The design of “Super E. coli” through automated reaction network generation and genome scale models

Genome-scale metabolic reconstructions capture the known metabolic capabilities of organisms and their analysis have provided many insights into complex biochemical networks. However, this approach relies on a database of known biochemical reactions and hence may fail to take into account unique novel metabolic reactions. Even for well-characterized model organisms like E. coli, new reactions remain to be discovered. Starting from the known biochemistry of E. coli metabolism, we investigated how many more reactions could be evolved and if these reactions could give extra potential to this organism for industrial application in the production of fuels and chemicals. We utilized the BNICE (Biochemical Network Integrated Computational Explorer) methodology which applies known biotransformation rules to generate a “super” network which captures all the possible reactions given a set of E. coli core metabolites and known biotransformation rules in E. coli. This super network is found to capture all the known E. coli reactions as well as novel pathways that can serve as potential novel biosynthesis pathways to valuable chemicals. In order to identify the role of these novel reactions in E. coli metabolic network, we embedded them within the genome scale model of E. coli and performed a thermodynamics flux balance analysis (TFBA) to investigate the feasibility of the novel pathways in the context of the genome scale model. By performing a Flux Variability Analysis (FVA), subject to thermodynamic constraints, the reliable sets of reactions that increase the yield through biomass were identified. To further investigate the feasibility of the novel proposed reactions, we used the BridgIT computational framework for the identification of candidate gene sequences. BridgIT has a database of all the known biochemical reactions and it makes associations between novel predicted reactions and known reactions by using a chemical similarity metrics and it links the novel reactions with known genes in genomes and organisms.

Discovery and Evaluation of Novel Pathways for Production of the Second Generation of Biofuels

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis, Ljubisa Miskovic, Milenko Tokic

In an effort of overcoming the limited availability of fossil energy resources the focus of the research and development in the area of biofuels has moved towards developing the second generation of fuels that should be produced via microbial fermentation. The idea is to use as a feedstock inexpensive and abundant waste materials such as lignocellulosic biomass. The second generation biofuels should satisfy several criteria such as lower emission, higher energy density and should be less corrosive to engines. However, currently used industrial workhorses such as E. coli and S. cerevisiae do not produce many of these molecules naturally. Furthermore, for majority of these molecules there are no known biochemical pathways. The need for discovery of novel biosynthetic pathways towards desired molecules sparked the development of computational tools that are capable of reconstructing feasible reaction steps between a given set of starting compounds (precursors) and a molecule of interest (a target compound). In this study, we performed the retrobiosynthesis analysis for all the candidates applying Biochemical Network Integrated Computational Explorer (BNICE.ch) and we reconstructed the metabolic network for 69 fuel compounds. We analyzed compounds (biological or chemical) and reactions one-step away from these target molecules and the appearance of the potential substrates in their reconstructed metabolic network. Based on the results of this analysis, coupled with other criteria such as the Gibes free energy of formation, combustion potential and expert opinion, we chose the 5 highest ranked fuel candidates for further pathway reconstruction studies. For these 5 fuel candidates we have created 1.8 million de novo pathways with different number of reaction steps for further evaluation. We have then embedded the reconstructed pathways in the genome-°©‐scale models of the two chassis organisms E. coli and P. putida and we performed the pathway evaluation with respect to their maximum theoretical yield using two methods (i) Flux Balance Analysis (FBA), and (ii) Thermodynamic-based Flux Analysis (TFA). All the pathways that did not satisfy the thermodynamic constraints were rejected. Our results indicated that in the evaluation and ranking of the pathways the thermodynamics is a necessary consideration. Specifically, we showed that the majority of the pathways that were determined as feasible based on FBA, they were not feasible based on TFA. Moreover, we also showed that some pathways that were determined as feasible in both organisms based on FBA, they were not feasible in both of them based on TFA. Also, the yield of the feasible pathways can differ depending on the organism. These results demonstrate how TFA can also guide the selection of a suitable chassis organism. For all discovered reactions in the feasible pathways we have also identified known enzymes from the KEGG database with the closest reaction similarity that could be engineered for the implementation of novel reactions. This study shows the full potential of computational tools for discovery and design of novel synthetic pathways and their relevance for future developments in the area of metabolic engineering and synthetic biology.

