Eric Fanchon

Crystal structure of UDP-N-acetylmuramoyl-L- alanine:D-glutamate ligase from Escherichia coli

UDP‐N‐acetylmuramoyl‐L‐alanine:D‐glutamate ligase (MurD) is a cytoplasmic enzyme involved in the biosynthesis of peptidoglycan which catalyzes the addition of D‐glutamate to the nucleotide precursor UDP‐N‐acetylmuramoyl‐L‐alanine (UMA). The crystal structure of MurD in the presence of its substrate UMA has been solved to 1.9 Å resolution. Phase information was obtained from multiple anomalous dispersion using the K‐shell edge of selenium in combination with multiple isomorphous replacement. The structure comprises three domains of topology each reminiscent of nucleotide‐binding folds: the N‐ and C‐terminal domains are consistent with the dinucleotide‐binding fold called the Rossmann fold, and the central domain with the mononucleotide‐binding fold also observed in the GTPase family. The structure reveals the binding site of the substrate UMA, and comparison with known NTP complexes allows the identification of residues interacting with ATP. The study describes the first structure of the UDP‐N‐acetylmuramoyl‐peptide ligase family.

Refined crystal structure of the 2[4Fe-4S] ferredoxin from Clostridium acidurici at 1.84 Å resolution

The crystal structure of the 2[4Fe-4S] ferredoxin from Clostridium acidurici has been determined at a resolution of 1.84 A and refined to an R-factor of 0.169. Crystals belong to space group P4(3)2(1)2 with unit cell dimensions a = b = 34.44 A and c = 74.78 A. The structure was determined by molecular replacement using the previously published model of an homologous ferredoxin and refined by molecular dynamics techniques. The model contains the protein and 46 water molecules. Only two amino acid residues, Asp27 and Asp28, are poorly defined in the electron density maps. The molecule has an overall chain fold similar to that of other [4Fe-4S] bacterial ferredoxins of known structure. The two [4Fe-4S] clusters display similar bond distances and angles. In both of them the co-ordination of one iron atom (bound to Cys11 and Cys40) is slightly distorted as compared with that of the other iron atoms. A core of hydrophobic residues and a few water molecules contribute to the stability of the structure. The [4Fe-4S] clusters interact with the polypeptide chain through eight hydrogen bonds each, in addition to the covalent Fe-Scys bonds. The ferredoxin from Clostridium acidurici is the most typical clostridial ferredoxin crystallized so far and the biological implications of the newly determined structure are discussed.

Robustness Analysis and Behavior Discrimination in Enzymatic Reaction Networks

Characterizing the behavior and robustness of enzymatic networks with numerous variables and unknown parameter values is a major challenge in biology, especially when some enzymes have counter-intuitive properties or switch-like behavior between activation and inhibition. In this paper, we propose new methodological and tool-supported contributions, based on the intuitive formalism of temporal logic, to express in a rigorous manner arbitrarily complex dynamical properties. Our multi-step analysis allows efficient sampling of the parameter space in order to define feasible regions in which the model exhibits imposed or experimentally observed behaviors. In a first step, an algorithmic methodology involving sensitivity analysis is conducted to determine bifurcation thresholds for a limited number of model parameters or initial conditions. In a second step, this boundary detection is supplemented by a global robustness analysis, based on quasi-Monte Carlo approach that takes into account all model parameters. We apply this method to a well-documented enzymatic reaction network describing collagen proteolysis by matrix metalloproteinase MMP2 and membrane type 1 metalloproteinase (MT1-MMP) in the presence of tissue inhibitor of metalloproteinase TIMP2. For this model, our method provides an extended analysis and quantification of network robustness toward paradoxical TIMP2 switching activity between activation or inhibition of MMP2 production. Further implication of our approach is illustrated by demonstrating and analyzing the possible existence of oscillatory behaviors when considering an extended open configuration of the enzymatic network. Notably, we construct bifurcation diagrams that specify key parameters values controlling the co-existence of stable steady and non-steady oscillatory proteolytic dynamics.

