Till date we have made a lot of posts on Systems Biology, its applications and it scope. Indeed, Systems Biology has brought a big revolution in cell biology and pathway analysis. When seen in combination with treatment of diseases and drug discovery, it proves even more handy. Here we discuss Systems Biology in combination with drug discovery.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs and ultimately the whole organism. As currently employed, the term ’systems biology’ encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to man. Much of the academic focus is on developing fundamental computational and informatics tools required to integrate large amounts of reductionist data (global gene expression, proteomic and metabolomic data) into models of regulatory networks and cell behavior. Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization.

There are basically three advances in the practical applications of systems biology to drug discovery. These are:

1. Informatic integration of ‘omics’ data sets (a bottom-up approach)

Omics approaches to systems biology focus on the building blocks of complex systems (genes, proteins and metabolites). These approaches have been adopted wholeheartedly by the drug industry to complement traditional approaches to target identification and validation, for generating hypotheses and for experimental analysis in traditional hypothesis-based methods.

2. Computer modeling of disease or organ system physiology from cell and organ response level information available in the literature (a top-down approach to target selection, clinical indication and clinical trial design).
The goal of modeling in systems biology is to provide a framework for hypothesis generation and prediction based on in silico simulation of human disease biology across the multiple distance and time scales of an organism. More detailed understanding of the systems behavior of intercellular signaling pathways, such as the identification of key nodes or regulatory points in networks or better understanding of crosstalk between pathways, can also help predict drug target effects and their translation to organ and organism level physiology.

3.  The use of complex human cell systems themselves to interpret and predict the biological activities of drugs and gene targets (a direct experimental approach to cataloguing complex disease-relevant biological responses).

Pathway modeling as yet remains too disconnected from systemic disease biology to have a significant impact on drug discovery. Top-down modeling at the cell-to-organ and organism scale shows promise, but is extremely dependent on contextual cell response data. Moreover, to bridge the gap between omics and modeling, we need to collect a different type of cell biology data—data that incorporate the complexity and emergent properties of cell regulatory systems and yet ideally are reproducible and amenable to storing in databases, sharing and quantitative analysis.

This is how Systems Biology has aided in Drug Discovery Research and paved its path to cure many vital diseases.

Read our other posts on Systems Biology – http://biointelligence.wordpress.com/category/systems-biology/

I have just completed a paper titled Reachability, Persistence, and Constructive Chemical Reaction Networks. The paper is available at http://math.gillesgnacadja.info/files/ConstructiveCRNT.html.

This post represents my first experimentation with blogging. The plan is to blog on mathematics and systems biology topics that I find very intriguing and like cogitating about. As with most hobbies however, there are time constraints. I do not have much time for such cogitation and I expect to have even less time for blogging. I welcome and intend to respond to all comments, but I will often be unable to respond in a timely fashion.

Due to continued research there is a continuous groth in the amount of biological data available. The exponential growth of the amount of biological data available raises two problems:

1. Efficient information storage and management and, on the other hand, the extraction of useful information from these data.

2. It requires the development of tools and methods capable of transforming all these heterogeneous data into biological knowledge about the underlying mechanism.

 There are various biological domains where machine learning techniques are applied for knowledge extraction from data. The below figure shows the main areas of biology such as genomics, proteomics, microarrays, evolution and text mining where computational methods are being applied.

In addition to all the above applications, computational techniques are used to solve other problems, such as efficient primer design for PCR, biological image analysis and backtranslation of proteins (which is, given the degeneration of the genetic code, a complex combinatorial problem). Machine learning consists in programming computers to optimize a performance criterion by using example data or past experience. The optimized criterion can be the accuracy provided by a predictive model—in a modelling problem—, and the value of a fitness or evaluation function—in an optimization problem. Machine learning uses statistical theory when building computational models since the objective is to make inferences from a sample. The two main steps in this process are:

 1. To induce the model by processing the huge amount of data

2. To represent the model and making inferences efficiently.

 The process of transforming data into knowledge is both iterative and interactive. The iterative phase consists of several steps. In the first step, we need to integrate and merge the different sources of information into only one format. By using data warehouse techniques, the detection and resolution of outliers and inconsistencies are solved. In the second step, it is necessary to select, clean and transform the data. To carry out this step, we need to eliminate or correct the uncorrected data, as well as decide the strategy to impute missing data. This step also selects the relevant and non-redundant variables; this selection could also be done with respect to the instances. In the third step, called data mining, we take the objectives of the study into account in order to choose the most appropriate analysis for the data. In this step, the type of paradigm for supervised or unsupervised classification should be selected and the model will be induced from the data. Once the model is obtained, it should be evaluated and interpreted—both from statistical and biological points of view—and, if necessary, we should return to the previous steps for a new iteration. This includes the solution of conflicts with the current knowledge in the domain. The model satisfactorily checked—and the new knowledge discovered—are then used to solve the problem.

