Showing posts with label Bioinformatics. Show all posts
Showing posts with label Bioinformatics. Show all posts

Careers in Bioinformatics


Biology is no longer restricted to its qualitative quests. The huge amount of data being generated from the Genomics and proteomics research and other associated techniques has transformed biology into an increasingly quantitative science. There is a growing need for Bioinformatics skill to deal with this explosion of data. For many, especially those who are very little acquainted with the subject, Bioinformatics deals with research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. It involves the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data. To put it in simpler terms, Bioinformatics is where the domain of fundamental biolog y meets with those of mathematics, statistics and computational sciences to analyze the data generated by large number of individual experimental data.
The Bioinformatics domain is expanding fast and wide and this has already been stimulated to a significant extent by the completion of the human DNA sequence in the spring of 2003. The world bioinformatics market value is expected to be around $7 billion dollars by the year 2007. Considering the current spending in the biotechnology sector towards informatics and relevant hardware, software and services and the growth of the biotech industry around the world, there would be a substantial amount of spending on bioinformatics. Though India has still a small space among the world bioinformatics leaders, the opportunities for growth are huge. With revenues reaching around $19 million dollars in the year 2005, India is all set to touch an expected mark of $3 billion dollars by the end of year 2010.

Since the domain of Bioinformatics is rooted in life sciences as well as computer and information sciences and technologies, an enormous need has emerged for people skill ed in not only the fundamentals of biological science but with a sound grasp of computational science as well. People with formal training, or experience in bioinformatics are not finding it hard to search for employment. There is a great need for bioinformaticists in both the industrial and academic spheres. In fact, job opportunities in the field are so good that, at the moment, potential employers are finding it difficult to meet their requirement of skilled bioinformatics talent. This is an even greater concern for academic institutes as the flow of skilled manpower is always biased towards the industries, attributed to lucrative salaries and long term employment benefits.

The rise in opportunities in this field has led to an increase in the number of bioinformatics academic programs around the country. There are far more academic training programs available now than they were a few years ago, with more universities planning to roll out their programs. The De partment of Biotechnology, under the Ministry of Science and Technology, India had established the Biotechnology Information System (BTIS) network way back in 1987 to create an infrastructure that enables it to harness, biotechnology through the application of Bioinformatics. BTIS is today recognized as one of the major scientific network in the world dedicated to provide the-state-of-the-art infrastructure, education, manpower and tools in bioinformatics. Under BTIS, there are Ten Distributed Information Centers. The Apex Center is located at the Department of Biotechnology. There are 46 Sub-Distributed Information Centers, located in various universities and research institutes of national importance. Long term courses at the level of post MSc Diploma in Bioinformatics are being offered at Pune University, Jawaharlal Nehru University, Calcutta University and Madurai Kamaraj University, in this inter-disciplinary area. More information about the Distributed Information Cent ers and Sub-Centers scattered across the various states of India could be obtained from the BTIS official website. Major institutes/universities in India, offering a specialized course in this line are Institute of Bioinformatics and Applied Biotechnology (IBAB), Bangalore, Indian Institute of Technology, Delhi, Indian Institute of Technolog,Kharagpur, Center of Cellular and Molecular Biology (Hyderabad), National Institute of Immunology (New Delhi), Institute of Microbial Technology (Chandigarh). Madurai Kamaraj University in Madurai claims to have been the first in the country to initiate a bioinformatics programme and advanced diploma in bioinformatics at its School of Biotechnology. There is an Advanced (Graduate) Diploma in Bioinformatics offered by the Bioinformatics Center at the Jawaharlal Nehru University. An Advanced Diploma in Bioinformatics is being offered at the Bioinformatics Center at The University of Pune. Bioinformatics Institute of India is conducting a one year correspondence course on Industry Program in Bioinformatics, Biomedical Informatics, Clinical Trial and Clinical Research, Pharma Regulatory Affairs, Biotechnology. BII is also offering various online programs through their web site.

