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
