Faithful modeling of transient expression and its application to elucidating negative feedback regulation

Amir Rubinstein, Vyacheslav Gurevich, Zohar Kasulin-Boneh, Lilach Pnueli, Yona Kassir, Ron Y. Pinter

Research output: Contribution to journalArticlepeer-review

Abstract

Modeling and analysis of genetic regulatory networks is essential both for better understanding their dynamic behavior and for elucidating and refining open issues. We hereby present a discrete computational model that effectively describes the transient and sequential expression of a network of genes in a representative developmental pathway. Our model system is a transcriptional cascade that includes positive and negative feedback loops directing the initiation and progression through meiosis in budding yeast. The computational model allows qualitative analysis of the transcription of early meiosis-specific genes, specifically, Ime2 and their master activator, Ime1. The simulations demonstrate a robust transcriptional behavior with respect to the initial levels of Ime1 and Ime2. The computational results were verified experimentally by deleting various genes and by changing initial conditions. The model has a strong predictive aspect, and it provides insights into how to distinguish among and reason about alternative hypotheses concerning the mode by which negative regulation through Ime1 and Ime2 is accomplished. Some predictions were validated experimentally, for instance, showing that the decline in the transcription of IME1 depends on Rpd3, which is recruited by Ime1 to its promoter. Finally, this general model promotes the analysis of systems that are devoid of consistent quantitative data, as is often the case, and it can be easily adapted to other developmental pathways.

Original languageEnglish
Pages (from-to)6241-6246
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume104
Issue number15
DOIs
StatePublished - 10 Apr 2007
Externally publishedYes

Keywords

  • Budding yeast
  • Computational modeling
  • Meiosis
  • Systems biology
  • Transcriptional networks

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