Studies on the Biosynthesis of the Stephacidin and Notoamide Natural Products: A Stereochemical and Genetic Conundrum

The stephacidin and notoamide natural products belong to a group of prenylated indole alkaloids containing a bicyclo[2.2.2]diazaoctane core. Biosynthetically, this bicyclic core is believed to be the product of an intermolecular Diels–Alder (IMDA) cycloaddition of an achiral azadiene. Since all of the natural products in this family have been isolated in enantiomerically pure form to date, it is believed that an elusive Diels–Alderase enzyme mediates the IMDA reaction. Adding further intrigue to this biosynthetic puzzle is the fact that several related Aspergillus fungi produce a number of metabolites with the opposite absolute configuration, implying that these fungi have evolved enantiomerically distinct Diels–Alderases. We have undertaken a program to identify every step in the biogenesis of the stephacidins and notoamides, and by combining the techniques of chemical synthesis and biochemical analysis we have been able to identify the two prenyltransferases involved in the early stages of the stephacidin and notoamide biosyntheses. This has allowed us to propose a modified biosynthesis for stephacidin A, and has brought us closer to our goal of finding evidence for, or against, the presence of a Diels–Alderase in this biosynthetic pathway.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83757/1/442_ftp.pd

Knowledge-based generalization of metabolic networks: a practical study

International audienceThe complex process of genome-scale metabolic network reconstruction involves semi- automatic reaction inference, analysis, and refinement through curation by human experts. Unfortunately, decisions by experts are hampered by the complexity of the network, which can mask errors in the inferred network. In order to aid an expert in making sense out of the thousands of reactions in the organism's metabolism, we developed a method for knowledge-based generalization that provides a higher-level view of the network, highlighting the particularities and essential structure, while hiding the details. In this study, we show the application of this generalization method to 1286 metabolic networks of organisms in Path2Models that describe fatty acid metabolism. We compare the generalized networks and show that we successfully highlight the aspects that are important for their curation and comparison

Dériver des associations de gène pour des modèles métaboliques

We define a formal procedure for inferring gene-protein-reaction (GPR) relations from complete metabolic models, using a logical representation of knowledge and a small set of inference rules. We show that different use cases of metabolic models requires difference GPR relations. Three examples from the yeast Saccharomyces cerevisae illustrate the procedure and demonstrate its usefulness.Nous définissons une procédure formelle pour l’inférence de relations gène-protéine- réaction (GPR) à partir de modèles métaboliques complets, par moyen d’une représentation logique des connaissances et un petit ensemble de règles d’inférence. Nous montrons que les différents cas d’utilisation requièrent des différentes relations GPR. Trois exemples tirés de la levure Saccharomyces cerevisae illustrent la procédure et démontrent son utilité

Knowledge-based generalization of metabolic networks: An applicational study

International audienceGenome-scale metabolic networks are complex systems that describe thousands of reactions participating in the organism's metabolism. During the process of genome network reconstruction these reactions are automatically inferred from pathway and reaction databases, and existing models for similar organisms, using genomic data [1]. The inferred draft network is then refined during several iterations of error detection, gap filling, analysis and improvement [2]. Although automatic tools for model inference and analysis are becoming more and more powerful, they may still miss some reactions or add those ones that should not belong to the network of the target organism. That is why the analysis by a human expert is needed during the network refinement process. However, being tailored for a computer simulation, and thus including all the reactions thought to participate in the organism's metabolism, genome-scale networks can be too complicated and detailed for a human. The errors may be hidden in the multitude of reactions. To help a human expert understanding these detailed networks, we developed a method for knowledge-based generalization that focusses on the higher-level relationships in the network, while omitting the details [3]. The generalization process groups chemical species present in the network into semantically equivalent classes, based on their hierarchical relationships in the ChEBI ontology [4], and merges them into a generalized chemical species. For instance, butyryl-CoA, hexanoyl-CoA and octanoyl-CoA species can be generalized into fatty acyl-CoA. After the species generalization, reactions that share the same generalized reactants and the same generalized products, are factored together into a generalized reaction. This provides a higher-level view of the network. In this poster, we show the application of this generalization method to the network of the yeast Y. lypolitica [5]. We analyze the generalized network, and illustrate how it can be used for easier error detection: We show the changes that both initial and generalized networks undergo if the catalyzing enzyme for some of the reactions is missing. The application of the generalization procedure also facilitates network comparison, which we show by comparing the generalized Y. lypolitica network to the networks of several other organisms

