Reverse Engineering and Automatic Synthesis of Metabolic Pathways from Observed Data Using Genetic Programming

Recent work has demonstrated that genetic programming is capable of automatically creating complex networks (such as analog electrical circuits and controllers) whose behavior is modeled by continuous-time differential equations (both linear and nonlinear) and whose behavior matches prespecified output values. The concentrations of substances participating in networks of chemical reactions are also modeled by non-linear continuous-time differential equations. This paper demonstrates that it is possible to automatically create (reverse engineer) a network of chemical reactions from observed time-domain data. Genetic programming starts with observed time-domain concentrations of input substances and automatically creates both the topology of the network of chemical reactions and the rates of each reaction within the network such that the concentration of the final product of the automatically created network matches the observed time-domain data. This paper describes how genetic programming automatically created a metabolic pathway involving four chemical reactions that takes in glycerol and fatty acid as input, uses ATP as a cofactor, and produces diacyl-glycerol as its final product. In addition, this paper describes how genetic programming similarly created a metabolic pathway involving three chemical reactions for the synthesis and degradation of ketone bodies. Both automatically created metabolic pathways contain at least one instance of three noteworthy topological features, namely an internal feedback loop, a bifurcation point where one substance is distributed to two different reactions, and an accumulation point where one substance is accumulated from two sources.