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Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

Giurumescu, Claudiu A. and Sternberg, Paul W. and Asthagiri, Anand R. (2009) Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions. PLoS Computational Biology, 5 (4). Art. No. e1000354. ISSN 1553-734X. PMCID PMC2661366. https://resolver.caltech.edu/CaltechAUTHORS:20090820-142952457

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PDF (Figure S1. Model schematic of regulatory network and fate assignments) - Supplemental Material
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PDF (Figure S2. Extended set of phenotypes that occur upon changing the level of inductive signal) - Supplemental Material
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PDF (Figure S3. Phenotypic diversity caused by quantitative changes in gradient steepness) - Supplemental Material
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PDF (Figure S4. Letter representations of the phenotypes observed in C. elegans, C. briggsae and C. remanei) - Supplemental Material
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PDF (Figure S5. An illustration of our word representation for the order of phenotypes that occurs as inductive signal is increased) - Supplemental Material
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PDF (Table S1. Fate assignment based on threshold values) - Supplemental Material
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PDF (Table S2. The values of dimensional parameters used to determine the center values for the dimensionless parameters) - Supplemental Material
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PDF (Table S3. Range of values for dimensionless model parameters) - Supplemental Material
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PDF (Table S4. List of phenotypes with PSO values that are two standard deviations below the mean) - Supplemental Material
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PDF (Table S6. Characteristic words associated with each species) - Supplemental Material
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Abstract

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pcbi.1000354DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2661366/PubMed CentralArticle
ORCID:
AuthorORCID
Sternberg, Paul W.0000-0002-7699-0173
Additional Information:© 2009 Giurumescu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This work was supported by the Institute for Collaborative Biotechnologies Grant DAAD 19-03-D-0004 from the U.S. Army Research Office (to A.R.A.), the Center for Biological Circuit Design at Caltech, and the Jacobs Institute for Molecular Engineering for Medicine. P.W.S. is an investigator with the Howard Hughes Medical Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions: Conceived and designed the experiments: CAG. Performed the experiments: CAG. Analyzed the data: CAG PWS ARA. Contributed reagents/materials/analysis tools: PWS. Wrote the paper: CAG ARA. The authors have declared that no competing interests exist.
Group:Jacobs Institute for Molecular Engineering for Medicine
Funders:
Funding AgencyGrant Number
Army Research Office (ARO)DAAD 19-03-D-0004
Jacobs Institute for Molecular Engineering for MedicineUNSPECIFIED
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Issue or Number:4
PubMed Central ID:PMC2661366
Record Number:CaltechAUTHORS:20090820-142952457
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20090820-142952457
Official Citation:Giurumescu CA, Sternberg PW, Asthagiri AR (2009) Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions. PLoS Comput Biol 5(4): e1000354. doi:10.1371/journal.pcbi.1000354
Usage Policy:This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ID Code:15194
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:09 Sep 2009 15:47
Last Modified:03 Oct 2019 00:55

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