Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies

David J. Wooten, Jorge Gómez Tejeda Zañudo, David Murrugarra, Austin M. Perry, Anna Dongari-Bagtzoglou, Reinhard Laubenbacher, Clarissa J. Nobile, Réka Albert

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 17 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

Original languageEnglish
Article numbere1008690
JournalPLoS Computational Biology
Issue number3
StatePublished - Mar 2021

Bibliographical note

Funding Information:
This work was supported by National Science Foundation (NSF) grants PHY 1545832, MCB-1715826, and IIS-1814405 to R.A., National Institutes of Health (NIH) National Institute of General Medical Sciences (NIGMS) award R35GM124594 to C.J.N., and the Kamangar family in the form of an endowed chair to C.J.N. R.L. was partially supported by NIH grants R011AI135128, U01EB024501, and R01GM127909, and NSF grant CBET-1750183. A.D.B. was supported by NIH grants R01DE013986 and R01GM127909. The content is the sole responsibility of the authors and does not represent the views of the funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2021 Wooten 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.

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics


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