ArticlePhysical biology2012
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.
Article in Physical biology, 2012. 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.
- Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.Behavior research methods · 2026Review
- Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers.bioRxiv : the preprint server for biology · 2026Article
- Morphogens in the evolution of size, shape and patterning.Development (Cambridge, England) · 2024Review
- Article
- Feed-forward regulation adaptively evolves via dynamics rather than topology when there is intrinsic noise.Nature communications · 2019Article
- How to design an optimal sensor network for the unfolded protein response.Molecular biology of the cell · 2018Article
- φ-evo: A program to evolve phenotypic models of biological networks.PLoS computational biology · 2018Article
- Trade-offs between microbial growth phases lead to frequency-dependent and non-transitive selection.Proceedings. Biological sciences · 2018Article
- Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions.PloS one · 2016Article
- NetExplore: a web server for modeling small network motifs.Bioinformatics (Oxford, England) · 2015Article
- Mechanistic links between cellular trade-offs, gene expression, and growth.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Computer-assisted design for scaling up systems based on DNA reaction networks.Journal of the Royal Society, Interface · 2014Article
- The geometry of the Pareto front in biological phenotype space.Ecology and evolution · 2013Article
- Design principles of regulatory networks: searching for the molecular algorithms of the cell.Molecular cell · 2013Review
- Evolutionary tradeoffs between economy and effectiveness in biological homeostasis systems.PLoS computational biology · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
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Registered trials
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.