Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells

Petros Mina, Mario di Bernardo, Nigel J Savery, Krasimira Tsaneva-Atanasova

Research output: Contribution to journalArticle (Academic Journal)peer-review

19 Citations (Scopus)


Population-level measurements of phenotypic behaviour in biological systems may not necessarily reflect individual cell behaviour. To assess qualitative changes in the behaviour of a single cell, when alone and when part of a community, we developed an agent-based model describing the metabolic states of a population of quorum-coupled cells. The modelling is motivated by published experimental work of a synthetic genetic regulatory network (GRN) used in Escherichia coli cells that exhibit oscillatory behaviour across the population. To decipher the mechanisms underlying oscillations in the system, we investigate the behaviour of the model via numerical simulation and bifurcation analysis. In particular, we study the effect of an increase in population size as well as the spatio-temporal behaviour of the model. Our results demonstrate that oscillations are possible only in the presence of a high concentration of the coupling chemical and are due to a time scale separation in key regulatory components of the system. The model suggests that the population establishes oscillatory behaviour as the system's preferred stable state. This is achieved via an effective increase in coupling across the population. We conclude that population effects in GRN design need to be taken into consideration and be part of the design process. This is important in planning intervention strategies or designing specific cell behaviours.
Original languageEnglish
Pages (from-to)20120612
JournalJournal of the Royal Society Interface
Issue number78
Publication statusPublished - 2013

Structured keywords

  • Bristol BioDesign Institute



Fingerprint Dive into the research topics of 'Modelling emergence of oscillations in communicating bacteria: a structured approach from one to many cells'. Together they form a unique fingerprint.

Cite this