Genomic and post-genomic studies

Previous studies of the DOLPHIN project mainly deal with genomic and postgenomic applications. These have been realized in collaboration with academic and industrial partners (IBL: Biology Institute of Lille; IPL: Pasteur Institute of Lille; IT-Omics firm).

First, genomic studies aim at analyzing genetic factors which may explain multi-factorial diseases such as diabetes, obesity or cardiovascular diseases. The scientific goal was to formulate hypotheses describing associations that may have any influence on diseases under study.

Secondly, in the context of post-genomic, a very large amount of data are obtained thanks to advanced technologies and have to be analyzed. Hence, one of the goals of the project was to develop analysis methods in order to discover knowledge in data coming from biological experiments.

These problems can be modeled as classical data mining tasks (Association rules, feature selection). As the combinatoric of such problems is very high and the quality criteria not unique, we proposed to model these problems as multi-objective combinatorial optimization problems. Evolutionary approaches have been adopted in order to cope with large scale problems.

Nowadays the technology is still going fast and the amount of data increases rapidly. Within the collaboration with Genes Diffusion, specialized in genetics and animal reproduction for bovine, swine, equine and rabbit species, we study combinations of Single Nucleotide Polymorphisms (SNP) that can explain some phenotypic characteristics. Therefore feature selection for regression is addressed using metaheuristics.

Optimization for health care

The collaboration with the Alicante company, a major actor in the hospital decision making, deals with knowledge extraction by optimization methods for improving the process of inclusion in clinical trials. Indeed, conducting a clinical trial, allowing for example to measure the effectiveness of a treatment, involves selecting a set of patients likely to participate to this test. Currently existing selection processes are far from optimal, and many potential patients are not considered. The objective of this collaboration consists in helping the practitioner to quickly determine if a patient is interesting for a clinical trial or not. Exploring different data sources (from a hospital information system, patient data...), a set of decision rules have to be generated. For this, approaches from multi-objective combinatorial optimization are implemented, requiring extensive work to model the problem, to define criteria optimization and to design specific optimization methods.

In another project we address the problems, proper models and suitable solving approaches to optimize the design and planning of a medical laboratory. It comes from the observation that human biology laboratories tend to get bigger and more complicated. The goal of this project is to design and apply solutions to optimize human biology laboratory decisions and operations. As a result of mutualisation phenomenon, huge medical laboratories are emerging all over the world. Medical laboratories are responsible for analyzing medical tests ordered by physicians on patient’s samples. Large number of prescriptions and tubes are received by a laboratory make a complicated workflow into the system. The aim of this thesis is to design and plan a medical laboratory to minimize the costs and time required to perform the tests. Medical laboratory optimization is an immense problem which encompasses many subproblems. In the literature, there are only few studies that refer directly to medical laboratory planning and optimization from different points of view. In this thesis, the problem of designing and planning of medical laboratory is studied in a comprehensive form for both existing and new labs to provide practical models and solutions that can be implemented to real cases. The problem is studied from two different aspects: strategic and operational problems. In strategic level, machine selection and analyzer configuration problem and facilities layout problem are modeled and solved to achieve an optimal medical laboratory design. Mathematical programming is used for this phase. For operational level, the problems of assignment and scheduling are emerged. Mathematical programming, heuristic and meta-heuristic algorithms are proposed to deal with such problems. Simulation as a powerful tool is applied to verify and validate the solutions proposed from the previous analytical steps and also to aid decision making in some problems0. FlexSim is a simulation software which is used in this study and provide us the ability to model and analyze complex systems. It is worth mentioning that this project is raised by Normand-Info, a Beckman Coulter subsidiary which is an international company mainly producing human biology instruments. Thus, it is a collaborative research work with industry.