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Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

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Abstract

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise and/or stochastic behaviour. This paper explores a fitness measure determined from a set of statistical features characterizing the time series’ sequence of values, rather than the actual values themselves. Through a series of experiments involving modular gene regulatory network models based on the stochastic π-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

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Authors and Affiliations

  1. Brock University, 500 Glenridge Ave., St. Catharines, ON, Canada, L2S 3A1

    Janine Imada & Brian J. Ross

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  1. Janine Imada
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  2. Brian J. Ross
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Correspondence to Janine Imada.

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Imada, J., Ross, B.J. Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces. New Gener. Comput. 29, 365–390 (2011). https://doi.org/10.1007/s00354-009-0115-7

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  • DOI: https://doi.org/10.1007/s00354-009-0115-7

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