Toward predictive modeling of large and complex biological signaling networks

Rachel A. Hillmer; Fumiaki Katagiri

doi:10.1016/j.pmpp.2016.01.009

Toward predictive modeling of large and complex biological signaling networks

Rachel A. Hillmer, Fumiaki Katagiri

Plant and Microbial Biology

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

The main goal of this article is to identify critical issues that arise when building in silico models that can predict the behavior of complex biological signaling networks. We discuss practical approaches to overcome, circumvent, or moderate these issues. Here we focus on modeling spatially homogenous systems, such as those consisting of cells of the same type receiving the same input. Proper modeling of spatially heterogeneous systems requires additional model features and added modeling scales, beyond those required for spatially homogenous systems. These different modeling scales are defined here as different layers in the hierarchical relationships in a network. For example, protein molecules and cells that contain the protein molecules belong to different scales.

Original language	English (US)
Pages (from-to)	77-83
Number of pages	7
Journal	Physiological and Molecular Plant Pathology
Volume	95
DOIs	https://doi.org/10.1016/j.pmpp.2016.01.009
State	Published - Jul 1 2016

Bibliographical note

Funding Information:
We thank Yungil Kim for valuable discussion in preparing this perspective. The relevant work in F. K.’s laboratory was partly supported by grants from the National Science Foundation , MCB-0918908 and IOS-1121425 . R. A. H. was supported by a Doctoral Dissertation Fellowship and a Plant Biological Sciences Summer Fellowship from University of Minnesota .

Publisher Copyright:
© 2016 Elsevier Ltd

Keywords

Network reconstitution
Signaling allocation

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title = "Toward predictive modeling of large and complex biological signaling networks",

abstract = "The main goal of this article is to identify critical issues that arise when building in silico models that can predict the behavior of complex biological signaling networks. We discuss practical approaches to overcome, circumvent, or moderate these issues. Here we focus on modeling spatially homogenous systems, such as those consisting of cells of the same type receiving the same input. Proper modeling of spatially heterogeneous systems requires additional model features and added modeling scales, beyond those required for spatially homogenous systems. These different modeling scales are defined here as different layers in the hierarchical relationships in a network. For example, protein molecules and cells that contain the protein molecules belong to different scales.",

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Toward predictive modeling of large and complex biological signaling networks

Abstract

Bibliographical note

Keywords

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