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Home page > Research > Metabolic Genomics Group > Computational Systems Biology (NeMo) > Computational Systems Biology (NeMo)

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Computational Systems Biology (NeMo)

Global modeling of bacterial metabolism
Coupling of metabolic flux
Software tools for the metabolic modeling
Metabolomics and metabolic models
Analysis of phenotypic profiles
Group members



Created at the end of 2004, the Computational Systems Biology group focuses on the function and evolution of metabolism (1) using mathematical and computational methods, in close collaboration with teams of experimentalists. The group builds models of metabolism at the level of the cell, integrating various types of biological data, confronts model predictions against experimental data, and analyzes these predictions using computational methods.

Metabolic modeling at genome scale

From annotations to models...
The availability of the complete genomic sequence and corresponding functional annotation for a given genome makes it possible to perform a first inventory of the set of metabolic reactions which are likely to occur in the organism, i.e. reactions catalyzed by the enzymes for which coding genes have been identified. This inventory provides a starting point for the initial reconstruction of metabolic models, together with physiological knowledge and a specific curation effort. As additional genes are annotated as having a role in metabolism, or as existing enzymatic annotations are refined, the model can be improved.

...and from models to annotations
As soon as it is sufficiently complete to allow testable predictions about the global metabolic behavior of the cell, a metabolic model may be used to evaluate the coherence of the network of reactions deduced from functional annotations with existing knowledge on the physiology of the organism. This evaluation is performed by comparing predictions about the metabolic capacities of the cell, such as its capacity to grow on a given medium or to produce specific metabolites or combinations thereof, with physiological information or new experimental data. An incoherence between model predictions and experimental results may signal missing or inaccurate elements in the annotation. In combination with the use of other functional clues, detailed analysis of model predictions then permits the design of experimental strategies to complete or correct the annotation.

Metabolic models and experimental data
Metabolic models are doubly constrained by experimental data : they are reconstructed from data on enzymatic reactions and regulatory mechanisms predicted from the genome sequence or validated experimentally; and their predictions are tested against experimental measurements. Their complexity and level of detail must therefore be commensurate with the nature and quantity of the data which is experimentally available. Quantitative parameters are not yet available at the scale of an entire metabolic network. Qualitative models can nevertheless yield biologically relevant insights using structural analyses and predictions which can be experimentally verified.
A large majority of the phenotypic traits () of a bacterium (for example: is it able to grow on a given medium? Which metabolites is it able to produce ? How does a genetic disruption modify its metabolic capabilities ?) are direct consequences of the internal state of its metabolism, i.e. of the set of metabolite fluxes and concentrations. Metabolic models provide an effective handle on this relationship, linking genotype and phenotype via a description of the states of the metabolic network.

Modeling framework
The “constraint-based” (* Price et al. (2004) modeling framework (Price et al. (2004)) is well adapted to the study of the metabolism of the whole cell, at a scale for which quantitative data are not available, and complex mathematical treatment is not possible. It describes the dynamics of the metabolic network in a stationary state and permits characterization of the distribution of the flux of material within the network of metabolic reactions as a function of the chemical composition of the environment, the law of conservation of mass and supplementary biological or biochemical constraints. When a “constraint-based” model is sufficiently complete, it also becomes possible to make predictions about the flux of individual reactions as well as predictions of growth phenotypes in a given medium depending on a hypothesis regarding a task which the cell is trying to optimize (production of biomass, degradation of a toxic substance,...?).

As abstract and simplified representations of metabolism, these models have two roles :

  • Descriptive : they present a view of a structured ensemble of the metabolic reactions which take place in the cell.
  • Predictive : they permit prediction of certain qualitative and quantitative aspects of the metabolic behavior of the cell in a given environment.

Far from constituting an exhaustive or necessarily very detailed vision of the metabolic functioning of the real cell, these global metabolic models are mainly tools for advancing our understanding of the function and metabolic capacities of a cell, but also the role of each of these reactions which constitute the pathway, and therefore of the function of the corresponding genes. They also constitute a starting point for more targeted and detailed models, via extensions of the modeling framework and integration of supplementary experimental data.


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  A portion of a metabolic network. (From Biochemical Pathways, Roche Applied Science,



Each project targets a biological question in a specific experimental context and/or focuses on the development of new methods.

Bibliography :
[*] Price ND, Reed JL, et al.
“Genome-scale models of microbial cells: evaluating the consequences of constraints.”
Nat Rev Microbiol. 2004. 2(11): 886-97.

Glossary :
(1) Metabolism : Metabolism is the ensemble of molecular transformations and energy transfers which constantly take place in the cells of living organisms. It is an orderly process, which includes processes of degradation (catabolism) and organic synthesis (anabolism).
(2) Phenotype : The phenotype is the ensemble of anatomic, morphologic, physiologic and ethological characteristics which typify a given living organism. The phenotype represents the expression of the genotype as a function of specific conditions in the environment. It is the ensemble of the observed traits of an organism, resulting from the translation of its genotype. It is highly dependent on the proteins that it is capable of producing.

Collaborations :

  • Metabolic Thesaurus project
  • European excellence network ENFIN (Coordination of the workpackage: “Development of methods for systems modeling”)
  • European excellence network BioSapiens : development of standards and methods for the representation and analysis of biological networks in the context of genome annotation.
  • Participation in the BioPAX consortium for the development of a standard of representation of biological networks.

For more information :

Genomes to Life (DOE): the “Genomes to Life” program site of the American Department of Energy provides a large panorama of questions related to function, evolution and the utilization of prokaryotes in which research uses approaches integrating modeling and experimental strategies.

Last update on 1 April 2008

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