Tech Briefs

Easy exchange of model information will support post-genomics research in systems biology.

A continuing research project is dedicated to development of mathematical and software infrastructure in support of post genomics research in systems biology. One near-term objective of the project is to contribute to deeper understanding of the organizational principles of biological networks. A distinguishing theme of this project is a focus on scalable methods of robustness and theoretically sound methods of the use of experimental data to validate (or invalidate) models; this theme stands in contrast to the heretofore prevalent theme of relying purely on simulation.

A central goal of modeling and simulation in systems biology is to connect molecular mechanisms to network functions to questions of biomedical relevance. Unfortunately, many of the most critical questions involve events that are extremely rare at the level of individual cells in an organism, yet are catastrophic to the organism as a whole. Consequently, simulation methods that may be adequate for studying generic or typical behavior are inadequate for use in exploring worst-case scenarios, which are computationally intractable using conventional methods. In an effort to overcome this limitation of conventional methods, the present project is extending best practice software tools and algorithms for robustness analysis that have become standards in engineering to models of biological relevance, which are typically nonlinear, hybrid, uncertain, and stochastic. This effort includes integration of formal inference methods from previously fragmented theories in computer science with those of control and dynamical systems.

The theoretical framework being developed in this project represents an unprecedented opportunity to create a system for analysis and validation (or invalidation) of models and for iterative experimentation on models that may be of a large-scale, stochastic, nonlinear, nonequilibrium, and mixed continuous and discrete nature with multiple time and spatial scales. The remarkable quality of the theoretical framework is that it can be used to prove conjectures regarding such complex, difficult-to-analyze models. Examples of conjectures that can be proven are (1) a given model cannot fit the experimental data, no matter what parameters are used and (2) a given model is robust, no matter how its parameters are varied. Heretofore, there has been no way of proving such conjectures except in cases of much simpler models. The combination of the capability of proving such conjectures and sophisticated robustness analysis methods is what is needed to make it possible to analyze realistic biological models and relate them to experimental data to help answer the question, “What is the next experiment that would best differentiate among the current alternative hypotheses?”