Online research in philosophy

Endothelial cells, when cultured on gelled basement membrane matrix exert forces of tension through which they deform the matrix and at the same time they aggregate into clusters. The cells eventually form a network of cord-like structures connecting cell aggregates. In this network, almost all of the matrix has been pulled underneath the cell cords and cell clusters. This phenomenon has been proposed as a possible model for the growth and development of planar vascular systems in vitro. Our hypothesis is that the matrix is reorganized and the cellular networks form as a result of traction forces exerted by the cells on the matrix and the latter's elasticity. We construct and analyze a mathematical model based on this hypothesis and examine conditions necessary for the formation of the pattern. We show cell migration is not necessary for pattern formation and that isotropic, strain-stimulated traction is sufficient to form the observed patterns.

While the control of cell migration by biochemical and biophysical factors is largely documented, a precise quantification of cell migration parameters in different experimental contexts is still questionable. Indeed, these phenomenological parameters can be evaluated from data obtained either at the cell population level or at the individual cell level. However, the range within which both characterizations of cell migration are equivalent remains unclear. We analyse here to which extent both sources of data could be integrated within a unified description of cell migration by considering the motility of the endothelial cell line EAhy926. Using time-lapse video-microscopy and associated analysis of digital image time series, we quantified EAhy926 random motility coefficient, migration speed and trajectory persistence time in two different migration assays: the in vitro wound healing assay, and the cell-populated agarose drop assay. In order to analyse the agreement between independent quantifications of cell motility based either on individual cell analysis or cell population dynamic analysis, a theoretical multi-agents cellular model was developed and discussed as a possible theoretical framework able to unify these multi-scale data. Model simulations especially reveal the potential bias induced by cell proliferation and cell-cell adhesion when cell migration parameters are estimated from the extensively used in vitro wound healing assay.

How environmental mechanical forces affect cellular functions is a central problem in cell biology. Theoretical models of cellular biomechanics provide relevant tools for understanding how the contributions of deformable intracellular components and specific adhesion conditions at the cell interface are integrated for determining the overall balance of mechanical forces within the cell. We investigate here the spatial distributions of intracellular stresses when adherent cells are probed by magnetic twisting cytometry. The influence of the cell nucleus stiffness on the simulated nonlinear torque-bead rotation response is analyzed by considering a finite element multi-component cell model in which the cell and its nucleus are considered as different hyperelastic materials. We additionally take into account the mechanical properties of the basal cell cortex, which can be affected by the interaction of the basal cell membrane with the extracellular substrate. In agreement with data obtained on epithelial cells, the simulated behaviour of the cell model relates the hyperelastic response observed at the entire cell scale to the distribution of stresses and strains within the nucleus and the cytoskeleton, up to cell adhesion areas. These results, which indicate how mechanical forces are transmitted at distant points through the cytoskeleton, are compared to recent data imaging the highly localized distribution of intracellular stresses.

Increasing interest has been paid to applications of fluorescence measurements to analyze physiological mechanisms in living cells. However, few studies have taken advantage of DNA quantification by fluorometry for dynamic assessment of chromatin organization as well as cell motion during the cell cycle. This approach involves both optimal conditions for DNA staining and cell tracking methods. In this context, this report describes a stoichiometric method for nuclear DNA specific staining, using the bisbenzimidazole dye Hoechst 33342 associated with verapamil, a calcium membrane channel blocker. This method makes it possible to correlate variations of nuclear DNA content with cell motion in cells that are maintained alive. Motion measurement is the second goal of this paper and it explains the snake-spline method, and the associated cell following method.

