Network Reconstructions Allow Us To Unravel the Complexity of Life

Ever since Gregor Mendel discovered discrete quanta of information that determine form and function of organism passed from one generation to the next, the genotype-phenotype relationship (GPR) has been of fundamental importance in the life sciences.  For monogeneic traits the GPR can be readily understood.  However, for most phenotypic traits involve multiple gene products making the GPR a challenge to construct and understand.  With the publication of the first full genome sequences in the mid-1990's it became possible, in principle, to identify all the gene products involved in complex biological processes in a single organism.  The well-studied biochemistry of metabolic transformations made it possible to reconstruct genome-scale metabolic networks for a target organism in a bottom-up fashion.  Network reconstructions can be converted into a mathematical format yielding mechanistic GPRs for microbial metabolism allowing the computation of phenotypic traits based on the genetic composition of the target organism.  Since the appearance of the first such genome-scale reconstruction in 1999, a large number of them have appeared; perhaps most notably for human metabolism in 2007.  Such metabolic GPR have been the basis for a large number of studies.  For the purposes of this talk we mention the use of genome-scale metabolic reconstructions to: (1) map multiple omics data types, (2) discover knowledge gaps in the networks, (3) understand complex biological processes, such as bacterial adaptation, and (4) to design metabolic phenotypes.  In addition, we describe recent developments in achieving similar bottom-up reconstructions of the translation/transcription machinery, the transcriptional regulatory networks, and select signaling processes.