Designing synthetic networks in silico: a generalised evolutionary algorithm approach

Robert W Smith,Bob Van Sluijs,Christian Fleck

doi:10.1186/s12918-017-0499-9

Robert W Smith, Bob Van Sluijs + Show 1 more

Open Access

https://doi.org/10.1186/s12918-017-0499-9

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Abstract

BackgroundEvolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes).ResultsThe key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction.ConclusionsIn this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Highlights

Evolution has led to the development of biological networks that are shaped by environmental signals
One method commonly used to optimise the topology of promoter circuits is Mixed Integer Non-Linear Programming, a minimisation optimisation routine where parameters can be altered within certain ranges [10,11,12]
Network structure Within our Evolutionary Algorithms (EAs) we describe biological networks at three levels: i) at the node level whereby a binary string determines how a node within the network is regulated and how it functions, ii) an adjacency matrix that shows how the nodes are connected, and iii) a set of parameters that determine the rate of each reaction

Summary

Introduction

Evolution has led to the development of biological networks that are shaped by environmental signals. Whilst the forward engineering approach has proven highly successful, the opposite challenge (‘reverse engineering’ a network design from a known desired response) is of importance. This would allow one to obtain novel network designs that may possess complex functionality. One method commonly used to optimise the topology of promoter circuits is Mixed Integer Non-Linear Programming, a minimisation optimisation routine where parameters can be altered within certain ranges [10,11,12] This method has been extended to optimise networks for multiple objectives, resulting in a Pareto front that allows one to observe the trade-offs between different system constraints [10]. Efficient means of executing and solving the reverse engineering problem have yet to be developed in a generalised manner for the synthetic biology community

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