Section: Research Program
Computational Systems Biology
Bridging the gap between the complexity of biological systems and our capacity to model and quantitatively predict system behaviors is a central challenge in systems biology. We believe that a deeper understanding of the concept and theory of biochemical computation is necessary to tackle that challenge. Progress in the theory is necessary for scaling, and enabling the application of static analysis, module identification and decomposition, model reductions, parameter search, and model inference methods to large biochemical reaction systems. A measure of success on this route will be the production of better computational modeling tools for elucidating the complex dynamics of natural biological processes, designing synthetic biological circuits and biosensors, developing novel therapy strategies, and optimizing patient-tailored therapeutics.
Progress on the coupling of models to data is also necessary. Our approach based on quantitative temporal logics provides a powerful framework for formalizing experimental observations and using them as formal specification in model building. Key to success is a tight integration between in vivo and in silico work, and on the mixing of dry and wet experiments, enabled by novel biotechnologies. In particular, the use of micro-fluidic devices makes it possible to measure behaviors at both single-cell and cell population levels in vivo, provided innovative modeling, analysis and control methods are deployed in silico.
In synthetic biology, while the construction of simple intracellular circuits has shown feasible, the design of larger, multicellular systems is a major open issue. In engineered tissues for example, the behavior results from the subtle interplay between intracellular processes (signal transduction, gene expression) and intercellular processes (contact inhibition, gradient of diffusible molecule), and the question is how should cells be genetically modified such that the desired behavior robustly emerges from cell interactions.