ArticlePLoS computational biology2018
φ-evo: A program to evolve phenotypic models of biological networks.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Morphogens in the evolution of size, shape and patterning.Development (Cambridge, England) · 2024Review
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- Article
- Computational Systems Biology of Morphogenesis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Poly-aneuploid cancer cells promote evolvability, generating lethal cancer.Evolutionary applications · 2020Article
- Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise.Nature communications · 2019Article
- Automated, predictive, and interpretable inference ofProceedings of the National Academy of Sciences of the United States of America · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular networks are at the core of most cellular decisions, but are often difficult to comprehend. Reverse engineering of network architecture from their functions has proved fruitful to classify and predict the structure and function of molecular networks, suggesting new experimental tests and biological predictions. We present φ-evo, an open-source program to evolve in silico phenotypic networks performing a given biological function. We include implementations for evolution of biochemical adaptation, adaptive sorting for immune recognition, metazoan development (somitogenesis, hox patterning), as well as Pareto evolution. We detail the program architecture based on C, Python 3, and a Jupyter interface for project configuration and network analysis. We illustrate the predictive power of φ-evo by first recovering the asymmetrical structure of the lac operon regulation from an objective function with symmetrical constraints. Second, we use the problem of hox-like embryonic patterning to show how a single effective fitness can emerge from multi-objective (Pareto) evolution. φ-evo provides an efficient approach and user-friendly interface for the phenotypic prediction of networks and the numerical study of evolution itself.
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Registered trials
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