Complex and unexpected dynamics in simple genetic regulatory networks

Abstract

One aim of synthetic biology is to construct increasingly complex genetic networks from interconnected simpler ones to address challenges in medicine and biotechnology. However, as systems increase in size and complexity, emergent properties lead to unexpected and complex dynamics due to nonlinear and nonequilibrium properties from component interactions. We focus on four different studies of biological systems which exhibit complex and unexpected dynamics. Using simple synthetic genetic networks, small and large populations of phase-coupled quorum sensing repressilators, Goodwin oscillators, and bistable switches, we review how coupled and stochastic components can result in clustering, chaos, noise-induced coherence and speed-dependent decision making. A system of repressilators exhibits oscillations, limit cycles, steady states or chaos depending on the nature and strength of the coupling mechanism. In large repressilator networks, rich dynamics can also be exhibited, such as clustering and chaos. In populations of Goodwin oscillators, noise can induce coherent oscillations. In bistable systems, the speed with which incoming external signals reach steady state can bias the network towards particular attractors. These studies showcase the range of dynamical behavior that simple synthetic genetic networks can exhibit. In addition, they demonstrate the ability of mathematical modeling to analyze nonlinearity and inhomogeneity within these systems.

Chimera states in hybrid coupled neuron populations

“…Synchronization is widely considered to be essential for the proper functioning of a large variety of natural and artificial systems, ranging from physical experiments to chemical reactions and physiological processes. Prominent examples include communication networks [1,2], coupled lasers [3][4][5][6], Josephson junctions [7,8], oxidation and catalytic surface reactions [9][10][11], power grids [12] as well as circadian oscillators [13,14] and genetic oscillator networks [15][16][17]. Apart from these, synchronization in neural systems has remained a very popular research area during the last decades, because it is widely assumed to be a possible underlying mechanism for various behavioral and cognitive functions, e.g., attention, information processing, and neural control of movement [18][19][20][21][22].…”

Synthetic biology routes to bio-artificial intelligence

“…As such, a complete description of the relevant models is not necessary to understand the concepts presented here. Readers who do wish to examine the mathematical models further should refer to the cited literature and reviews by Bates et al [11] and Borg et al [12]. Specific technical details can be provided via the corresponding author.…”