Institute of Fundamental Technological Research
Polish Academy of Sciences

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Bing Tian

University of Texas Medical Branch (US)


Recent publications
1.  Bertolusso R., Tian B., Zhao Y., Vergara L., Sabree A., Iwanaszko M., Lipniacki T., Brasier A.R., Kimmel M., Dynamic cross talk model of the epithelial innate immune response to double-stranded RNA stimulation: Coordinated dynamics emerging from cell-level noise, PLOS ONE, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0093396, Vol.9, No.4, pp.e93396-1-21, 2014

Abstract:
We present an integrated dynamical cross-talk model of the epithelial innate immune reponse (IIR) incorporating RIG-I and TLR3 as the two major pattern recognition receptors (PRR) converging on the RelA and IRF3 transcriptional effectors. bioPN simulations reproduce biologically relevant gene-and protein abundance measurements in response to time course, gene silencing and dose-response perturbations both at the population and single cell level. Our computational predictions suggest that RelA and IRF3 are under auto- and cross-regulation. We predict, and confirm experimentally, that RIG-I mRNA expression is controlled by IRF7. We also predict the existence of a TLR3-dependent, IRF3-independent transcription factor (or factors) that control(s) expression of MAVS, IRF3 and members of the IKK family. Our model confirms the observed dsRNA dose-dependence of oscillatory patterns in single cells, with periods of 1–3 hr. Model fitting to time series, matched by knockdown data suggests that the NF-κB module operates in a different regime (with different coefficient values) than in the TNFα-stimulation experiments. In future studies, this model will serve as a foundation for identification of virus-encoded IIR antagonists and examination of stochastic effects of viral replication.

Our model generates simulated time series, which reproduce the noisy oscillatory patterns of activity (with 1–3 hour period) observed in individual cells. Our work supports the hypothesis that the IIR is a phenomenon that emerged by evolution despite highly variable responses at an individual cell level.

Affiliations:
Bertolusso R. - Rice University (US)
Tian B. - University of Texas Medical Branch (US)
Zhao Y. - University of Texas Medical Branch (US)
Vergara L. - University of Texas Medical Branch (US)
Sabree A. - Rice University (US)
Iwanaszko M. - Silesian University of Technology (PL)
Lipniacki T. - IPPT PAN
Brasier A.R. - University of Texas Medical Branch (US)
Kimmel M. - Rice University (US)

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