Metabolic network modelling, also known as metabolic network reconstruction or metabolic pathway analysis, allows for an in-depth insight into the molecular mechanisms of a particular organism. In particular, these models correlate the genome with molecular physiology. A reconstruction breaks down metabolic pathways (such as glycolysis and the citric acid cycle) into their respective reactions and enzymes, and analyzes them within the perspective of the entire network. In simplified terms, a reconstruction collects all of the relevant metabolic information of an organism and compiles it in a mathematical model. Validation and analysis of reconstructions can allow identification of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. This knowledge can then be applied to create novel biotechnology. In general, the process to build a reconstruction is as follows:

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Mechanism(s) of electron transfer and metabolic changes during iron reduction by Clostridium acetobutylicum

Cornelia List

Microorganisms catalyze many of the redox transformations driving the iron cycle in the geosphere. For instance, iron cycling includes the reduction of solid-phase and soluble ferric iron (Fe(III)) to ferrous iron (Fe(II)). However, in order to reduce solid-phase Fe(III), the reduction requires the delivery of electrons extracellularly, requiring a specialized electron transfer system. The mechanism of iron reduction by Gram-negative bacteria has been de-scribed in some detail, but very little is known for Gram-positive bacteria. These microorgan-isms are likely to contribute to iron reduction significantly, particularly in environments where Gram-negative bacteria fare poorly, such as metal contaminated sites, or hydrother-mal environments. Some Gram-positive bacteria use metal reduction as a form of enhanced fermentation, where the metal is a sink for excess reducing equivalents. In these cases, metal reduction is facultative and is not a mechanism for direct energy conservation. Here, we use Clostridium acetobutylicum, a Gram-positive fermenter, as a model organism for its ability to reduce iron and contaminant heavy metals. A major advantage of this model organism is that genetic tools are available to study genes involved in the metal reduction process. We investigated the reduction of solid-phase and soluble Fe(III) by C. acetobutyli-cum and found that it reduces both forms of iron. The bacteria do not require direct contact with solid-phase Fe(III) (as hydrous ferric oxide, HFO) for the reduction to occur. In fact, flavins were identified as likely to be involved in the extracellular electron transfer (EET) in this organism. We found that HFO reduction is increased by utilizing exogenous (resazurin, resorufin, anthraquinone-2,6-disulfonate) or endogenous (riboflavin, RF) electron shuttles. Inactivation of a putative flavin transporter (CAC2841) decreased HFO and Fe(III)-citrate reduction. We proposed that a complex CAC2841/ CAC3100/ CAC3101/ CAC3102 imports RF and CAC2841/ CAC3100 exports flavin adenine dinucleotide (FAD). FAD might be hy-drolyzed by CAC2761 and CAC2766, which interact with membrane-anchored flavoproteins potentially involved in EET (CAC2762 and CAC2767). Additionally, we performed a transcriptomic analysis of Fe(III)-citrate- and HFO-reducing cultures and compared them to fermentation alone. We found two Fe-S oxidoreductases (CA1254 and CAC1021) upregulated with Fe(III)-citrate, which have an unknown function and could be putative Fe(III)-citrate reductases. Additional upregulated redox active pro-teins with potential involvement in solid-phase Fe(III) reduction were identified as CA1044 and CAC3371. These proteins should be considered for further studies. Furthermore, iron reduction impacts bacterial metabolism by buffering the pH and shifting the carbon and electron flow to less reduced metabolites as compared to fermentation. In addition to catabolic pathways, metabolic modeling showed that iron reduction influences anabolic pathways. Finally, metabolic modeling suggests that NADH is the physiological elec-tron donor for iron reduction in this system. This study contributes to a better understanding of the mechanism of iron reduction by Gram-positive bacteria and provides implications for further studies to understand the EET by fermenters in natural environments.