 An article published in the journal ‘Briefings in Bioinformatics’ gives an insight of various machine learning techniques used in Bioinformatics. It also throws light on some major techniques such as Bayesian classifiers, logistic regression, discriminant analysis, classification trees, nearest neighbour, neural networks, Support vector machines, clustering, Hidden Markov Models and much more.

 The article can be found here: http://bib.oxfordjournals.org/cgi/content/full/7/1/86?maxtoshow=&HITS=&hits=&RESULTFORMAT=&fulltext=bioinformatics&andorexactfulltext=and&searchid=1&FIRSTINDEX=0&resourcetype=HWCIT

I’ve added a document on
exploring pathway data to blipdoc. I’ll highlight some of the salient points on this blog at a later date.

Exellent review about types of network analysis algorithms, advantages and disadvantages of each. You need to know the notation, but it’s still perfectly conscise review. Figure summarizing features of each analysis – a must!

BMC Bioinformatics. 2007 Sep 27;8 Suppl 6:S5. 

Inferring cellular networks–a review.

Markowetz F, Spang R.

Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. florian@genomics.princeton.edu

PMID: 17903286 [PubMed - indexed for MEDLINE]

The Kyoto Encyclopedia of Genes and Genomes (KEGG) PATHWAY database is a valuable comprehensive collection of manually curated pathway maps for metabolism, genetic information processing and other functions. It is an integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information as shown below. Genomic and chemical information represents the molecular building blocks of life in the genomic and chemical spaces, respectively, and systems information represents functional aspects of the biological systems, such as the cell and the organism, that are built from the building blocks. KEGG has been widely used as a reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling.

In order to construct such metabolic pathways, the KEGGConvertor has been implemented. It is a tool implemented in JAVA. KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. It permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy.
KEGG Convertor is available here: http://www.grissom.gr/keggconverter/

Here are some useful and handy bioinformatics links which would aid in study of bioinformatics and various related fields:

http://www.cellbiol.com/

http://www.expasy.org/links.html

http://www.biochemweb.org/databases.shtml

http://bioinformatics.byu.edu/

http://www-personal.umich.edu/~lpt/chemlinks.htm

http://www.sciencegateway.org/tools/index.html

http://molbiol-tools.ca/

http://dorakmt.tripod.com/mtd/biomed.html

http://www.bio.ku.dk/mundy/links.htm

http://users.breathe.com/hachen/mol_biol_sites.html

http://www.whitney.ufl.edu/resources/molecular-links.htm

http://bioinformatics.ws/index.php/Bioinformatics_tools_and_algorithms

http://fruitfly4.aecom.yu.edu/molbio.html

http://www.biologie.uni-erlangen.de/mpp/pages/tools_prot.html

http://staff.umt.edu.my/~cha_ts/Cha%20Bioinfo.html

Wednesday am, Philadelphia, PA
I spent this morning in a fascinating session on the Systems Biology and Biomarkers session. The presenters and audience were a real eclectic mix of pharma scientists, biologists, mathematicians, data modelers, IT – and us, of course – the type that spans all boxes.

The presentations spanned a range of domains and topics. We argued in depth about concepts, constructs, contexts, meaning, semantics, and data/modeling challenges. It was fascinating to say the least, especially because all of the presentations were grounded in real world experience.

This is precisely what such events should be about because it elevated the conversation to a refreshingly higher order of thinking. We continued well into lunch with the same group. In fact, it is precisely this type of out-of-the-box environment that companies need to foster if they want to achieve breakthrough ideas.

Frank Tobin’s talk on the use of practical mathematical models for discovery and development, and their impact on how we collect and use data, was especially insightful. It seems to be a logical pathway for any drug development program but most companies tend to abandon the “art of the long run” for short-term goals even though these models clearly provide a way to understand disease foundations.

Usha Reddy’s presentation on the collaboration between Merck and the Moffett research center highlighted real-world challenges of data and process flows in a multi-party environment. She was intrigued by how we handle multiple formats with our Semantic Data Exchanger™. As the number of collaborators and CROs increase, so does the challenge of standardizing data across hundreds, and even thousands of organizations; I think we took people here by surprise when we showed them that with our technology, they can now think about standardization challenges differently and solve them in a relatively simple way.

We ended up, I believe, taking away a rich set of insights relevant to our respective spheres of influence.

Hats off again to Douglas Connect and Bryn Mawr for hosting such a wonderful event.

I will be adding some more thoughts as my involvement in the conference winds down this afternoon.