When it comes to tapping a career in bioinformatics, what one may essentially require is: fundamental knowledge of biology along with some exposure to computational techniques and concepts. However, there is good scope for even those with Computer Science, Physical Chemistry and Physics background. In fact according to experts, in certain niches of the bioinformatics workshop like developing and applying numerical algorithms and data mining, computer programmers, Physical Chemists and Physicists tend to be more skilled as compared to their biological counterparts. The growing field of bioinformatics has made good room for people with IT background with keen interest in biology, especially molecular biology. The skills that are usually sought from an aspirant with an IT background are - (a) Simple and basic understanding of mo lecular entities like DNA, RNA, Proteins and molecular processes such as Transcription and Translation, which is for most part taken care by the training provided by the company. (b) Proficiency in using and administering databases like Oracle, SQL Server, MySQL, Sybase. (c) Programming skills in JAVA, C, C++, PERL, web scripting etc. (c) Good understanding (in certain cases) of operating system platforms like UNIX and LINUX. (d) Informatics related problem solving skills.

When it comes to tapping a career in bioinformatics, what one may essentially require is a strong fundamental knowledge of biology along with some exposure to computational techniques and concepts. However, there are opportunities even for graduates in Statistics, Physical Chemistry and Physics. According to experts, in certain niches of the bioinformatics such as developing and applying numerical algorithms and data mining, Physical Chemists and Physicists tend to be more skilled when compared to their biological counterparts. There are ample opportunities for those with an IT background with strong interest or knowledge in the biological sciences, especially molecular biology. People with just a biological sciences or computer sciences degree along with a Master's degree in bioinformatics or computational biology is one best profile to get going with the bioinformatics careers. People with a master's degree are usually looked on favorably by recruiters as it provides the basic foundation along with some practical experience which is usually lacking at a graduate level. A bit of working experience in IT may add a feather to your cap. However an undergraduate degree in bioinformatics is also an option that can be considered.

The major employer of bioinformatics talent is currently the drug designing industry and the bioinformatics software and solution based companies. Others including biotechnology companies, especially those involved in personal care p roducts, industrial organisms and agriculture have also shown great promise. Some prominent areas within bioinformatics where opportunities are arising are Proteomics, Pharmacogenomics, Functional Genomics, Sequence Mapping and Analysis, Data Mining and Management etc. While hunting for jobs related to bioinformatics, these days, some usual titles to consider would be:
For Biologists: Bioinformatician, Geneticist, Computational Biologist, Biostatistician etc.
For an IT professionals: Applications Programmer, Programmer Analyst, Object Modeler, Quality Assurance Engineer, Software development Engineer, Bioinformatics programmer / Analyst / Developer etc.

Apticraft Systems (P) Ltd, a software company based company in Indore employs biologists and software experts to develop software for use in life science research. Founded in 2001, Apticraft Systems is the vision of a few computer savvy technocrats, who have been actively involved in this field for the last 10 yrs. At Apticraft Systems, the goal is to create top quality software using the best software development practices. Apticraft has successfully offered its software development expertise and value added services to contractual clients who cater to renowned academic research institutions, government agencies, pharmaceutical and biotechnology companies, around the worl d.


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Bioinformatics/Biostatistics Jobs In Leading Organizations



There are many groups that belong to the major pharmaceutical clients who have a vision to play an important role in the development of personalized medicine. They facilitate the combination of therapeutics with diagnostics and thus improve the applications of biomarkers in drug discovery and development in cancer, cardiovascular & central nervous system.

Currently a large number of companies with varying technical backgrounds are involved in biomarkers and a massive growth of these markets are estimated from 2008 to 2018 in drug discovery, clinical trials, and bioinformatics. Your aspiration to become a part of Biostatistics would definitely come true with hard work, intensive study, intelligence and analytical synthesis at various levels in Oncology Biomarker Group.

As a key member of the Bioinformatics biostatistics group your scope of involvement will be broad, like mentoring junior Biomarker Analysts, follow timeliness and adequate quality of all deliverables for assigned trials and provide expertise in statistics, data mining and bioinformatics and their applications to clinical trials.

