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.
Highlights
An algorithm for in silico therapeutic target discovery was presented in its first version [1]
An example is the work of Hyunho Chu and co-workers [15]. They built a molecular interaction network involved in colorectal tumorigenesis and studied its dynamics, its attractors and their basins, with stochastic Boolean modelling
The technical features resulting from these improvements are illustrated on a simple example network while their biological significance is assessed in a case study, namely a published logic-based model of bladder tumorigenesis [16]
Summary
An algorithm for in silico therapeutic target discovery was presented in its first version [1]. An example is the work of Hyunho Chu and co-workers [15] They built a molecular interaction network involved in colorectal tumorigenesis and studied its dynamics, its attractors and their basins, with stochastic Boolean modelling. They highlighted what they termed the flickering, that is the displacement of the system from one basin to another one due to stochastic noise. Concerning kali, three improvements were made: (i) adding the possibility to work with asynchronous Boolean networks, (ii) implementing a finer assessment of therapeutic targets and (iii) adding the possibility to use multivalued logic. The technical features resulting from these improvements are illustrated on a simple example network while their biological significance is assessed in a case study, namely a published logic-based model of bladder tumorigenesis [16]
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