Knowledge-based generalisation for metabolic models

Informations générales
Nom
ZHUKOVA
Prénom
Anna
Diplôme
Thèse
Année
2014
Détails de la thèse/HDR
Jury
Marie-Dominique Devignes
Guillaume Fertin
Pascal DURRENS
Colette JOHNEN
Delphine ROPERS
Directeur (pour les thèses)
David James SHERMAN
Résumé en anglais
Genome-scale metabolic models describe the relationships
between thousands of reactions and biochemical molecules, and are used
to improve our understanding of organism’s metabolism. They found
applications in pharmaceutical, chemical and bioremediation
industries.The complexity of metabolic models hampers many tasks that
are important during the process of model inference, such as model
comparison, analysis, curation and refinement by human experts. The
abundance of details in large-scale networks can mask errors and
important organism-specific adaptations. It is therefore important to
find the right levels of abstraction that are comfortable for human
experts. These abstract levels should highlight the essential model
structure and the divergences from it, such as alternative paths or
missing reactions, while hiding inessential details.To address this
issue, we defined a knowledge-based generalization that allows for
production of higher-level abstract views of metabolic network models.
We developed a theoretical method that groups similar metabolites and
reactions based on the network structure and the knowledge extracted
from metabolite ontologies, and then compresses the network based on
this grouping. We implemented our method as a python library, that is
available for download from metamogen.gforge.inria.fr.To
validate our method we applied it
to 1 286 metabolic models from the Path2Model project, and showed that
it helps to detect organism-, and domain-specific adaptations, as well
as to compare models.Based on discussions with users about their ways of
navigation in metabolic networks, we defined a 3-level representation of
metabolic networks: the full-model level, the generalized level, the
compartment level. We combined our model generalization method with the
zooming user interface (ZUI) paradigm and developed Mimoza, a
user-centric tool for zoomable navigation and knowledgebased exploration
of metabolic networks that produces this 3-level representation. Mimoza
is available both as an on-line tool and for download
atmimoza.bordeaux.inria.fr .