title: Transcriptional regulation of lineage commitment--a stochastic model of cell fate decisions.
abstract: Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.
authors.affiliation: Computational Biology & Biological Physics, Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
authors.forename: Jose
authors.initials: J
authors.lastname: Teles
authors.forename: Cristina
authors.initials: C
authors.lastname: Pina
authors.forename: Patrik
authors.initials: P
authors.lastname: Edén
authors.forename: Mattias
authors.initials: M
authors.lastname: Ohlsson
authors.forename: Tariq
authors.initials: T
authors.lastname: Enver
authors.forename: Carsten
authors.initials: C
authors.lastname: Peterson
mesh_terms: D002454:Cell Differentiation / Q000235:genetics*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D050989:GATA2 Transcription Factor / Q000096:biosynthesis / Q000235:genetics / Q000378:metabolism; D005786:Gene Expression Regulation; D016179:Granulocyte Colony-Stimulating Factor / Q000096:biosynthesis / Q000235:genetics / Q000378:metabolism; D007377:Interleukin-3 / Q000096:biosynthesis / Q000235:genetics / Q000378:metabolism; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D011993:Recombinant Fusion Proteins / Q000096:biosynthesis / Q000235:genetics / Q000378:metabolism; D013269:Stochastic Processes
chemical_list: D050989:GATA2 Transcription Factor; D007377:Interleukin-3; D012333:RNA, Messenger; D011993:Recombinant Fusion Proteins; C404145:myelopoietin; D016179:Granulocyte Colony-Stimulating Factor