ArticleNature communications2025
Limits on the computational expressivity of non-equilibrium biophysical processes.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Combinatorial decision-making driven by multicomponent surface condensates.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Unifying theories in high-dimensional biophysics: approaches, challenges and opportunities.NPJ systems biology and applications · 2026Article
- Biomolecular condensates as cellular memory modules: Thermodynamic principles and plant stress adaptation.Biophysical journal · 2026Review
- Thermodynamic limits in far-from-equilibrium molecular templating networks.Newton ((New York, N.Y.) · 2026Article
- Article
- Ecosystems as adaptive living circuits.bioRxiv : the preprint server for biology · 2025Article
- Article
- Fast nonlinear integration drives accurate encoding of input information in large multiscale systems.Communications physics · 2025Article
- Principles of Computation by Competitive Protein Dimerization Networks.bioRxiv : the preprint server for biology · 2024Article
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Abstract
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of general biochemical networks, we reveal several unanticipated and universal limitations on the classification ability of generic biophysical processes. These limits arise from a fundamental non-equilibrium thermodynamic constraint that we have derived. Importantly, we show that these limitations can be overcome using common biochemical mechanisms that we term input multiplicity, examples of which include enzymes acting on multiple targets. Analogous to how increasing depth enhances the expressivity and classification ability of neural networks, our work demonstrates how tuning input multiplicity can potentially enable an exponential increase in a biological system's ability to classify and process information.
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