title: Phenotypes and tolerances in the design space of biochemical systems.
abstract: One of the major unsolved problems of modern biology is deep understanding of the complex relationship between the information encoded in the genome of an organism and the phenotypic properties manifested by that organism. Fundamental advances must be made before we can begin to approach the goal of predicting the phenotypic consequences of a given mutation or an organism's response to a novel environmental challenge. Although this problem is often portrayed as if the task were to find a more or less direct link between genotypic and phenotypic levels, on closer examination the relationship is far more layered and complex. Although there are some intuitive notions of what is meant by phenotype at the level of the organism, it is far from clear what this term means at the biochemical level. We have described design principles that are readily revealed by representation of molecular systems in an appropriate design space. Here, we first describe a generic approach to the construction of such a design space in which qualitatively distinct phenotypes can be identified and counted. Second, we show how the boundaries between these phenotypic regions provide a method of characterizing a system's tolerance to large changes in the values of its parameters. Third, we illustrate the approach for one of the most basic modules of biochemical networks and describe an associated design principle. Finally, we discuss the scaling of this approach to large systems.
authors.affiliation: Department of Biomedical Engineering, University of California, One Shields Avenue, Davis, CA 95616-5294, USA. masavageau@ucdavis.edu
authors.forename: Michael A
authors.initials: MA
authors.lastname: Savageau
authors.forename: Pedro M B M
authors.initials: PM
authors.lastname: Coelho
authors.forename: Rick A
authors.initials: RA
authors.lastname: Fasani
authors.forename: Dean A
authors.initials: DA
authors.lastname: Tolla
authors.forename: Armindo
authors.initials: A
authors.lastname: Salvador
mesh_terms: D000220:Adaptation, Biological; D053858:Metabolic Networks and Pathways; D010641:Phenotype