Detalle de: Mixed Integer Multiobjective Optimization Approaches for Systems and Synthetic Biology

In this work we tackle a number of computational challenges in systems and synthetic biology exploiting optimization based approaches. Our framework combines three important capabilities: multiple optimization objectives (taking into account trade-offs between conflicting goals), simultaneous exploration of topology and parameter spaces (through a mixed integer modeling framework) and high computational efficiency. We illustrate the capacities of the mixed integer multiobjective framework in three different applications: i) automated design of synthetic bistable genetic switches, ii) exloring design principles underlying biochemical bistable switches in living cells iii) advanced identification of cellular process models from experimental data.