FIP: a highly automated beamline for multiwavelength anomalous diffraction experiments

FIP is a French Collaborating Research Group (CRG) beamline at the European Synchrotron Radiation Facility (ESRF) dedicated exclusively to crystallography of biological macromolecules, with a special emphasis on multiwavelength anomalous diffraction data collection in the 0.7-1.81 A wavelength range. The optics, consisting of long cylindrical grazing-angle mirrors associated with a cryocooled double-crystal monochromator, delivers an optimal beam in the corresponding energy range. The high level of automation, which includes automated crystal centring, automated data-collection management and data processing, makes the use of this beamline very easy. This is illustrated by the large number of challenging structures that have been solved since 1999.

Applications of a formal approach to decipher discrete genetic networks.

BackgroundA growing demand for tools to assist the building and analysis of biological networks exists in systems biology. We argue that the use of a formal approach is relevant and applicable to address questions raised by biologists about such networks. The behaviour of these systems being complex, it is essential to exploit efficiently every bit of experimental information. In our approach, both the evolution rules and the partial knowledge about the structure and the behaviour of the network are formalized using a common constraint-based language.ResultsIn this article our formal and declarative approach is applied to three biological applications. The software environment that we developed allows to specifically address each application through a new class of biologically relevant queries. We show that we can describe easily and in a formal manner the partial knowledge about a genetic network. Moreover we show that this environment, based on a constraint algorithmic approach, offers a wide variety of functionalities, going beyond simple simulations, such as proof of consistency, model revision, prediction of properties, search for minimal models relatively to specified criteria.ConclusionsThe formal approach proposed here deeply changes the way to proceed in the exploration of genetic and biochemical networks, first by avoiding the usual trial-and-error procedure, and second by placing the emphasis on sets of solutions, rather than a single solution arbitrarily chosen among many others. Last, the constraint approach promotes an integration of model and experimental data in a single framework.

A declarative constraint-based method for analyzing discrete genetic regulatory networks

Dynamical modeling has proven useful for understanding how complex biological processes emerge from the many components and interactions composing genetic regulatory networks (GRNs). However, the development of models is hampered by large uncertainties in both the network structure and parameter values. To remedy this problem, the models are usually developed through an iterative process based on numerous simulations, confronting model predictions with experimental data and refining the model structure and/or parameter values to repair the inconsistencies. In this paper, we propose an alternative to this generate-and-test approach. We present a four-step method for the systematic construction and analysis of discrete models of GRNs by means of a declarative approach. Instead of instantiating the models as in classical modeling approaches, the biological knowledge on the network structure and its dynamics is formulated in the form of constraints. The compatibility of the network structure with the constraints is queried and in case of inconsistencies, some constraints are relaxed. Common properties of the consistent models are then analyzed by means of dedicated languages. Two such languages are introduced in the paper. Removing questionable constraints or adding interesting ones allows to further analyze the models. This approach allows to identify the best experiments to be carried out, in order to discriminate sets of consistent models and refine our knowledge on the system functioning. We test the feasibility of our approach, by applying it to the re-examination of a model describing the nutritional stress response in the bacterium Escherichia coli.

Modeling the molecular network controlling adhesion between human endothelial cells: inference and simulation using constraint logic programming

Cell-cell adhesion plays a critical role in the formation of tissues and organs. Adhesion between endothelial cells is also involved in the control of leukocyte migration across the endothelium of blood vessels. The most important players in this process are probably identified and the overall organization of the biochemical network can be drawn, but knowledge about connectivity is still incomplete, and the numerical values of kinetic parameters are unknown. This calls for qualitative modeling methods. Our aim in this paper is twofold: (i) to integrate in a unified model the biochemical network and the genetic circuitry. For this purpose we transform our system into a system of piecewise linear differential equations and then use Thomas theory of discrete networks. (ii) to show how constraints can be used to infer ranges of parameter values from observations and, with the same model, perform qualitative simulations.

