Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Data Integration in Logic-based Models of Biological Mechanisms

Version 1 : Received: 7 May 2021 / Approved: 10 May 2021 / Online: 10 May 2021 (15:38:20 CEST)

How to cite: Hall, B.; Niarakis, A. Data Integration in Logic-based Models of Biological Mechanisms. Preprints 2021, 2021050219 (doi: 10.20944/preprints202105.0219.v1). Hall, B.; Niarakis, A. Data Integration in Logic-based Models of Biological Mechanisms. Preprints 2021, 2021050219 (doi: 10.20944/preprints202105.0219.v1).

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.

Subject Areas

Logic-based models; Boolean models; executable models; qualitative dynamical modelling; omic data integration; in silico simulations; formal verification

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