Literature DB >> 29031098

Stepwise construction of a metabolic network in Event-B: The heat shock response.

Usman Sanwal1, Luigia Petre2, Ion Petre3.   

Abstract

There is a high interest in constructing large, detailed computational models for biological processes. This is often done by putting together existing submodels and adding to them extra details/knowledge. The result of such approaches is usually a model that can only answer questions on a very specific level of detail, and thus, ultimately, is of limited use. We focus instead on an approach to systematically add details to a model, with formal verification of its consistency at each step. In this way, one obtains a set of reusable models, at different levels of abstraction, to be used for different purposes depending on the question to address. We demonstrate this approach using Event-B, a computational framework introduced to develop formal specifications of distributed software systems. We first describe how to model generic metabolic networks in Event-B. Then, we apply this method for modeling the biological heat shock response in eukaryotic cells, using Event-B refinement techniques. The advantage of using Event-B consists in having refinement as an intrinsic feature; this provides as a final result not only a correct model, but a chain of models automatically linked by refinement, each of which is provably correct and reusable. This is a proof-of-concept that refinement in Event-B is suitable for biomodeling, serving for mastering biological complexity.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Keywords:  Biomodeling; Event-B; Heat shock response; Model hierarchy; Model refinement; Rodin

Mesh:

Year:  2017        PMID: 29031098     DOI: 10.1016/j.compbiomed.2017.09.021

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  1 in total

1.  Scalable reaction network modeling with automatic validation of consistency in Event-B.

Authors:  Usman Sanwal; Thai Son Hoang; Luigia Petre; Ion Petre
Journal:  Sci Rep       Date:  2022-01-25       Impact factor: 4.379

  1 in total

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