Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors

Resource type
Authors/contributors
Title
Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors
Abstract
Biochemical reaction networks are one of the most widely used formalisms in systems biology to describe the molecular mechanisms of high-level cell processes. However, modellers also reason with influence diagrams to represent the positive and negative influences between molecular species and may find an influence network useful in the process of building a reaction network. In this paper, we introduce a formalism of influence networks with forces, and equip it with a hierarchy of Boolean, Petri net, stochastic and differential semantics, similarly to reaction networks with rates. We show that the expressive power of influence networks is the same as that of reaction networks under the differential semantics, but weaker under the discrete semantics. Furthermore, the hierarchy of semantics leads us to consider a positive Boolean semantics that cannot test the absence of a species, that we compare with the negative Boolean semantics with test for absence of a species in gene regulatory networks à la Thomas. We study the monotonicity properties of the positive semantics and derive from them an algorithm to compute attractors in both the positive and negative Boolean semantics. We illustrate our results on models of the literature about the p53/Mdm2 DNA damage repair system, the circadian clock, and the influence of MAPK signaling on cell-fate decision in urinary bladder cancer.
Publication
IEEE/ACM Trans. Comput. Biol. Bioinformatics
Volume
15
Issue
4
Pages
1138–1151
Date
July 2018
DOI
10/ggdf94
ISSN
1545-5963
Short Title
Influence Networks Compared with Reaction Networks
Accessed
2019-11-23T07:40:24Z
Library Catalog
ACM Digital Library
Extra
ZSCC: 0000002
Citation
Fages, F., Martinez, T., Rosenblueth, D. A., & Soliman, S. (2018). Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 15(4), 1138–1151. https://doi.org/10/ggdf94
BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
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