How to deal with large models?

Abstract

Mol Syst Biol. 5: 240 The regulatory systems that allow cells to adapt to their environments are exceedingly complex, and although we know a great deal about the intricate mechanistic details of many of these systems, our ability to make accurate predictions about their system‐level behaviors is severely limited. We would like to make such predictions for a number of reasons. How can we reverse dysfunctional molecular changes of these systems that cause disease? More generally, how can we harness and direct cellular activities for beneficial purposes? Our ability to make accurate predictions about a system is also a measure of our fundamental understanding of that system. As evidenced by our mastery of technological systems, a useful understanding of a complex system can often be obtained through the development and analysis of a mathematical model, but predictive modeling of cellular regulatory systems, which necessarily relies on quantitative experimentation, is still in its infancy. There is much that we need to learn before modeling for practical applications becomes routine. In particular, we need to address a number of issues surrounding the large number of parameters that are typically found in a model for a cellular regulatory system. In a recent article published in Molecular Systems Biology , Peter Sorger et al report a significant contribution not only to our system‐level understanding of an important signal‐transduction system but also to our understanding of the methodology needed for developing and testing a large‐scale mathematical model for this type of system (Chen et al , 2009). The power of sensitivity analysis is demonstrated. In this study, to which William Chen, Birgit Schoeberl and Paul Jasper contributed equally, an ordinary differential equation (ODE) model for immediate‐early events in signaling by the epidermal growth factor (EGF) receptor (EGFR, which is also called ErbB1) (Schoeberl et …