Fusing Individual-based Models with Genome-scale Metabolic Networks - a Novel Approach for Quantifying Emergent Microbial Community Spatiotemporal Dynamics

Metabolic capabilities of organisms govern their growth characteristics and their capability to adapt to changing environmental conditions. In natural systems, substrates can change dynamically and spatially with multiple species competing for resources. Genome-scale metabolic network models (GEMs) offer quantitative frameworks for consideration of the metabolism of organisms in the concourse. Community modeling has been used in studies to analyze multi-species consortia, however the high dimensionality of GEMs limits their use in complex systems especially for applications in spatially-explicit individual-based models (IBM). The use of IBM permits exploration of species interaction with their environment and neighbors within a prescribed physical domain. This study proposes to capitalize on recently developed algorithms, redGEM and lumpGEM for the unbiased and systematic reduction of GEMs into reduced metabolic models (rGEMs) that retain compatibility with their parent GEMs. The reduced computational burden of rGEMs makes them scalable and makes their incorporation in IBM frameworks feasible. We formulated a hybrid model fusing an individual-based model (IBM) with advanced thermodynamics-based flux analysis (TFA) and reanalyzed a 3-member consortium comprised of E. coli, S. enterica and M. extorquens. While the IBM provides detailed description of the physical environment, dispersion and motility characteristics of organisms, the rGEMs and TFA provide a reduced description of the stoichiometric properties and bioenergetics constraints imposed on organisms, including intracellular and extracellular concentrations of all defined metabolites at the local scale experienced by an individual. We report the first application of such a hybrid approach that systematically merges stoichiometric models and individual-based models for study of interacting multispecies communities in simple physical spaces (surfaces). The expansion to consideration of microbial communities in complex environments will be discussed.

A novel approach to find the missing links in genome-scale metabolic models: The BridgeIt mrthod

Vassily Hatzimanikatis, Marianne Seijo, Keng Cher Soh

Genome-scale metabolic reconstructions (GSMRs) are valuable resources in the analysis and understanding of cellular metabolism. They are based on genome sequence and annotation, and they are to develop bottom-up mathematical models of metabolic networks. These models are used in a wide variety of studies ranging from metabolic engineering to evolutionary studies. However, there are incomplete pathways and orphan metabolites in all GSMRs, even for the most well studied organisms. These knowledge gaps are due to the lack of experimental or homologous information, as current methods rely on a database of known reactions to generate possible pathways for bridging these gaps, and they fall short when there is no sequence homology. We present a novel computational framework called BridgIT that is able to generate hypothetical reactions and pathways that bridge gaps in reconstructed pathways. The novel reactions generated are based the third level of enzyme commission classification system (EC), which is consistent with known biochemical reactions, protein structures, genomic sequences, and enzyme properties that follow the EC classification. Within the BridgIT framework, we generate all biochemically plausible reactions and pathways, which can link two or more metabolites. These pathways are then ranked according to their length, thermodynamic feasibility, and network feasibility. We next use chemical similarity metrics to link the generated hypothetical reactions with known reactions through their substrate and product similarity. The protein and gene sequences of the linked known reactions are used to identify possible sequences within the GSMR to further refine and improve the annotation of the existing GSMR. We demonstrate the ability of this method to identify gaps that can be easily filled by known reactions and also gaps that require novel reactions which existing methods fail to do so.

2012

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

Noushin Hadadi, Vassily Hatzimanikatis, Keng Cher Soh

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.

2014

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