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. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.