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Quantitative Modeling Approaches for Understanding the Role of Phosphatases in Cell Signaling Regulation: Applications in Metabolism

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Protein Tyrosine Phosphatase Control of Metabolism

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Abstract

The regulation of cell signaling occurs through a complex set of coupled processes occurring over multiple length and time scales. Computational modeling approaches have been applied to dissect this complexity over these various time and length scales, which range from the molecular level to the cell and tissue level, but these approaches have not focused heavily on the regulation or roles of phosphatases. Because of the clear importance of phosphatases in cell signaling, significant opportunities exist to expand our understanding of the regulation of cell signaling in metabolism and other cell regulatory processes by focusing modern computational approaches on phosphatases and the processes they regulate. The aim of this chapter is to provide a brief review of some computational modeling approaches that have been usefully applied to study the regulation of signaling, mainly by kinases, over a range of length and time scales and to describe opportunities to apply similar approaches for understanding signaling regulation by phosphatases. Some specific examples of key relevance to metabolism are described.

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Abbreviations

AKT:

Protein kinase B

DEP1/RPTPJ:

Density-enhanced phosphatase 1/receptor-like protein tyrosine phosphatase J

EGF:

Epidermal growth factor

EGFR:

Epidermal growth factor receptor

GAB1:

GRB2-associated binder 1

GLUT4:

Glucose transporter 4

GRB2:

Growth factor receptor-bound protein 2

HER2:

Human epidermal growth factor receptor 2

HER3/ErbB3:

Human epidermal growth factor receptor 3

IR:

Insulin receptor

IRS:

Insulin receptor substrate

MAP:

Mitogen-activated protein

MAPK:

Mitogen-activated protein kinase

MEK:

Mitogen-activated protein kinase kinase

MKP3/DUSP6:

MAP kinase phosphatase 3/dual specificity phosphatase 6

NSCLC:

Non-small cell lung cancer

PAG/Cbp:

Phosphoprotein associated with glycosphingolipid-enriched microdomains/Csk-binding protein

PDGFR:

Platelet-derived growth factor receptor

PDK1:

Phosphoinositide-dependent kinase-1

PI3K:

Phosphoinositide 3-kinase

PLSR:

Partial least squares regression

PTEN:

Phosphatase and tensin homolog

PTP:

Protein tyrosine phosphatase

PTP1B:

Protein tyrosine phosphatase 1B

Raf:

Mitogen-activated protein kinase kinase kinase for ERK pathway

ROS:

Reactive oxygen species

RPTP:

Receptor-like protein tyrosine phosphatase

RTK:

Receptor tyrosine kinase

SH2:

Src homology 2

SHP1:

Src homology 2 domain containing phosphatase 1

SHP2:

Src homology 2 domain containing phosphatase 2

Src:

Proto-oncogene tyrosine kinase

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Acknowledgements

The author gratefully acknowledges Prof. Douglas A. Lauffenburger (MIT) and Prof. Ravi Radhakrishnan (University of Pennsylvania) for helpful feedback on this book chapter.

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  1. Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, 19104-6393, USA

    Matthew J. Lazzara Ph.D.

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  1. Matthew J. Lazzara Ph.D.
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Correspondence to Matthew J. Lazzara Ph.D. .

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  1. Department of Animal Biology, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Kendra K. Bence

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Lazzara, M.J. (2013). Quantitative Modeling Approaches for Understanding the Role of Phosphatases in Cell Signaling Regulation: Applications in Metabolism. In: Bence, K. (eds) Protein Tyrosine Phosphatase Control of Metabolism. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7855-3_2

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