Computing with bacterial constituents, cells and populations: from bioputing to bactoputing

The relevance of biological materials and processes to computing—aliasbioputing—has been explored for decades. These materials include DNA, RNA and proteins, while the processes include transcription, translation, signal transduction and regulation. Recently, the use of bacteria themselves as living computers has been explored but this use generally falls within the classical paradigm of computing. Computer scientists, however, have a variety of problems to which they seek solutions, while microbiologists are having new insights into the problems bacteria are solving and how they are solving them. Here, we envisage that bacteria might be used for new sorts of computing. These could be based on the capacity of bacteria to grow, move and adapt to a myriad different fickle environments both as individuals and as populations of bacteria plus bacteriophage. New principles might be based on the way that bacteria explore phenotype space via hyperstructure dynamics and the fundamental nature of the cell cycle. This computing might even extend to developing a high level language appropriate to using populations of bacteria and bacteriophage. Here, we offer a speculative tour of what we term bactoputing, namely the use of the natural behaviour of bacteria for calculating.

Reduction and Fixed Points of Boolean Networks and Linear Network Coding Solvability

Linear network coding transmits data through networks by letting the intermediate nodes combine the messages they receive and forward the combinations toward their destinations. The solvability problem asks whether the demands of all the destinations can be simultaneously satisfied by using linear network coding. The guessing number approach converts this problem into determining the number of fixed points of coding functions f : An → An over a finite alphabet A (usually referred to as Boolean networks if A = {0, 1}) with a given interaction graph that describes which local functions depend on which variables. In this paper, we generalize the so-called reduction of coding functions in order to eliminate variables. We then determine the maximum number of fixed points of a fully reduced coding function, whose interaction graph has a loop on every vertex. Since the reduction preserves the number of fixed points, we then apply these ideas and results to obtain four main results on the linear network coding solvability problem. First, we prove that non-decreasing coding functions cannot solve any more instances than routing already does. Second, we show that the triangle-free undirected graphs are linearly solvable if and only if they are solvable by routing. This is the first classification result for the linear network coding solvability problem. Third, we exhibit a new class of non-linearly solvable graphs. Fourth, we determine large classes of strictly linearly solvable graphs.

Iron for proliferation of cell lines and hematopoietic progenitors: Nailing down the intracellular functional iron concentration.

Iron is an essential nutrient which must be provided in sufficient amounts to support growth of eukaryotic cells. All organisms devote specialized pathways to ensure proper delivery. Yet, a quantitative assessment of the intra-cellular iron concentration needed to allow the cell cycle to proceed in mammalian cells is missing. Starting from iron-depleted cell lines or primary hematopoietic progenitors prepared with clinically implemented iron chelators, replenishment via transferrin and other iron sources has been quantitatively monitored through the main endogenous markers of the cellular iron status, namely proteins involved in the uptake (transferrin receptor), the storage (ferritin), and the sensing (Iron Regulatory Proteins) of iron. When correlated with measurements of iron concentrations and indicators of growth, this minimally intrusive approach provided an unprecedented estimate of the intracellular iron concentration acting upon iron-centered regulatory pathways. The data were analyzed with the help of a previously developed theoretical treatment of cellular iron regulation. The minimal cellular iron concentration required for cell division was named functional iron concentration (FIC) to distinguish it from previous estimates of the cellular labile iron. The FIC falls in the low nanomolar range for all studied cells, including hematopoietic progenitors. These data shed new light on basic aspects of cellular iron homeostasis by demonstrating that sensing and regulation of iron occur well below the concentrations requiring storage or becoming noxious in pathological conditions. The quantitative assessment provided here is relevant for monitoring treatments of conditions in which iron provision must be controlled to avoid unwanted cellular proliferation.