2016

Facing The Unknown: Suggesting Alternative Novel Biochemistry in Escherichia coli and Saccharomyces cerevisiae

Meriç Ataman, Noushin Hadadi, Vassily Hatzimanikatis

While advances in genome sequencing have greatly facilitated the inference of metabolic networks, the presence of unknown biochemistry in the organisms still challenges the analysis and understanding of metabolism. Even for one of the most known organism like Escherichia coli, there are around 10% of its open reading frames (ORFs) that remain to be annotated. The experimental identification of metabolic capabilities in an organism will benefit from the guidance from computational analysis. The study of genome-scale models represents an attractive approach to identify the metabolic capabilities required for growth and for the connectivity of all metabolites that are part of the metabolic network. For this purpose, we develop a gap-filling approach that identifies known and novel alternative reactions to the ones integrated in the latest genome-scale models of E. coli (iJO1366) and Saccharomyces cerevisiae (iTO977). The novel reactions were obtained from the recently developed repository of all possible biochemical reactions (ATLAS of biochemistry). Our method uses a mixed-integer linear programming (MILP) formulation to identify alternative metabolic reactions that satisfy mass balance constraints. We then evaluate the thermodynamic feasibility of the novel reactions at the intracellular conditions. We further used a cheminformatics tool to compare the sequence similarity of the alternative gap-filled enzymes with the ORF of closely related organisms. Here, we present a comparative study between the unknown biochemistry from E. coli and S. cerevisiae, and we highlight the currently unknown metabolic functions that represent the most attractive candidates for experimental characterization.

2017

Modeling, predicting and mining metabolism at atom-level resolution

Jasmin Maria Hafner

Living organisms can catalyze many thousands of biochemical reactions that they use to convert energy and matter, which provides them with the essentials for life. The sum of these chemical reactions happening in an organism is called metabo-lism. Understanding metabolism is crucial to elucidate the fundamental principles of biology, and further to enable us to redesign it for the sustainable, biosynthetic pro-duction of bio-based fuels, commodity chemicals and medicines. Mathematical mod-els are essential to organize and understand the complexity of metabolism. They usu-ally represent metabolism as a network of reactions, but they tend to neglect the exact molecular structure of the metabolites. To redesign metabolic reactions, how-ever, a mechanistic understanding of metabolic reactions and their catalysts, proteins called enzymes, is essential. In this work, a mathematical description of enzymatic reaction mechanism, called generalized reaction rules, is applied to computationally simulate and predict meta-bolic processes at the level of atoms. Each reaction rule describes the catalytic activi-ty of an enzyme, or a group of enzymes, at the mechanistic level by encoding the re-arrangement of atoms in the reaction. The reaction rules are called âgeneralizedâ, because they mimic the ability of a single enzyme to catalyze multiple reactions by acting on a range of substrates. Using these reaction rules, we first developed a computational representation of me-tabolism that allowed tracking single atoms throughout complex metabolic reaction networks. The principle of atom-tracking was then used to develop a graph-theory based method to represent and analyze metabolic networks, and to reliably identify of metabolic pathways for the biosynthesis of chemicals. Next, we applied the gener-alized reaction rules to predict all possible novel, hypothetical reactions from known biological compounds, and we stored the five million generated novel reactions them in a database called ATLAS. Finally, the developed tools and resources were applied to specific engineering and research problems, such as the biosynthetic pathway design for the biofuel bisabolene and the plastic precursor 1,4-butanediol. We further pre-dicted a biosynthesis route for the pharmaceutical tetrahydropalmatine and engi-neered a yeast strain to produce it. Finally, we show that our tools can be used to mine available genome sequences to find organisms that can degrade xenobiotics. Our findings suggest that the atom-level representation of metabolism can greatly contribute to its understanding, exploration and prediction. Given the complexity of atom-level modeling of metabolic processes, we propose metrics that can approxi-mate the atom-level information to conserve the information at the level of big, hy-pothetical metabolic networks like ATLAS. This database plus the developed pathway search techniques form a valuable resource for scientist to help characterizing un-known biosynthesis pathways towards secondary metabolites, and for metabolic engi-neers for designing novel bioproduction pathways for chemicals. Hopefully, these considerations will contribute to a better understanding of metabolism, advance the exploration of the bioproduction of drugs and other valuable molecules, and acceler-ate metabolic engineering efforts to realize the switch from a petroleum-based chem-ical industry towards a more sustainable, bio-based production of societyâs chemical needs.