Expert curation for building network-based dynamical models: a case study on atherosclerotic plaque formation

Abstract

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. Database URL: http://biomodels.caltech.edu

Modeling cholesterol metabolism and atherosclerosis

“…(Parton et al, 2019). The model complements previous work which has encased atherosclerosis in these frameworks (Bekkar et al, 2018), and well‐designed simulations pin‐pointed a combination of pharmacological agents which effectively reversed atheroma development.…”

Automated inference of Boolean models from molecular interaction maps using CaSQ

“…We should note that CaSQ infers preliminary Boolean rules, so the modeller still needs to fine-tune the model and find the best logical rules to reproduce data accurately. Bekkar et al (2018) show that logical models with added human curation perform better than models where rules are extracted automatically from a given topology. As demonstrated in the results, the CaSQ tool produces models that are largely in agreement with the model a human modeller would build, accelerating the time of model construction impressively.…”

Boolean function metrics can assist modelers to check and choose logical rules

“…Regardless of how a logical model is constructed, it has been shown in practice that expert curation, i.e. the manual fine-tuning of the logical rules to fit experimental data, can result in highly predictive models [14,15], yet this is not trivially obtained with automatically constructed networks [16]. Because of the large function space complemented with a sparsity of observations and inherent noise in existing data, there is a wide range of plausible BRFs.…”