Abstract

Background:The gap between genotype and phenotype is filled by complex biochemical systems most of which are poorly understood. Because these systems are complex, it is widely appreciated that quantitative understanding can only be achieved with the aid of mathematical models. However, formulating models and measuring or estimating their numerous rate constants and binding constants is daunting. Here we present a strategy for automating difficult aspects of the process.Methods:The strategy, based on a system design space methodology, is applied to a class of 16 designs for a synthetic gene oscillator that includes seven designs previously formulated on the basis of experimentally measured and estimated parameters.Results:Our strategy provides four important innovations by automating: (1) enumeration of the repertoire of qualitatively distinct phenotypes for a system; (2) generation of parameter values for any particular phenotype; (3) simultaneous realization of parameter values for several phenotypes to aid visualization of transitions from one phenotype to another, in critical cases from functional to dysfunctional; and (4) identification of ensembles of phenotypes whose expression can be phased to achieve a specific sequence of functions for rationally engineering synthetic constructs. Our strategy, applied to the 16 designs, reproduced previous results and identified two additional designs capable of sustained oscillations that were previously missed.Conclusions:Starting with a system’s relatively fixed aspects, its architectural features, our method enables automated analysis of nonlinear biochemical systems from a global perspective, without first specifying parameter values. The examples presented demonstrate the efficiency and power of this automated strategy.

Highlights

  • Biological systems display an enormous variety of phenotypes that emerge through complex interactions between their genotype and environment

  • The phenotypes manifested by the model representing the system are the result of several mappings: genome to genotypically determined structural parameters of the model, environment to environmentally determined input parameters of the model, and the gene-by-environment space of model parameters to the quantitative phenotypes of the model representing the biochemical system. In view of this last mapping, we have defined ‘phenotype’ as the attributes of a biochemical system in steady-state determined by a unique set of values in the gene-by-environment space of model parameters

  • Elucidation of the mechanistic link from genotype and environment to phenotype is a nearly intractable problem for two primary reasons. (a) The phenotype corresponding to a unique point in parameter space is the manifestation of a complex system that is analytically intractable and requires sampling numerous simulations for its characterization. (b) The parameter space represents an infinite number of phenotypes in a homogenous continuum

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Summary

BACKGROUND

The gap between genotype and phenotype is filled by complex biochemical systems most of which are poorly understood. Because these systems are complex, it is widely appreciated that quantitative understanding can only be achieved with the aid of mathematical models. We present a strategy for automating difficult aspects of the process. METHODS: The strategy, based on a system design space methodology, is applied to a class of 16 designs for a synthetic gene oscillator that includes seven designs previously formulated on the basis of experimentally measured and estimated parameters. CONCLUSIONS: Starting with a system’s relatively fixed aspects, its architectural features, our method enables automated analysis of nonlinear biochemical systems from a global perspective, without first specifying parameter values. Npj Systems Biology and Applications (2015) 1, 15003; doi:10.1038/npjsba.2015.3; published online 28 September 2015

INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
Design identifier
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