Biomedical subjects

Jean-Marc Schwartz

Publications and source records attributed to Jean-Marc Schwartz.

4 recordsLinked to original sources

PubMed2006

Identification of metabolic units induced by environmental signals.

MOTIVATION: Biological cells continually need to adapt the activity levels of metabolic functions to changes in their living environment. Although genome-wide transcriptional data have been gathered in a large variety of environmental conditions, the connections between the expression response to external changes and the induction or repression of specific metabolic functions have not been investigated at the genome scale. RESULTS: We present here a correlation-based analysis for identifying the expression response of genes involved in metabolism to specific external signals, and apply it to analyze the transcriptional response of Saccharomyces cerevisiae to different stress conditions. We show that this approach leads to new insights about the specificity of the genomic response to given environmental changes, and allows us to identify genes that are particularly sensitive to a unique condition. We then integrate these signal-induced expression data with structural data of the yeast metabolic network and analyze the topological properties of the induced or repressed subnetworks. They reveal significant discrepancies from random networks, and in particular exhibit a high connectivity, allowing them to be mapped back to complete metabolic routes.

Adaptation, Physiological↗

PubMed2006

Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis.

BACKGROUND: Elementary mode analysis of metabolic pathways has proven to be a valuable tool for assessing the properties and functions of biochemical systems. However, little comprehension of how individual elementary modes are used in real cellular states has been achieved so far. A quantitative measure of fluxes carried by individual elementary modes is of great help to identify dominant metabolic processes, and to understand how these processes are redistributed in biological cells in response to changes in environmental conditions, enzyme kinetics, or chemical concentrations. RESULTS: Selecting a valid decomposition of a flux distribution onto a set of elementary modes is not straightforward, since there is usually an infinite number of possible such decompositions. We first show that two recently introduced decompositions are very closely related and assign the same fluxes to reversible elementary modes. Then, we show how such decompositions can be used in combination with kinetic modelling to assess the effects of changes in enzyme kinetics on the usage of individual metabolic routes, and to analyse the range of attainable states in a metabolic system. This approach is illustrated by the example of yeast glycolysis. Our results indicate that only a small subset of the space of stoichiometrically feasible steady states is actually reached by the glycolysis system, even when large variation intervals are allowed for all kinetic parameters of the model. Among eight possible elementary modes, the standard glycolytic route remains dominant in all cases, and only one other elementary mode is able to gain significant flux values in steady state. CONCLUSION: These results indicate that a combination of structural and kinetic modelling significantly constrains the range of possible behaviours of a metabolic system. All elementary modes are not equal contributors to physiological cellular states, and this approach may open a direction toward a broader identification of physiologically relevant elementary modes among the very large number of stoichiometrically possible modes.

Algorithms↗

PubMed2005

A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes.

MOTIVATION: It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions. RESULTS: We introduce an approach for decomposing steady-state flux distributions onto elementary modes based on quadratic programming. The decomposition is performed so as to favour modes that are closest to the actual state of the system, i.e. most relevant for biological interpretation. As an illustration, an application of this approach to a model of yeast glycolysis is presented. AVAILABILITY: Software is available upon request from the authors.

Algorithms↗

PubMed2004

Modelling liver tissue properties using a non-linear visco-elastic model for surgery simulation.

In this work, we introduce an extension of the linear elastic tensor-mass method allowing fast computation of non-linear and visco-elastic mechanical forces and deformations for the simulation of biological soft tissue. We aim at developing a simulation tool for the planning of cryogenic surgical treatment of liver cancer. Percutaneous surgery simulation requires accurate modelling of the mechanical behaviour of soft tissue, and previous experimental characterizations have shown that linear elasticity is only a coarse approximation of the real properties of biological tissues. We first show that our model can simulate different types of non-linear and visco-elastic mechanical behaviours at speeds which are compatible with real-time applications. Then an experimental setup is presented which was used to characterize the mechanical properties of deer liver tissue under perforation by a biopsy needle. Experimental results demonstrate that a linear model is not suitable for simulating this application, while the proposed model succeeds in accurately modelling the axial load measured on the needle.

Animals↗