The number of people looking forward for a good career as a biostaticians is increasing vastly; this is because of the awareness that the job opportunities are good with best pay scale. Choosing such stream is really worthy and helps to fly high in the career with in a short span of time. The opportunities for biostaticians are very good all around the world, one can get their desired job without any risk just by applying their efforts in a proper and planned way. Wish you success in all your endeavors.
About the Author

Makro Scientific delivers custom client-centric solutions by offering contract outsourcing and premium staffing to the best known Science Organizations throughout the world. Click for more on biostatistics jobs .

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Andrew Dalke's Slides for Introduction to Python


I came across a nice set of slides from a course that Andrew Dalke taught a few years ago. They give a great introduction to Python for people interested in Bioinformatics. Here is a link (scroll to the very bottom of the page).

Andrew has a
blog, and a company with a presence on the web (where the slides are hosted), and he's on Stack Overflow as dalke.

Thanks, Andrew.



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UC San Diego Undergraduates Forge New Area of Bioinformatics


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What Is the HapMap?

What Is the HapMap?

This is an article that recently appeared on The International HapMap project,
http://www.hapmap.org/.

The HapMap is a catalog of common genetic variants that occur in human beings. It describes what these variants are, where they occur in our DNA, and how they are distributed among people within populations and among populations in different parts of the world. The International HapMap Project is not using the information in the HapMap to establish connections between particular genetic variants and diseases. Rather, the Project is designed to provide information that other researchers can use to link genetic variants to the risk for specific illnesses, which will lead to new methods of preventing, diagnosing, and treating disease.
Figure 1: When DNA sequences on a part of chromosome 7 from two random individuals are compared, two single nucleotide polymorphisms (SNPs) occur in about 2,200 nucleotides.
The DNA in our cells contains long chains of four chemical building blocks -- adenine, thymine, cytosine, and guanine, abbreviated A, T, C, and G. More than 6 billion of these chemical bases, strung together in 23 pairs of chromosomes, exist in a human cell. (See
http://www.dnaftb.org/dnaftb/ for basic information about genetics.) These genetic sequences contain information that influences our physical traits, our likelihood of suffering from disease, and the responses of our bodies to substances that we encounter in the environment.
The genetic sequences of different people are remarkably similar. When the chromosomes of two humans are compared, their DNA sequences can be identical for hundreds of bases. But at about one in every 1,200 bases, on average, the sequences will differ (Figure 1). One person might have an A at that location, while another person has a G, or a person might have extra bases at a given location or a missing segment of DNA. Each distinct "spelling" of a chromosomal region is called an allele, and a collection of alleles in a person's chromosomes is known as a genotype.

Differences in individual bases are by far the most common type of genetic variation. These genetic differences are known as single nucleotide polymorphisms, or SNPs (pronounced "snips"). By identifying most of the approximately 10 million SNPs estimated to occur commonly in the human genome, the International HapMap Project is identifying the basis for a large fraction of the genetic diversity in the human species.

For geneticists, SNPs act as markers to locate genes in DNA sequences. Say that a spelling change in a gene increases the risk of suffering from high blood pressure, but researchers do not know where in our chromosomes that gene is located. They could compare the SNPs in people who have high blood pressure with the SNPs of people who do not. If a particular SNP is more common among people with hypertension, that SNP could be used as a pointer to locate and identify the gene involved in the disease.

However, testing all of the 10 million common SNPs in a person's chromosomes would be extremely expensive. The development of the HapMap will enable geneticists to take advantage of how SNPs and other genetic variants are organized on chromosomes. Genetic variants that are near each other tend to be inherited together. For example, all of the people who have an A rather than a G at a particular location in a chromosome can have identical genetic variants at other SNPs in the chromosomal region surrounding the A. These regions of linked variants are known as haplotypes (Figure 2).

In many parts of our chromosomes, just a handful of haplotypes are found in humans. [
See The Origins of Haplotypes.] In a given population, 55 percent of people may have one version of a haplotype, 30 percent may have another, 8 percent may have a third, and the rest may have a variety of less common haplotypes. The International HapMap Project is identifying these common haplotypes in four populations from different parts of the world. It also is identifying "tag" SNPs that uniquely identify these haplotypes. By testing an individual's tag SNPs (a process known as genotyping), researchers will be able to identify the collection of haplotypes in a person's DNA. The number of tag SNPs that contain most of the information about the patterns of genetic variation is estimated to be about 300,000 to 600,000, which is far fewer than the 10 million common SNPs.

