Robust Design of Biological Circuits: Evolutionary Systems Biology Approach

Abstract

Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.

Period doubling induced by thermal noise amplification in genetic circuits

“…1a, with its associated dynamics for the protein p ( t ) and mRNA m ( t ) concentrations, respectively2829: In Eqs. (1), α i and β i stand for the reaction and degradation rates, respectively, while g ( p ) is the promoter-inhibitor activity function.…”

Reconstructing biological gene regulatory networks: where optimization meets big data

“…This leads to a flipping of each of the genes from the inactive (0) state to the active (1) state due to the repression of g1 by g2 and demonstrates the importance of Boolean networks in understanding steady states and robustness in GRN [58]. More detailed models include ordinary differential equation (ODE) models, which are able to model the dynamics behaviour of each gene in the network, are commonly used [18,97,98,116,131,133]. This increase in knowledge of the system, i.e.…”

Systems approaches for synthetic biology: a pathway toward mammalian design

“…Known as evolutionary algorithms, these methods can predict state changes in the behavior of signaling pathways over time, through adaptation or random mutation, by modeling this rewiring directly (Hallinan et al, 2010; Chen et al, 2011; Mobashir et al, 2012). In the same vein, these methods allow for the de novo construction and optimization of genetic networks by way of simulation (Bloom and Arnold, 2009), “evolving” a set of viable pathway designs that meet the specified constraints (Hallinan et al, 2010).…”