title: Inferring single-cell gene expression mechanisms using stochastic simulation.
abstract: BACKGROUND
  Stochastic promoter switching between transcriptionally active (ON) and inactive (OFF) states is a major source of noise in gene expression. It is often implicitly assumed that transitions between promoter states are memoryless, i.e. promoters spend an exponentially distributed time interval in each of the two states. However, increasing evidence suggests that promoter ON/OFF times can be non-exponential, hinting at more complex transcriptional regulatory architectures. Given the essential role of gene expression in all cellular functions, efficient computational techniques for characterizing promoter architectures are critically needed.
  
  
  RESULTS
  We have developed a novel model reduction for promoters with arbitrary numbers of ON and OFF states, allowing us to approximate complex promoter switching behavior with Weibull-distributed ON/OFF times. Using this model reduction, we created bursty Monte Carlo expectation-maximization with modified cross-entropy method ('bursty MCEM(2)'), an efficient parameter estimation and model selection technique for inferring the number and configuration of promoter states from single-cell gene expression data. Application of bursty MCEM(2) to data from the endogenous mouse glutaminase promoter reveals nearly deterministic promoter OFF times, consistent with a multi-step activation mechanism consisting of 10 or more inactive states. Our novel approach to modeling promoter fluctuations together with bursty MCEM(2) provides powerful tools for characterizing transcriptional bursting across genes under different environmental conditions.
  
  
  METHODS
  R source code implementing bursty MCEM(2) is available upon request.
  
  
  BACKGROUND
  absingh@udel.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 and Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.
authors.forename: Bernie J
authors.initials: BJ
authors.lastname: Daigle
authors.affiliation: Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 and Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Soltani
authors.affiliation: Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 and Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.
authors.forename: Linda R
authors.initials: LR
authors.lastname: Petzold
authors.affiliation: Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 and Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.
authors.forename: Abhyudai
authors.initials: A
authors.lastname: Singh
mesh_terms: D000818:Animals; D003198:Computer Simulation; D015870:Gene Expression; D005972:Glutaminase / Q000235:genetics; D051379:Mice; D008957:Models, Genetic; D009010:Monte Carlo Method; D011401:Promoter Regions, Genetic; D059010:Single-Cell Analysis; D013269:Stochastic Processes
chemical_list: D005972:Glutaminase