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Understanding Evolving Bacterial Colonies

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Springer Handbook of Bio-/Neuroinformatics

Part of the book series: Springer Handbooks ((SHB))

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Abstract

Microbial colonies are collections of cells of the same organism (in contrast to biofilms, which comprise multiple species). Within an evolving colony, cells communicate, pass information to their daughters, and assume roles that depend on their spatiotemporal distribution. Thus, they possess a collective intelligence which renders them model systems for studying biocomplexity. Since the early 1990s, a plethora of models have been proposed to investigate and understand bacterial colonies. The majority of these are based on continuum equations incorporating physical and biological phenomena, such as chemotaxis, bacterial diffusion, nutrient diffusion and consumption, and cellular reproduction. Continuum approaches have greatly advanced our knowledge of the likely drivers of colony evolution, but are limited by the fact that diverse methods yield the same or similar solutions. Some researchers have turned instead to agent-based, heuristic approaches, which provide a natural description of complex systems. Yet others have recognized that chemotaxis constitutes an optimization problem, as bacteria weigh nutrient requirement against competition and energy expenditure. This chapter begins with a brief introduction to bacterial colonies and why they have attracted research interest. The experiments on which many of the published models have been based, and the modeling approaches used, are discussed (Sect. 7.1). In Sect. 7.2 a wide cross-section of published models for comparison and contrast is presented. Limitations of existing models are discussed in Sects. 7.3–7.7, and the chapter concludes with current and future trends in this important research area (Sect. 7.8).

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Abbreviations

2-D:

two-dimensional

BCGA:

bacterial colony growth algorithm

BFA:

bacterial foraging algorithm

DBM:

low nutrient/soft agar conditions

DC:

direct current

DLA:

low nutrient/hard agar conditions

DNA:

deoxyribonucleic acid

KBP:

knotted branching pattern

UV:

ultraviolet

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  1. Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, 1142, Auckland, New Zealand

    Leonie Z. Pipe

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  1. KEDRI – Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 120 Mayoral Drive, 1010, Auckland, New Zealand

    Nikola Kasabov

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Pipe, L.Z. (2014). Understanding Evolving Bacterial Colonies. In: Kasabov, N. (eds) Springer Handbook of Bio-/Neuroinformatics. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_7

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