Complex and unexpected dynamics in simple genetic regulatory networks

Yanika Borg*, Ekkehard Ullner, Afnan Alagha, Ahmed Alsaedi, Darren Nesbeth, Alexey Zaikin

*Corresponding author for this work

Research output: Contribution to journalLiterature review

2 Citations (Scopus)
7 Downloads (Pure)

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.

Original languageEnglish
Article number1430006
Number of pages34
JournalInternational Journal of Modern Physics B
Volume28
Issue number14
Early online date25 Mar 2014
DOIs
Publication statusPublished - 10 Jun 2014

Keywords

  • synthetic biology
  • genetic regulatory networks
  • complex dynamics
  • mathematical modeling
  • repressilator
  • artificial cell differentiation
  • synchronization
  • noise
  • cellular decision making
  • coherence resonance
  • stochastic resonance
  • noisy precursors
  • oscillators
  • system
  • biology
  • instabilities
  • expression
  • gate

Cite this

Complex and unexpected dynamics in simple genetic regulatory networks. / Borg, Yanika; Ullner, Ekkehard; Alagha, Afnan; Alsaedi, Ahmed; Nesbeth, Darren; Zaikin, Alexey.

In: International Journal of Modern Physics B, Vol. 28, No. 14, 1430006, 10.06.2014.

Research output: Contribution to journalLiterature review

Borg, Yanika ; Ullner, Ekkehard ; Alagha, Afnan ; Alsaedi, Ahmed ; Nesbeth, Darren ; Zaikin, Alexey. / Complex and unexpected dynamics in simple genetic regulatory networks. In: International Journal of Modern Physics B. 2014 ; Vol. 28, No. 14.
@article{154d62abe5554aa89eeced6a9d70829a,
title = "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.",
keywords = "synthetic biology, genetic regulatory networks, complex dynamics, mathematical modeling, repressilator, artificial cell differentiation, synchronization, noise, cellular decision making, coherence resonance, stochastic resonance, noisy precursors, oscillators, system, biology, instabilities, expression, gate",
author = "Yanika Borg and Ekkehard Ullner and Afnan Alagha and Ahmed Alsaedi and Darren Nesbeth and Alexey Zaikin",
year = "2014",
month = "6",
day = "10",
doi = "10.1142/S0217979214300060",
language = "English",
volume = "28",
journal = "International Journal of Modern Physics B",
issn = "0217-9792",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "14",

}

TY - JOUR

T1 - Complex and unexpected dynamics in simple genetic regulatory networks

AU - Borg, Yanika

AU - Ullner, Ekkehard

AU - Alagha, Afnan

AU - Alsaedi, Ahmed

AU - Nesbeth, Darren

AU - Zaikin, Alexey

PY - 2014/6/10

Y1 - 2014/6/10

N2 - 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.

AB - 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.

KW - synthetic biology

KW - genetic regulatory networks

KW - complex dynamics

KW - mathematical modeling

KW - repressilator

KW - artificial cell differentiation

KW - synchronization

KW - noise

KW - cellular decision making

KW - coherence resonance

KW - stochastic resonance

KW - noisy precursors

KW - oscillators

KW - system

KW - biology

KW - instabilities

KW - expression

KW - gate

U2 - 10.1142/S0217979214300060

DO - 10.1142/S0217979214300060

M3 - Literature review

VL - 28

JO - International Journal of Modern Physics B

JF - International Journal of Modern Physics B

SN - 0217-9792

IS - 14

M1 - 1430006

ER -