Jean-Marc Schwartz

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.

Cytokinesis mediated through the recruitment of cortexillins into the cleavage furrow.

The fact that substrate‐anchored Dictyostelium cells undergo cytokinesis in the absence of myosin II underscores the importance of other proteins in enabling the cleavage furrow to constrict. Cortexillins, a pair of actin‐bundling proteins, are required for normal cleavage. They are targeted to the incipient furrow in wild‐type and, more prominently, in myosin II‐null cells. No other F‐actin bundling or cross‐linking protein tested is co‐localized. Green fluorescent protein fusions show that the N‐terminal actin‐binding domain of cortexillin I is dispensable and the C‐terminal region is sufficient for translocation to the furrow and the rescue of cytokinesis. Cortexillins are suggested to have a targeting signal for coupling to a myosin II‐independent system that directs transport of membrane proteins to the cleavage furrow.

Dynein motor regulation stabilizes interphase microtubule arrays and determines centrosome position.

Cytoplasmic dynein is a microtubule‐based motor protein responsible for vesicle movement and spindle orientation in eukaryotic cells. We show here that dynein also supports microtubule architecture and determines centrosome position in interphase cells. Overexpression of the motor domain in Dictyostelium leads to a collapse of the interphase microtubule array, forming loose bundles that often enwrap the nucleus. Using green fluorescent protein (GFP)–α‐tubulin to visualize microtubules in live cells, we show that the collapsed arrays remain associated with centrosomes and are highly motile, often circulating along the inner surface of the cell cortex. This is strikingly different from wild‐type cells where centrosome movement is constrained by a balance of tension on the microtubule array. Centrosome motility involves force‐generating microtubule interactions at the cortex, with the rate and direction consistent with a dynein‐mediated mechanism. Mapping the overexpression effect to a C‐terminal region of the heavy chain highlights a functional domain within the massive sequence important for regulating motor activity.

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

BackgroundElementary 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.ResultsSelecting 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.ConclusionThese 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.

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

BackgroundGenome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources.ResultsIn this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation.ConclusionThis semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications.

A global view of drug-therapy interactions

BackgroundNetwork science is already making an impact on the study of complex systems and offers a promising variety of tools to understand their formation and evolution in many disparate fields from technological networks to biological systems. Even though new high-throughput technologies have rapidly been generating large amounts of genomic data, drug design has not followed the same development, and it is still complicated and expensive to develop new single-target drugs. Nevertheless, recent approaches suggest that multi-target drug design combined with a network-dependent approach and large-scale systems-oriented strategies create a promising framework to combat complex multi-genetic disorders like cancer or diabetes.ResultsWe here investigate the human network corresponding to the interactions between all US approved drugs and human therapies, defined by known relationships between drugs and their therapeutic applications. Our results show that the average paths in this drug-therapy network are shorter than three steps, indicating that distant therapies are separated by a surprisingly low number of chemical compounds. We also identify a sub-network composed by drugs with high centrality measures in the drug-therapy network, which represent the structural backbone of this system and act as hubs routing information between distant parts of the network.ConclusionThese findings provide for the first time a global map of the large-scale organization of all known drugs and associated therapies, bringing new insights on possible strategies for future drug development. Special attention should be given to drugs which combine the two properties of (a) having a high centrality value in the drug-therapy network and (b) acting on multiple molecular targets in the human system.

Observing metabolic functions at the genome scale

BackgroundHigh-throughput techniques have multiplied the amount and the types of available biological data, and for the first time achieving a global comprehension of the physiology of biological cells has become an achievable goal. This aim requires the integration of large amounts of heterogeneous data at different scales. It is notably necessary to extend the traditional focus on genomic data towards a truly functional focus, where the activity of cells is described in terms of actual metabolic processes performing the functions necessary for cells to live.ResultsIn this work, we present a new approach for metabolic analysis that allows us to observe the transcriptional activity of metabolic functions at the genome scale. These functions are described in terms of elementary modes, which can be computed in a genome-scale model thanks to a modular approach. We exemplify this new perspective by presenting a detailed analysis of the transcriptional metabolic response of yeast cells to stress. The integration of elementary mode analysis with gene expression data allows us to identify a number of functionally induced or repressed metabolic processes in different stress conditions. The assembly of these elementary modes leads to the identification of specific metabolic backbones.ConclusionThis study opens a new framework for the cell-scale analysis of metabolism, where transcriptional activity can be analyzed in terms of whole processes instead of individual genes. We furthermore show that the set of active elementary modes exhibits a highly uneven organization, where most of them conduct specialized tasks while a smaller proportion performs multi-task functions and dominates the general stress response.

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.

Golvesin-GFP fusions as distinct markers for Golgi and post- Golgi vesicles in Dictyostelium cells

Golvesin is a new protein associated with membranes of the Golgi apparatus and post‐Golgi vesicles in Dictyostelium cells. An internal hydrophobic sequence of 24 amino‐acid residues is responsible for anchoring golvesin to the membranes of these organelles. In an attempt to visualize organelle dynamics in vivo, we have used specific antibody and other labels to localize golvesin—green fluorescent protein (GFP) constructs to different cellular compartments. With a GFP tag at its N‐terminus, golvesin shows the same localization as the untagged protein. It is transferred to two post‐Golgi compartments, the endosomal and contractile vacuole systems. Endosomes are decorated with GFP—golvesin within less than 10 min of their internalisation, and keep the label during the acidic phase of the pathway.

Modularity in protein complex and drug interactions reveals new polypharmacological properties.

Recent studies have highlighted the importance of interconnectivity in a large range of molecular and human disease-related systems. Network medicine has emerged as a new paradigm to deal with complex diseases. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex system. We here present the first systematic and comprehensive set of relationships between protein complexes and associated drugs and analyzed their topological features. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections, indicating that its topology is highly distinct from a random network and that it contains a rich and heterogeneous internal modular structure. To unravel the relationships between modules of protein complexes, drugs and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases driven by gain-of-function mutations.