Cooperative RNA Polymerase Molecules Behavior on a Stochastic Sequence-Dependent Model for Transcription Elongation

Author(s)

Costa, Pedro Rafael ^[1] ; Acencio, Marcio Luis ^[1] ; Lemke, Ney ^[1]

Affiliation

^[1] UNESP Univ Estadual Paulista, Inst Biociencias Botucatu, Dept Fis & Biofis, Sao Paulo - Brazil

• Document type: Journal article
• Source: PLoS One; v. 8, n. 2 FEB 21 2013.
• Web of Science Citations: 5

Abstract

The transcription process is crucial to life and the enzyme RNA polymerase (RNAP) is the major component of the transcription machinery. The development of single-molecule techniques, such as magnetic and optical tweezers, atomic-force microscopy and single-molecule fluorescence, increased our understanding of the transcription process and complements traditional biochemical studies. Based on these studies, theoretical models have been proposed to explain and predict the kinetics of the RNAP during the polymerization, highlighting the results achieved by models based on the thermodynamic stability of the transcription elongation complex. However, experiments showed that if more than one RNAP initiates from the same promoter, the transcription behavior slightly changes and new phenomenona are observed. We proposed and implemented a theoretical model that considers collisions between RNAPs and predicts their cooperative behavior during multi-round transcription generalizing the Bai et al. stochastic sequence-dependent model. In our approach, collisions between elongating enzymes modify their transcription rate values. We performed the simulations in Mathematica (R) and compared the results of the single and the multiple-molecule transcription with experimental results and other theoretical models. Our multi-round approach can recover several expected behaviors, showing that the transcription process for the studied sequences can be accelerated up to 48% when collisions are allowed: the dwell times on pause sites are reduced as well as the distance that the RNAPs backtracked from backtracking sites. (AU)

• FAPESP's process: 09/10382-2 - Machine learning for molecular systems biology
• Grantee: Ney Lemke
• Support Opportunities: Regular Research Grants
• FAPESP's process: 10/20684-3 - Development of machine learning approaches based on biological networks for prediction and determination of rules governing the emergence of phenotypes of interest
• Grantee: Marcio Luis Acencio
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 10/03338-4 - Stochastic kinetic model for euchariotic transcription considering collisions among RNA polymerases II
• Grantee: Pedro Rafael Costa
• Support Opportunities: Scholarships in Brazil - Master