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Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

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Abstract

A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using “post-translational” synthetic biology that exploits reversible protein modification.

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Acknowledgments

We would like to thank Caleb Bashor and Mo Khalil for many useful conversations and comments on the manuscript. PM was supported by a Simons Investigator in the Mathematical Modeling of Living Systems grant and a Sloan Fellowship. AHL was supported by a National Science Foundation Graduate Research Fellowship (NSF GRFP) under Grant No. DGE-1247312. DJS was supported by NIH Grant K25GM098875.

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  1. Department of Physics, Boston University, Boston, MA, 02215, USA

    Pankaj Mehta & Alex H. Lang

  2. Department of Physics, Northwestern University, Evanston, IL, USA

    David J. Schwab

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  1. Pankaj Mehta
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Mehta, P., Lang, A.H. & Schwab, D.J. Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks. J Stat Phys 162, 1153–1166 (2016). https://doi.org/10.1007/s10955-015-1431-6

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