Quantitative Modeling the Saccharomyces cerevisiae FLR1 Regulatory Network Using an S-System Formalism
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- TL;DR
- This paper presents a mathematical model for the genetic network that regulates how the yeast Saccharomyces cerevisiae responds to stress from the fungicide mancozeb.
- Problem
- Finding an accurate mathematical model for the 5-gene regulatory network responsible for the yeast's stress response to mancozeb.
- Method
- An S-system formalism was used to model gene interactions, with parameter estimation performed by decoupling the differential equations into an algebraic system and using the Levenberg-Marquardt algorithm to fit predictions to experimental data.
- Results
- Forcing the network connectivity to follow a putative topology did not yield better results than using an unrestricted network topology.
- Contributions
- Demonstrated the application of an S-system formalism and parameter estimation techniques to model a specific 5-gene regulatory network in yeast.
- Limitations
- The approach achieved only partial success when trained on non-mutant datasets, and further work is needed to improve the accuracy of time-course predictions.
- Takeaways
- While the modeling approach showed partial success on non-mutant datasets, restricted network topologies did not outperform unrestricted ones, indicating that further refinement is necessary.
- Applications
- Not specified in the abstract.
- Topics
- Quantitative Modeling, Genetic Regulatory Networks, Systems Biology
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
We address the problem of finding a mathematicalmodel for the genetic network regulating the stress response ofthe yeast Saccharomyces cerevisiae to the fungicide mancozeb.An S-system formalism was used to model the interactions ofthis 5 gene network. Parameter estimation was accomplishedby decoupling the resulting system of nonlinear ordinarydifferential equations into a larger nonlinear algebraic system,and using the Levenberg-Marquardt algorithm to fit themodels predictions to experimental data. The introduction ofconstraints in the model, related to the putative topology ofthe network, was explored. The results show that forcing thenetwork connectivity to adhere to this topology did not leadto better results than the ones obtained using an unrestrictednetwork topology. Overall, the modeling approach obtainedpartial success when trained on the non-mutant datasets,although further work is required if one wishes to obtain moreaccurate prediction of the time courses.