Optimal resource allocation in cellular sensing systems
Open accessAbstract
Significance: Cells continually have to sense their environments to make decisions—to stay put or move, to differentiate or proliferate, or even to live or die. However, they are thwarted by noise at the cellular scale. Cells use signaling networks to filter this noise as much as possible and sense accurately. To operate these networks, resources are required: time, protein copies, and energy. We present a theory for the optimal design of cellular sensing systems that maximize sensing precision given these resources. It reveals a new design principle, namely that of optimal resource allocation. It describes how these resources must be allocated so that none are wasted. We show that the chemotaxis network of Escherichia coli obeys this principle.
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The equivalence between increasing environmental volatility ε and decreasing energy availability or memory accuracy δ explains the monotonic phase transition from memory-based to memoryless estimation strategies.
“Therefore, increasing environmental volatility ε is the same as decreasing energy availability or memory accuracy δ , which explains the monotonic phase transition from memory-based to memoryless estimation strategies [13].”
From Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies (2026) · cites this paper as [13]
The Results discuss energy-dependent reliability of stored receptor samples, and the Discussion describes limits on sensing precision, but neither section addresses environmental volatility ε or a transition from memory-based to memoryless estimation. Searches for environmental volatility, memory accuracy, and that phase transition found no passage establishing the claimed equivalence. Page numbers were not available in our copy. (LLM, conf=0.74)
Experimental — accuracy not yet measuredRecent human experiments reported that increasing sensory uncertainty induces a nonmonotonic phase transition between memoryless and memory-based estimation strategies, while increasing environmental volatility induces a monotonic transition from memory-based to memoryless estimation strategies.
“Recent human experiments have reported that while increasing sensory uncertainty induces a nonmonotonic phase transition between memoryless and memory-based estimation strategies, increasing environmental volatility induces a monotonic transition from memory-based to memoryless estimation strategies [13].”
From Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies (2026) · cites this paper as [13]
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Experimental — accuracy not yet measuredIncreasing environmental volatility ε is the same as decreasing energy availability or memory accuracy δ.
“Therefore, increasing environmental volatility ε is the same as decreasing energy availability or memory accuracy δ , which explains the monotonic phase transition from memory-based to memoryless estimation strategies [13].”
From Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies (2026) · cites this paper as [13]
The Results and Discussion (pages unavailable) say that energy protects the reliability of stored receptor samples and that limited resources constrain sensing precision. They do not address environmental volatility ε, equate it with energy availability or memory accuracy δ, or describe a phase transition from memory-based to memoryless estimation strategies. I searched for those terms and their relationship in our copy. (LLM, conf=0.76)
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Cites
Irreversibility and heat generation in the computing process
Landauer, R. (1961) · IBM Journal of Research and Development · doi:10.1147/rd.53.0183 · Cited by 4,980 · see citing works
An Introduction to Systems Biology: Design Principles of Biological Circuits
Alon, U. (2006) · doi:10.1201/9781420011432 · Cited by 2,825 · see citing works
Irreversibility and heat generation in the computing process
Landauer, R. W. (2000) · IBM Journal of Research and Development · doi:10.1147/rd.441.0261 · Cited by 2,617 · see citing works
Berg, H. C., & Purcell, E. M. (1977) · Biophysical Journal · doi:10.1016/s0006-3495(77)85544-6 · Cited by 1,902 · see citing works
Robustness in simple biochemical networks
Barkai, N., & Leibler, S. (1997) · Nature · doi:10.1038/43199 · Cited by 1,513 · see citing works
An amplified sensitivity arising from covalent modification in biological systems.
Goldbeter, A., & Koshland, D. E. (1981) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.78.11.6840 · Cited by 1,209 · see citing works
Robustness in bacterial chemotaxis
Alon, U. S., Surette, M. G., Barkai, N., & Leibler, S. (1999) · Nature · doi:10.1038/16483 · Cited by 1,129 · see citing works
Optimality and evolutionary tuning of the expression level of a protein
Dekel, E., & Alon, U. (2005) · Nature · doi:10.1038/nature03842 · Cited by 766 · see citing works
Receptor sensitivity in bacterial chemotaxis
Sourjik, V., & Berg, H. C. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.011589998 · Cited by 544 · see citing works
The energy–speed–accuracy trade-off in sensory adaptation
Lan, G., Sartori, P., Neumann, S., Sourjik, V., & Tu, Y. (2012) · Nature Physics · doi:10.1038/nphys2276 · Cited by 452 · see citing works
Physical limits to biochemical signaling
Bialek, W., & Setayeshgar, S. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504321102 · Cited by 445 · see citing works
Design principles of a bacterial signalling network
Kollmann, M., Løvdok, L., Bartholomé, K., Timmer, J., & Sourjik, V. (2005) · Nature · doi:10.1038/nature04228 · Cited by 285 · see citing works
