Asparagine protects pericentral hepatocytes during acute liver injury.

The nonessential amino acid asparagine can only be synthesized de novo by the enzymatic activity of asparagine synthetase (ASNS). While ASNS and asparagine have been implicated in the response to numerous metabolic stressors in cultured cells, the in vivo relevance of this enzyme in stress-related pathways remains unexplored. Here, we found ASNS to be expressed in pericentral hepatocytes, a population of hepatic cells specialized in xenobiotic detoxification. ASNS expression was strongly enhanced in 2 models of acute liver injury: carbon tetrachloride (CCl4) and acetaminophen. We found that mice with hepatocyte-specific Asns deletion were more prone to pericentral liver damage than their control littermates after toxin exposure. This phenotype could be reverted by i.v. administration of asparagine. Unexpectedly, the stress-induced upregulation of ASNS involved an ATF4-independent, noncanonical pathway mediated by the nuclear receptor, liver receptor homolog 1 (LRH-1; NR5A2). Altogether, our data indicate that the induction of the asparagine-producing enzyme ASNS acts as an adaptive mechanism to constrain the necrotic wave that follows toxin administration and provide proof of concept that i.v. delivery of asparagine can dampen hepatotoxin-induced pericentral hepatocellular death.

A circuit-preserving mapping from multilevel to Boolean dynamics.

Many discrete models of biological networks rely exclusively on Boolean variables and many tools and theorems are available for analysis of strictly Boolean models. However, multilevel variables are often required to account for threshold effects, in which knowledge of the Boolean case does not generalise straightforwardly. This motivated the development of conversion methods for multilevel to Boolean models. In particular, Van Ham's method has been shown to yield a one-to-one, neighbour and regulation preserving dynamics, making it the de facto standard approach to the problem. However, Van Ham's method has several drawbacks: most notably, it introduces vast regions of "non-admissible" states that have no counterpart in the multilevel, original model. This raises special difficulties for the analysis of interaction between variables and circuit functionality, which is believed to be central to the understanding of dynamic properties of logical models. Here, we propose a new multilevel to Boolean conversion method, with software implementation. Contrary to Van Ham's, our method doesn't yield a one-to-one transposition of multilevel trajectories; however, it maps each and every Boolean state to a specific multilevel state, thus getting rid of the non-admissible regions and, at the expense of (apparently) more complicated, "parallel" trajectories. One of the prominent features of our method is that it preserves dynamics and interaction of variables in a certain manner. As a demonstration of the usability of our method, we apply it to construct a new Boolean counter-example to the well-known conjecture that a local negative circuit is necessary to generate sustained oscillations. This result illustrates the general relevance of our method for the study of multilevel logical models.

Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation.

MOTIVATION: Understanding the temporal behaviour of biological regulatory networks requires the integration of molecular information into a formal model. However, the analysis of model dynamics faces a combinatorial explosion as the number of regulatory components and interactions increases. RESULTS: We use model-checking techniques to verify sophisticated dynamical properties resulting from the model regulatory structure in the absence of kinetic assumption. We demonstrate the power of this approach by analysing a logical model of the molecular network controlling mammalian cell cycle. This approach enables a systematic analysis of model properties, the delineation of model limitations, and the assessment of various refinements and extensions based on recent experimental observations. The resulting logical model accounts for the main irreversible transitions between cell cycle phases, the sequential activation of cyclins, and the inhibitory role of Skp2, and further emphasizes the multifunctional role for the cell cycle inhibitor Rb. AVAILABILITY AND IMPLEMENTATION: The original and revised mammalian cell cycle models are available in the model repository associated with the public modelling software GINsim (http://ginsim.org/node/189). CONTACT: thieffry@ens.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A discrete model of Drosophila eggshell patterning reveals cell-autonomous and juxtacrine effects.

The Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.

Identification of key regulators in glycogen utilization in E. coli based on the simulations from a hybrid functional Petri net model.