Once the information on tag SNPs from the HapMap is available, researchers will be able to use them to locate genes involved in medically important traits. Consider the researcher trying to find genetic variants associated with high blood pressure. Instead of determining the identity of all SNPs in a person's DNA, the researcher would genotype a much smaller number of tag SNPs to determine the collection of haplotypes present in each subject. The researcher could focus on specific candidate genes that may be associated with a disease, or even look across the entire genome to find chromosomal regions that may be associated with a disease. If people with high blood pressure tend to share a particular haplotype, variants contributing to the disease might be somewhere within or near that haplotype.

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1st International Conference on Bioinformatics and Systems Biology @ Annamalai University, Tamil Nadu

The Annamalai University is a unitary, teaching and residential university.The Department of Zoology, Anna University with sponsership from ICMR, DSt, DBT ir organizing a 3-day International Conference on Bioinformatics and Systems Biology in the month of February 2010.

Scope of the Conference
The genome – sequencing and structural genomics efforts worldwide have contributed to huge data on protein sequences and novel protein folds.
The conference will deal to protein fold prediction, comparative protein modelling, secondary structure prediction, protein dynamics and simulations, ab-initio prediction and the validation of protein models and various aspects of structure determination and analysis by X – ray crystallography.


Themes of the Conference

Papers are solicited on, but not limited to, the following topics:
• Agricultural bioinformatics
• Bioinformatics databases & Bioinformatics of diseases
• Chemoinformatics
• Computational genomics and proteomics
• DNA Assembly, clustering and mapping
• DNA Bar coding and Bioinformatics
• Drug discovery & Data mining, Database ontologies
• Gene expression and microarrays, Gene identification
• Hidden Markov models
• Immunoinformatics of diseases, Molecular sequence analysis
• Neuroinformatics
• Phylogeny reconstruction algorithms
• Protein structure prediction and modelling
• Parallel algorithms for biological analysis , Simulation and system dynamics

Bioinformatics and Systems Biology 2010 invites high-quality original full papers on any topic related to Bioinformatics and System Biology. The submitted papers must have not been published or under the consideration for publication in any other journal or conference with formal proceedings. All accepted papers will have to be presented by one on the authors at the conference.
The online submission system will be open from
December 2, 2009 to January 20, 2010.

Registration
All the participants and their accompanying members are requested to register by paying the necessary registration fees. Participants are requested to fill the enclosed registration form (Photo copy may be used) and mail it to the Organizing Secretary along with the copies of abstract and registration fees payable by DD from a Nationalized bank in favour of “The Registrar, Annamalai University” Payable at Annamalainagar (or) Chidambaram.

Accomodation
Limited accommodation will be arranged by the organizers in the University guest house / Hostel etc. (First come first serve basis).

Important Dates
Paper submission open December 2, 2009
Paper submission deadline January 20, 2010
Paper acceptance decision January 28, 2010
Poster submission open December 30, 2009
Poster submission deadline January 28, 2010
Poster acceptance decision January 30, 2010
Registration open December 2, 2009
Early-bird registration upto January 30, 2010
Conference Feb 19 - 21, 2010

Organizing Secretary
Dr.SABESAN, Ph.D.
Prof. & Wing Head, Dept. of Zoology, D.D.E.
Annamalai University,Annamalainagar-608 002, Tamil Nadu,India
E-mail :
sabesan1956@gmail.com, sabesan_muthukumar@yahoo.com
sabesan_in@redifmail.com, Mobile : 09994932234

Original Notification Download Registration Form


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Your own Bioinformatics workstation using VigyaanCD



VigyaanCD at
http://www.vigyaancd.org/
has a nice collection of bioinformatics software and is worth downloading. It will boot directly from the CD and needs very little linux expertise as the X-windows system is almost like windows.

