eYE sEE bIOINFORMATICS

SYSTEM BIOLOGY(WHOLE-ISTIC APPROACH TO UNDERSTAND BIOLOGY)

noreply@blogger.com (Vikas) — Tue, 27 Jul 2010 14:30:00 +0000

Few days back i was talking with one of my friend about scope of Bioinformatics in India. He was life science graduates and currently working in one of eminent research lab in India.While talking to him about my work when i told him dude... I am working in System Biology based company where we are doing insilico drug studies and predicting biological responses in different disease condition by using our state of art developed technology. Then he gave me such a weird look and laughed as i have cracked "joke of the century". I was shocked when i came to know that he is completely unaware of the immense potential of System Biology.That's why i decided to write this post about system biology so that people like him can understand System Biology.

SYSTEM BIOLOGY is a whole-istic approach to understanding biology which aims at system-level understanding of biology, and to understand biological systems as a system or In simpler words it is examination of the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism.

Immune System and nerves System are two prominent examples of biological system

.Although idea of system level understanding is not new to biology,there have been attempts at such an approach in the past which originated in other fields of science. Scientists from other disciplines - such as physicists or (later) systems theorists - have been interested in applying their science to biology for quite some time

But concept of System Biology was originally conceived by NORBERT WIENER, the founding father of cybernetics in 1948, who explicitly considered technical as well as biological systems as objects for the same scientific approach.At the same time other attempts were made, some still under the name of cybernetics, that kept the idea alive through the following decades. Prominent are

Biochemical Systems Theory (BST), developed in the late 1960s, and a related approach
Metabolic Control Theory (MCT), proposed in the mid 1970s.

to create simplified mathematical models of biological systems in steady state and resulted in quite a number of tools and methods for analyzing systems modeled in the respective way. But all these attempts suffered from inadequate data to base their theories and models.

Breakthrough advances in molecular biology in the last decades in wake of human genome project , providing new data, enabling applied work in this area, making the in silico model of an organism envisionable.These advancements can be subsumed into two groups :

Bottom-up( independent experimental data into a conclusive representation of a gene regulatory network) and
Top-down approaches(uses high-throughput data from DNA micro-array and other new measurement technologies).

These advances in measurement, data acquisition and handling technologies provide a wealth of new data to improve existing models. That data can be divided into four categories or key properties:

system structures, system dynamics, control methods, and design methods.

Progress in these areas required ``breakthroughs in our understanding of computational sciences, genomics, and measurement technologies, and integration of such discoveries with existing knowledge''which brings together scientists from a lot of different disciplines, such as biology, systems theory, computer science, physics, chemistry, and interdisciplinary areas of applied science like measurement instruments development.

APPROACHES FOR SYSTEM BIOLOGY

There are basically two approaches to SYSTEM BIOLOGY. One group wants to use a new system-oriented approach. Another wants to continue the successful work along proven lines and make progress by ``integrating the different levels of information pertaining to genes, mRNAs, proteins, and pathways'' which have up to date been used individually and interestingly both these approaches can coexist without hurting each other,even they can profit from each other's discoveries.

System biologist are trying to integrate the biological knowledge and to understand how the molecules act together within the network of interaction that makes up life.As earlier said currently we are having enormous amount of biological data, which cannot be understood by simply drawing lines between interacting molecules Model building promises to be the key in advancing understanding. Already numerous centers devoted to Systems Biology being opened worldwidea, and other research collaborations bringing together expertise in mathematics, information science and biology being funded.

WHAT IS MODEL BUILDING

Model building as an aid to understand complex systems is also the method of choice in areas like ecology or economy.This huge amount of data is familiar to engineers, for example, those designing control systems for modern passenger jets.So By taking clue and lessons from system analysis of advanced technologies and engineering theory suggest that the systems can be divided into subsystems, so one does not have to tackle and solve the whole system at once which will ease the task of understanding of complex interactions at systems level.

MODEL BUILDING IN BIOLOGY

Same Complexity as faced by other branches of Science is experienced in Biology while looking at signal signaling molecule.Prof. Kitano said "biological system is not just an assembly of genes and proteins.Therefore, its properties cannot be fully understood merely by drawing diagrams of their interconnections." Currently Biologists are using "conceptiol" models describing their pictured view of the events involved. Those models are in general purely qualitative. There is need to introduce mathematics like non-linear differential equations to elucidate the underlying mechanism on a quantitative basis to gain systems-level understanding, not just a pictured view.

