Understanding Bacterial Cell−Cell Communication with Computational Modeling

Abstract

In the past decades, bacterial cell-cell communication has captivated interest of a broad scientific community drawn from a wide spectrum of disciplines including biology, physics, chemistry, mathematics, and engineering. Extensive exchange of experimental techniques and theoretical paradigms resulted in burgeoning development of the field as well as inevitable mixing of research cultures. As is often the case when multiple disciplines address a complex scientific problem, mathematical equations can provide a unifying platform which synergizes the efforts. Indeed, integration of many disparate experimental results in the form of models that span multiple scales from molecules to populations has already greatly benefited the field. In the present contribution, I will briefly survey the key developments in the rapidly growing field of modeling approaches toward understanding bacterial cell-cell communication on a systemic level. Complex prokaryotic metabolism generates a diverse array of chemicals that enter the extracellular environment and can potentially function as signaling molecules. The list of bacteria-produced substances known to function as cell-cell communication signals grows constantly1,2 and is likely to continue expansion in the foreseeable future. Once outside the bacterial cell, these molecules find themselves in diverse, often hostile, environments where they freely diffuse until adsorbed to surfaces, chemically degraded, assimilated by other organisms, or perceived by potential signal recipients. Even in the absence of degradation, the intensity of this undirected, diffusion-propagated chemical signal rapidly falls with the distance from the signal source. Thus, success of any cell-cell communication mediated by freely diffusing molecules, defined as the ratio of received to the total number of secreted molecules, strongly depends on the characteristic cell-to-cell distance. Apart from some exceptional situations in which bacteria might find themselves enclosed in tiny diffusion-impermeable compartments (see section 3), this implies that cell-cell communication becomes a significant factor only when the local cell density reaches certain threshold level. Not surprisingly, most of the known cell-cell signaling and communication in bacteria is cell density dependent. In the majority of examined systems (a few notable exceptions have also been characterized, see e.g., ref 3 and discussion in section 7), the mode of bacterial cell-cell communication is autocrine, i.e., cells capable of producing the signal are also the cells that can receive the signal. The received signal is directly translated into a change in transcription regulation, the decision-making level of a prokaryotic cell at which cell-cell communication is integrated with other sensory inputs. The ability of bacteria to regulate gene expression programs in response to autocrine diffusible signals is typically referred to as quorum sensing, * To whom correspondence should be addressed. E-mail: Andrew.Goryachev@ ed.ac.uk. Phone: 44-131-650-7807. Andrew Goryachev is a computational cell biologist with a multifaceted background and broad interests in the dynamics of cellular regulatory networks. Trained as a biophysicist at the Moscow Institute of Physics and Technology, he received his Ph.D. in theoretical computational Chemistry at the University of Toronto. Presently, he is a Lecturer at the Centre for Systems Biology, University of Edinburgh, UK. He has a longstanding interest in bacterial quorum sensing to understanding of which he contributed by developing computational models on intracellular as well as population-wide scales. Chem. Rev. 2011, 111, 238–250 238