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Biomedical subjects

Philippe Tracqui

Publications and source records attributed to Philippe Tracqui.

3 recordsLinked to original sources

Critical conditions for pattern formation and in vitro tubulogenesis driven by cellular traction fields.

In vitro angiogenesis assays have shown that tubulogenesis of endothelial cells within biogels, like collagen or fibrin gels, only appears for a critical range of experimental parameter values. These experiments have enabled us to develop and validate a theoretical model in which mechanical interactions of endothelial cells with extracellular matrix influence both active cell migration--haptotaxis--and cellular traction forces. Depending on the number of cells, cell motility and biogel rheological properties, various 2D endothelial patterns can be generated, from non-connected stripe patterns to fully connected networks, which mimic the spatial organization of capillary structures. The model quantitatively and qualitatively reproduces the range of critical values of cell densities and fibrin concentrations for which these cell networks are experimentally observed. We illustrate how cell motility is associated to the self-enhancement of the local traction fields exerted within the biogel in order to produce a pre-patterning of this matrix and subsequent formation of tubular structures, above critical thresholds corresponding to bifurcation points of the mathematical model. The dynamics of this morphogenetic process is discussed in the light of videomicroscopy time lapse sequences of endothelial cells (EAhy926 line) in fibrin gels. Our modeling approach also explains how the progressive appearance and morphology of the cellular networks are modified by gradients of extracellular matrix thickness.

Biomechanical Phenomena↗

Cytomechanics of cell deformations and migration: from models to experiments.

A cytomechanical model bas been proposed to analyse cell-cell interactions and cell migration through chemotaxis. We consider as the leading assumption that the cell cortical tension is locally modified by the protrusive activity of neighbour cells and binding of chemoattractant molecules to membrane receptors respectively. The model derives from the one initially proposed by Alt and Tranquillo (1995), which successfully describes experimentally observed cyclic autonomous cell shape changes. It is based on force balance equations coupling intracellular hydrostatic pressure and cell cortex contraction. Considering the protrusive dynamics of L929 fibroblats observed by videomicroscopy, we simulated the influence of neighbouring protrusions on a cell spontaneous pulsating behaviour. We further investigated the role of an extracellular gradient as another kind of external stimulus. The model illustrates how binding of chemoattractant molecules can induce a cell morphological instability that, above an intracellular stress threshold, will break the cell-substratum attachment. As a result, realistic cell chemotaxis can be simulated.

Animals↗

Hyperstructures, genome analysis and I-cells.

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.

Algorithms↗