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Lowering Pharma firewalls: Just for Bioinformatics or Chemoinformatics also

Notion of pre-competitive collaboration has been in under experiment steadily for quite sometime now. Notable examples are the Airbus consortium of European aircraft manufacturers, the Sematech consortium of US semiconductor manufacturers, banks working together to launch Visa and Mastercard, our recent moon lust and many more. But this was never a case for pharmaceutical industry until now which is now lowering industry firewalls to shift funding and focus from early- to late-stage projects by developing cooperation in the areas with little potential for differentiation most notably a shared informatics infrastructure through public–private partnerships. Pre-competitive collaboration in this process means that everyone will have same common pool of data and resources. Competition will be still there but for better ideas, for better models and to discover first.

Pre-competitive informatics initiatives
A very interesting opinion piece appeared in September issue of Nature Reviews Drug Discovery discussing the importance of pre-competitive informatics initiatives in drug discovery. Article suggest that many companies are already beginning to embrace this idea, and that for some companies
This was a very timely review in the wake of several initiatives such a Innovative Medicines Initiative (IMI), EBI industry programme, Pistoia Alliance and many others. The idea of lowering industry firewalls caught more attention after the announcement of Sage Bionetworks, a non-profit medical research organization established this year with initiatives of Merck duo Eric Schadt and Stephen Friend. A similar kind of effortOpen Source Drug Discovery(OSDD) was launched by CSIR, India earlier this year with a initial investment of US $38 million. OSDD consortium is trying to implement open source model for Drug Discovery and public-private partnership is one of the major focus of this initiative. Exciting isn't it? But wait there is a twist in story, there are no definitive answers for what type of data is pre-competitive and what is not? The definition of of pre-competitive is fluidic and it depends on several factors, one of them is whether data belongs to biology or chemistry. Article suggests that any data and tools used by biologists should be under consideration for pre-competitive sharing but those used by chemist should remain the competitive or proprietary (which is very much according to current trends). I could not find any rational reason behind this argument except the fact that there is overwhelming amount of public data in biology domain and day by day companies and institution are finding it hard to manage, integrate and use them for drug discovery. I will go further and suggest that much of these initiatives serves no benefits unless otherwise the data and tools belonging to chemistry domain is also considered as pre-competitive. Ironically much of the data and tools released by pharmaceutical companies under these initiatives are yet to proof their importance. For instance much hyped Life Science Grid released by Eli Lilly (which went open source in year 2008) failed to attract even an average user base. Lilly released only the biology side of the grid which includes a selected group of non proprietary plug-ins, including those for Gene Browser, NCBI Entrez, and Gene Ontology. Forgive me but there are already better tools for the biology in public domain. In my opinion unhindered access to data and tool is prerequisite for the success of the pre-competitive landscape which require more active contributions from the industry participants. Currently the systems is evolving and for now something is better than nothing.
Apart from the issues related to definition of pre-competitive boundaries there are several other bottlenecks, for instance the who will fund the long-term maintainability of such an infrastructure, those remain unresolved .


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CIRCOS - VISUALIZING THE GENOME, AMONG OTHER THINGS








Circos is designed for visualizing genomic data such as alignments, conservation, and generalized 2D data, such as line, scatter, heatmap and histogram plots. Circos is very flexible — you can use it to visualize any kind of data, not just genomics. Circos has been used to visualize customer flow in the auto industry, volume of courier shipments, database schemas, and presidential debates.

The creation of Circos was motivated by a need to visualize intra- and inter-chromosomal relationships within one or more genomes, or between any two or more sets of objects with a corresponding distance scale. Circos is similar to chromowheel and, to a lesser extent, genopix.

Circos uses a circular composition of ideograms to mitigate the fact that some data, like combinations of intra- and inter-chromosomal relationships (alignments, duplications, assembly paired-ends, etc) are very difficult to organize when the underlying ideograms (or contigs) are arranged as lines. In many cases, it is impossible to keep the relationship lines from crossing other structures and this deteriorates the effectiveness of the graphic.

Specific features are included to help viewing data on the genome. The genome is a large structure with localized regions of interest, frequently separated by large oceans of uninteresting sequence. To help visualize data in this context, Circos can create images with variable axis scaling, permitting local magnification of genomic regions to be controlled without cropping. Scale smoothing ensures that the magnification level changes smoothly. In combination with axis breaks and custom ideogram order, the final image can be easily tuned to offer the clearest illustration of your data.

