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Cell Syst. 2019 Dec 13. pii: S2405-4712(19)30430-2. doi: 10.1016/j.cels.2019.11.010. [Epub ahead of print]

Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks.

Author information

1
Department of Systems Biology, Columbia University, New York, NY, USA; Department of Physics, University of Florida, Gainesville, FL, USA. Electronic address: dixitpd@gmail.com.
2
Department of Systems Biology, Columbia University, New York, NY, USA.
3
Laboratory of Systems Pharmacology, HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA.
4
Department of Systems Biology, Columbia University, New York, NY, USA; Department of Biomedical Informatics, Columbia University, New York, NY, USA; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA. Electronic address: dv2121@cumc.columbia.edu.

Abstract

Predictive models of signaling networks are essential for understanding cell population heterogeneity and designing rational interventions in disease. However, using computational models to predict heterogeneity of signaling dynamics is often challenging because of the extensive variability of biochemical parameters across cell populations. Here, we describe a maximum entropy-based framework for inference of heterogeneity in dynamics of signaling networks (MERIDIAN). MERIDIAN estimates the joint probability distribution over signaling network parameters that is consistent with experimentally measured cell-to-cell variability of biochemical species. We apply the developed approach to investigate the response heterogeneity in the EGFR/Akt signaling network. Our analysis demonstrates that a significant fraction of cells exhibits high phosphorylated Akt (pAkt) levels hours after EGF stimulation. Our findings also suggest that cells with high EGFR levels predominantly contribute to the subpopulation of cells with high pAkt activity. We also discuss how MERIDIAN can be extended to accommodate various experimental measurements.

KEYWORDS:

maximum entropy; parameter inference; population heterogeneity; signaling networks

PMID:
31864963
DOI:
10.1016/j.cels.2019.11.010

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