-Shree Nath

The Gene Ontology project is a major bioinformatics initiative with the aim of standardizing the representation of gene and gene product attributes across species and databases. The aims of the Gene Ontology project are threefold:
1. Firstly, to maintain and further develop its controlled vocabulary of gene and gene product attributes.
2. Secondly, to annotate genes and gene products, and assimilate and disseminate annotation data.
3. Thirdly, to provide tools to facilitate access to all aspects of the data provided by the Gene Ontology project.

QuickGO is a web-based tool which allows easy browsing of the
Gene Ontology and all associated GO annotations provided by the
GOA group. It provides a comprehensive set of both electronic and
manual annotations from a large number of curation groups.QuickGO users can view and search information provided for GO terms (identifiers, words/phrases in the title or definition, cross-references and synonyms), as well as protein data from Uni- ProtKB (accession numbers, names and gene symbols). Results are ranked so that terms most closely matching the query are returned first. Individual words and combinations of words are scored according to the field in which they occur and their frequency within GO.

QuickGO is updated weekly with protein names, gene symbols, accessions and taxonomy data from UniProtKB. Single or multiple protein accessions can be queried and selected proteins will display all associated GO annotations, both electronic and manual.

QuickGo can be accessed from the EBI website. Here is the link:
http://www.ebi.ac.uk/QuickGO/

PHILADELPHIA – The American Association for Cancer Research will host its first Frontiers in Basic Cancer Research Meeting in Boston from Oct. 8-11, 2009. The meeting is expected to draw nearly 500 leading scientists to present on high-profile topics like epigenetics, metastasis and systems biology, and create synergies among the many subfields of basic science.

Tyler Jacks, Ph.D., director of the David H. Koch Institute for Integrative Cancer Research at MIT and president of the AACR, hosted a news briefing to highlight some of the important research that will be presented at this meeting.

The press briefing took place on Oct. 9, 2009, at 12:30 p.m. ET, in the Stuart Room of the Boston Park Plaza Hotel

Download* the mp3 of the press briefing (5.17 MB, 22 minutes and 35 seconds)

“Basic science is the foundation of clinical research that leads to patient breakthroughs. These research projects are examples of work that is being done in the laboratory that will one day directly impact patient care,” said Jacks.

Jacks cited Gleevec, the anti-leukemia drug that has revolutionized the prognosis of patients with leukemia, as an example of a patient breakthrough that began in the lab.

“What people often forget is that the laboratory work on Gleevec began 40 years before any patient was even treated. Progress is deliberate, but it does continue,” said Jacks.

The following abstracts were presented at the press conference:

#A22. Pooled analysis of phosphatidylinositol 3-kinase (PI3K) pathway variants and risk of prostate cancer

Scientists have observed an association between a collection of PI3K gene variations and prostate cancer risk that became stronger after considering the patient’s age at diagnosis and family history, according to data presented at the American Association for Cancer Research Frontiers in Basic Cancer Research Meeting.

Stella Koutros, Ph.D., a post-doctoral fellow at the National Cancer Institute, said this was the first time PI3K genes had been evaluated in this setting, though further studies are needed to confirm the relationship and determine the role of the pathway in prostate cancer etiology.

“We observed an association in a very large nested case-control study drawn from several prospective cohorts, but the information will need to be confirmed,” said Koutros.

Koutros and colleagues observed 8,309 cases of prostate cancer and 9,286 controls. They examined 89 single nucleotide polymorphisms (SNP) on the following PI3K pathway genes: PIK3C2B, PIK3AP1, PIK3C2A, PIK3CD and PIK3R3.

They found that the SNP rs7556371 in PIK3C2B was significantly associated with between an 8 percent and 21 percent increased risk of prostate cancer overall.

Men who carried the rs7556371 risk allele had a 47 percent increased risk of early-onset prostate cancer (diagnosed at age 65 or younger) compared to men who did not carry the variant. Among men with a family history of the disease, those carrying the risk allele had a 57 percent increased risk of prostate cancer compared to men who did not carry the PI3K variation.

The greatest increase in risk was observed in men who had both a family history of prostate cancer and early-onset disease. Carriers of this risk allele had a 2.31-fold increased risk for prostate cancer compared to those without the PI3K variation. The variation was not associated with differences in disease aggressiveness.

#C49. AKT inhibitor has potent antitumor activity in human lung cancer xenograft models

Scientists at Cellceutix Corporation may have developed a new compound that could significantly delay lung tumor growth, according to data presented at the AACR Frontiers in Basic Cancer Research Meeting.

Lung cancer accounts for 215,000 new cases and 130,000 deaths in the United States every year, making it the single leading cause of cancer death. Non-small cell lung cancer accounts for 80 percent of all bronchogenic neoplasms, with 90 percent of diagnosed patients dying within five years.