All aspects of the output image are tunable, making Circos a flexible and extensible tool for the generation of publication-quality, circularly composited renditions of genomic data and related annotations.

Circos is written in Perl and produces bitmap (PNG) and vector (SVG) images using plain text configuration and input files.

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Evloving computing definitions and realms


Melanie Swan has written an interesting piece about Expanding notion of Computing on her blog Broader Perspective. In response to the question how computing definitions and realms are evolving, she writes

the traditional linear Von Neumann model is being extended with new materials, 3D architectures, molecular electronics and solar transistors. Novel computing models are being investigated such as quantum computing, parallel architectures, cloud computing, liquid computing and the cell broadband architecture like that used in the IBM Roadrunner supercomputer. Biological computing models and biology as a substrate are also under exploration with 3D DNA nanotechnology, DNA computing, biosensors, synthetic biology, cellular colonies and bacterial intelligence, and the discovery of novel computing paradigms existing in biology such as the topological equations by which ciliate DNA is encrypted.
Evolving_Computational_Models
A related but more broader presentation about future of life sciences

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Backup and fault tolerance in systems biology: Striking similarity with Cloud computing

Striking similarity between biological systems and computing paradigms is not new, and in past there have been several attempts to draw an analogy between systems biology and computing systems. For interested readers I will recommend my last post which examine how systems biology of human can be describes asa grid of super-computers. Over the time researchers have developed several bio-inspired fault-tolerance methods to support fault detection and removal in both hardware and softwares systems, such as fault-tolerant hardware inspired by ideas of embryology and immune systems. Fault tolerance is the ability of a system to retain intended functionality even in the presence of faults, and in case of living cells fault-tolerance is due to the intrinsic robustness of their gene regulatory networks which can be easily observed in case of mutation-insensitivity expression of genes with phenotypic feature. In recent issue of journal Molecular Systems Biology, Anthony Gitter and other co-authors suggest that gene regulatory networks also have backup plans very much like cloud computing networks or MapReduce framework where failure of a computing node is managed by by re-executing its task on another node. Fault-tolerant is seen as mechanism to retain the functionality of master gene in very extreme circumstances through a controller mechanism, while backup plan employs another gene with reasonable sequence similarity to master gene in order to perform the tasks which are key for the survival of cell itself. Their finding suggest that
[T]he overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets.
TF Backup in Gene Regulatory NetworksThe yellow TF which has sequence similarity as well as shared interactions with green TF can replace the green TF when it is knocked out and is able to recruit the transcription machinery leading to only small overlap between binding and knockout results

In order to understand the systems biology of robustness provided by redundant TFs and their role in broader cellular context authors explored dependence of findings on the TFs' homology relationships and shared protein interaction network. They observed that TFs with the most similar paralogs had no overlap between their binding and knockout data, while protein interaction networks provide physical support for knockout effects.
Further Gitter describes
importance of his research as,
It's extremely rare in nature that a cell would lose both a master gene and its backup, so for the most part cells are very robust machines. We now have reason to think of cells as robust computational devices, employing redundancy in the same way that enables large computing systems, such as Amazon, to keep operating despite the fact that servers routinely fail
Master Backup MapReduceA simple backup mechanism in MapReduce framework

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BioPAX or SBML?

Katherine Mejia asked this question on twitter, well just now I want to keep it short,

  • Both BioPAX (Level 3) and SBML (Level 2) can encode signaling pathways, metabolic pathways and regulatory pathways, although SBML can represent finer details.
  • If your aim is simulation and prediction then SBML seems to be reasonable choice.
  • SBML has better software and API support than BioPAX, no doubt this one is major advantage with SBML.
  • Similarly BioPAX is rich standard when it comes to encode the interactions such as genetics, molecular or non-molecular.
BioPAX or SBML or CellML Visual comparison of SBML, BioPAX, CellML and PSI



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Bioinformatics and Systems Biology: Multidisciplinary scientists versus Interdisciplinary scientist

