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D Thieffry

Publications and source records attributed to D Thieffry.

26 records · Page 2Linked to original sources
PubMed1997

Definite-clause grammars for the analysis of cis-regulatory regions in E. coli.

Based on an extensive collection of sigma 70 associated regulatory mechanisms, a grammatical model has been constructed that define the functional positions and combinations of sites within DNA regulatory regions. The syntactic rules and the dictionary implemented in a Prolog program were coupled to consensus matrices used as "sensors" to integrate a syntactic recognizer. A systematic comparison between the syntactic recognizer and the standard weight matrix methodology is presented using 12 regulatory proteins and the whole collection of about 130 sigma 70 DNA regulatory regions. On the average an increased sensitivity of 5 to 10 fold is obtained with this novel approach.

Binding Sites↗
PubMed1996

Toxicity and neuroendocrine regulation of the immune response: a model analysis.

Various models have been proposed for the regulation of the primary immune response. Most of the models focus on the ability of the immune system to control a multiplying pathogen, and take into account the cross-regulations between different immune components. In the present study, we integrate the immune system in the general physiology of the host and consider the interaction between the immune and neuroendocrine systems. In addition to pathogen growth and toxicity, our four-variable model takes into account the toxic consequences for the organism of the immune response itself, as well as a neuro-hormonal retro-control of this immune response. Formally, the dynamics of the model is first explored on the basis of a discrete caricature, with special emphasis on the role of the constitutive feedback loops for determining the essential dynamical behavior of the system. This logical analysis is then completed by a classical continuous approach using differential equations. From a biological point of view, our model accounts for four stable regimes which can be described as "pathogen elimination/organism healthy", "pathogen elimination/ organism death", "pathogen growth/organism death" and "chronic infection". The size of the basins of attraction of these different regimes varies as a function of some crucial parameters. Our model allows moreover to interpret the interplay between pathogen immunogenicity and neuro-hormonal feedback, the effects of stress on immunity and the toxic shock syndrome, in terms of transitions among the steady states.

Animals↗
PubMed1996

Syntactic recognition of regulatory regions in Escherichia coli.

MOTIVATION: One of the most common methodologies to identify cis-regulatory sites in regulatory regions in the DNA is that of weight matrices, as testified by several articles in this issue. An alternative to strengthen the computational predictions in regulatory regions is to develop methods that incorporate more biological properties present in such DNA regions. The grammatical implementation presented in this paper provides a concrete example in this direction. RESULTS: On the basis of the analysis of an exhaustive collection of regulatory regions in Escherichia coli, a grammatical model for the regulatory regions of sigma 70 promoters has been developed. The terminal symbols of the grammar represent individual sites for the binding of activator and repressor proteins, and include the precise position of sites in relation to transcription initiation. Combining these symbols, the grammar generates a large number of different sentences, each of which can be searched for matching against a collection of regulatory regions by means of weight matrices specific for each set of sites for individual proteins. On the basis of this grammatical model, a Prolog syntactic recognizer is presented here. Specific subgrammars for ArgR, LexA and TyrR were implemented. When parsing a collection of 128 sigma 70 promoter regions, the syntactic recognizer produces a much lower number of false-positive sites than the standard search using weight matrices.

Algorithms↗
PubMed1996

Escherichia coli as a model system with which to study cell differentiation.

This article concerns the elaboration of epigenetic models for differentiation. I discuss how results and conclusions arising from studies of prokaryotes were extrapolated to explain differentiation during metazoan development. In this respect, I focus on the presentation of a multi-stable biochemical model by Delbrück in 1949, and on a series of works dealing with enzyme adaptation in Escherichia coli that culminated in Jacob and Monod's operon model. These influential contributions are discussed in the context of debates on nuclear versus cytoplasmic heredity, on the regulation of gene expression, as well as on the mechanisms at the basis of cell differentiation.

Cell Differentiation↗
PubMed1995

Dynamical behaviour of biological regulatory networks--I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state.

In the field of biological regulation, models dictated by experimental work are usually complex networks comprising intertwined feedback loops. In this paper the biological roles of individual positive loops (multistationarity, differentiation) and negative loops (homeostasis, with or without oscillations, buffering of gene dosage effect) are discussed. The relationship between feedback loops and steady states is then clarified, and the problem: "How can one conveniently disentangle complex networks?" is then considered. Initiated long ago, logical descriptions have been generalized from various viewpoints; these developments are briefly discussed. The recent concept of the loop-characteristic state, defined as the logical state located at the level of the thresholds involved in the loop, together with its application, are then presented. Biological applications are also discussed.

Arginine↗
PubMed1995

Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda.

A number of bacterial and viral genes take part in the decision between lysis and lysogenization in temperate bacteriophages. In the lambda case, at least five viral genes (cI, cro, cII, N and cIII) and several bacterial genes are involved. Several attempts have been made to model this complex regulatory network. Our approach is based on a logical method described in the first paper of the series which formalizes the interactions between the elements of a regulatory network in terms of discrete variables, functions and parameters. In this paper two models are described and discussed, the first (two-variable model) focused on cI and cro interactions, the second (four-variable model) considering, in addition, genes cII and N. The treatment presented emphasizes the roles of positive and negative feedback loops and their interactions in the development of the phage. The role of the loops between cI and cro, and of cI on itself (which both have to be positive loops) was discovered earlier; this group's contribution to this aspect mainly deals with the possibility of treating these loops as parts of a more extended network. In contrast, the role of the negative loop of cro on itself had apparently remained unexplained. We realized that this loop buffers the expression of genes cro itself. cII, O and P against the inflation due to the rapid replication of the phage. More generally, negative auto-control of a gene appears an efficient way to render its expression insensitive (or less sensitive) to gene dosage, whereas a simple negative control would not provide this result.

Bacteria↗
PubMedDate not supplied

Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis.

MOTIVATION: A large number of molecular mechanisms at the basis of gene regulation have been described during the last few decades. It is now becoming possible to address questions dealing with both the structure and the dynamics of genetic regulatory networks, at least in the case of some of the best-characterized organisms. Most recent attempts to address these questions deal with microbial or animal model systems. In contrast, we analyze here a gene network involved in the control of the morphogenesis of flowers in a model plant, Arabidopsis thaliana. RESULTS: The genetic control of flower morphogenesis in Arabidopsis involves a large number of genes, of which 10 are considered here. The network topology has been derived from published genetic and molecular data, mainly relying on mRNA expression patterns under wild-type and mutant backgrounds. Using a 'generalized logical formalism', we provide a qualitative model and derive the parameter constraints accounting for the different patterns of gene expression found in the four floral organs of Arabidopsis (sepals, petals, stamens and carpels), plus a 'non-floral' state. This model also allows the simulation or the prediction of various mutant phenotypes. On the basis of our model analysis, we predict the existence of a sixth stable pattern of gene expression, yet to be characterized experimentally. Moreover, our dynamical analysis leads to the prediction of at least one more regulator of the gene LFY, likely to be involved in the transition from the non-flowering state to the flowering pathways. Finally, this work, together with other theoretical and experimental considerations, leads us to propose some general conclusions about the structure of gene networks controlling development.

Arabidopsis↗