“For patients with lung cancer, there are hardly any effective treatments available, so we investigated this unique compound that has activity against the AKT protein and found it to be extremely successful,” said Krishna Menon, Ph.D., chief scientific officer of Cellceutix Corporation in Beverly, Mass.

Menon and colleagues tested the compound, which the company currently calls Kevetrin, in two human xenograft models: A549 and NCI-H1975, both of them multiple drug resistant. They tested 200 mg/kg of Kevetrin three times a day every other day against paclitaxel, one of the currently approved therapies for lung cancer, 22 mg/kg four times per day every other day.

In the A549 model, Kevetrin significantly delayed tumor growth by 11 days in the first experiment and by 30 days in a repeat experiment. By contrast, paclitaxel delayed growth by zero days in the first experiment and only three days in a subsequent experiment.

In the NCI-H1975 model, Kevetrin significantly delayed tumor growth by 34 days in the first experiment and by 28 days in a subsequent experiment. Similar to the previous model, paclitaxel delayed growth by just four and 14 days, respectively.

The researchers measured weight loss as a marker of toxicity and found it to be less than 5 percent in all experiments.

“It is encouraging that this drug has such little toxicity,” said Menon.

#A25. Array-based comparative genomic hybridization of hepatocellular carcinoma reveals a unique genomic aberration pattern in tumors with poor prognosis

Scientists at the National Cancer Institute have identified a 10-gene signature that could predict survival in both liver and breast cancer, according to data presented at the American Association for Cancer Research Frontiers in Basic Cancer Research Meeting.

Stephanie Roessler, Ph.D., a visiting fellow in the Laboratory of Human Carcinogenesis at the NCI, and colleagues analyzed 13,000 genes to determine the genomic regions of human hepatocellular carcinoma (HCC) that are associated with poor prognosis. By integration of genomic aberration and gene expression they identified 10 potential tumor suppressor genes, which are associated with a 2.1-fold increased risk of death.

Tumor suppressor genes, as the name implies, prevent tumors when they are active.

“We and others found that genomic profiling of both liver cancer and breast cancer show overlapping regions of gain or loss,” said Roessler. “When we analyze downstream pathways of these genes, we may be able to identify areas for more personalized medicine approaches in the future.”

Roessler and colleagues performed their analysis in human liver and breast cancer tissue. Starting with 13,000 genes, they narrowed their search to 419 and then to 134 before determining the 10 that were significantly associated with impaired survival.

“We looked not only at the DNA copy number, but also at the gene expression, so this will help us identify genes that have a downstream function that can be manipulated by biologic therapies,” said Roessler.

#A63. Inhibition of ROCK signaling inhibits breast cancer metastasis to human bone

Scientists have found that a kinase called ROCK is overexpressed in metastatic breast cancer, according to data presented at the AACR Frontiers in Basic Cancer Research Meeting. In this in vivo study, inhibiting ROCK in the earliest stages of breast cancer decreased metastasis by approximately 85 percent.

“This is preliminary research that will require further laboratory and clinical studies, but our results suggest that ROCK inhibition may be a drug therapy target for metastatic breast cancer,” said Sijin Liu, Ph.D., a research instructor at Tufts University School of Medicine and the Sackler School of Graduate Biomedical Sciences at Tufts in Boston. Liu and colleagues used an experimental inhibitor called Y27632 to block ROCK action, which resulted in metastasis to the bone decreasing by approximately 85 percent.

The researchers used a mouse model with luminescent imaging to study the effects and found that Y27632 inhibited the overall frequency of metastasis by 36 percent compared to controls. Specifically, only five out of 14 tumors metastasized in treated mice compared with eight out of 12 in the control group.

The laboratory experiments suggested that ROCK inhibition may work by targeting a set of microRNAs. Those microRNAs, 17 through 92, were elevated in metastatic cells compared with non-metastatic cells and responded to treatment with Y27632.

“In this study, we showed that there is a specific microRNA cluster associated with ROCK expression and breast cancer metastasis,” said Liu, who worked under the supervision of Michael Rosenblatt, M.D., professor of medicine and dean of Tufts University School of Medicine.

