Metabolic Pathway Analysis: from small to genome-scale networks

The need for mathematical modelling of biological processes has grown alongside with the achievements in the experimental field leading to the appearance and development of new fields like systems biology. Systems biology aims at generating new knowledge through modelling and integration of experimental data in order to develop a holistic understanding of organisms. In the first part of my PhD thesis, I compare two different levels of abstraction used for computing metabolic pathways, constraint-based and graph theoretical methods. I show that the current representations of metabolism as a simple graph correspond to wrong mathematical descriptions of metabolic pathways. On the other hand, the use of stoichiometric information and convex analysis as modelling framework like in elementary flux mode analysis, allows to correctly predict metabolic pathways. In the second part of the thesis, I present two of the first methods, based on elementary flux mode analysis, that can compute metabolic pathways in such large metabolic networks: the K-shortest EFMs method and the EFMEvolver method. These methods contribute to an enrichment of the mathematical tools available to model cell biology and more precisely, metabolism. The application of these new methods to biotechnological problems is also explored in this part. In the last part of my thesis, I give an overview of recent achievements in metabolic network reconstruction and constraint-based modelling as well as open issues. Moreover, I discuss possible strategies for integrating experimental data with elementary flux mode analysis. Further improvements in elementary flux mode computation on that direction are put forward

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

This article has been made available through the Brunel Open Access Publishing Fund. Copyright @ 2013 Pey et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling. Results: We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed. Conclusions: A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.Basque Governmen

In Silico Evidence for Gluconeogenesis from Fatty Acids in Humans

The question whether fatty acids can be converted into glucose in humans has a long standing tradition in biochemistry, and the expected answer is “No”. Using recent advances in Systems Biology in the form of large-scale metabolic reconstructions, we reassessed this question by performing a global investigation of a genome-scale human metabolic network, which had been reconstructed on the basis of experimental results. By elementary flux pattern analysis, we found numerous pathways on which gluconeogenesis from fatty acids is feasible in humans. On these pathways, four moles of acetyl-CoA are converted into one mole of glucose and two moles of CO2. Analyzing the detected pathways in detail we found that their energetic requirements potentially limit their capacity. This study has many other biochemical implications: effect of starvation, sports physiology, practically carbohydrate-free diets of inuit, as well as survival of hibernating animals and embryos of egg-laying animals. Moreover, the energetic loss associated to the usage of gluconeogenesis from fatty acids can help explain the efficiency of carbohydrate reduced and ketogenic diets such as the Atkins diet

Identification of metabolic pathways using pathfinding approaches: A systematic review

Metabolic pathways have become increasingly available for variousmicroorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis ofmetabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches inmetabolic pathway analysis are discussed. Threemajor types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways withmathematical benchmarkingmetrics is provided. This review would lead to better comprehension ofmetabolismbehaviors in living cells, in terms of computed pathfinding approaches. © The Author 2016

Inulin-grown Faecalibacterium prausnitzii cross-feeds fructose to the human intestinal epithelium

Many chronic diseases are associated with decreased abundance of the gut commensal Faecalibacterium prausnitzii. This strict anaerobe can grow on dietary fibers, e.g., prebiotics, and produce high levels of butyrate, often associated to epithelial metabolism and health. However, little is known about other F. prausnitzii metabolites that may affect the colonic epithelium. Here, we analyzed prebiotic cross-feeding between F. prausnitzii and intestinal epithelial (Caco-2) cells in a “Human-oxygen Bacteria-anaerobic” coculture system. Inulin-grown F. prausnitzii enhanced Caco-2 viability and suppressed inflammation- and oxidative stress-marker expression. Inulin-grown F. prausnitzii produced excess butyrate and fructose, but only fructose efficiently promoted Caco-2 growth. Finally, fecal microbial taxonomy analysis (16S sequencing) from healthy volunteers (n = 255) showed the strongest positive correlation for F. prausnitzii abundance and stool fructose levels. We show that fructose, produced and accumulated in a fiber-rich colonic environment, supports colonic epithelium growth, while butyrate does not

Microenvironmental modulation of the intestinal epithelial barrier

A healthy gut is essential for human health and healthy aging. Indeed, many diseases are characterized by a disturbed function of the gut, which is often reflected by an abnormal composition of the gut microbiota, e.g., the gut bacteria that help in food digestion and immune regulation. Most of these gut bacteria are anaerobes, which means that they grow and are active under oxygen-free conditions. Instead, all human cells require oxygen to stay alive. Only one monolayer of cells, the intestinal epithelium, separates these anaerobic gut bacteria from all the internal organs of our body. The oxygen-sensitive hypoxia-inducible factor-1α (HIF1α) is a very important factor to maintain a strong intestinal epithelium, a barrier that is often compromised in patients with gastrointestinal disorders, such as inflammatory bowel disease (IBD). The aim of this thesis was to identify factors in the gut microenvironment that control the human intestinal epithelial barrier, with particular focus on the role of HIF1α. For this, we combined detailed IBD patient data with laboratory experiments using an in-house developed system in which oxygen-requiring human epithelium cells can be cocultured with anaerobic gut bacteria, e.g., the Human-oxygen Bacteria-anaerobe (HoxBan) system. We found that specific gut bacteria, dietary fibers, iron levels and inflammatory factors directly control the intestinal epithelial barrier function, in most cases involving HIF1α. As drugs that activate HIF1α are currently under investigation for various diseases, they may also be promising to improve gut health, for example in patients with IBD

A new computational method to split large biochemical networks into coherent subnets

<p>Abstract</p> <p>Background</p> <p>Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators) to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation.</p> <p>The method proposed here (Netsplitter) allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning.</p> <p>Results</p> <p>Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for <it>Arabidopsis thaliana </it>encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called <it>efficacy </it>is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species.</p> <p>Conclusions</p> <p>For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of subnet sizes with the removal of fewer mass balance constraints. In addition, the user can interactively control which metabolite nodes are selected for cutting and when to stop further partitioning as the desired granularity has been reached. Finally, the blocking transformation at the heart of the procedure provides a powerful visual display of network structure that may be useful for its exploration independent of whether partitioning is required.</p