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Emil Andersson

Emil Andersson

Doctoral student

Emil Andersson

Multi-scale Dynamical Modeling of T Cell Development from an Early Thymic Progenitor State to Lineage Commitment

Author

  • Victor Olariu
  • Mary A. Yui
  • Pawel Krupinski
  • Wen Zhou
  • Julia Deichmann
  • Emil Andersson
  • Ellen V. Rothenberg
  • Carsten Peterson

Summary, in English

Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.

Department/s

  • Computational Biology and Biological Physics

Publishing year

2021

Language

English

Publication/Series

Cell Reports

Volume

34

Issue

2

Document type

Journal article

Publisher

Cell Press

Topic

  • Cell and Molecular Biology
  • Cell Biology

Keywords

  • epigenetic modeling
  • experimental validations
  • kinetic measurements
  • population modeling
  • proliferation measurements
  • single-cell measurements
  • stochastic simulations
  • T cell development
  • transcriptional modeling

Status

Published

ISBN/ISSN/Other

  • ISSN: 2211-1247