Abstract
Computational modeling of engineered gene circuits is an important while challenged task in systems biology. In order to describe and predict the response behaviors of genetic circuits using reliable model parameters, this paper applies an optimal experimental design(OED) method to obtain input signals. In order to obtain informative observations, this study focuses on maximizing Fisher information matrix(FIM)-based optimal criteria and to provide optimal inputs. Furthermore, this paper designs a two-stage optimization with the modified E-optimal criteria and applies harmony search(HS)-based OED algorithm to minimize estimation errors. The proposed optimal identification methodology involves estimation errors and the sample size to pursue a trade-off between estimation accuracy and measurement cost in modeling gene networks. The designed cost function takes two major factors into account, in which experimental costs are proportional to the number of time points. Experiments select two types of synthetic genetic networks to validate the effectiveness of the proposed HS-OED approach. Identification outcomes and analysis indicate the proposed HS-OED method outperforms two candidate OED approaches, with reduced computational effort.
Highlights
In synthetic biology, synthetic gene circuits and networks offer the opportunity to modify behaviors of cellular systems in a controllable and stable way
The model quality is judged by the modified E-optimal criteria and the measurement cost is reflected the number of collected data points
In order to guide the modular construction of synthetic gene networks, a novel optimal identification method that provides accurate predictive models at a low experimental cost is proposed
Summary
Synthetic gene circuits and networks offer the opportunity to modify behaviors of cellular systems in a controllable and stable way. Relying only on prior experience is not enough to design input signals in modeling of synthetic gene circuits In this way, optimal experimental design(OED) provides a feasible way to design input signals, including selection of sampling periods and the number of measured points. The problem of OED is usually converted to an optimization problem that involves the judgment of model quality, which is related the scalar functions of FIM [24] Several factors such as measured time points, sampling time and efficient information contained in measurements have influence on the modeling quality of synthetic gene circuits. Under the framework of deterministic modeling, the paper proposes an two-stage identification method to obtain model parameters at a low experimental cost In this method, the model quality is judged by the modified E-optimal criteria and the measurement cost is reflected the number of collected data points. Optimal identification of synthetic gene networks outcomes that involve two kinds of synthetic gene networks illustrate that the proposed optimal identification is able to achieve a tradeoff between estimation accuracy and measurement cost
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