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C Reder

Publications and source records attributed to C Reder.

7 recordsLinked to original sources

Mutated mtDNA distribution in exponentially growing cell cultures and how the segregation rate is increased by the mitochondrial compartments.

A cell contains many copies of mitochondrial DNA. The distribution of a mitochondrial gene mutation in a cell culture is governed by the way in which the mtDNA molecules of a cell are replicated and partitioned between the two daughter cells during mitosis. Assuming that this partition process is random, we describe the evolution of the mitochondrial genetic state of a cell culture. The mutated mtDNA is ultimately segregated and the rate of the trend to segregation is relatively slow. It is nevertheless greatly accelerated if the model takes into account the spatial mtDNA partition by the mitochondrial compartments.

Cell Division↗

Why are most flux control coefficients so small?

Experimental values of flux control coefficients are usually small. We examine assumptions about the nature of metabolic networks or of the rate equations that might lead to this result. We show that a general result cannot be obtained and we discuss this question with some examples.

Animals↗

Application of the metabolic control theory to the study of the dynamics of substrate cycles.

Substrate cycles are ubiquitous structures of the cellular metabolism (e.g. Krebs cycle, fatty acids beta-oxydation cycles, etc...). Moiety-conserved cycles (e.g. adenine nucleotides and NADH/NAD, etc...) are also important. The role played by such cycles in the metabolism and its regulation is not clearly understood so far. However, it was shown that these cycles can generate multistationarity (bistability), irreversible transitions, enhancement of sensitivity, temporal oscillations and chaotic motions (Hervagault & Canu, 1987; Hervagault & Cimino, 1989; Reich & Sel'kov, 1981; Ricard & Soulié, 1982). [formula: see text] Fig. 1: Scheme of the open binary substrate cycle under study. The substrate S is converted into P with a net rate v2. Substrate P is converted in turn into S with a net rate v3. Step v2 is inhibited by excess of the substrate, S. In addition, the cycle operates under open conditions, that is zero-order input of S at rates alpha 0(v1) and first order outputs of S and P at rates alpha S and alpha P(v4), respectively. The metabolic control theory (see also Fell, 1990), which shows how a metabolic network reacts to small perturbations in the vicinity of a steady state, and is formulated with the so-called "control coefficients", was applied to such a cycle in order to get a better knowledge on the importance of each step at the regulatory point of view. The behaviour of a binary substrate cycle (fig. 1) in which one of the enzymes may be subjected to inhibition by excess of its substrate (v2) was studied theoretically.(ABSTRACT TRUNCATED AT 250 WORDS)

Models, Biological↗

CONTROL: software for the analysis of the control of metabolic networks.

The program CONTROL is based on metabolic control theory and uses the method developed by Reder (1988). In this theory, two sets of parameters are defined in the vicinity of a steady-state: the elasticity coefficients which describe the local behaviour of the isolated enzymes, and the control coefficients which express the response of the whole metabolic network to perturbations at a given step. The theory shows that relationships exist between the control coefficients (summation relationships or structural relationships) and also between the two types of coefficients (control and elasticity coefficients: connectivity relationships). The program CONTROL is divided into two parts (sub-menus). The first one calculates all the control coefficients (flux and concentrations) of a metabolic network from the elasticity coefficients. Using the second menu, the symbolic relationships are obtained between the control coefficients (summation relationships) and between the control coefficients and the elasticity coefficients (connectivity relationships). These two sub-menus can be applied independently to any metabolic network (to date limited to 19 steps and 19 metabolites).

Algorithms↗

Analysis of multiscale biochemical systems: graph methods.

The applicability of usual biochemical modelling by ordinary differential equations is restricted if some of the metabolite concentrations leave the range of observability during the time span of interest. Therefore, a method is needed for testing whether the solution of a differential equation system remains comparable with given reference concentrations, which depend on the conditions of measurement. In the present paper, such a method based on graph theory and non-standard analysis is developed for the special case of networks of monomolecular reactions endowed with linear kinetics.

Algorithms↗

Metabolic control theory: the geometry of the triangle.

The definitions of flux control coefficients and elasticity coefficients as given in the Metabolic Control Theory are introduced by geometrical considerations. It is shown that a method to determine the (non-normalized) control coefficients from the elasticity coefficients as well as the well-known summation and connectivity relationships can easily be derived in a geometric way. This method is explained in the light of a simple example, but it can be applied to any metabolic system. A software implementation (program "control") and possible applications to biotechnology are briefly presented.

Biotechnology↗

Metabolic control theory: a structural approach.

In the general framework of metabolic control theory, we describe a method of mathematical modelling that provides a way of analysing the sensitivity of a metabolic system to perturbations of the environment or of the internal state of this system. The method can be applied to any metabolic system, involving for instance conservation relationships, non-specific external parameters, etc., and leads in particular to a characterization of the control matrices and to a generalization of the summation and connectivity theorems. In this paper, we emphasize the structural characterizations and properties of the systems which depend only on the structure of the metabolic network, and not on the reaction kinetics. The advantage of this approach lies of course in the fact that the structure of the metabolic network is an invariant of the system which depends neither on the environment nor on the internal state of this system. The aim of this paper is to show the efficiency of such a structural approach.

Kinetics↗