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  • Review Article
  • Published:

Pairing computation with experimentation: a powerful coupling for understanding T cell signalling

Nature Reviews Immunology volume 10, pages 59–71 (2010)Cite this article

Subjects

Key Points

  • T cell receptor (TCR) stimulation, a series of biochemical signalling reactions, must occur for T cells to become activated. These biochemical processes involve dynamic, stochastic and cooperative events involving many proteins. They occur over multiple timescales and take place in the membrane, cytoplasm and nucleus. The complexity of these dynamic processes can make it difficult to intuit mechanistic principles from experimental observations. We describe why and how computational and theoretical approaches can complement experiments to aid the elucidation of such mechanistic principles.

  • If a detailed conceptual model of a signalling module and measurements of various rate parameters and protein concentrations are available, a computational model can convert this knowledge into a quantitative predictive tool. More importantly, when such a detailed picture is not available, theoretical and computational analyses can complement experiments to identify possible mechanisms for a new phenomenon, and thereby reveal new aspects of the T cell signalling pathway. This is because computational models can be used to eliminate hypotheses that are inconsistent with known facts, reveal gaps in our knowledge of the signalling pathway and design experiments that can discriminate between viable hypotheses.

  • Different theoretical and computational approaches are described, with special emphasis on the differences between them, the suitability of each approach for answering different types of biological questions and how information obtained from them can complement specific types of experimental studies.

  • Examples of successes emerging from studies wherein experimental and computational approaches are used in a complementary manner are described. Attention is devoted to studies that determine how feedback loops in the T cell signalling pathway regulate cellular responses (such as digital signalling and ligand discrimination) and how spatial organization of membrane-associated signalling components influence the earliest events in T cell signalling. Some potentially fruitful future directions of research are noted.

Abstract

T cells are activated when extracellular stimuli, such as a ligand binding to the T cell receptor, are converted into functional outputs by the T cell signalling network. T cell receptor signalling is a highly complex, stochastic and dynamic process involving many interacting proteins. This complexity often confounds intuition, making it difficult to develop mechanistic principles that underly experimental observations. In this Review, we describe how computational approaches can partner successfully with biological experimentation to help address this challenge, and we illustrate this paradigm by summarizing recent work that shows new aspects of the T cell signalling network.

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Figure 1: Types of computational and theoretical research methods.
Figure 2: Digital and analogue responses of signalling networks stimulated by continuous increases in stimulus.
Figure 3: An example of complementary in silico and in vitro studies elucidating the function of a feedback loop in the membrane-proximal T cell receptor signalling pathway.
Figure 4: An example of complementary in silico and in vitro studies showing the effects of positive and negative feedback regulation of LCK on T cell activation.
Figure 5: The kinetic segregation model.
Figure 6: Signalling in the immunological synapse.

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Acknowledgements

We thank J. Roose, H. Eisen and A. Weiss for comments on the manuscript. A.K.C. is grateful to M. Davis, A. Shaw and A. Weiss for continuing collaborations and lessons in T cell signalling. Financial support was provided by the US National Institutes of Health (NIH) Director's Pioneer Award and 1PO1/AI071195/01.

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  1. Departments of Chemical Engineering, Chemistry and Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    Arup K. Chakraborty

  2. Departments of Pediatrics and Physics, Battelle Center for Mathematical Medicine, the Research Institute at the Nationwide Children's Hospital, Ohio State University, 700 Children's Drive, Columbus, 43205, Ohio, USA

    Jayajit Das

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  1. Arup K. Chakraborty
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Correspondence to Arup K. Chakraborty.

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Glossary

Hysteresis

A biological system's memory of its recent history. For example, a cell may respond to a certain stimulus weakly if it is subject to stimulation for the first time, but it may respond strongly to the same stimulus if it has recently been robustly stimulated.

MicroRNA

Small (∼21–23 nucleotides in length), single-stranded RNA molecules that regulate the expression of genes by binding to the 3′ untranslated regions of homologous target mRNAs.

Immunological synapse

A large junctional structure that is formed at the cell surface between a T cell that is interacting with an antigen-presenting cell. Important molecules involved in T cell activation, including the T cell receptor, numerous signal-transduction molecules and molecular adaptors, accumulate in an orderly manner at this site.

Kinetic proofreading

A model of T cell activation in which a series of sequential modifications (such as phosphorylation) of the TCR needs to be completed for the triggering of downstream signalling events. TCR modifications are reversed if the ligand and TCR dissociate prior to reaching the terminal state of modification. The model, therefore, suggests that there is a threshold time that a TCR needs to remain bound to a peptide–MHC complex for T cell activation.

Anergy

A state of unresponsiveness that is sometimes observed in T and B cells that are chronically stimulated or are stimulated through the antigen receptor in the absence of co-stimulatory signals.

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Chakraborty, A., Das, J. Pairing computation with experimentation: a powerful coupling for understanding T cell signalling. Nat Rev Immunol 10, 59–71 (2010). https://doi.org/10.1038/nri2688

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