Mixed-formalism hierarchical modeling and simulation with BioRica

Poster présenté également lors des JOBIM / Journées ouvertes biologie informatique mathématiques ; 7 au 9 septembre 2010 ; Montpellier http://www.jobim2010.fr/?q=frInternational audienceBackground : A recurring challenge for in silico modeling of cell behavior is that experimentally validated models are so focused in scope that it is difficult to repurpose them. Hierarchical modeling is one way of combining specific models into networks. Effective use of hierarchical models requires both formal definition of the semantics of such composition, and efficient simulation tools for exploring the large space of complex behaviors. Objectives : BioRica (Soueidan et al, 2007) is a high-level hierarchical modeling framework integrating discrete and continuous multi-scale dynamics within the same semantics domain. It is an adaptation of the AltaRica formalism (Arnold et al., 2000). It explicitly addresses model reusability, repurposing and other engineering best practices that are necessary for sustainable, incremental development of comprehensive models incorporating individually validated components. The goal of the present work was to make the BioRica framework accessible for a wider audience. Methods : The BioRica approach expresses each existing model (in SBML) as a BioRica node, which are hierarchically composed to build a BioRica system. Individual nodes can be of two types. Discrete nodes are composed of states, and transitions described by constrained events, which can be non deterministic. This captures a range of existing discrete formalisms (Petri nets, finite automata, etc.). Stochastic behavior can be added by associating the likelihood that an event fires when activated. Markov chains or Markov decision processes can be concisely described. Timed behavior is added by defining the delay between an event's activation and the moment that its transition occurs. Continuous nodes are described by ODE systems, potentially a hybrid system whose internal state flows continuously while having discrete jumps. Results : The system has been implemented as a distributable software package. The BioRica model compiler and associated tools are available from the INRIA, (address to be provided). Discussion : By providing a reliable and functional software tool backed by a rigorous semantics, we hope to advance real adoption of hierarchical modeling by the systems biology community. By providing an understandable and mathematically rigorous semantics, this will make is easier for practicing scientists to build practical and functional models of the systems they are studying, and concentrate their efforts on the system rather than on the tool

Knowledge-based generalization of metabolic models

International audienceGenome-scale metabolic model reconstruction is a complicated process beginning with (semi-)automatic inference of the reactions participating in the organism's metabolism, followed by many iterations of network analysis and improvement. Despite advances in automatic model inference and analysis tools, reconstruction may still miss some reactions or add erroneous ones. Consequently, a human expert's analysis of the model will continue to play an important role in all the iterations of the reconstruction process. This analysis is hampered by the size of the genome-scale models (typically thousands of reactions), which makes it hard for a human to understand them. To aid human experts in curating and analyzing metabolic models, we have developed a method for knowledge-based generalization that provides a higher-level view of a metabolic model, masking its inessential details while presenting its essential structure. The method groups biochemical species in the model into semantically equivalent classes based on the ChEBI ontology, identifies reactions that become equivalent with respect to the generalized species, and factors those reactions into generalized reactions. Generalization allows curators to quickly identify divergences from the expected structure of the model, such as alternative paths or missing reactions, that are the priority targets for further curation. We have applied our method to genome-scale yeast metabolic models and shown that it improves understanding by helping to identify both specificities and potential errors

Three-level representation of metabolic networks

National audienceThe complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner

Modeling Stochastic Switched Systems with BioRica

National audienceModeling physycal and biological dynamic systems needs to combine different types of models in a non-ambiguous way. We present an approach to integrate continuous, discrete, stochastic, deterministic and non-deterministic elements by using Transition Systems theory, reuse, composition of models, and the framework BioRica. The systems are described by interacting con- tinuous and discrete models, and in addition continuous models are decomposed into two compo- nents: controlled and controller model. We define Stochastic Switched Systems whose continuous dynamics is modeled by differential equations and its discrete dynamics by transition systems, al- lowing stochastic and non-deterministic behaviours. We illustrated the use of our approach with examples of intrinsically and approximated hybrid systems. Our approach allows us to give a first step to integrate and to extend models of complex systems, such as cell differentiation

Hierarchical study of Guyton Circulatory Model

National audienceThis article presents an initial study of the Guyton Circulatory Model using BioRica. This model consists of 18 connected modules, each of which caracterise a separate physiological subsystem. We have focused the present analysis in the Renin- Angiotensin-Aldosterone System (RAAS). The use of BioRica allowed us to build an hierarchical model for this system by means of directly mapping modules to BioRica nodes. The results of each node were validated by comparison with published results