BACKGROUND: Glycogen and glucose are two sugar sources available during the lag phase of E. coli, but the mechanism that regulates their utilization is still unclear. METHODS: Attempting to unveil the relationship between glucose and glycogen, we propose an integrated hybrid functional Petri net (HFPN) model including glycolysis, PTS, glycogen metabolic pathway, and their internal regulatory systems. RESULTS AND CONCLUSIONS: By comparing known biological results to this model, basic necessary regulatory mechanism for utilizing glucose and glycogen were identified as a feedback circuit in which HPr and EIIAGlc play key roles. Based on this regulatory HFPN model, we discuss the process of glycogen utilization in E. coli in the context of a systematic understanding of carbohydrate metabolism.

Glycogen is the primary source of glucose during the lag phase of E. coli proliferation.

In the studies of Escherichia coli (E. coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, we performed a profiling of concentration of metabolites by using batch culture. As a first step, we focused on glucose uptake and the behavior of the first metabolite, G6P (glucose-6-phosphate). A computational formula was derived to express the glucose uptake rate by a single cell from two kinds of experimental data, extracellular glucose concentration and cell growth, being simulated by Cell Illustrator. In addition, average concentration of G6P has been measured by CE-MS. The existence of another carbon source was suggested from the computational result. After careful comparison between cell growth, G6P concentration, and the computationally obtained curve of glucose uptake rate, we predicted the consumption of glycogen in lag phase and its accumulation as an energy source in an E. coli cell for the next proliferation. We confirmed our prediction experimentally. This behavior indicates the importance of glycogen participation in the lag phase for the growth of E. coli. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

Modular logical modelling of the budding yeast cell cycle.

Systems biologists are facing the difficult challenge of modelling and analysing regulatory networks encompassing numerous and diverse components and interactions. Furthermore, available data sets are often qualitative, which complicates the definition of truly quantitative models. In order to build comprehensive and predictive models, there is clearly a need for incremental strategies, enabling the progression from relatively small to large scale models. Leaning on former models, we have defined a logical model for three regulatory modules involved in the control of the mitotic cell cycle in budding yeast, namely the core cell cycle module, the morphogenetic checkpoint, and a module controlling the exit from mitosis. Consistency with available data has been assessed through a systematic analysis of model behaviours for various genetic backgrounds and other perturbations. Next, we take advantage of compositional facilities of the logical formalism to combine these three models in order to generate a single comprehensive model involving over thirty regulatory components. The resulting logical model preserves all relevant characteristics of the original modules, while enabling the simulation of more sophisticated experiments.

Logical modelling of cell cycle control in eukaryotes: a comparative study.

Dynamical modelling is at the core of the systems biology paradigm. However, the development of comprehensive quantitative models is complicated by the daunting complexity of regulatory networks controlling crucial biological processes such as cell division, the paucity of currently available quantitative data, as well as the limited reproducibility of large-scale experiments. In this context, qualitative modelling approaches offer a useful alternative or complementary framework to build and analyse simplified, but still rigorous dynamical models. This point is illustrated here by analysing recent logical models of the molecular network controlling mitosis in different organisms, from yeasts to mammals. After a short introduction covering cell cycle and logical modelling, we compare the assumptions and properties underlying these different models. Next, leaning on their transposition into a common logical framework, we compare their functional structure in terms of regulatory circuits. Finally, we discuss assets and prospects of qualitative approaches for the modelling of the cell cycle.

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle.

MOTIVATION: To understand the behaviour of complex biological regulatory networks, a proper integration of molecular data into a full-fledge formal dynamical model is ultimately required. As most available data on regulatory interactions are qualitative, logical modelling offers an interesting framework to delineate the main dynamical properties of the underlying networks. RESULTS: Transposing a generic model of the core network controlling the mammalian cell cycle into the logical framework, we compare different strategies to explore its dynamical properties. In particular, we assess the respective advantages and limits of synchronous versus asynchronous updating assumptions to delineate the asymptotical behaviour of regulatory networks. Furthermore, we propose several intermediate strategies to optimize the computation of asymptotical properties depending on available knowledge. AVAILABILITY: The mammalian cell cycle model is available in a dedicated XML format (GINML) on our website, along with our logical simulation software GINsim (http://gin.univ-mrs.fr/GINsim). Higher resolution state transitions graphs are also found on this web site (Model Repository page).