ArticleRoyal Society open science2018
Therapeutic target discovery using Boolean network attractors: improvements of kali.
Article in Royal Society open science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Complex networks interactions between bioactive compounds and adipose tissue vis-à-vis insulin resistance.Frontiers in endocrinology · 2025Article
- Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.iScience · 2024Article
- Inferring gene regulatory networks using transcriptional profiles as dynamical attractors.PLoS computational biology · 2023Article
- Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses.Frontiers in bioinformatics · 2023Article
- Boolean modelling as a logic-based dynamic approach in systems medicine.Computational and structural biotechnology journal · 2022Review
- Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.Frontiers in big data · 2021Article
- Multi-parameter exploration of dynamics of regulatory networks.Bio Systems · 2020Review
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2 authors.
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Abstract
In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modelled biological system. Given a logic-based model of pathological mechanisms, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness. kali is illustrated on an example network and used on a biological case study. The case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results. However, like any computational tool, kali can predict but cannot replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery.
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