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The mission of the American Association for Cancer Research is to prevent and cure cancer. Founded in 1907, the AACR is the world’s oldest and largest professional organization dedicated to advancing cancer research. The membership includes 30,000 basic, translational and clinical researchers; health care professionals; and cancer survivors and advocates in the United States and nearly 90 other countries. The AACR marshals the full spectrum of expertise from the cancer community to accelerate progress in the prevention, diagnosis and treatment of cancer through high-quality scientific and educational programs. It funds innovative, meritorious research grants, research fellowship and career development awards. The AACR Annual Meeting attracts more than 16,000 participants who share the latest discoveries and developments in the field. Special conferences throughout the year present novel data across a wide variety of topics in cancer research, treatment and patient care. The AACR publishes six major peer-reviewed journals: Cancer Research; Clinical Cancer Research; Molecular Cancer Therapeutics; Molecular Cancer Research; Cancer Epidemiology, Biomarkers & Prevention; and Cancer Prevention Research. The AACR also publishes CR, a magazine for cancer survivors and their families, patient advocates, physicians and scientists. CR provides a forum for sharing essential, evidence-based information and perspectives on progress in cancer research, survivorship and advocacy.

Media Contact:
Jeremy Moore
(267) 646-0557
jeremy.moore@aacr.org

In Boston, October 8-11:
(617) 457-2444

Biological Systems are basically formed when a group of molecules interact together. A type of Biological Systems is a biological pathway. Basically a biological pathway comparises of interacting genes, proteins, and small molecules.An understanding of the components, products, and biological effects of biosystems can lead to better understanding of biological processes in normal and disease states, elucidation of possible drug effects and side effects, and other insights to complex processes that have implications for health and medicine.

NCBI has designed a BioSystems database which has a centralized access to existing pathway databases.

Current source databases supported by Biosystems database are:

1. KEGG: Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/) by the Kanehisa Laboratory of the Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.

2. BioCyc (http://biocyc.org/) is a collection of organism-specific pathway/genome databases (PGDBs), and the EcoCyc (http://ecocyc.org/) subset of BioCyc is included in the NCBI BioSystems database.

3. Reactome (http://www.reactome.org/) is a curated knowledge base of biological pathways, and the human subset of Reactome is included in the NCBI BioSystems database. More about the Biosystems database can be read here: http://www.ncbi.nlm.nih.gov/Structure/biosystems/docs/biosystems_help.html

Here’s another multicellular, multiscale simulation using CompuCell3D and SOSlib. This is a more complex simulation as compared to the oscillating contact energies simulation I showed before (even if it is, perhaps, a little less visually interesting).

This simulation incorporates cell growth and division along with cell-cell communication through delta-notch signaling. ‘Internally’ there is a 2-ODE model running in association with each cell (I found this ODE model in Collier et al. 1996 and Eglen and Willshaw 2002). The numerical solution of these models (concurrently with the CC3D simulation) is accomplished using the SBML ODE Solver library (SOSlib). At the CC3D level, cell color is set depending on whether the internal (normalized) concentration of delta is above 0.5 or below 0.5. This is not intended to indicate distinct cell types. Rather, for visualization purposes, cell color is set in this manner.

Note that, with the creation of each new cell (by mitosis), a new SOSlib integrator instance must be created and associated with the new cell. The initial state of the new cell is set to be the same as the parent cell. However, as can be seen in the simulation, the child cell begins to behave in a manner distinct from the parent cell (as indicated by changing cell color).

Studying Reactome, actually led me to explore some more databases of pathways and reactions. While browing I eventually landed on a paper “Pathway databases and tools for their exploitation: benefits, current limitations and challenges” authored by Anna Bauer-Mehren, Laura I Furlong & Ferran Sanz. So, my todays post gives an abstract of what this paper is talking about.

Cell signalling studies have been going on from over a decade. This process basically refers to the biochemical processes using which cells respond to cues in their internal or external environment. This eventually led to the creation of chain of reactions and development of databases to store them in a compiled manner. Several databases containing information on cell signalling pathways have now been developed in conjunction with methodologies to access and analyse the data. At present, there are several repositories of information on cell signalling pathways that cover a wide range of signal transduction mechanisms and include high quality data in terms of annotation and cross references to biological databases.

Some of the online pathway databases have been nicely listed here: http://www.nature.com/msb/journal/v5/n1/fig_tab/msb200947_T2.html

This table basically lists Reactome, KEGG, Wikipathways, Nature interaction databases, pathway commons and many more….

The paper also explains the main standards for representation of biological networks, BioPAX and SBML. Furthermore, the advantages and drawbacks of current methods for pathway retrieval and integration, using the EGFR signalling as an illustrative example, have been discussed.

The paper is available here: http://www.nature.com/msb/journal/v5/n1/full/msb200947.html

Oncomine is an exellent platform for co- and antiexpression analysis. Press release. One can get an idea what poorly annotated genes are doing in different systems, tissues and conditions. The up side is it’s free version is poverful enough. Tho down side is Oncomine’s database is all about cancer only.

