rules that seemingly explain complex behaviours across different systems and disciplines has been a great gift of systems biology. It is of course important to point out that such ideas have a much longer history from homoeostasis in biology and control theory in engineering.

Bottom-up network models

Alongside the search for design principles in regulatory interactions the formal mathematical and computational modelling of biological systems gained prominence (e.g. Bhalla and Iyengar 1999). Mathematical models are much older than systems biology but they started to be used more extensively and visibly with the rise of systems biology. Formalising all of the past knowledge of a system was shown to be a useful way to test if what is known was sufficient to explain the behaviour of the system. Models were also perturbed in silico to find the most relevant parameters and generate novel hypothesis to be tested experimentally. This model refinement cycle has been used with success for example in the modelling of cell cycle (Novak and Tyson 1993, Tyson Noval 2001; among many others) or circadian clock (Locke et al. 2005; Locke et al. 2010; Pokhilko et al. 2012). However, this iteration between formal modelling and experiments has not really taken off across many other systems. The reason for the lack of excitement is not clear to me although I have the impression that often the models are not used extensively beyond asking if what we know about a system sufficiently explains all of observed outcomes and perturbations.

Top-down systems biology and everything in between

From the start there has been a division between the researchers that identified themselves as part of the systems biology community. Bottom-up researchers have been focused on the formal modelling of systems, the discovery of design principles and emerging behaviours. Top-down researchers would argue that a truly comprehensive view of a system is needed. These scientists have been more focused on further developing and applying methods to systematically measure biological systems. The emphasis in this camp has been on developing generalizable strategies that can take large-scale observations and identify rules, regardless of the system of interest. I would say that these works, my own included, have been less powerful in identifying elegant universal rules. By this I mean, for example, those initial attempts to find common principles across biological and man-made networks. Instead of principles, what have been readily transposed across systems have been approaches such as machine learning methods. Drug screens with behavioural phenotypes, genetic interaction networks or developmental defect screens with gene knock-downs can all be analysed in the same ways. Such systematic studies have driven costs down (per observation) and contrary to “representative” experiments in small scale studies, the large-scale measurements tend to be properly benchmarked for accuracy and coverage.

What is still missing are ways to bridge the divide between these two camps. Ways to start from large-scale measurements that result in models that can be studied for design features. Studies that include perturbation experiments come closer to achieve this. Examples for network reconstruction methods have shown that it should be possible to achieve this but we are not quite there yet (Hill et al. 2016).

From systems biology to systems everything

As scientific movement systems biology started in cell biology, as far as I can tell, but has since then permeated many other areas of research. As examples, I have heard of systems genetics, systems neuroscience, systems medicine, evolutionary systems biology and systems structural biology. In 2017 we still face a flood of data and highly complex nonlinear systems. However, the reductionist approaches now typically go hand-in-hand with attempts to formalise knowledge in quantitative ways to identify the key relationships that explain the function of interest. In a sense, the movement of systems biology has succeeded to such an extent that it seems less exciting to me as field in itself. It is a fantastic approach that is currently being used across most of biology but there is less developments that alter how we do science. I am curious as to what other researchers that identify themselves with doing systems biology think - What have been great achievements of systems biology? What are the great challenges that are not simply applications of systems biology? Questions to think about for the (equally arbitrary) celebration of the 20 years of the field in 2020.

Predicting E3 or protease targets with paired protein & gene expression data (negative result)

noreply@blogger.com (Pedro Beltrao) — Fri, 10 Feb 2017 17:03:00 +0000

Cancer datasets as a resource to study cell biology

The amazing resources that have been developed in the context of cancer biology can serve as tools to study "normal" cell biology. The genetic perturbations that happen in cancer can be viewed almost as natural experiments that we can use to ask varied questions. Different cancer consortia have produced, for the same patient samples or the same cancer cell lines, data that ranges from genomic information, such as exome sequencing, to molecular, cellular and disease traits including gene expression, protein abundance, patient survival and drug responses. These datasets are not just useful to study cancer biology but more globally to study cell biology processes. If we were interested in asking what is the impact of knocking out a gene we could look into these data to have, at least, an approximate guess of what could happen if this gene is perturbed. We can do this because it is likely that almost any given gene will have changes in copy number or deleterious mutations given a sufficiently large sample of tumours or cell lines. Of course, there will be a whole range of technical issues to deal with since it would not be a "clean" experiment comparing the KO with a control.

