Transcriptional Regulation of Lineage Commitment - A Stochastic Model of Cell Fate Decisions

• DOI: 10.1371/journal.pcbi.1003197
• Publication Date: 2013-08-22T21:07:17Z
• Authors: Jose Teles; Cristina Pina; Patrik Edén; Mattias Ohlsson; Tariq Enver; Carsten Peterson
• Keywords: Stochastic modelling

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 involves a limited set genes or entire transcriptional program, and what extent gene expression configures multiple trajectories into commitment. Importantly, transient nature transition confounds experimental capture committing cells. We develop computational framework that simulates stochastic events, affords mechanistic exploration fate transition. use combined modeling approach guided classifier methods infers time-series events growth characteristics profiling hematopoietic captured immediately before after define putative regulators probabilistic rules through machine learning methods, employ clustering correlation analyses interrogate regulatory interactions in Against this background, we Monte Carlo model transcription where parameters governing promoter status, mRNA production decay are fitted static distributions. time converted physical using cell culture kinetic data. Probability function as defined logistic regression obtained single-cell Our should be applicable similar differentiating systems single data available. Within our system, identify robust solutions for population within physiologically reasonable values explore predictions with regard molecular scenarios entry The suggests distinct dependencies different commitment-associated on dynamics activity, which globally influence probability