- Meeting ID: 867 6409 6440
- passcode: 149120
13h00 - 13h50 – Carito Guziołowski (LS2N, École centrale de Nantes)
Modeling Biological Networks as Logic Programs
In this talk it is proposed to explore two representations of a biological system using computational modeling based on logic programs.
One is done by using the sign-consistency approach, where a regulatory network is combined with a dataset of gene expression observations, using a logic program. This logic program, written in Answer Set Programming, expresses a rule that has to be valid for each species in the network, which relates the sign of a network species with its direct predecessors influences and signs. This rule is tested in a global way, through all the network species by using an efficient solver, clasp. The sign-consistency modeling framework we proposed is named Iggy. Iggy was applied to model Multiple Myeloma (MM) patients gene expression data. Our results on this system were to propose MM markers, that is, species in the network, coupled with our computational predictions, that allow to identify patients having a better survival.
The second one is done through the learning of Boolean network families. In this approach, given a regulatory network and a set of network species observations upon multiple perturbations over the system, the objective is to learn a family of Boolean Networks (BNs), compatible with the network topology, that fits the perturbation data with minimal error. The first system we conceived is named caspo. In its early stages caspo was applied to model pro-growth and inflammatory pathways. Briefly we will mention several suites of caspo, such as caspo-ts, which deals with perturbation time-series data, and outputs a family of dynamic BNs.
Dernière modification le 06/09/2024