ArticleProceedings of the National Academy of Sciences of the United States of America2023
Trade-offs between cost and information in cellular prediction.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
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- Actionable Forecasting as a Determinant of Biological Adaptation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Temporal Contrastive Learning through implicit non-equilibrium memory.Nature communications · 2025Article
- Stochastic thermodynamics for biological functions.Quantitative biology (Beijing, China) · 2025Review
- Time-reversal symmetry breaking in the chemosensory array reveals a general mechanism for dissipation-enhanced cooperative sensing.Nature communications · 2024Article
- A biological circuit to anticipate trend.Evolution letters · 2024Article
- Sensing a moving target: A new model reveals how cells sense dynamic signals.Biophysical journal · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Here, we show for two classes of input signals that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal: Push-pull networks can reach this information bound for Markovian signals, while networks that take a temporal derivative can reach the bound for predicting the future derivative of non-Markovian signals. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.