ArticleCommunicative & integrative biology2020
Emergence of informative higher scales in biological systems: a computational toolkit for optimal prediction and control.
Article in Communicative & integrative biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Mind Everywhere: A Framework for Conceptualizing Goal-Directedness in Biology and Other Domains-Part Two.Biological theory · 2026Article
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
- Information dynamics and the emergence of high-order individuality in ecosystems.Communications biology · 2025Article
- Consilience in Causation: Causal Emergence Is Found Across Measures of Causation.Entropy (Basel, Switzerland) · 2025Article
- Associative conditioning in gene regulatory network models increases integrative causal emergence.Communications biology · 2025Article
- Revealing non-trivial information structures in aneural biological tissues via functional connectivity.PLoS computational biology · 2025Article
- The Multiscale Wisdom of the Body: Collective Intelligence as a Tractable Interface for Next-Generation Biomedicine.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- Finding emergence in data by maximizing effective information.National science review · 2025Article
- Collective intelligence: A unifying concept for integrating biology across scales and substrates.Communications biology · 2024Review
- Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies.Entropy (Basel, Switzerland) · 2024Review
- Emergence as the conversion of information: a unifying theory.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Degeneracy measures in biologically plausible random Boolean networks.BMC bioinformatics · 2022Article
- On the prediction of neuronal microscale topology descriptors based on mesoscale recordings.The European journal of neuroscience · 2021Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
The biological sciences span many spatial and temporal scales in attempts to understand the function and evolution of complex systems-level processes, such as embryogenesis. It is generally assumed that the most effective description of these processes is in terms of molecular interactions. However, recent developments in information theory and causal analysis now allow for the quantitative resolution of this question. In some cases, macro-scale models can minimize noise and increase the amount of information an experimenter or modeler has about "what does what." This result has numerous implications for evolution, pattern regulation, and biomedical strategies. Here, we provide an introduction to these quantitative techniques, and use them to show how informative macro-scales are common across biology. Our goal is to give biologists the tools to identify the maximally-informative scale at which to model, experiment on, predict, control, and understand complex biological systems.
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