24 SEP 2009
INTRODUCING ONCOMINE 4 RESEARCH EDITION with Dr. Dan Rhodes
1:00-2:00 p.m. EDT

• Learn how to make discoveries and validate hypotheses with cancer genomics data using the new Oncomine 4.
• We’ll discuss how Oncomine was utilized in recent high-impact studies published in Science, Cancer Cell and PNAS.

REGISTER NOW

While studying about Biological pathways and databases, I landed on the home the Reactome Database, Indeed its a great creation. Here is a small introduction to “Reactome”.

Reactome is a free, online, open-source, curated resource of core pathways and reactions in human biology.It is a database which is maintained by the Reactome editorial staff and cross-referenced to the NCBI Entrez Gene, Ensembl and UniProt databases, the UCSC and HapMap Genome Browsers, the KEGG Compound and ChEBI small molecule databases, PubMed, and GO.curated human data are used to infer orthologous events in 22 non-human species including mouse, rat, chicken, puffer fish, worm, fly, yeast, two plants and E.coli.

The Reactome website (www.reactome.org) can be browsed like an online textbook. The website’s front page features a large ‘reaction map’ that summarizes all of the currently curated or inferred pathways, and a table of contents that describes each of the top-level pathways in the database. In the reaction map, each reaction is represented as a small arrow, and arrows are joined end to end to indicate that the output of one reaction becomes the input of the next. The reactions are organized in distinctive patterns to allow researchers to become familiar with the different parts of the reaction network.

Here is a article which talk about Reactome in detail: http://genomebiology.com/2007/8/3/r39

Reactome can be accessed from here: www.reactome.org

Reactome also hosts some tools for data analysis. These are Skypainter and Boiomart. Most probably, my next post would be on these tools. So, keep visiting…!!!

Simple and comprehensive approach to identify properties of unknown genes, conserved between organisms. Microarrays from different species, table of  genes similar between organisms (identified by BLAST). Identification of co-expressed genes, and building probabilistic networks out of them. Several unknown genes are classified by this method, and tested by RNAi.

Science. 2003 Oct 10;302(5643):249-55. Epub 2003 Aug 21. Links

But before the main article it is well worth reading of a comment by John Quackenbush, talking about quite negative (and wrong) public perception of microarray technology, co-expression approach up- and down-sides, and the future directions from this article leading to real biology findings.

J’aime bien dire que je suis cyberpunk. J’aime bien le cyberpunk en général, Gibson en particulier, même que j’ai eu l’occasion de tailler une bavette avec le mec aux dernières Utopiales et que j’étais ravi.

C’est sorti du bouquin de la SF et ça fait son chemin IRL.

Je ne suis pas Biopunk. Pour le Bio je joue dans la catégorie pro, même si par moments je m’essaie aux hacks qui font lever les sourcils des collègues. Mais qui souvent finissent par marcher. Et alors là, je ne vous dit pas la jubilation ! Mais dans les cercles vertueux quand ça marche ce n’est plus un hack, c’est un procédé

J’aurais pu être Biopunk et informaticien. Il a fallu que je fasse un choix entre informatique et biologie moléculaire, à une époque où la bioinformatique n’était même pas de la SF. Deux disciplines qui s’intéressent au traitement de l’information, dans deux systèmes différents. Si quelqu’un vous dit qu’elles sont différentes c’est qu’il ne comprend pas l’une, ou l’autre, peut-être même les deux.

La francophonie semble découvrir les biopunk (beaucoup disent biohackers) et les réactions me font marrer, parfois jaune, mais marrer quand même. D’habitude je passe volontiers sur ce genre de sujets, sachant que le risque de m’énerver n’est pas négligeable et je n’aime pas beaucoup m’énerver.

Là je suis tombé sur le titre du billet de Samuel Desgasne sur Rue89 : “ADN : les biohackers créent des monstres dans leur garage. Quel titre à la con ! Le contenu pas mieux. Si le mec peut me trouver où avoir la séquence complète de mon génome pour 3700€ (je dis bien la séquence complète) je suis prêt à me la payer. Sans blagues. Just for the fun et je la posterai même sur le Net et je la comparerai à celles de Venter et de Watson.

Puis j’ai fait le tour du Net pour voir, même Zenith en parle ! Pour dire. Le sujet est hot.

On raconte tout et n’importe quoi, on parsème de ce qui pourrait être sensationnel pour le lectorat, de monstres directement issus de son complexe de Frankenstein (une pensée tendre pour le bon Docteur), aux considérations de crime contre la nature ou la volonté divine, où divine est le mot clé. Laisser pisser est probablement la meilleure façon de faire.

Dans les blogs scientifiques c’est différent, quand c’est un scientifique qui se prononce, sans l’excuse d’être un journaliste sans bagage biologique apparent, ou un bigot qui se préoccupe du statut bafoué de sa mouture du créateur. Et troisième résultat sur Google, le billet de Tom repris au C@fé des sciences.

