Computational Challenges in Systems Biology

Publisher Summary This chapter examines the challenges and some of the recent advances in computational systems biology. Research in computational systems biology has moved beyond interaction networks based simply on clustering and correlation. There are two paradigms in computational systems biology: the iterative cycle of biochemical model—mathematical model—computational model, and integration of novel data and legacy knowledge to develop context-specific biochemical, mathematical, and computational models. Challenges in building biochemical models include the complexity of proteomic states and interactions, integration of diverse data to infer biochemical interactions, and the temporal state of biochemical models. Challenges in building mathematical models include incorporating statistical/probabilistic information into analytical models, using qualitative constraints in mathematical models, and incomplete knowledge and coarse-graining. Challenges in computational modeling include the absence of knowledge about model parameters such as rate constants, local versus global concentrations of species and multiple scales of distance and time, and variation among different cell types and subpopulation variability, or variability among biological repeats. Advanced research in coarse graining will pave the way for progress in the development of multiscale multidomain modeling that can connect fundamental research in network biology to clinical research.