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Receptor Tyrosine Kinases: Structure, Functions and Role in Human Disease pp 111–132Cite as

Computational and Modeling Aspects of RTK Networks

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Abstract

Receptor tyrosine kinases, along with G protein-coupled receptors and the group of cytokine receptors, transmit a great majority of extracellular cues to the cytoplasm and nucleus of target cells. Here we focus on one subgroup of receptor tyrosine kinases, whose prototype is the epidermal growth factor receptor (EGFR). Due to ligand-induced homo- and heterodimerization by EGFR (also called ERBB1) and other family members, extracellular signals are processed by a layered signaling network, which generates a complex, time-dependent output. Mass-action models well describe the emergent behavior of the network, but their establishment requires detailed experimental data. For example, mass-action models incorporate feedback regulatory loops and explain ligand-specific rewiring of the network, as well as the emergence of ultrasensitivity. Other computational models are employed when the volume of experimental data is limited. Both mass-action models and the more abstractive models help uncover fragile nodes amenable for therapeutic intervention. Likewise, co-option of network’s robustness by disease states might be modeled and help understand sensitivity, as well as resistance, to drugs targeting signal transduction by the ERBB and related networks.

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Authors and Affiliations

  1. Department of Biological Regulation, Weizmann Institute of Science, Rehovot, 76100, Israel

    Yehoshua Enuka, Morris E. Feldman & Yosef Yarden Ph.D.

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  1. Yehoshua Enuka
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  2. Morris E. Feldman
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  3. Yosef Yarden Ph.D.
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Correspondence to Yosef Yarden Ph.D. .

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Editors and Affiliations

  1. Department of Human Oncology, University of Wisconsin, Madison, Wisconsin, USA

    Deric L. Wheeler

  2. Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel

    Yosef Yarden

Glossary

Autocrine

 Secretion of a ligand that activates a receptor on the secreting cell itself.

Boolean model

 A discrete modeling method that produces a qualitative dynamic description of system behavior.

Emergent behavior

 The complex behavior of a system that arises from functional interactions of simple components.

Endocytosis

 The process of inward folding of the plasma membrane, thereby creating intracellular vesicles that uptake materials from outside a cell.

Fuzzy c-means

 A clustering method where data points are assigned a score for belonging to each cluster. Thus, it enables to identify which data points fit better to particular clusters.

Granularity

 The detailed level of the data included in a model. Higher model’s granularity means that the system is broken up into many smaller parts.

Mitogen-activated protein kinase (MAPK)

 A group of four kinases that are activated in a cascade. The dynamical activation pattern of these kinases leads to diverse cell fates.

Paracrine

 Secretion of a ligand that stimulates adjacent cells.

Self-organizing map

 A computational method based on unsupervised learning that clusters data into groups. Additionally, it is able to graphically present the relationships among different groups.

Species

 Forms of macromolecules, such as protein isoforms originating from RNA splicing, posttransnationally modified proteins and protein complexes.

Ultrasensitivity

 The output response of an enzymatic reaction where, above a certain threshold, a small increase in input causes a large increase in output response. The ultrasensitive stimulus/response curve is steeper than the hyperbolic Michaelis-Menten curve. It enables bistable behavior of signaling networks.

Unsupervised learning

 A computational approach that makes no prior assumptions on data organization. This method might reveal unanticipated biological insights.

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Enuka, Y., Feldman, M.E., Yarden, Y. (2015). Computational and Modeling Aspects of RTK Networks. In: Wheeler, D., Yarden, Y. (eds) Receptor Tyrosine Kinases: Structure, Functions and Role in Human Disease. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2053-2_6

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