What is biological complexity? How many sorts exist? Are there levels of complexity? How are they related to one another? How is complexity related to the emergence of new phenotypes? To try to get to grips with these questions, we consider the archetype of a complex biological system, Escherichia coli. We take the position that E. coli has been selected to survive adverse conditions and to grow in favourable ones and that many other complex systems undergo similar selection. We invoke the concept of hyperstructures which constitute a level of organisation intermediate between macromolecules and cells. We also invoke a new concept, competitive coherence, to describe how phenotypes are created by a competition between maintaining a consistent story over time and creating a response that is coherent with respect to both internal and external conditions. We suggest how these concepts lead to parameters suitable for describing the rich form of complexity termed hypercomplexity and we propose a relationship between competitive coherence and emergence.

Estimation of the repartition of asynchronous cells in the cell cycle can be explained by two hypotheses:– - the cells are supposed to be distributed into three groups: cells with a 2c DNA content (G0/1 phase), cells with a 4c DNA content (G2+M phase) and cells with a DNA content ranging from 2c to 4c (S phase); – - there is a linear relationship between the amount of fluorescence emitted by the fluorescent probe which reveals the DNA and the DNA content. According to these hypotheses, the cell cycle can be represented by the following equation.

What makes a cell? How are cells able to replicate themselves in a stable manner? How did cellular life emerge on our planet? The answer to these fundamental questions lies at the base of biology. Cellular life is the basic unit of living organization and defines the presence of a stable information reservoir connected through the external world by a well-defined boundary. Inside the cell, chains of computations and chemical reactions take place, sustained by self-assembled molecular machines. At the cell membrane, the environment and the cell communicate with each other through proteins that are able to detect small concentration changes and send the appropriate signals. But modern cells are very sophisticated entities and extracting the logic of cell organization from them is a gigantic effort. In order to answer these questions, we need to look at cells in a more basic level: a reduction of complexity is needed. Protocells, roughly defined as the simplest instances of autonomous cell-like structures, have been a matter of exploration for decades. Although most of the original work in this area was largely theoretical, the end of the twentieth century was marked by very active experimental research on minimal cells. One approach has been the top-down strategy, where living organisms (the simplest life forms) are being selectively modified to reduce their genome complexity. Such a minimal genome would be the smallest one able to sustain reproduction and growth under a given set of external constraints (mainly available molecules). The second approach is the bottom-up one, very much in the tradition of research into the origins of life. Under this approach, extremely simple life forms are built from basic chemical components, including lipids and a basic..

This paper is just a comment to the impressive work by A. C. Ehresmann and J.-P. Vanbremeersch on the theory of Memory Evolutive Systems (MES). MES are truly higher order systems. Hyperstructures represent a new concept which I introduced in order to capture the essence of what a higher order structure is—encompassing hierarchies and emergence. Hyperstructures are motivated by cobordism theory in topology and higher category theory. The morphism concept is replaced by the concept of a bond. In the paper I briefly introduce hyperstructures motivated geometrically and suggest further developments of the MESs along these lines, which could widen up new areas of applications.

We assume the existence of a specific G1 protein which is an initiator of DNA replication. This initiator is supposed to be synthesized according to Michaelis-Menten kinetics. In order to start DNA replication, it is assumed that this G1 specific protein must be produced in a required amount. Intra-cellular growth inhibitors and extra-cellular growth factors control the production of the initiator. This model allows to calculate the average G1 phase time as a function of the various chemical concentrations of nutrients, enzymes, growth inhibitors and growth factors. This model is compared to cell kinetics experiments on a leukemic cell line responding to Interleukin 3 deprivation. The curves giving the experimental average G1 phase times with respect to Interleukin-3 concentrations are fitted by the mathematical model with a quite good agreement.

In the natural sciences higher order structures often occur. There seems to be a need for good methods of describing what we mean by higher order structures in various contexts. This is what hyperstructures are intended to do. We motivate and introduce this new concept. Next we illustrate how it can be applied in various types of genomic analysis—particular the correlations between single nucleotide polymorphisms and diseases. The suggested structure is quite general and may be applied to a variety of situations. Finally we discuss how data sets (f. ex. genomic) may lead to topological spaces, giving new invariants and lead to the prediction of hyperstructures.