AbstractGene Regulatory Networks (GRN) inference is of great interest for biologists, considering the substantial information these networks can provide. This thesis shows how the GRN inference can be translated into a Constraint Satisfaction Problem (CSP), and benefit from the Constraint Logic Programming (CLP) paradigm.
Starting from current known modeling techniques, this thesis details how to model a GRN inference problem as a CSP. Based on this theoretical result, the prototype of a tool capable of reasoning over GRN is built as a Web application, back-end and front-end.
This tool aims at allowing the biologists to infer GRN from experimental data, but also
assess hypotheses on parameters of the networks. Required degrees of freedom, based on the biological modeling and assumptions, are provided to the user.
Different implementations of the core of the CSP, part of the back-end, are provided,
and their performances are assessed thanks to a systematic tests framework developed. This assessment helps defining heuristic allowing to automatically or manually choose, in the tool, what methodology using depending on the user inputs or expectations. As an illustration, a case-based application of the tool is provided, the simplified lac operon network.
|Date of Award||27 Aug 2018|
|Supervisor||Jean-Marie JACQUET (Supervisor)|
- Gene Regulatory Networks
- Constraint Logic