Les hackers sont des bricoleur. Le terme a été utilisé pour désigner essentiellement les bricoleurs en informatique, et il y en a eu grâce à Microsoft !

Les biohackers sont des bricoleurs d’organismes biologiques. Et ils ne doivent pas être très loin de l’informatique, d’une façon ou d’une autre.

Un hacker n’est pas obligatoirement quelqu’un de sympathique, demandez aux responsables sécurité des gros systèmes pour savoir combien ils trouvent sympathiques les hackers qui rentrent dans leurs systèmes ne serais-ce que pour détourner leurs ressources, sans rien casser ou pirater. Ils les abhorrent. Presque autant qu’ils les admirent. Moi je les aime bien tant qu’ils ne foutent pas le bordel. Je suis du genre à laisser ma WiFi sans mot de passe en utilisant un proxy d’anonymat. Faut lubrifier les accès. Mais que je vois une MAC qui déconne et je suis prêt à lui balancer ma collection de virus (si je trouve où j’ai pu planquer ce foutu CD).

Tom semble effrayé par les montres qui risquent de s’échapper, des OGM résistants aux antibiotiques, pensez donc ! Ca doit être grave ça. Ca peut l’être en fait, ça dépend de la modification génétique. Si le biopunk de votre quartier s’est mis en tête de faire des tomates à la GFP vous pouvez dormir tranquille. Même s’il a décidé de faire des E. coli rouge fluo. Et il peut mettre au tout à l’égout ses saloperies résistantes aux antibiotiques sans grand risque.

Maintenant, s’il est en train de s’employer à produire une bactérie pathogène multi-resistante c’est autre chose. Non, oubliez ça. Pas besoin de bidouiller le génome de la bête, suffit de la cultiver en sélectionnant les naturellement résistantes. Plus simple et ça demande moins de connaissances.

Passons à la métaphore filée “biologie=informatique”.

Une bactérie a quelque chose d’équivalent à un programme informatique, son génome. Ca ne justifie certainement pas une égalité entre biologie et informatique. Mais l’indignation de Tom est tirée par les cheveux.

Une bactérie est certes le produit de plusieurs milliards d’années d’évolution, il en est de même des bestioles qui on inventé l’informatique, qui n’a pas été conçue from scratch, mais par une espèce de mammifère sur le substrat des mèmes qu’il véhicule.

Comment ça il n’y a pas de séparation claire, nette et sans bavures entre hardware et software bactérien ? Parle à mon transfert de gènes horizontal inter-espèces (qui pose autant de problèmes de multi-résistance aux antibio), parle à la colonisation nucléaire par les gènes mitochondriaux, tape une causette avec Agrobacterium tumefaciens et après on verra si le soft est si lié au hard que ça en biolo. Tout est une question de compatibilité et il suffit de hacker le hard pour pouvoir transférer tranquillement du soft. Lis donc les travaux de l’équipe de Venter pour apprendre comment ça marche.

Puis, pour rapprocher un peu biologie et informatique on pourrait faire un petit tour du côté de la puissance des algorithmes génétiques pour résoudre des problèmes en industrie. Je ne ferai pas l’insulte à Tom de lui donner des liens comme je l’aurais fait pour Jean Staune. Je suis certain qu’il en connaît plein, peut-être plus que moi.

Et pour faire le pas dans l’autre sens on pourrait regarder vers les calculs utilisant l’ADN, qui vont se populariser (dans les labos au moins, pas dans les garages pour l’instant) avec la baisse des coûts des appareils de séquençage.

Question sécurité tout est relatif. Quand on hacke un gros système tout ce que l’on risque est de foutre le bordel dans le dit système, pas que griller sa machine ! D’ailleurs je ne vois pas comment on pourrait griller sa machine (je plaisante, j’en ai grillé un certain nombre quand j’étais jeunot). Ca dépend où l’on a foutu les connections.

Le génie biologique n’est plus prometteur. Il a déjà commencé à livrer ce qu’il promettait il y a trente-cinq ans et une partie est déjà autours de nous. Plein de produits issus de l’utilisation d’OGM, plein d’OGM directement sur le marché en tant que produits. Et fuck pour ceux qui pensent que la biologie puisse être vue comme sous-discipline de l’ingénierie, ils ont qu’à ouvrir les yeux. Le génie biologique a 35 ans, ils étaient où tout ce temps, cachés sous une pierre ?

L’une des dernières facettes, pas la première et probablement pas la dernière, du génie biologique c’est la biologie synthétique. Elle a des beaux jours devant elle. Personne ne dit que ça va être simple, mais ça avance. Booter un génome synthétique, c’est ça le graal du moment et il est peu probable que ça sorte d’un garage.