The word model derives from the Latin language and refers to the simplified representation of a real system e.g. representation of TNF Signaling Network using mathematics.Systems and models are often structured and hierarchically build(modularity).

" Modularity is a concept of treating subsystems of complex molecular networks as functional units that perform identifiable tasks perhaps even able to be characterized in familiar engineering terms.It would also be the ideal basis for future developments to even more complex models, once the cellular and sub-cellular levels can be described in sufficient detail. Modular approach in biology facilitates to produce fully qualified models of first cells, then organs, then even complete organisms."

On the cellular level, for example, usually the metabolic, gene, and protein networks are distinguished, although, of course, interconnected, and organs and then the organism form the next higher levels.

TYPES OF MODEL

Process of Model building also varies based on source of Information.Here i am giving few examples,

Models can be based on a priori knowledge about system elements, e.g. physical laws,

Models based on a behavior seen, e.g. curve fitting, where the correlation between input and output is then usually treated as a black box.

Model is static when all output signals only depend on the input signal at the same time,Where only the topology is relevant for the behavior.

Model is dynamic, where the system behavior also depends on the past. This implies storage in the system. Models can be deterministic, i.e. for a certain input and certain initial conditions of the state variables, there is one output or models can be probabilistic, i.e. the output is predicted within a range of values, and each value has an assigned probability. Certainly probabilistic models are useful in biology, and are necessary if small numbers of molecules are involved. However, probabilistic models are more complicated and also more demanding in terms of computer performance to be solved.

MODELING AS AN REPEATING PROCESS

Modeling is an repeating process where in initial stage one has to consider the questions the model is supposed to answer, and then clearly define the model system, e.g. which cell line is to be modeled. Then in next step one has to intensively perform the extensive scoping to gain information. This literature provides a lot of information, but this information often is only qualitative not quantitative and often obtained on different model systems.

Often above information is sufficient to define the structure of the model, i.e. what molecules are there and who interacts.Again before modeling, the type of model has to be considered and how detailed it should be, again this depends on the questions the model should be able to answer. After the structure of the model has been defined, it has to be implemented kinetically (if a dynamic model approach is chosen, as is the case here), i.e. the kinetic parameters have to be adjusted, so that the model can explain the experimental data. The operating model resulting thereof has then to be verified by testing model predictions and comparing those with literature or new own experimental data that have not already been used in the model implementation. Also, negative tests can be performed, i.e. is the model not doing what it should not do? The experiments have to be designed to distinguish a right from a wrong model. With new data at hand, the model can be refined, maybe only by adjusting parameters, but maybe also by changing the structure, thus starting the next iteration round.

CONCLUSION

One major goal of these efforts clearly is a better understanding of how cells work. Model-building is a tool to that end as well as a standardized form of representation for knowledge about a system. This is different from the way biologist defined models in the past, using prose descriptions of concepts and ideas.

But once the knowledge exists, what can be done with it?

``The most feasible application of systems biology research is to create a detailed model of cell regulation, focused on particular signal-transduction cascades and molecules to provide system-level insights into mechanism-based drug discovery. Such models may help to identify feedback mechanisms that offset the effects of drugs and predict systemic side effects.''

Endless application possibilities exists :

Easier drug design; 'personalized' drugs, i.e. built for purpose
Side effect free medicines, developed for (or at least adapted to) individual patients,
Directed, reliable manipulation of gene information (e.g. treatment of tumors or hereditary diseases); and more.

It may even be possible to use a multiple drug system to guide the state of malfunctioning cells to the desired state with minimal side effects. Such a systemic response cannot be rationally predicted without a model of intracellular biochemical and genetic interactions.With such models another transfer from engineering practice would become possible: Newly designed drugs could be tested in simulations before going into clinical testing. This would reduce risks to test subjects and patients and could eventually eliminate the need for animal testing.

For these applications to be realistic, though, apart from vastly increased computing power it will be absolutely necessary to be able to tune the level of Modularity.

