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

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

From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment

Data, Parameters & Nonlinearities: Development and Applications of Large-scale Dynamic Models of Metabolism

Dynamic nonlinear models of metabolism offer a significant advantage as compared to constraint-based stoichiometric descriptions. However, progress in the development of large-scale nonlinear models has been hindered by both structural and quantitative uncertainties. In particular, the knowledge about kinetic rate laws and their parameters is till today still very limited when compared to the number of stoichiometric reactions known to be present in a large-scale metabolic model. In addition, strategies to systematically identify and implement large-scale dynamic models for metabolism are still lacking. In this contribution, we propose a novel methodology for development of dynamic nonlinear models for metabolism. Using the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework, we integrate thermodynamics and available omics and kinetic data into a large-scale stoichiometric model. The resulting set of log-linear kinetic models is used to compute kinetic parameters of the involved enzymatic reactions such as the maximal velocities and Michaelis constants. These kinetic parameters are in turn used to compute populations of stable, nonlinear, dynamic models sharing the same stable steady-state as the log-linear ones. The computed models offer unprecedented possibilities for system analysis, e.g. to study the responses of metabolism upon large perturbations; to investigate time course evolutions in and around the steady state; and to identify multiple steady-states and their basins of attraction. We illustrate the features of the generated models in the case of optimally grown E. coli, where our analysis of the estimated maximal reaction rates highlights the significance of network thermodynamics in constraining the variability of these quantities

Towards kinetic modeling of genome-scale metabolic networks without sacrificing stoichiometric, thermodynamic and physiology constraints

Mathematical modeling is an essential tool for a comprehensive understanding of cell metabolism and its interactions with the environmental and process conditions. Recent developments in the construction and analysis of stoichiometric models made it possible to define limits on steady-state metabolic behavior using flux balance analysis. However, detailed information about enzyme kinetics and enzyme regulation is needed to formulate kinetic models that can accurately capture the dynamic metabolic responses. The use of mechanistic enzyme kinetics is a difficult task due to uncertainty in the kinetic properties of enzymes. Therefore, the majority of recent works consider only the mass action kinetics for the reactions in the metabolic networks. In this work, we applied the ORACLE framework and constructed a large-scale, mechanistic kinetic model of optimally grown E. coli. We investigated the complex interplay between stoichiometry, thermodynamics, and kinetics in determining the flexibility and capabilities of metabolism. Our results indicate that enzyme saturation is a necessary consideration in modeling metabolic networks and it extends the feasible ranges of the metabolic fluxes and metabolite concentrations. Our results further suggest that the enzymes in metabolic networks have evolved to function at different saturation states to ensure greater flexibility and robustness of the cellular metabolism

Identification of Feasible Metabolic Fluxes and Metabolite Concentrations using Large-scale Kinetic Models

The constraints imposed during modeling must satisfy biologically representative phenotypes of the studied organism. Simultaneously, identification and analysis of these constraints enhances our understanding of the evolution/operational paradigms of the organism. It was postulated by Varma and Palsson[1] that it is possible to define limits on metabolic behavior using flux balance analysis, but in order to accurately capture the metabolic responses, detailed information about enzyme kinetics and their regulation is needed. Since development of mechanistic kinetic models is a difficult task due to uncertainty in kinetic properties of enzymes, a substantial number of recent works consider only the mass action (MA) term in their model formulation. As kinetics is one of crucial factors in governing the metabolic capabilities of a cell, i.e. realizable metabolic flux and concentration states, considering only the mass action term does not necessarily provide a realistic description of the feasible space of fluxes and concentrations. In this work, using the ORACLE[2] framework, we constructed a large-scale mechanistic kinetic model of optimally grown E. coli that considers the enzyme saturations as observed in biological systems. Using this model, we performed an analysis of the complex interplay between stoichiometry, thermodynamics, and kinetics in determining flexibility and capabilities of metabolic networks. Our analysis indicates that enzyme saturation is an important and necessary consideration in modeling metabolic networks. Extended ranges of feasibility, both in the space of metabolic fluxes and metabolite concentrations, of kinetic models involving the enzyme saturation suggests that the enzymes in metabolic networks have evolved to function at different saturation states so as to ensure higher flexibility and robustness of the cell

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories

Computational Studies on Cellular Bioenergetics

Genome-scale metabolic reconstructions have become indispensable tools for understanding and investigation of the metabolism of any organism in this post-genomic era. However, there are still knowledge gaps in our understanding of the metabolism of even the most well-studied organism, hence a gapfilling algorithm based on novel reaction generating framework was implemented and shown to be able to refine the reconstruction as well as improve our knowledge of the network. The analysis of these large-scale models has their own set of challenges and difficulties that limit their full utility in practical application areas and higher usage by industry and experimentalists. Constraint-based analyses of metabolic models are highly popular methods, as it only requires knowledge of stoichiometry and key input/output fluxes of the system to infer the underlying intracellular fluxes. However due to the underdetermined nature of most large metabolic networks, additional constraints have to be incorporated to ascertain the actual state of the network. Thermodynamic constraints eliminate thermodynamically infeasible solutions and also provide another layer of information onto metabolic networks. Using Thermodynamics-based Flux Balance Analysis (TFBA) of metabolic models and taking into account pH and ionic strength differences, we study how these factors can affect overall cellular energetics. TFBA also enables us to integrate metabolomics data to reduce the solution space of the problem. As genome-scale networks have too many degrees of freedom for systematic analysis and conceptually too difficult to manage, a consistent approach of reducing genome-scale models to core metabolic models that retain most of the network characteristics of the original model is proposed. Such core metabolic models are valuable resources for simplifying analysis that can be extrapolated to the original model. A novel framework for characterizing the flux and thermodynamic state of metabolic networks is proposed in order to systematically analyze the possible intracellular states given the set of known parameters. The characterized states can be further analyzed using metabolic control analysis to provide insights as to how the control of the network is distributed and also provide a bridge to the formulation of large-scale kinetic models

DREAMS of metabolism

Metabolic networks have been studied for decades, and sophisticated computational frameworks are needed to augment experimental approaches to harness these complex networks. BNICE (Biochemical Network Integrated Computational Explorer), a computational approach for the discovery of novel biochemical pathways, overcomes many of the current limitations. BNICE and similar frameworks can be used in a myriad of different areas: (i) design of novel pathways for metabolic engineering and bioremediation; (ii) retrosynthesis of metabolic compounds; (iii) evolution analysis between metabolic pathways of different organisms; (iv) analysis of metabolic pathways; (v) mining of 'omics' data; and (vi) selection of targets for enzyme engineering. We will discuss the issues and challenges in building such a framework and the gamut of applications that they can offer

Network thermodynamics in the post-genomic era

Network models have been used to study the underlying processes and principles of biological systems for decades, providing many insights into the complexity of life. Biological systems require a constant flow of free energy to drive these processes that operate away from thermodynamic equilibrium. With the advent of high-throughput omics technologies, more and more thermodynamic knowledge about the biological components, processes and their interactions are surfacing that we can integrate using large-scale biological network models. This allows us to ask many fundamental questions about these networks, such as, how far away from equilibrium must the reactions in a network be displaced in order to allow growth, or what are the possible thermodynamic objectives of the cell