ArticlePLoS computational biology2009
Quantifying global tolerance of biochemical systems: design implications for moiety-transfer cycles.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 31 citations in OpenAlex.
- Mechanistic Modeling of Biochemical Systems without A Priori Parameter Values Using the Design Space Toolbox v.3.0.iScience · 2020Article
- Mapping the phenotypic repertoire of the cytoplasmic 2-Cys peroxiredoxin - Thioredoxin system. 1. Understanding commonalities and differences among cell types.Redox biology · 2018Article
- Rapid Discrimination Among Putative Mechanistic Models of Biochemical Systems.Scientific reports · 2016Article
- Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems.Frontiers in genetics · 2016Article
- Unrelated toxin-antitoxin systems cooperate to induce persistence.Journal of the Royal Society, Interface · 2015Article
- Strategy revealing phenotypic differences among synthetic oscillator designs.ACS synthetic biology · 2014Article
- Molecular mechanisms of multiple toxin-antitoxin systems are coordinated to govern the persister phenotype.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Phenotypic deconstruction of gene circuitry.Chaos (Woodbury, N.Y.) · 2013Article
- Article
- Biological robustness: paradigms, mechanisms, and systems principles.Frontiers in genetics · 2012Article
- Design of the lac gene circuit revisited.Mathematical biosciences · 2011Review
- Biomedical engineering strategies in system design space.Annals of biomedical engineering · 2011Review
- Article
- Phenotypic repertoire of the FNR regulatory network in Escherichia coli.Molecular microbiology · 2011Article
- Automated construction and analysis of the design space for biochemical systems.Bioinformatics (Oxford, England) · 2010Article
- Relating mutant genotype to phenotype via quantitative behavior of the NADPH redox cycle in human erythrocytes.PloS one · 2010Article
- Architecture-dependent robustness and bistability in a class of genetic circuits.Biophysical journal · 2010Article
- Review
- Elucidating the genotype-phenotype map by automatic enumeration and analysis of the phenotypic repertoire.NPJ systems biology and applicationsArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Robustness of organisms is widely observed although difficult to precisely characterize. Performance can remain nearly constant within some neighborhood of the normal operating regime, leading to homeostasis, but then abruptly break down with pathological consequences beyond this neighborhood. Currently, there is no generic approach to identifying boundaries where local performance deteriorates abruptly, and this has hampered understanding of the molecular basis of biological robustness. Here we introduce a generic approach for characterizing boundaries between operational regimes based on the piecewise power-law representation of the system's components. This conceptual framework allows us to define "global tolerance" as the ratio between the normal value of a parameter and the value at such a boundary. We illustrate the utility of this concept for a class of moiety-transfer cycles, which is a widespread module in biology. Our results show a region of "best" local performance surrounded by "poor" regions; also, selection for improved local performance often pushes the operating values away from regime boundaries, thus increasing global tolerance. These predictions agree with experimental data from the reduced nicotinamide adenine dinucleotide phosphate (NADPH) redox cycle of human erythrocytes.
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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.