2016/11/30 by Arnaud Poret, Carito Guziolowski · 15 citations
Biochemistry, Genetics and Molecular Biology · Engineering · #Asynchronous communication #Biological network #Boolean function #Boolean model #Boolean network #DNA and Biological Computing #Gene Regulatory Network Analysis #Kali #Reachability #Slime Mold and Myxomycetes Research #q-bio.MN #q-bio.QM
paper · pdf · open access · doi:10.1098/rsos.171852
published in Royal Society Open Science 5(2), 171852 (Royal Society)
openalex created_date 2017/11/17 · arxiv created 2018/01/08 · openalex publication_date 2018/02/01 · arxiv updated 2018/03/13 · openalex updated_date 2026/08/05
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.