Seminar Series Systems Biology for Food, Feed, and Health

On September 12th the Wageningen Centre for Systems Biology (WCSB) will organise the first of the seminar series on systems biology. The seminar is free, will take an hour and will be followed by drinks. No registration is necessary, so feel free to come by!

Organisator Wageningen Centre for Systems Biology

do 12 september 2013 16:00 tot 17:00

Zaal/kamer C8
Logo wcsb

Jörg Stelling, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

"Automated Generation of Predictive Models for Systems Biology"

Limited mechanistic knowledge, conflicting hypotheses, and still relatively scarce experimental data hamper the development of mathematical models as analysis tools for systems biology. In particular, model identification is often challenging because it concerns network topologies (e.g., presence or absence of interactions between components) and parameters (e.g., binding affinities) simultaneously. Here, we discuss how to address these problems by the automation of model generation for small-scale dynamic (signaling)networks and for large-scale (metabolic) networks.

For cell signaling analysis, our ‘topological filtering’ method integrates all hypotheses on a pathway and automatically generates a set of simple(r) models compatible with observational data. For control of the yeast stress-responsive transcription factor Msn2, iterations between model predictions and rationally designed experiments identified a single, highly plausible circuit topology. Model analysis suggested that dynamic phenomena such as (rapid) switching of Msn2 localization and phosphorylation states enable efficient stress-response signaling. Similar principles could apply to mammalian signaling pathways involving ‘shuttling’ transcription factors.

At a larger scale we addressed the problem that current models cannot accurately predict plant growth and metabolism, especially when considering perturbations or expected climate changes. We developed methods for automated model construction to establish a compartmentalized, genome-scale metabolic model of Arabidopsis thaliana. Integration of model predictions and experimental in vivo data in a systems perspective revealed important knowledge gaps, such as diurnal growth patterns that must be accounted for in plant metabolism analysis, and previously unknown structural limitations of plant growth that quantitatively explain differences between laboratory and field experiments in studies on the impact of climate change on C3 plant growth. Overall, the systematic construction of systems biology models can yield detailed insights into non-obvious mechanisms from the molecular to the physiological level.

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