First of all, though, before any of these visions can become reality, has to come a fundamental understanding of the processes in cells at the smallest level (i.e. level of smallest systems). The basis for macro-level insights is still micro-level knowledge, a basis that has been built continuously up till now and will increase as technologies improve.This is needed not only to understand the mechanisms to be used and manipulated. The ability to assess the risks that are inherent in manipulating so complex and intricately balanced a machinery as molecular processes in or between cells will be possibly even more important. Here simulations could considerably reduce the risk of creating potentially dangerous mutations and help clarify genetic mechanisms of inheritance and gene transfer and their consequences. This would help to understand the complexity of biological systems and make it more manageable than it is today.

That's all from my side i had put all my random thoughts about these vast branch of bioinformatics.Your thoughts and suggestions are welcomed.

will be back in eYE sEE bIOINFORMATICS with few more topics

Thanks all

LIFE SCIENCE CLOUD COMPUTING

noreply@blogger.com (Vikas) — Wed, 07 Jul 2010 20:37:00 +0000

"Cloud Computing in Bio informatics" .Before going straight into application of Cloud Computing in Bio informatics without telling about Cloud Computing is enough to make you people confused and your mind in position of asking lot of questions like

"What is this Cloud Computing ??????"

"Why we will use it for Bioinformatics ?????"

"Where we are going to use this technology ?????"

"Who are technology providers ??????"
So ultimately with these lot of WWWW in your mind you will stop reading this post but wait buddy... this is just a title of this post and i m just trying to add some humour though i know you people must be thinking hey man .. stop this non-sense and come to point and keeping this fact in mind i am straight coming to topic

FIRST W(What is Cloud Computing)
Cloud computing is a general term for anything that involves delivering hosted services over the Internet. Services broadly divided into three categories:

Infrastructure-as-a-Service (IaaS)
Platform-as-a-Service (PaaS)
Software-as-a-Service (SaaS). The name cloud computing was inspired by the cloud symbol that's often used to represent the Internet in flowcharts and diagrams.

Distinct characteristics that differentiate it from traditional hosting are

It is sold on demand, typically by the minute or the hour.
It is elastic -- a user can have as much or as little of a service as they want at any given time.
Service is fully managed by the provider (the consumer needs nothing but a personal computer and Internet access).
Significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet and a weak economy, have accelerated interest in cloud computing.

Above is pictorial representation of Key players in Cloud Computing

To knock your head with this topic Please refer to /http://www.janakiramm.net/

NOW SECOND W(Why we will use it for Bio informatics)
Life Science research and technology is growing exponentially at 400x speed which is generating huge amount of data to provide researchers better insight into biology of system. With rise of system biology where scientists are taking holistic approach by considering all details of molecular and biochemical processes in picture and generating biochemical network containing lot of differential equations whose analysis and simulation will take lot of computational resources likewise with development in genome sequencing technology huge amount of data generated in very less span of time storing and analysis of this data again needs huge spending on infrastructure development of hardware and software.
Private or Public, every Cloud implementation has to respect four key tenets. They are

Elasticity
Pay-By-Use
Self Service
Programmability

These above key properties will helps to tackle huge infrastructure problem facing by Life science industry.

THIRD W(Where we will use this technology)

"Cloud computing could help analyze genomes"Analyzing the vast amount of genetic information within a single genome - the set of genes/chromosomes that make up the full DNA sequence of a living being - usually takes a large amount of time and requires an expensive set of computers to be on hand. Cloud computing - a method with which the researchers may purchase computing power on a distributed network of computers without needing to invest in a large amount of hardware - to speed up the DNA sequencing process is a solution for that.

The current crop of next-generation sequencers (NGS) produce a much richer set of data per sequencing run, offer researchers more insight into the biological systems they are studying, perform their work faster than before, and frequently cost less to run. However, such lab equipment places new demands on life sciences storage solutions. The reason: Each faster and more frequent run produces orders of magnitude more data than was the case with previous generation systems.

To Simulate Complex biochemical network generated with rise of system biology.

FOURTH W(Who are technology providers)

Below is List of Current Cloud Services providers in INDIA.