En attendant le génie biologique avance tranquillement son chemin, si tranquillement que certains l’ont presque oublié. C’est certes amusant de faire compter jusqu’à trois à une cellule (moi ça m’amuse), mais c’est plus jouissif de faire produire un anti-malarien au dixième du prix actuel.

Revenons aux biopunk/biohackers. Ils vont foutre un peu le bordel, ils vont provoquer des nouvelles réglementations, ils vont inquiéter les citoyens qui ne veulent pas d’OGM dans leur assiette, ils vont faire dresser les cheveux de ceux qui voient leurs envies comme un blasphème. Tant mieux. Tant mieux.

DIYbio, faut les aider, pas leur casser les pieds.

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence ‘labeling’ problems. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification.

A Markov model is a probabilistic process over a finite set, {S₁, …, S_k}, usually called its states. Each state-transition generates a character from the alphabet of the process.

A Hidden Markov Model (HMM) is simply a Markov Model in which the states are hidden. For example, suppose we only had the sequence of throws from the 3-coin example above, and that the upper-case v. lower-case information had been lost.

HTHHTHHTTTHTTTHHTHHHHTTHTTHTTHT...

We can never be absolutely sure which coin was used at a given point in the sequence but we can calculate the probability.

What’s Hidden in HMM?

It’s useful to imagine an HMM generating a sequence. When we visit a state, we emit a residue from the state’s emission probability distribution. Then, we choose which state to visit next according to the state’s transition probability distribution. The model thus generates two strings of information. One is the underlying state path (the labels), as we transition from state to state. The other is the observed sequence (the DNA), each residue being emitted from one state in the state path.

The state path is a Markov chain, meaning that what state we go to next depends only on what state we’re in. Since we’re only given the observed sequence, this underlying state path is hidden—these are the residue labels that we’d like to infer. The state path is a hidden Markov chain.

Here is a link to an interesting paper on HMMs: http://www.nature.com/nbt/journal/v22/n10/full/nbt1004-1315.html

Probably a landmark paper defining two types of basic networks containing three nodes and being able to adapt most efficiently. Video explains the essense in 4 minutes.

Cell. 2009 Aug 21;138(4):760-73. Links

This Pathways Compendium provides an index of Alzheimer disease pathway models contributed by researchers and companies.

Probable Interaction Networks Involved in Pathology of Alzheimer Disease: Predicting Targets and Therapeutic Agents – NetPro™ based Study. Developed by researchers at Molecular Connections Private Limited, an in silico discovery services company. Users can click on a specific node (molecule) to get information on all interactions of the molecule in the given network. Click on the interaction arrows for information on the specific interaction.

The Alzheimer Research Forum is a Pioneering Biomedical Web Community. Founded 13 years ago when the Web was still in its infancy, the “Alzforum” has more than 5,000 registered members and is familiar to most Alzheimer scientists in the world.

XTractor Premium – A Platform for discovery, knowledge sharing, analysis and modelling of published biomedical facts.

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Directions for development of unified language to describe biological processes. Although it does not address multi-layered information space, the article recognize three parts of the data that should be described uniquely:

1. Process diagram. Represent processes that convert physical entities into other entities, change their states of change their location.
2. Entity relationship diagram. Represent the interactions between entities and the rules that control them.
3. Activity flow diagram. Represent the influence of biological activities on each other.

http://www.ncbi.nlm.nih.gov/pubmed/19668183

Nat Biotechnol. 2009 Aug;27(8):735-41. Epub 2009 Aug 7. Links

Systems Biology is emerging as one of the biggest research trends these days. Talking about pathways, metabolomics, cellular cycles, interactions is common in this field.

While reading on Interaction Datasets , I came across “BioGrid“. Here is a small post on the same.

BioGRID can be explained as Biological General Repository for Interaction Datasets. It distributes collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies.

BioGRID interactions are recorded as relationships between two proteins or genes (i.e. they are binary relationships) with an evidence code that supports the interaction and a publication reference. The term “interaction” includes, as well as direct physical binding of two proteins, co-existence in a stable complex and genetic interaction. It should not be assumed that the interaction reported in BioGRID is direct and physical in nature; the experimental system definitions below indicate the nature of the supporting evidence for an interaction between the two biological entities. It should also be noted that some interactions in BioGRID have various levels of evidential support. BioGRID simply curates the result of the experiment from the publication and we do not guarantee that any individual interaction is true, well-established or the current consensus view of the community. Curating all available evidence supporting for an interaction enables orthogonal data from various sources to be collated, allowing users of the database to decide confidence in the existence and/or physiological relevance of that interaction.

More information on Biogrid can be found at: www.thebiogrid.org

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