Studying complex assembly using protein abundance data

More recently the CPTAC consortium and other groups have released proteomics measurements for some of the reference cancer samples. Given the work that we have been doing in studying post-translational control we started a few projects making use of these data. One idea that we tried and have recently made available online via a pre-print was to study gene dosage compensation. When there are copy number changes, how often are these propagated to changes in gene expression and then to protein level ? This was work done by Emanuel Gonçalves (@emanuelvgo), jointly with Julio Saez-Rodriguez lab. There were several interesting findings from this project, one of these was that we could identify members of protein complexes that indirectly control the degradation of other complex subunits. This was done by measuring, in each sample, how much of the protein abundance changes are not explained by its gene expression changes. This residual abundance change is most likely explained either by changes in the translation or degradation rate of the protein (or noise). We think that, for protein complex subunits, this residual mainly reflects degradation rates. Emanuel then searched for complex members that had copy number changes that predicted the "degradation" rate of other subunits of the same complex. We think this is a very robust way to identify such subunits that act as rate-limiting factors for complex assembly.

Predicting E3 or protease targets

If what I described above works to find some subunits that control the "degradation" of other subunits of a complex then why not use the exact same approach to find the targets of E3 ligases or proteases ? Emanuel gave this idea a try but in some (fairly quick) tests we could not see a strong predictive signal. We collected putative E3 targets from a few studies in the literature (Kim et al. Mol Cell Biol. 2015; Burande et al, Mol Cell Proteomics. 2009; Lee et al. J Biol Chem. 2011; Coyaud et al. Mol Cell Proteomics. 2015; Emanuele MJ et al. Cell 2011). We also we collected protease targets from the Merops database. We then tried to find a significant association between the copy number or gene expression changes of a given E3 with the proxy for degradation, as described above, of any other protein. Using the significance of the association as the predictor with would expect a stronger association between an E3 and their putative substrates than with other random genes. Using a ROC curve as descriptor of the predictive power, we didn't really see robust signals. The figure above shows the results when using gene expression changes in the E3 to associate with the residuals (i.e. abundance change not explained by gene expression change) of the putative targets. The best result, was obtained for CUL4A (AUC=0.59) in this case but overall the predictions are close to random.

A similar poor result was generally observed for protease targets from the merops database although we didn't really make a strong effort to properly map the merops interactions to all human proteins. Emanuel tried a couple of variations. For the E3s he tried restricting the potential target list to proteins that are known to be ubiquitylated in human cells but that did not improve the results. Also, surprisingly, the genes listed as putative targets of these E3s are not very enriched in genes that increase in ubiquitylation after proteasome inhibition (from Kim et al. Mol Cell. 2011) with the clearest signal observed in the E3 targets proposed by Emanuele MJ and colleagues (Emanuele MJ et al. Cell 2011).

Why doesn't it work ?

There are many reasons for the lack of capacity to predict E3/protease targets in this way. The residuals that we calculate across samples may reflect a mixture of effects and degradation may be only a small component. The regulation of degradation is complex and, as we have shown for the complex members, it may be dependent on other factors besides the availability of the E3s/proteases. It is possible that the E3s/proteases are highly regulated and/or redundant such that we would not expect to see a simple relationship between changing the expression of one E3/protease and the abundance level of the putative substrate. The list of E3/protease targets may contain false positives and of course, we may have not found the best way to find such associations in these data. In any case, we though it could be useful to provide this information in some format for others that may be trying similar things.

