Therapeutic target discovery using Boolean network attractors: improvements of kali

TL;DR

This paper presents improvements to the kali algorithm for identifying therapeutic targets in Boolean network models, enabling asynchronous, multivalued logic, and refined target assessment to better support drug discovery.

Contribution

The paper introduces enhancements to the kali algorithm, including asynchronous dynamics, multivalued logic, and improved target evaluation for better therapeutic target prediction.

Findings

kali now supports asynchronous Boolean networks

The algorithm can use multivalued logic for more nuanced modeling

kali successfully identified therapeutic targets in a bladder tumorigenesis model

Abstract

In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modeling 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 modeled 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 can not replace human expertise: it is a supporting tool for coping with the complexity of biological systems in the field of drug discovery.