Same old wine now in new bottle, a debate which bioinformatics community has followed for the years now became a major agenda for systems biology community as well. During recent Systems Biology Inter-DTC Conference at University of Manchester Systems Biology Centre, there was a special session on the subject “can scientists be multidisciplinary?”. Steve Checkley has written andetailed opinion about this secession and following debate. Current status of systems biology is quite similar what we have seen 5 year back in bioinformatics community, both have very similar fundamental issues. Part of the problem is interchangeable definitions of interdisciplinarity and multidisciplinarity. According to Wikipedia, multidisciplinarity is defined as
Multidisciplinarity is the act of joining together two or more disciplines without integration. Each discipline yields discipline specific results while any integration would be left to a third party observer. An example of multidisciplinarity would be a panel presentation on the many facets of the AIDS pandemic (medicine, politics, epidemiology) in which each section is given as a stand-alone presentation.
A multidisciplinary community or project is made up of people from different disciplines and professions who are engaged in working together as equal stakeholders in addressing a common challenge. The key question is how well can the challenge be decomposed into nearly separable subparts, and then addressed via the distributed knowledge in the community or project team. The lack of shared vocabulary between people and communication overhead is an additional challenge in these communities and projects. However, if similar challenges of a particular type need to be repeatedly addressed, and each challenge can be properly decomposed, a multidisciplinary community can be exceptionally efficient and effective. A multidisciplinary person is a person with degrees from two or more academic disciplines, so one person can take the place of two or more people in a multidisciplinary community or project team. Over time, multidisciplinary work does not typically lead to an increase nor a decrease in the number of academic disciplines.
while interdisciplinarity can be described as

"Interdisciplinarity" in referring to an approach to organizing intellectual inquiry is an evolving field, and stable, consensus definitions are not yet established for some subordinate or closely related fields.
An interdisciplinary community or project is made up of people from multiple disciplines and professions who are engaged in creating and applying new knowledge as they work together as equal stakeholders in addressing a common challenge. The key question is what new knowledge (of an academic discipline nature), which is outside the existing disciplines, is required to address the challenge. Aspects of the challenge cannot be addressed easily with existing
distributed knowledge, and new knowledge becomes a primary subgoal of addressing the common challenge. The nature of the challenge, either its scale or complexity, requires that many people have interactional expertise to improve their efficiency working across multiple disciplines as well as within the new interdisciplinary area. An interdisciplinarary person is a person with degrees from one or more academic disciplines with additional interactional expertise in one or more additional academic disciplines, and new knowledge that is claimed by more than one discipline. Over time, interdisciplinary work can lead to an increase or a decrease in the number of academic disciplines.

Unlike multidisciplinarity which brings two or more disciplines together without any integration with each discipline approaching the problem from their own perspective, interdisciplinarity utilizes an integrated approach to solve those problems. Both multidisciplinary co-operation and interdisciplinary integration have same intent- providing practical solutions to practical problems either confined in the separateness of unidisciplinarity or inherent in present conditions of specialization. Indeed multidisciplinary co-operation has been always favored over interdisciplinary integration without very strong reasons. For example pharmaceutical companies have always preferred multidisciplinary team of computer scientist and biologist rather than recruiting interdisciplinary bioinformaticians. Although interdisciplinarity scientists are highly trained in several disciplines, but in general they are not considered as specialist for any of them. In case of systems biology, as Steve report, pharmaceutical companies want to recruit hard core mathematicians rather than trained systems biologist for very same argument that multidisciplinary team of mathematicians and biologist can have a better impact on the research. No doubt interdisciplinary trained scientist are good for solving problems, but they can not be hired for implementing solutions which require more than interdisciplinary approach or let say specialist. Contrary to that there is nothing which stops a trained specialist to be a generalist or interdisciplinary. Existing R&D environment in both academic and industry is preoccupied and optimized for the specialist as leading role while interdisciplinary trained scientist have their secondary roles. Ironically road to interdisciplinarity is one way or it is too early to predict that. At the end it does not matter if you are specialist or interdisciplinary, success depends on the passion to do the right things on right time, whatever you do be at best you will be always successful. It will be premature to say that interdisciplinary scientist will not get their place in a specialist world, even interdisciplinary scientist will turn into specialist some day. Just now pharmaceutical companies are playing safe as systems biology is an emerging discipline but is yet to prove itself.

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