Sourjik, V., & Berg, H. C. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.192463199 · Cited by 285 · see citing works
Cellular Stoichiometry of the Components of the Chemotaxis Signaling Complex
Li, M., & Hazelbauer, G. L. (2004) · Journal of Bacteriology · doi:10.1128/jb.186.12.3687-3694.2004 · Cited by 274 · see citing works
Energetic costs of cellular computation
Mehta, P. D., & Schwab, D. J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207814109 · Cited by 235 · see citing works
The Berg-Purcell Limit Revisited
Kaizu, K., de Ronde, W. H., Paijmans, J., Takahashi, K., Tostevin, F., & ten Wolde, P. R. (2014) · Biophysical Journal · doi:10.1016/j.bpj.2013.12.030 · Cited by 145 · see citing works
Stochastic Signal Processing and Transduction in Chemotactic Response of Eukaryotic Cells
Ueda, M., & Shibata, T. (2007) · Biophysical Journal · doi:10.1529/biophysj.106.100263 · Cited by 141 · see citing works
Maximum Likelihood and the Single Receptor
Endres, R. G., & Wingreen, N. S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.158101 · Cited by 134 · see citing works
Physical Limits on Cellular Sensing of Spatial Gradients
Hu, B., Chen, W., Rappel, W., et al. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.048104 · Cited by 127 · see citing works
Energy Dissipation and Noise Correlations in Biochemical Sensing
Govern, C. C., & ten Wolde, P. R. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.258102 · Cited by 119 · see citing works
Self-citationLimits of Sensing Temporal Concentration Changes by Single Cells
Mora, T., & Wingreen, N. S. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.248101 · Cited by 99 · see citing works
Physical Responses of Bacterial Chemoreceptors
Vaknin, A., & Berg, H. C. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.12.024 · Cited by 90 · see citing works
Information-theoretic versus thermodynamic entropy production in autonomous sensory networks
Barato, A. C., Hartich, D., & Seifert, U. (2013) · Physical Review E · doi:10.1103/physreve.87.042104 · Cited by 79 · see citing works
Gao, R., & Stock, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214587110 · Cited by 79 · see citing works
Receptor Noise and Directional Sensing in Eukaryotic Chemotaxis
Rappel, W., & Levine, H. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.228101 · Cited by 71 · see citing works
Quantifying noise levels of intercellular signals
Wang, K. Y., Rappel, W., Kerr, R. A., et al. (2007) · Physical Review E · doi:10.1103/physreve.75.061905 · Cited by 61 · see citing works
Danielson, M. A., Biemann, H.-P., Koshland, D. E., & Falke, J. J. (1994) · Biochemistry · doi:10.1021/bi00186a009 · Cited by 59 · see citing works
Chemical Sensing by Nonequilibrium Cooperative Receptors
Skoge, M. L., Naqvi, S., Meir, Y., & Wingreen, N. S. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.248102 · Cited by 59 · see citing works
Fundamental Limits on Sensing Chemical Concentrations with Linear Biochemical Networks
Govern, C. C., & ten Wolde, P. R. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.218103 · Cited by 49 · see citing works
Self-citationUnraveling Adaptation in Eukaryotic Pathways: Lessons from Protocells
Palo, G. D., & Endres, R. G. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003300 · Cited by 30 · see citing works
Noise Filtering Strategies in Adaptive Biochemical Signaling Networks
Sartori, P., & Tu, Y. (2011) · Journal of Statistical Physics · doi:10.1007/s10955-011-0169-z · Cited by 29 · see citing works
Flagged first, then by citations
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Optimal Prediction by Cellular Signaling Networks
Becker, N. B., Mugler, A., & ten Wolde, P. R. (2015) · Physical Review Letters · doi:10.1103/physrevlett.115.258103 · Cited by 63 · see citing works
Physical Limit to Concentration Sensing in a Changing Environment
Mora, T., & Nemenman, I. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.198101 · Cited by 49 · see citing works
Connection between the Bacterial Chemotactic Network and Optimal Filtering
Nakamura, K., & Kobayashi, T. J. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.128102 · Cited by 19 · see citing works
Tottori, T., & Kobayashi, T. J. (2025) · Physical Review Research · doi:10.1103/gvl6-cvby · Cited by 6 · see citing works
Resource limitations induce phase transitions in biological information processing
Tottori, T., & Kobayashi, T. J. (2025) · Physical Review Research · doi:10.1103/nz4j-tyv1 · Cited by 3 · see citing works
Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies
Tottori, T., & Kobayashi, T. J. (2026) · Physical Review Letters · doi:10.1103/5ynb-7k4v
- Authors
- Govern, C. C., & ten Wolde, P. R.
- Affiliations
- Institute for Atomic and Molecular Physics (NL)
- FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2014-11-24
- Volume
- 111
- Issue
- 49
- Pages
- 17486-17491
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4267345
- OpenAlex
- W2009008752
- Cited by
- 141
- References
- 31
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 13 · 2023: 12 · 2022: 11 · 2021: 9 · 2020: 9 · 2019: 10 · 2018: 10 · 2017: 13 · 2016: 17 · 2015: 14 · 2014: 2
- FWCI
- 6.29
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- AMOLF Institutional Repository (AMOLF) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- cellular sensing, resource allocation, energy consumption, chemotaxis, Escherichia coli, design principles, signaling networks
- MeSH terms
- Methyl-Accepting Chemotaxis Proteins, Bacterial Proteins, Chemotaxis, Escherichia coli, Ligands, Membrane Proteins, Models, Biological, Molecular Conformation, Thermodynamics, Time Factors, Signal Transduction, Escherichia coli Proteins, Cell Proliferation
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