In a previous article, an algorithm for discovering therapeutic targets in Boolean networks modeling disease mechanisms was introduced. In the present article, the updates made on this algorithm, named kali, are described. These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system. Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes . However, like any computational tool, kali can predict but can not replace human expertise: it is an aid for coping with the complexity of biological systems  .
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. This 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 .
R
discovering
identifying
R
disease
pathological
R
updates
improvements
R
updates are :
improvements are
R
smarter search for therapeutic targets ,
finer assessment of therapeutic targets
R
correspond to
, such as a Boolean network, are associated with
R
logical model of a pathophysiology, either Boolean or multivalued,
logic-based model of pathological mechanisms,
R
which biological components should be therapeutically disturbed in order
therapeutic targets able
R
the likelinessof pathological phenotypes
their likeliness
R
a simple
an
R
shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes
used on a biological case study. This case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results
R
an aid
a supporting tool
A
in the field of drug discovery