ZENITH INFOTECH
TCS
WIPRO TECHNOLOGIES
NETMAGIC SOLUTIONS
INFOSYS TECHNOLOGIES
RELIANCE DATA CENTER

It is a Long List for Details refer http://www.techno-pulse.com/2010/03/india-based-cloud-service-providers.html
You must be thinking that there is already long list of Cloud Service Provider available in market than what is scope of Bio informatics in Cloud

and thats great!!!
It means now you all are convinced and started looking into commercial application of cloud computing so i feel its right time to tell you that all of above mentioned service providers are providing services to IT organizations ,Banking Sector and Government organization none of them are providing solution for Life Science.
It means Life Science field is still untapped and having lot of commercial potential.
In my Knowledge there is one bangalore based organization http://www.geschickten.com/ which is offering bioinformatics solution .even they are also in their starting phase.

So my Friends what are you waiting for Just Comment on this post and start Googling about Cloud Computing you will find lot of material and look for more Opportunities in this Field.

Thats all for Today!!!!!!!!!

Will Be back with new technology

SCOPE OF BIOINFORMATICS

noreply@blogger.com (Vikas) — Mon, 05 Jul 2010 15:49:00 +0000

You all are thinking oh again one more post for scope of Bioinformatics. But believe me this post is different from other articles except name. We all are aware of what actually bio informatics is but now question arises is what is scope of this field then we will find huge list of answers like
"Oh Bio informatics its very popular field and have lot of scope and potential you should go for it"
                                                                                                                             -- Academician
"This field has ruined my life there is no scope of Bio informatics in india IT is best option to choose"
                                                                                                                            -- Msc in Bio informatics
"Yeah Bio informatics great field buddy very interesting though it has no scope in india but who cares i will fly abroad to do MBA"
                                                                                                                            -- B.tech in Bio informatics
These are few excerpts by our daily to daily conversations regarding bio-informatics with different people. all are having their different views about this promising field.Ya it is correct that current scenario is not looking much promising but after sometime situation will be different but for that we need to change our view and ways to handle bio-informatics. Below i am presenting few statistics :

"The offshoring of bioinformatics services to India has been driven by the growing demand for biotechnology products, India's rich biodiversity driving its clinical trials industry, a strong base for pharmaceutical research and development and IT services, and well-educated low cost English speaking human capital"
"India's entry into the product patent regime in 2005 has further boosted the confidence of buyers to outsource bioinformatics services to India."
"ValueNotes / KnowGenix report forecasts that the Indian bioinformatics services outsourcing opportunity grows at a CAGR of 25% rising from USD 32 million in 2007 to USD 62 million by 2010."
"Indian bioinformatics vendor space comprises pure play bioinformatics service providers, multinationals and IT companies servicing the life sciences vertical."
"Pharma and biotech CRAMS players have also started offering informatics services."
"Indian vendors offer integrated bioinformatics solutions such as biological and chemical databases, data analysis, data mining, bio-medical text mining and customized tool development among others."

These are few excerpts from current status of Bio-informatics and its very clear from above points that future of bio informatics is very bright but still we are far behind from point from where we can lead world and give new dimensions to bio informatics.I found few reasons based on my observation which i am discussing below.

In past there is lot of hype about bioinformatics.That's why we have many universities in India which are offering degree/diplomas (M.Sc., B.Sc., B.Tech, etc. ) in bioinformatics. Interestingly most of universities do not have trained bioinformaticians still they are able to attract lots of students.Because of that students are facing problem in finding the jobs.
Job opportunity in Bioinformatics industry is limited by the fact that in a typical company the number of Bioinformatician is around 20%. Rest are programmers, pure biologist, pure statistician and mathematician, testing guys, support, technical writers and administrative staff etc.Still there is place for good candidates specially who can work with logistics, understand mathematics specially algorithms and can incorporate them to biological problems. Some companies working on biological database employ a lot of Biologist with some understanding of Bioinformatics.

That means we need paradigm shift in educational structure where students not only need to learn theoretical concepts,mugging literature they also need to learn how to do things practically for this we need good finishing schools where we can gain both knowledge and experience of doing things.

"IBAB(INSTITUTE OF BIOINFORMATICS AND APPLIED BIOTECHNOLOGY)" is such a numero uno which will take you on complete tour of bio informatics and you will get perfectly blended mixture of academics and industrial experience.Course structure is designed in such a way that student will get knowledge of currently prevailing practices in bio informatics along with other soft skills needed to survive in corporate culture.Here you will get golden chance to study under guidance of well known people of their field .which will makes you competent enough to lead in bio informatics.For further reference please visit http://www.ibab.ac.in/

IBAB is an Institute which is catering our current need of creating expertise and leaders both locally and globally . It is their continuous endeavor to polish our talented pool to make them competent enough to explore more and more opportunities in Bio informatics and make our own position across globe.