State of the lab 4 – the one before the four year review

noreply@blogger.com (Pedro Beltrao) — Fri, 13 Jan 2017 21:25:00 +0000

It has been 4 years since I started as a group leader at the EMBL-EBI (see past yearly reports – 1, 2 and 3). This year the group composition has been mostly stable, with the exception of interns that have rotated through the group. We had Bruno Ariano (twitter) visiting us for 6 months working on a project to build an improved functional interaction network for Plasmodium. Matteo Martinis has joined the group for a few months and is working with David Ochoa on comparing in-vivo effects of kinase inhibitors with their known in-vitro kinase inhibition effects. Finally, Areeb Jawed has joined Cristina and Bede, for some months, in their efforts to develop genetic methods to study protein modification sites. I think we had a great year in terms of publishing and I had the luxury of not trying to apply for additional funding. That luxury is short lived as we have funding that is ending this year that I will try to replace.

The one before the four year review

All EMBL units are evaluated every 4 years by a panel of external reviewers. The next review for the research at EMBL-EBI is coming up now in March and we have been preparing the required documentation for this. Naturally this forced me to think about what we have achieved as a group for the past 4 years and what we aim to do for the next 4. It is impossible not to go through this process without being drawn into some introspection and without comparing our performance with that of those around me. I think we did well in this period of time, we got two significant grants funded (HFSP and ERC) and published some articles that I feel have been significant contributions towards the study of kinase signaling. I remember my interview for this position when they asked me what I would expect to achieve in the next 5 years. My first though was: “Really ? That question ?”, but I think we did achieve we I had hoped at the time. Still, at EMBL-EBI we are surrounded by some fantastic colleagues that keep the bar really high. It is hard to be satisfied and I am certainly motivated by our research environment to try to help our group to keep up the good work. This review will also determine if our group receives a 4 year extension after the first 5 years. I am confident but still apprehensive and curious about what the reviewers will say.

Studying cell signaling states using phosphoproteomics

During the past four years we have worked on several aspects of kinase based cell signaling. I mentioned before our work on trying to describe the evolutionary history of protein phosphorylation (blog post) and to predict the kinase specificity from interactions networks and phosphoproteomic data (blog post). I haven't described yet our work on studying cell signaling states that has been published a few months ago When David Ochoa started in the group around 3.5 years we reasoned that, by collecting information on how phosphosites abundances change across a large number of conditions, we would be able to use the profile of co-regulation across conditions to learn about how cell signaling systems work. This is copying by analogy what has been done in gene expression studies since Mike Eisen's work. David made use of published conditional phosphoproteomic studies to compile a very large compendium of different conditions. There are issues related to the incomplete coverage of mass spectrometry measurements and potential batch effects of the different studies. David tried to work around these, primarily by focusing the analysis on groups of phosphosites (e.g. targets of the same kinase) instead of individual positions. Using this data he derived an atlas of changes in activities for around 200 human kinases across nearly 400 different conditions. We show in this work how this can be used to advance our knowledge of kinase signaling (Ochoa et al. Mol Sys Bio 2016, and the phosfate webserver).

For me this work was the fist time we could measure a large number of cell signaling states. To see what is the structure of this state-space and what we can learn from this. What kinases are most often regulated ? What kinases define particular signaling states ? What states act as “opposing” states and how may we use this information to promote or inhibit specific states or transitions through the state-space ? These are all questions that we can address with this atlas. The fact that the data was collected from different publications, using different protocols and machines will certainly have an impact on the accuracy and resolution of this atlas. However, the quality and coverage and these types of experiments will only improve and I think this direction of research will continue to be exciting for long period of time.

Since this work we have also tried to benchmark different approaches to predict the changes of kinase activity from phosphoproteomic information (preprint). In collaboration with Julio Saez-Rodriguez's lab we also used some of the same concepts to relate the changes of metabolism with predicted changes in kinase, phosphatase and transcription factor activities (Gonçalves et al. PLOS Comp Bio 2016).

