GA-based Design Algorithms for the Robust Synthetic Genetic Oscillators with Prescribed Amplitude, Period and Phase

Abstract

In the past decade, the development of synthetic gene networks has attracted much attention from many researchers. In particular, the genetic oscillator known as the repressilator has become a paradigm for how to design a gene network with a desired dynamic behaviour. Even though the repressilator can show oscillatory properties in its protein concentrations, their amplitudes, frequencies and phases are perturbed by the kinetic parametric fluctuations (intrinsic molecular perturbations) and external disturbances (extrinsic molecular noises) of the environment. Therefore, how to design a robust genetic oscillator with desired amplitude, frequency and phase under stochastic intrinsic and extrinsic molecular noises is an important topic for synthetic biology.In this study, based on periodic reference signals with arbitrary amplitudes, frequencies and phases, a robust synthetic gene oscillator is designed by tuning the kinetic parameters of repressilator via a genetic algorithm (GA) so that the protein concentrations can track the desired periodic reference signals under intrinsic and extrinsic molecular noises. GA is a stochastic optimization algorithm which was inspired by the mechanisms of natural selection and evolution genetics. By the proposed GA-based design algorithm, the repressilator can track the desired amplitude, frequency and phase of oscillation under intrinsic and extrinsic noises through the optimization of fitness function.The proposed GA-based design algorithm can mimic the natural selection in evolutionary process to select adequate kinetic parameters for robust genetic oscillators. The design method can be easily extended to any synthetic gene network design with prescribed behaviours.

A Novel Synthesizing Genetic Logic Circuit: Frequency Multiplier

“…Other computational evolutionary algorithms are also used to deal with this problem [25], [26], [27]. A genetic algorithm is applied to mimic the mechanisms of natural selection to search for optimal parameters to achieve the desired performance for synthesizing robust genetic oscillators [25]. In addition to the parameter optimization problem, the structure optimization problem of genetic networks also needs to be considered [28].…”

“…A robust synthetic design approach based on H 1 optimization control theory is proposed to build a robust synthetic genetic oscillator tracking a sustained periodic oscillating behavior under the stochastic perturbational environment by regulating degradation rates of mRNAs and proteins [22], [23], [24]. Other computational evolutionary algorithms are also used to deal with this problem [25], [26], [27]. A genetic algorithm is applied to mimic the mechanisms of natural selection to search for optimal parameters to achieve the desired performance for synthesizing robust genetic oscillators [25].…”

Optimal Design of Robust Synthetic Biological Oscillators

“…Most importantly, the synthetic oscillator must be robust to the internal parameter uncertainties such as thermal fluctuation and external environment disturbances in the host cell because these fluctuations may lead sustained oscillations to be damped oscillations, stable steady states or chaos. At present, these synthetic oscillators still can't perform reliably for a long time and need further tuning before application, hence, design a robust synthetic oscillator to withstand the influences of intrinsic and extrinsic stochastic molecular perturbations is necessary [33]. The robust oscillatory networks based on the S-system model when the networks are influenced by intrinsic fluctuations and extrinsic disturbances can be described as ( ) ( )…”

Mechanistic modelling of a recombinase‐based two‐input temporal logic gate

“…One such method, the genetic algorithm (GA), is a particularly powerful global optimisation tool and is exploited regularly in biological model parameter inference [32,33]. The GA converges to the global minimum within the allocated parameter space by evolving an initial population of randomly generated solutions over a large number of generations (see Section 5).…”