This Post is to make people aware of unlimited potential and opportunities in bioinformatics what eYE sEE in bIOINFORMATICS along with the reason why we are lagging in this field and what measures we should take to overcome these limitations.

Thats All for today !!!!!!!!!!

Will be back with new topic ........

THANKS
VIKAS SHARMA

See Bioinformatics VickyPedia

noreply@blogger.com (Vikas) — Fri, 25 Jun 2010 21:58:00 +0000

In my last Post i explained Bio-informatics as Link-up between Biology and IT where later came into picture to solve huge data handling problem faced by former at the beginning of the "genomic revolution", such as nucleotide and amino acid sequences. Development of this type of database involved not only design issues but the development of complex interfaces whereby researchers could both access existing data as well as submit new or revised data.

        This field is already popular in west and spread its root but still trying to create impact in India.. This technology has completely rotated the wheel of biological research from conventional wet lab experiments to dry lab(Computer labs).

        Now Bio-informatics is not mere alliance of biology and computer science it also includes statistics,mathematics ,various stochastic and non-stochastic probabilities model and Bayesian interference and by collating knowledge from these different branches what eYE can sEE bIOINFORMATICS is evolved such that the most pressing task now involves the analysis and interpretation of various types of data, including nucleotide and amino acid sequences, protein domains, and protein structures. The actual process of analyzing and interpreting data is referred to as computational biology.Now Bio-informatics is

the development and implementation of tools that enable efficient access to, and use and management of, various types of information.
the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets, such as methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences.

Covering all these in a single post is impractical and frankly speaking is difficult for me. So we will discuss one by one .i will request you all to please participate in this discussion and input your suggestions and views how Bio-informatics has turned the pages in biological research.

I hope you all are aware duo of James d Watson and Francis Crick who, using x-ray data collected by Rosalind Franklin, proposed the double helix structure of the DNA molecule in 1953 and received the 1962 Noble Prize in Physiology and Medicine for their body of research on nucleic acids. In this Pictures it is seen that one of them is standing on small bench to calculate the A:T G:C base pairing so you can imagine that how laborious it was at that time to do all these calculation and keep all records jotted on papers.Similarly take example of structure predictions methods which involves complex calculations which are humanly impossible to keep track of all the data.Likewise Not many people remember those days where people have to wait for almost more than a month period of time to perform single knock down studies/Over expression studies which currently are matter of minutes by using computational system biology.

Major Research Area in Bio-informatics :

Sequence Analysis
Genome Annotation
Predictions of Protein Structure
Modeling Biological System
High Throughput image Analysis
Protein-Protein Docking
Drug Designing
Drug Discovery
Gene Expression Analysis

This is what eYE sEE in bIO-INFORMATICS and now we are entering in field of bio-informatics where first we will discuss about focus of bio-informatics and then trying to xplore more opportunities in this field..

Thats all for today !!!!!!!!!!!!

will be back with more topics for bio-informatics discussion ...

Marriage between IT and BIOLOGY

noreply@blogger.com (Vikas) — Tue, 15 Jun 2010 18:07:00 +0000

Hi all..
Lets start this blog with Basic of bio-informatics.According to definitions in various books bio-informatics is marriage between IT and BIOLOGY as per many scholars it is not an apt definition for bio-informatics and they are proposing many complex definition with well articulated jargons but i feel above definition is serving its purpose.If we will see marriage is mutual understanding between two persons who walks together and without one other is incomplete in the same manner bio-informatics is joint effort of both BIOLOGY and IT to ease each other's task.Bio-Informatics came into existence with rapid growth in biology field which generated enormous amount of data and then raised question How to manage this data ??????????????
Answer to the above question was IT which facilitates easy storage ,manipulation and retrieval of data.With technological advancements in IT now it is very easy to handle huge data and extract relevant information from this data.

So Friends this is my basic definition of bio-informatics . I will urge u all to please provide your inputs for this post in a manner so that a novice can also understands it and feels importance of bio-informatics in today's era

I am Signing Off for today and will be Back with Evolution of Bio-informatics in upcoming Post