QUANTITATIVE MODELING OF THESACCHAROMYCES CEREVISIAEFLR1 REGULATORY NETWORK USING AN S-SYSTEM FORMALISM
See where this sits in the topic map →Summary AI-generated
- TL;DR
- This study develops a quantitative mathematical model to understand how the yeast Saccharomyces cerevisiae regulates its stress response to the agricultural fungicide mancozeb.
- Problem
- Finding an accurate quantitative mathematical model for the genetic network that regulates the yeast's transcriptional response to mancozeb stress.
- Method
- An S-system formalism modeled a five-gene network controlling the FLR1 gene via four transcription factors. Parameter estimation decoupled the nonlinear ordinary differential equations into a larger algebraic system, using the Levenberg-Marquardt algorithm to fit predictions to experimental data alongside tested topological constraints.
- Results
- Enforcing a putative network topology did not improve model performance compared to using an unrestricted network topology.
- Contributions
- A quantitative modeling framework applied to the stress response regulatory network of Saccharomyces cerevisiae.
- Limitations
- The approach achieved only partial success on nonmutant datasets, and further work is required to accurately predict time courses.
- Takeaways
- Unrestricted network topologies performed just as well as constrained ones when modeling this yeast stress response, though the approach still requires refinement for accurate time-course predictions.
- Applications
- Not specified in the abstract.
- Topics
- Quantitative modeling; Genetic regulatory networks; Systems biology; Saccharomyces cerevisiae; Gene expression
- For industry
- Agriculture and biotechnology
- Why it matters
- Advances the understanding of yeast genetic networks and stress responses to agricultural chemicals through mathematical modeling.
Abstract
In this study we address the problem of finding a quantitative mathematical model for the genetic network regulating the stress response of the yeast Saccharomyces cerevisiae to the agricultural fungicide mancozeb. An S-system formalism was used to model the interactions of a five-gene network encoding four transcription factors (Yap1, Yrr1, Rpn4 and Pdr3) regulating the transcriptional activation of the FLR1 gene. Parameter estimation was accomplished by decoupling the resulting system of nonlinear ordinary differential equations into a larger nonlinear algebraic system, and using the Levenberg-Marquardt algorithm to fit the models predictions to experimental data. The introduction of constraints in the model, related to the putative topology of the network, was explored. The results show that forcing the network connectivity to adhere to this topology did not lead to better results than the ones obtained using an unrestricted network topology. Overall, the modeling approach obtained partial success when trained on the nonmutant datasets, although further work is required if one wishes to obtain more accurate prediction of the time courses.