EPFL2019

Optimization methods and objective functions for analysis of metabolic network models

Zhaleh Hosseini

Computational studies of metabolism aim to systematically analyze the metabolic behaviour of biological systems in different conditions. Genome-scale metabolic network models (GEMs) capture the connection between elements of the network by applying stoichiometric balances while taking into account gene-protein-reaction associations. Several methods have already been developed for the analysis of these models. These methods are mainly used to optimize a single or combination of objective functions subject to different constraints. In this thesis, we have summarized the optimization methods and objective functions by classifying them based on biological and mathematical features. Particularly, we suggest reformulations to convert some of the complex optimization classes to simpler ones. One of the reformulations is the conversion of mixed-integer linear fractional programming (MILFP) to mixed-integer linear programming (MILP). We show that this conversion is useful in studying coupling relationships in thermodynamically constrained metabolic network models. Coupling determines how different components of the network such as metabolites or reactions are interrelated. Particularly, flux Coupling Analysis (FCA) is a method for evaluating the dependencies between metabolic reactions. In FCA, two reactions are considered as coupled if the activity of one, constrains the activity of the other. So far, FCA has been used for analyzing metabolic reactions in flux-balanced models. In this work, we developed a new formulation, Thermodynamic Flux Coupling Analysis (TFCA), which calculates flux couplings of metabolic models that are subjected to thermodynamic constraints. With TFCA, we show that adding thermodynamic constraints can significantly change the coupling relationship of reactions of the network. Moreover, we show that calculating coupling relations helps in reducing the number of combinations of bidirectional reactions (BDRs), which in turn will facilitate the analysis of the metabolic network. In addition to proposing several mathematical reformulations to gain global optimality, we also addressed the issue of finding the proper cellular objective function in different conditions of cellular metabolism. This is not always straight-forward, since the metabolic activities of some organisms are not well-characterized, e.g., metabolism of dormancy phase in some bacteria and parasites. In this thesis, we studied the metabolic behaviour of dormant malaria parasite using genome-scale model of Plasmodium falciparum. We examined known and novel objective functions and scored them based on the modelâs consistency with experimental gene expression data. Our results suggested that minimizing energy dissipation can best describe the metabolic activities of the malaria parasites in the dormancy phase. In the last chapter, we focus on studying another poorly characterized metabolic system that is the process of iron reduction in Clostridium acetobutylicum. Research has shown that this organism can reduce Fe(III), but the mechanism behind this reduction is yet to be identified. In this thesis, we analyzed the metabolism of C. acetobutylicum using its reconstructed genome-scale metabolic network model and experimental transcriptomics data in the presence or absence of Fe(III). By performing several computational studies, we suggested that NAD(P) is involved in the reduction of iron and is the potential physiological electron donor to Fe(III).

EPFL2020

Integrating Transcriptomic Data with Thermodynamically Consistent Metabolic Models

Daniel Federico Hernandez Gardiol, Vikash Kumar Pandey

In metabolic modeling, experimental data is used to constrain the solution space of allowable flux distributions to biologically feasible phenotypes. This makes metabolic modeling a powerful tool for integrating information from different experimental sources. In this study we integrate transcriptomics with metabolomics for the first time. For this, we generated a new formulation of transcriptomics-based constraints (derived from IMAT), and we integrated Thermodynamics-Based Metabolic Flux Analysis (TMFA) constraints into an E. coli Genome Scale model. We found that when used separately, thermodynamic and transcriptomic constraints produced contradicting results. The optimal solutions for a model with transcript constraints alone were not thermodynamically feasible. This conflict forces us to choose between a solution space that is “optimally” constrained by transcriptomics or one that is thermodynamically feasible. All models should be thermodynamically consistent, whereas the integration of transcriptomics is only desirable. Adding transcription constraints is complicated because of the large amount of thresholds and/or assumptions that are required by the currently available methods. The proposed method enforces TMFA and transcript-based constraints together, ensuring solutions that are feasible for both data sets simultaneously. The combination reduces the solution space much more than each method would individually. Hence, we can reduce the bias introduced by transcriptomics-based constraints, while still obtaining a desired level of flexibility in our model.