Reverse engineering 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 linear and non-linear continuous-time differential equations 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. Specifically, genetic programming automatically created metabolic pathways involved in the phospholipid cycle and the synthesis and degradation of ketone bodies.
Department of Medicine, Department of Electrical Engineering, Stanford University, Stanford, California, USA. koza@stanford.edu
J R
JR
Koza
W
W
Mydlowec
G
G
Lanza
J
J
Yu
M A
MA
Keane
D001698:Biomedical Engineering; D003627:Data Interpretation, Statistical; D007657:Ketone Bodies / Q000378:metabolism; D008660:Metabolism; D008954:Models, Biological; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D010743:Phospholipids / Q000378:metabolism
D007657:Ketone Bodies; D010743:Phospholipids