Section: Other Grants and Activities
Participants : David Auber, Romain Bourqui, Jonathan Dubois, Paolo Simonetto.
Project: SysTryp (Metabolomic systems biology analysis of differentiation in trypanosomes)
Call: ANR Systems Biology (bilateral FR-UK)
start/end December 2007 – December 2010
Budget: 299 980 euros (grant French partners) / 89 338 euros (INRIA GRAVITÉ)
The project focuses on the study of the relationship between metabolism and cellular differentiation in the protozoan Trypanosoma brucei, by collecting high resolution mass spectrometry data and reconstructing networks based on this data. Relationships between static and dynamic networks will be determined and hypotheses generated by seeking and visualizing metabolic network modules that associate with differentiation.
A limitation in modeling of biochemical networks relates to a lack of general compatibility between static and dynamic modeling. Here we aim to reduce this gap and provide the means by which biochemists move seamlessly from the global view of metabolism within a model system, provided by static modeling, to a detailed representation derived from dynamic modeling. To do so, we will design and evaluate new combinatorial and visual means to detect, within large networks, modules corresponding to key pathways involved in the system under study. To validate these graph mining methods we will model one selected pathway using dynamic modeling and then check it experimentally. We will focus on the protozoan, Trypanosoma brucei, an extraordinary model system. These single celled organisms undergo a complex life cycle that takes them through the divergent environments of the mammalian bloodstream through various developmental stages within the tsetse fly. As a consequence the trypanosome remodels its cellular structure, and its metabolism, to adapt to these incongruent conditions.
Once within those environments, however, they enjoy relative stability, thus their capacity to retain homeostasis is apparently pre-programmed and their metabolic network less plastic than those seen in free living organisms like yeast. Here we propose to make comprehensive measurements of the trypanosome's metabolome as the parasites transform. Ab initio networks, where individual metabolites are linked based on chemical transformations between them, will be constructed along with a second set of networks of metabolites whose abundance changes in a coordinated fashion. The various networks will be used to assist in validating the accuracy of the overall network. Modules, comprising connected metabolites whose abundance changes in a coordinated fashion throughout the differentiation process will be identified and the components of a selected module will be subject to dynamic modeling. Predictions based on the modeling will then guide reverse genetics based experiments (using gene knockout and RNA interference) to remove genes encoding enzymes central to the modules predicted to be critical to differentiation. The impact of these genetic perturbations on the differentiation process and on the metabolome will be assessed experimentally.
In summary, the project aims:
To use high resolution mass spectrometry to identify the metabolite composition of trypanosomes and see how the metabolome changes during the differentiation process;
To use advanced bioinformatic techniques (based on metabolic connectivity and response correlation) to build metabolite networks from these cells;
To follow perturbations, and use static modeling to identify those parts of the network that are unchanged through the differentiation processes and those which respond, in a coordinated fashion, to changes during differentiation;
To use modularity properties to derive visualisation methods that allow identification of one or more modules associated with cellular differentiation, then use them in dynamic flux modeling studies to evaluate their cellular roles;
To use a functional genomics approach to find whether loss, or inappropriate expression of key enzymes involved in differentiation-response pathways have a predictable impact on this process.