Abstract
Biological signalling systems are complex, and efforts to build mechanistic models mustconfront a huge parameter space, indirect and sparse data, and frequently encountermultiscale and multiphysics phenomena. We present HOSS, a framework forHierarchical Optimization of Systems Simulations, to address such problems. HOSSoperates by breaking down extensive systems models into individual pathway blocksorganized in a nested hierarchy. At the first level, dependencies are solely on signallinginputs, and subsequent levels rely only on the preceding ones. We demonstrate thateach independent pathway in every level can be efficiently optimized. Once optimized,its parameters are held constant while the pathway serves as input for succeeding levels.We develop an algorithmic approach to identify the necessary nested hierarchies for theapplication of HOSS in any given biochemical network. Furthermore, we devise twoparallelizable variants that generate numerous model instances using stochasticscrambling of parameters during initial and intermediate stages of optimization. Ourresults indicate that these variants produce superior models and offer an estimate ofsolution degeneracy. Additionally, we showcase the effectiveness of the optimizationmethods for both abstracted, event-based simulations and ODE-based models.