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  • Review Article
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Systems biology of persistent infection: tuberculosis as a case study

Nature Reviews Microbiology volume 6, pages 520–528 (2008)Cite this article

Key Points

  • Study of infectious diseases involves a broad range of research disciplines from molecular biology to population dynamics. This article discusses the ways in which mathematical modelling can be used to integrate studies across these different disciplines.

  • The article focuses on tuberculosis (TB) as an example of a complex persistent infection with a major impact on global health.

  • Approaches to integrate molecular and cellular research with epidemiology are illustrated by a discussion of host and pathogenic diversity in the context of population-based models of TB.

  • Analogous population-based mathematical approaches can be used to model the immune response during TB.

  • An alternative approach of agent-based modelling is illustrated by a description of key factors that regulate formation of granulomas in TB.

Abstract

The human immune response does an excellent job of clearing most of the pathogens that we encounter throughout our lives. However, some pathogens persist for the lifetime of the host. Despite many years of research, scientists have yet to determine the basis of persistence of most pathogens, and have therefore struggled to develop reliable prevention and treatment strategies. Systems biology provides a new and integrative tool that will help to achieve these goals. In this article, we use Mycobacterium tuberculosis as an example of how systems-biology approaches have begun to make strides in uncovering important facets of the host–pathogen interaction.

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Acknowledgements

This Review was made possible by financial support from the UK Biotechnology and Biotechnology and Biological Sciences Research Council (BBSRC) via the Centre for Integrative Systems Biology at Imperial College (CISBIC), BB/C519670/1. Work described in this Review was supported, in part, by National Institutes of Health grants to D.K. (NIH R01 LM 009027 and NIH R01 HL 072682).

Author information

Authors and Affiliations

  1. Centre for Integrative Systems Biology at Imperial College (CISBIC), Imperial College London, London, SW7 2AZ, UK

    Douglas Young & Jaroslav Stark

  2. Department of Mathematics, Imperial College London, London, SW7 2AZ, UK

    Jaroslav Stark

  3. Division of Mycobacterial Research, National Institute for Medical Research, London, NW7 1AA, UK

    Douglas Young

  4. Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA

    Denise Kirschner

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  1. Douglas Young
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Correspondence to Douglas Young.

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Glossary

Ordinary differential equation

A system of equations that is based on the rates (derivatives) of change of dependent variables with respect to time. Most of the interesting differential equations are nonlinear and, with a few exceptions, cannot be solved exactly. Approximate solutions are determined using computer simulations.

Mendelian

Genetic inheritance of disease susceptibility through a single gene.

TH1

After priming by exposure to signals from antigen-presenting cells, T cells undergo a process of maturation to their final effector phenotype. Cytokines produced by TH1 cells (for example, interferon-γ) enhance the antimicrobial activity of macrophages and have an important role in protection against Mycobacterium tuberculosis. Cytokines produced by TH2 cells (for example, interleukin-4) are important in promoting antibody responses. Cells that have not committed to the TH1 or TH2 lineages are referred to as TH0.

Linkage analysis

A test for co-inheritance of genetic markers along with disease susceptibility in family groups.

Cellular automata

Discrete models that consist of a regular grid of cells, each of which has a finite number of states. The state of a cell at time t is a function of the states of a finite number of cells (called its neighbourhood) at time t−1. Every cell has the same rule for updating that is based on the values in this neighbourhood. Each time the rules are applied to the whole grid, a new generation is created.

Granuloma

A roughly spherical structure that comprises a focus of infection that is surrounded by immune cells. Dead cells at the centre of the granuloma may decompose, leaving a 'cheesy' residue that is referred to as caseum.

Markov chain

A discrete-time stochastic process with the property that the next state solely depends on the present state, but not on the previous states. If a sequence of states has the Markov property, then every future state is conditionally independent of every prior state.

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Young, D., Stark, J. & Kirschner, D. Systems biology of persistent infection: tuberculosis as a case study. Nat Rev Microbiol 6, 520–528 (2008). https://doi.org/10.1038/nrmicro1919

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