Onto the next four years

If I do get my contract extension, we will continue our current main research focus on studying cell signaling through the next four years. Although we will certainly continue to study long term evolutionary trends, such as the evolution of kinase specificity, we will complement this with trying to understand the impact of genetic variation for individuals of the same species with a strong focus on E. coli, S. cerevisae and H. sapiens (mutfunc). We have started to make use of cancer data as genetic resource to study human cell biology (preprint). We won't necessarily try to study cancer as a disease but I think that datasets for primary tumors and cancer cell lines are amazing resources to learn about how human cell biology and cell signaling work. The group will have its first big turnover of group members over the next 1 or 2 years which will be challenging professionally and personally. However this turnover will also allow for and shape future directions of the group which will also be exciting.

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When only truthiness matters

noreply@blogger.com (Pedro Beltrao) — Sun, 13 Nov 2016 00:00:00 +0000

As many others out there I am still trying to process the result of the US elections. I don’t usually write about politics but I think this does have relevance to science. The result brought me flashbacks of the outcome of the Brexit vote. In both occasions I woke up to a result that I found shocking and disheartening. Both times I went to work in a dazed state of denial trying to come to terms with the fact that so many people have viewpoints that are so different from mine. Personally, I find repugnant that both elections were so much about racism and fomenting protectionist and anti-immigration movements. There are many political and social issues around these elections that I am not going to touch on. The important point to science and scientists here is that these elections were won using many false statements and arguments. I know I am biased because these were not the outcomes I was hoping for. Still, I don’t think I am exaggerating when I say that the winning sides of both elections used a similar strategy of inventing a suitable reality that they pushed to their advantage. I am used to politicians bending the truth and making promises that they don’t keep but more and more they simply lie. As someone trained to be rational and critical to flaws in argument I live through it in complete disbelief. Trump did this all the time but one particular interview in the US elections really brought this point to home to me:

Newt Gingrich clearly states it here – it does not matter what the truth is, it matters what people feel the truth is. This is what Steven Colbert termed as truthiness, a joke that he should be thinking a lot about these days. The rise of truthiness is a danger to society. To get your way you no longer have to find arguments based on the present reality, you just have to be able to warp reality in your favor.

Filter bubbles and confirmation bias

The internet, with its immediate access to information and its global reach, should be a weapon in favor of reason. Instead it has actually increased our isolation as we sort ourselves by affinity to beliefs. I am this shocked with the results of these elections because I barely interact with those with the same set of beliefs of the winning groups. We live in filter bubbles (book, TED talk) in all the media that we consume and even in the places where we live in. This affinity based social sorting is amoral. The same ease of access that allows scientists to collaborate globally is bringing together any other likeminded group of people. In the book “Here Comes Everybody” Clay Shirky gives examples of groups of bulimics teaching each other techniques to avoid eating and how the internet may help terrorist groups. It is hard to break into these echo chambers because people tend to perceive as true whatever confirms their beliefs. This well-known phenomenon of confirmation bias gets magnified by communal reinforcement within the filter bubbles. Savvy social manipulators don’t have to change the opinions of those in these echo chambers, they can try to connect with and shepherd those within.

What do we do when truth and reason no longer matter? Scientific findings are no longer facts but just opinions and values. People can be pro or against vaccination for example. This is starting to have very serious and concrete consequences (e.g. global warming) and looks to be increasingly getting worse. Although in both elections the younger generations were less likely to have voted for the winning outcomes, I don’t think that echo chambers and the attack on reason are a generational problem. Maybe scientists should be having a more active role in promoting the importance rational thought or maybe it is a challenge that can only be solved by improving the education system.

