Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions
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.
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.Attached Files
Published - Giurumescu2009p4253Plos_Comput_Biol.pdf
Supplemental Material - journal.pcbi.1000354.s001.pdf
Supplemental Material - journal.pcbi.1000354.s002.pdf
Supplemental Material - journal.pcbi.1000354.s003.pdf
Supplemental Material - journal.pcbi.1000354.s004.pdf
Supplemental Material - journal.pcbi.1000354.s005.pdf
Supplemental Material - journal.pcbi.1000354.s006.pdf
Supplemental Material - journal.pcbi.1000354.s007.pdf
Supplemental Material - journal.pcbi.1000354.s008.pdf
Supplemental Material - journal.pcbi.1000354.s009.pdf
Supplemental Material - journal.pcbi.1000354.s010.pdf
Supplemental Material - journal.pcbi.1000354.s011.pdf
Supplemental Material - journal.pcbi.1000354.s012.pdf
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Additional details
- PMCID
- PMC2661366
- Eprint ID
- 15194
- Resolver ID
- CaltechAUTHORS:20090820-142952457
- Army Research Office (ARO)
- DAAD 19-03-D-0004
- Jacobs Institute for Molecular Engineering for Medicine
- Howard Hughes Medical Institute (HHMI)
- Created
-
2009-09-09Created from EPrint's datestamp field
- Updated
-
2021-11-08Created from EPrint's last_modified field
- Caltech groups
- Jacobs Institute for Molecular Engineering for Medicine