Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

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

Fructose metabolism in Chromohalobacter salexigens: interplay between the Embden–Meyerhof–Parnas and Entner–Doudoroff pathways

Background The halophilic bacterium Chromohalobacter salexigens metabolizes glucose exclusively through the Entner–Doudoroff (ED) pathway, an adaptation which results in inefficient growth, with significant carbon overflow, especially at low salinity. Preliminary analysis of C. salexigens genome suggests that fructose metabolism could proceed through the Entner–Doudoroff and Embden–Meyerhof–Parnas (EMP) pathways. In order to thrive at high salinity, this bacterium relies on the biosynthesis and accumulation of ectoines as major compatible solutes. This metabolic pathway imposes a high metabolic burden due to the consumption of a relevant proportion of cellular resources, including both energy molecules (NADPH and ATP) and carbon building blocks. Therefore, the existence of more than one glycolytic pathway with different stoichiometries may be an advantage for C. salexigens. The aim of this work is to experimentally characterize the metabolism of fructose in C. salexigens. Results Fructose metabolism was analyzed using in silico genome analysis, RT-PCR, isotopic labeling, and genetic approaches. During growth on fructose as the sole carbon source, carbon overflow was not observed in a wide range of salt concentrations, and higher biomass yields were reached. We unveiled the initial steps of the two pathways for fructose incorporation and their links to central metabolism. While glucose is metabolized exclusively through the Entner–Doudoroff (ED) pathway, fructose is also partially metabolized by the Embden–Meyerhof–Parnas (EMP) route. Tracking isotopic label from [1-13C] fructose to ectoines revealed that 81% and 19% of the fructose were metabolized through ED and EMP-like routes, respectively. Activities of enzymes from both routes were demonstrated in vitro by 31P-NMR. Genes encoding predicted fructokinase and 1-phosphofructokinase were cloned and the activities of their protein products were confirmed. Importantly, the protein encoded by csal1534 gene functions as fructose bisphosphatase, although it had been annotated previously as pyrophosphate-dependent phosphofructokinase. The gluconeogenic rather than glycolytic role of this enzyme in vivo is in agreement with the lack of 6-phosphofructokinase activity previously described. Conclusions Overall, this study shows that C. salexigens possesses a greater metabolic flexibility for fructose catabolism, the ED and EMP pathways contributing to a fine balancing of energy and biosynthetic demands and, subsequently, to a more efficient metabolism.University of Murcia and University of Seville was supported by projects: BIO2015-63949-R, BIO2014-54411-C2-1-REuropa MINECO/FEDER RTI2018-094393-B-C21Fundación Séneca (Grant no. 19236/PI/14

Shiny GATOM: Omics-based identification of regulated metabolic modules in atom transition networks

Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom

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

Pathway Hunter Tool (PHT) � A Platform for Metabolic Network Analysis and Potential Drug Targeting

Metabolic network analysis will play a major role in �Systems Biology� in the future as they represent the backbone of molecular activity within the cell. Recent studies have taken a comparative approach toward interpreting these networks, contrasting networks of different species and molecular types, and under varying conditions. We have developed a robust algorithm to calculate shortest path in the metabolic network using metabolite chemical structure information. A divide and conquer technique using Maximal Common Subgraph (MCS) approach and binary fingerprint was used to map each substrate onto its corresponding product. Then for the calculation of the shortest paths (using modified Breadth First Search algorithm) the two biochemical criteria �local� and �global� structural similarity were used, where �local similarity� is defined as the similarity between two intermediate molecules and �global similarity� is defined as the amount of conserved structure found between the source metabolite and the destination metabolites after a series of reaction steps. The pathway alignment was introduced to find enzyme(s) preference in the pathway of various organisms (a local and global outlook to metabolic networks). This was also used to predict potentially missing enzymes in the pathway. A novel concept called �load points� and �choke points� identifies hot spots in the network. This was used to find important enzymes in the pathogens metabolic network for potential drug targets

A retrosynthetic biology approach to metabolic pathway design for therapeutic production

<p>Abstract</p> <p>Background</p> <p>Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an <it>in situ </it>drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.</p> <p>Results</p> <p>In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.</p> <p>Conclusions</p> <p>We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.</p

Computational discovery and analysis of metabolic pathways

Finding novel or non-standard metabolic pathways, possibly spanning multiple species, has important applications in fields such as metabolic engineering, metabolic network analysis, and metabolic network reconstruction. Traditionally, this has been a manual process, but the large volume of metabolic data now available has created a need for computational tools to automatically identify biologically relevant pathways. This thesis presents new algorithms for automatically finding biologically meaningful linear and branched metabolic pathways in multi-genome scale metabolic networks. These algorithms utilize atom mapping data, which provides the correspondence between atoms in the substrates to atoms in the products of a chemical reaction, to find pathways which conserve a given number of atoms between desired start and target compounds. The first algorithm presented identifies atom conserving linear pathways by explicitly tracking atoms during an exploration of a graph structure constructed from the atom mapping data. The explicit tracking of atoms enables finding branched pathways because it provides automatic identification of the reactions and compounds through which atoms are lost or gained. The thesis then describes two algorithmic approaches for identifying branched metabolic pathways based upon atom conserving linear pathways. One approach takes one linear pathway at a time and attempts to add branches that connect loss and gain compounds. The other approach takes a group of linear pathways and attempts to merge pathways that move mutually exclusive sets of atoms from the start to the target compounds. Comparisons to known metabolic pathways demonstrate that atom tracking causes the algorithms to avoid many unrealistic connections, often found in previous approaches, and return biologically meaningful pathways. While the theoretical complexity of finding even linear atom conserving pathways is high, by choosing the appropriate representations and heuristics, and perhaps due to the structure of the underlying data, the algorithms in this thesis have practical running times on real data. The results also demonstrate the potential of the algorithms to find novel or non-standard pathways that may span multiple organisms