Group member profile - Marco Galardini

noreply@blogger.com (Pedro Beltrao) — Thu, 20 Oct 2016 17:18:00 +0000

Marco Galardini (webpage, Gscholar, twitter, EMBL-EBI page), a postdoc in the group, is the next member that kindly volunteered to write a group profile page. He is currently one of the few people in the group that is not working directly with protein PTM regulation but is looking instead more generally at the consequences of mutations on cellular growth phenotypes.

What was the path the brought you to the group? Where are you from and what did you work on before arriving in the group?

I like to think of my career so far as a simulated annealing process, where the temperature parameter is substituted by curiosity. I started by studying applied chemistry in high school; we had to spend lots of time in the lab and we got plenty of opportunities to get our hands dirty with both inorganic and organic chemistry. The latter is probably the reason why I then pursued a bachelor degree in biotechnology at the university of Florence, with a focus on industrial and environmental processes; during that time I also got interested in microbiology, mostly by the great diversity and versatility of the bacterial kingdom. When I discovered that the University of Bologna was offering a masters degree in Bioinformatics I jumped into it with great enthusiasm, eventually combining it with the interest in microbiology during an internship at the Nijmegen university.

After a short break as a software developer in a company I started a PhD in Florence, carrying on a comparative genomics study in the nitrogen-fixing plant symbiont Sinorhizobium meliloti (PhD thesis). Since this project combined computational biology, microbiology and the impact on the environment, I can say that it succeeded in combining the various academic interests I had developed during the years. Following the simulated annealing analogy I can say that I sometimes felt like I was in a local optima. Under the supervision of Marco Bazzicalupo, Emanuele Biondi and Alessio Mengoni (lab page) I was lucky enough to ride the wave of genomics in a moment where getting bacterial genomes was becoming increasingly easy; I was therefore able to describe the interesting functional and evolutionary features of the (relatively) complex genome of S. meliloti, while developing some computational methods on the side.

What are you currently working on?

I'm currently two years into a very exciting project that aims to develop models
to predict phenotypes for the Escherichia coli species, in close collaboration with Nassos Typas (EMBL). Bacterial species are known to harbor striking genetic variability between strains, both in the form of point mutations, but also with respect to their gene content (the so-called pangenome), due to recombination and lateral gene trasfer. Understanding how this variability translates to differences in phenotypes has been therefore the focus of this project. This has proven to be both a challenging and valuable experience, as we had to build a strain collection from scratch, phenotype it on different growth conditions and sequence a large fraction of those strains.

For this I owe a great deal of gratitude to various members of the Typas group who have helped me out in running the wet-lab experiments, namely Lucia Herrera and Anja Telzerow. I am now in the process of testing the predictive models, who have proven to show very promising results, with potential applications to other species, in and outside the bacterial kingdom.

What are some of the areas of research that excite you right now?

Despite the common claim that no great discoveries are made anymore, I think that science is moving faster and getting bigger every day; if we want to be optimistic it should only be a matter of time before this will start to have an impact on our everyday lives. Some examples involving microbiology include real-time tracking of infectious diseases (e.g. WGSA, or NextFlu) and microbial communities as environmental sensors (e.g. Smith et al. mBio 2015). I'm therefore very excited to see how the lag time between a discovery and its application shrinks; there are legitimate concerns of course (e.g. laws not catching up, democratization of new technologies), but I can't help being thrilled about it. I also enjoy reading about how human activities are becoming a new powerful selective pressure in evolution; antibiotic resistance is the best known example, but there are also positive examples like the reports of bacterial species evolving the ability to degrade plastic. This shows that the natural world is still worth exploring and that evolution can also act on very short time-scale.

What sort of things do you like outside of the science?

I used to be quite active in photography, with a preference for analogic media
such as black and white films and polaroids; despite not being very active right now, I'm still packing my camera when going for a short trip. I also have an interest in small DIY projects involving music; I have built some experimental synths running on Arduino, which were used in a band I used to play in. Apart from that, I enjoy reading and watching movies, going to contemporary art exhibitions, and a bit of cycling.