Elsevier

Current Opinion in Biotechnology

Volume 28, August 2014, Pages 149-155
Current Opinion in Biotechnology

Environmental sensing, information transfer, and cellular decision-making

https://doi.org/10.1016/j.copbio.2014.04.010Get rights and content

Highlights

  • We discuss current theoretical approaches to the quantitative study of cellular decision-making.

  • We focus on the application of information theory, sequential inference, decision theory, and optimality arguments.

  • We consider interpretations of mutual information in systems biology versus engineering.

The recognition that gene expression can be substantially stochastic poses the question of how cells respond to dynamic environments using biochemistry that itself fluctuates. The study of cellular decision-making aims to solve this puzzle by focusing on quantitative understanding of the variation seen across isogenic populations in response to extracellular change. This behaviour is complex, and a theoretical framework within which to embed experimental results is needed. Here we review current approaches, with an emphasis on information theory, sequential data processing, and optimality arguments. We conclude by highlighting some limitations of these techniques and the importance of connecting both theory and experiment to measures of fitness.

Introduction

Life for single cells is stochastic [1]. Cells sense fluctuating signals with biochemical networks that are themselves stochastic and history-dependent [2], and yet living organisms are able to flourish in nearly all environments. Understanding how cells prosper despite stochasticity and environmental variability is the focus of a relatively new area of systems biology, that of cellular decision-making 3, 4. By a cellular decision we mean the process by which a cell makes a ‘choice’ of phenotype from a range of possible phenotypes in response to or in anticipation of extracellular change. Such choices could include new gene expression, changes in cell morphology, intracellular re-arrangements, movement, or the option not to change phenotype at all.

In addition to the stochasticity of signal transduction, cells locally sense signals that fluctuate both in time and across space, whereas often it is the successful identification of broader environmental changes that is important in enabling an effective response [5]. Even bacteria appear to be able to solve this kind of inference problem, using local signals to identify, for example, that they are in the human gut and thereby anticipate likely future events 6, 7.

Here we review the theoretical approaches developed so far to understand cellular decision-making. Motivated by the surge of interest in biochemical stochasticity generated by the theoretical work of McAdams and Arkin in 1997 [8], we ask if theory is now poised to have a similar effect on the experimental study of decision-making in single cells.

Section snippets

Dose–response and information theory

Most theorists have focused on applying ideas from information theory, often inspired by neuroscience [9]. In systems biology, the experimental confirmation that gene expression is stochastic 10, 11 and the related discovery that genetically identical cells can vary significantly in their response to the same stimulus 12, 13, 14 implies that dose–response, or ‘input–output’, relationships are also often substantially stochastic. Information theory, through mutual information, provides an

Updating inferences over time

We have argued that higher mutual information between an input and output implies better inference of the input from the output. Mutual information has so far typically been quantified at individual time points or at steady-state but cells may well update their inferences over time as they sense and learn more about their environment [35]. Sharpening inferences over time in this fashion is referred to as sequential inference in the statistics literature. Any posterior distribution given all the

Optimality theory

Cells not only make inferences about the state of the environment but also make decisions [43], such as whether to differentiate or not, which affect the fitness of the cell. Mutual information is a generic approach implying a certain cost or ‘scoring’ for evaluating inferences (Box 1), and therefore does not suggest a cost function related to a specific cellular decision. Nevertheless it is possible to show (equating fitness with the expected long term growth rate and under certain additional

Conclusions

What insights does the above theory provide for experimental studies? Perhaps the most successful use of information theory to date has been the investigation of development in Drosophila. Nearly all the information needed to describe the patterning of the fly embryo is established early in development by the gap genes [26••]. We do not yet know whether this result is fundamental or if other organisms can reach maturity by establishing positional information later. In signal transduction, the

Acknowledgements

We thank Ilya Nemenman, Pieter Rein ten Wolde, Margartis Voliotis, and especially Gasper Tkacik for helpful discussions.

References (58)

  • H.H. McAdams et al.

    Stochastic mechanisms in gene expression

    Proc Natl Acad Sci USA

    (1997)
  • W. Bialek

    Biophysics: Searching for Principles

    (2013)
  • E.M. Ozbudak et al.

    Regulation of noise in the expression of a single gene

    Nat Genet

    (2002)
  • M.B. Elowitz et al.

    Stochastic gene expression in a single cell

    Science

    (2002)
  • O. Feinerman et al.

    Variability and robustness in T cell activation from regulated heterogeneity in protein levels

    Science

    (2008)
  • S.L. Spencer et al.

    Non-genetic origins of cell-to-cell variability in trail-induced apoptosis

    Nature

    (2009)
  • C.E. Shannon

    A mathematical theory of communication

    Bell Syst Tech J

    (1948)
  • G. Tkacik et al.

    Information transmission in genetic regulatory networks: a review

    J Phys Condens Matter

    (2011)
  • M.D. Brennan et al.

    How information theory handles cell signaling and uncertainty

    Science

    (2012)
  • A. Rhee et al.

    The application of information theory to biochemical signaling systems

    Phys Biol

    (2012)
  • I. Nemenman
  • J.M. Bernardo et al.

    Bayesian Theory

    (2000)
  • M.R. Bennett et al.

    Microfluidic devices for measuring gene network dynamics in single cells

    Nat Rev Genet

    (2009)
  • J.C.W. Locke et al.

    Using movies to analyse gene circuit dynamics in single cells

    Nat Rev Micro

    (2009)
  • C.J. Wang et al.

    Microfluidics technology for systems biology research

    Methods Mol Biol

    (2009)
  • R. Cheong et al.

    Information transduction capacity of noisy biochemical signaling networks

    Science

    (2011)
  • J.O. Dubuis et al.

    Positional information, in bits

    Proc Natl Acad Sci USA

    (2013)
  • S. Uda et al.

    Robustness and compensation of information transmission of signaling pathways

    Science

    (2013)
  • M. Voliotis et al.

    Information transfer by leaky, heterogeneous, protein kinase signaling systems

    Proc Natl Acad Sci USA

    (2014)
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