Version 1
: Received: 7 May 2021 / Approved: 10 May 2021 / Online: 10 May 2021 (15:38:20 CEST)
Version 2
: Received: 30 July 2021 / Approved: 30 July 2021 / Online: 30 July 2021 (15:05:03 CEST)
Benjamin A. Hall, Anna Niarakis,
Data integration in logic-based models of biological mechanisms,
Current Opinion in Systems Biology,
Volume 28,
2021,
100386,
ISSN 2452-3100,
https://doi.org/10.1016/j.coisb.2021.100386.
Benjamin A. Hall, Anna Niarakis,
Data integration in logic-based models of biological mechanisms,
Current Opinion in Systems Biology,
Volume 28,
2021,
100386,
ISSN 2452-3100,
https://doi.org/10.1016/j.coisb.2021.100386.
Benjamin A. Hall, Anna Niarakis,
Data integration in logic-based models of biological mechanisms,
Current Opinion in Systems Biology,
Volume 28,
2021,
100386,
ISSN 2452-3100,
https://doi.org/10.1016/j.coisb.2021.100386.
Benjamin A. Hall, Anna Niarakis,
Data integration in logic-based models of biological mechanisms,
Current Opinion in Systems Biology,
Volume 28,
2021,
100386,
ISSN 2452-3100,
https://doi.org/10.1016/j.coisb.2021.100386.
Abstract
Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signalling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high throughput data. Novel, literature-based representations of biological processes and emerging machine learning algorithms offer new opportunities for model construction. Here, we review recent efforts to incorporate omic data into logic-based models and discuss critical challenges in constructing and analysing integrative, large-scale, logic-based models of biological mechanisms.
Keywords
Logic-based models; Boolean models; executable models; qualitative dynamical modelling; omic data integration; in silico simulations; formal verification
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
