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Two Sides of Network Biology 2.0

By John Russell

July 20, 2009 | The roster of speakers at the Network Biology 2.0 symposium*, sponsored by Gene Network Sciences (GNS) and the Broad Institute, featured some of the most influential people in systems biology and personalized medicine: Jim Collins, George Church, Mark Boguski, and Peter Sorger to name a few. Eric Schadt was a no show, but with his dramatic move to Pacific Biosciences (see p. 8), he probably had an excuse.

Chaired by GNS co-founder and executive VP Iya Khalil, the symposium speakers discussed various approaches for taking diverse data, transforming that into models, validating those models, and using the model to infer new insight. Here are a few of the highlights:

• Examining extrinsic ligand-induced death pathways, Sorger used modeling at different levels—quantitative and Boolean—to study why some cells dies rapidly and other slowly. The modeling ruled out genetics and epigenetics and stochastic biochemical reaction rates. Sorger suggested a third hypothesis: unequal concentrations of the reactants in different cells are the drivers of variability. 
• Dana Pe’er (Columbia University) discussed changes in gene regulatory networks that drive phenotype. In one case, she applied Conexic, a Bayesian algorithm, to a melanoma dataset comprising 62 tumor samples and correctly identified most known ‘driver’ events, while also connecting these to their known targets. The analysis also suggested a number of novel drivers, including genes involved in regulation of protein trafficking and endosome biology.
• The GNS platform REFS (reverse engineering/forward simulation) can take large data sets and, without prior knowledge, use computational techniques to find novel associations. Paul McDonagh (GNS) has used REFS to analyze liver gene expression, serum lipid profiles and body weight in 120 male mice from a mouse intercross. An ensemble of 1024 networks accurately predicted animals that were not part of the training data and explained almost twice the variance compared to quantitative trait loci alone. Further in silico experiments identified dozens of additional transcripts predicted to play a significant role in controlling HDL and triglyceride levels.
• In a talk on “Customized Care 2020,” Mark Boguski (Harvard Medical School) argued precision diagnostics will be a disruptive innovation in medicine by 2020, and that clinical pathology will play a central role in personalized medicine. “Pathology is moving beyond diagnostics and classification of disease to providing customization information and therapeutic recommendations,” he said, adding that data-driven reverse engineering of disease processes will identify the relevant molecular pathways in individual patients. These pathways will then be forward-simulated in the presence of virtual drug combinations to predict individualized therapies.
• George Church (Harvard Medical School) argued that “we stand on the cusp of a remarkable opportunity for connecting data for personalized medicine via web 2.0 volunteerism, personally controlled health records, and inexpensive personal genomes.” Next-generation genome sequencing advances would be accompanied by understanding of regulatory variants (via allele-specific RNA quantitation) and environmental components (via pharmaceutical, microbiome and immune system datasets).

Pin Drop
One of the most interesting moments of the day occurred at the symposium’s concluding panel: “The Future of Integrative Genomics and Network Biology.” After a day packed with science and optimism, Gurinder “Mickey” Atwal (Institute for Advanced Study, Princeton, New Jersey) expressed resolute skepticism that systems biology could ever fulfill its promise of predictiveness.

You could have heard a pin drop as the audience’s buoyant spirits were suddenly sobered by this challenging view. A physicist by training, Atwal argued that the search for biology’s first principles around which to organize data and create truly predictive models was probably fundamentally flawed. He cited several long-term research projects that have made, essentially, no progress. Atwal hoped he was wrong, but fundamentally doubted the field would fulfill its expectations.

McDonagh took on the role of defender, and Boguski, whose 2020 talk brimmed with rosy forecasts for medicine, treaded carefully in rallying a counter argument. Stolovitzky offered that progress would be measured as the iterative process of experiment, model, and simulate gradually moved our understanding closer to comprehensive principles.

Usefully, the full assembly seemed to recognize the road to comprehensively predictive models for biology is unlikely to be short, even against the backdrop of solid science presented optimistically throughout the day and aiming for that very goal. But if Atwal saw the predictive biology glass forever half-empty, most saw it half-full and rising, though with varying ideas of how full it could get and when.

This article also appeared in the July-August 2009 issue of Bio-IT World Magazine.
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