Graph models and mathematical programming in biochemical network analysis and metabolic engineering design

Thanks to the technological innovations introduced in the biological research pipeline, it is possible to write a list of parts containing the molecular units building living systems. This list constitutes the starting point for Systems Biology, a new paradigmatic approach in life sciences, that focuses on the complex behaviors of living systems deriving from the interactions of their molecular basis. In this way biological systems are represented as networks of interacting entities. Computational models of biological networks will help us to deeply understand the development of living organisms. In this paper we first introduce a general graph based model of biological systems and we then focus our attention on metabolic networks. We show how metabolic networks optimization techniques based on linear programming (LP) can be used to meet objectives such as flux maximization and optimal growth. Moreover we show that Petri Nets are useful in biochemical networks representation and for steady state pathways identification. Results obtained by studying the robustness of Escherichia coli metabolic network with sensitivity analysis tools are presented. Finally we suggest how computational models of biochemical networks can be employed in metabolic engineering design.