Dans cette thèse, nous avons présenté trois méthodes différentes pour l’énumération de sousréseauxparticuliers d’un réseau métabolique: les histoires métaboliques, les ensembles minimaux deprécurseurs et les organisations chimiques. Pour chacune de ces trois méthodes, nous avons présentédes résultats théoriques, et pour les deux premières, nous avons en outre fourni une illustration surcomment les appliquer afin d’étudier le comportement métabolique des organismes vivants. Les histoiresmétaboliques sont définies comme des graphes acycliques dirigés maximaux dont les ensemblesde sources et de cibles sont limités à un sous-ensemble des noeuds. La motivation initiale de cette définitionétait d’analyser des données expérimentales de métabolomique, mais la méthode a égalementété explorée dans un contexte différent. Les ensembles de précurseurs métaboliques sont des ensemblesminimaux de nutriments qui permettent de produire des métabolites d’intérêt. Nous présentons troisméthodes différentes pour l’énumération de tels ensembles minimaux de précurseurs, et nous illustronsleur application dans une étude des échanges métaboliques dans un système symbiotique. Les organisationschimiques sont des ensembles de métabolites qui à la fois sont fermés et s’auto-maintiennent,ce qui reflète des caractéristiques de stabilité dans le sens où aucun nouveau métabolite ne peut êtreproduit et qu’aucun des métabolites déjà présents dans le système ne peut disparaître. / In this thesis, we presented three different methods for enumerating special subnetworks containedin a metabolic network: metabolic stories, minimal precursor sets and chemical organisations. Foreach of the three methods, we gave theoretical results, and for the two first ones, we further providedan illustration on how to apply them in order to study the metabolic behaviour of living organisms.Metabolic stories are defined as maximal directed acyclic graphs whose sets of sources and targets arerestricted to a subset of the nodes. The initial motivation of this definition was to analyse metabolomicsexperimental data, but the method was also explored in a different context. Metabolic precursor setsare minimal sets of nutrients that are able to produce metabolites of interest. We present threedifferent methods for enumerating minimal precursor sets and we illustrate the application in a studyof the metabolic exchanges in a symbiotic system. Chemical organisations are sets of metabolites thatare simultaneously closed and self-maintaining, which captures some stability feature in the

In this thesis, we presented three different methods for enumerating special subnetworks containedin a metabolic network: metabolic stories, minimal precursor sets and chemical organisations. Foreach of the three methods, we gave theoretical results, and for the two first ones, we further providedan illustration on how to apply them in order to study the metabolic behaviour of living organisms.Metabolic stories are defined as maximal directed acyclic graphs whose sets of sources and targets arerestricted to a subset of the nodes. The initial motivation of this definition was to analyse metabolomicsexperimental data, but the method was also explored in a different context. Metabolic precursor setsare minimal sets of nutrients that are able to produce metabolites of interest. We present threedifferent methods for enumerating minimal precursor sets and we illustrate the application in a studyof the metabolic exchanges in a symbiotic system. Chemical organisations are sets of metabolites thatare simultaneously closed and self-maintaining, which captures some stability feature in the

Lors de cette thèse, je me suis intéressée à la modélisation du métabolisme des micro-organismes. Nous nous sommes focalisé sur le métabolisme des petites molécules qui ne prend pas en compte les réactions associées aux macromolécules, telle que la synthèse des protéines.Nous avons ainsi utilisé différents formalismes de modélisation.Tout d'abord, nous avons développé TOTORO où les réseaux métaboliques sont représentés par des hypergraphes dirigés et qui permet d'identifier les réactions ayant participé à une transition métabolique. TOTORO a été utilisé sur un jeu de données sur la levure en présence de cadmium. Nous avons pu montrer que nous retrouvons les mécanismes connus de désintoxication.Ensuite, en utilisant une méthode de modélisation par contraintes, nous discutons d'un développement en cours, KOTOURA, qui propose d'utiliser les connaissances actuelles de concentrations de métabolites entre différentes conditions pour inférer de manière quantitative les possibles asynchronies des réactions lors du passage d'un état stable à un autre. Nous avons testé son implémentation sur des données simulées.Enfin, nous proposons MULTIPUS, une méthode d'extraction d'(hyper)-arbres de Steiner dirigés qui permet de sélectionner les voies métaboliques pour la production de composés au sein d'une communauté bactérienne. Les réseaux métaboliques sont modélisés en utilisant des hypergraphes dirigés et pondérés. Nous proposons un algorithme de programmation dynamique paramétré ainsi qu'une formulation utilisant la programmation par ensemble réponse. Ces deux propositions sont ensuite comparées dans deux cas d'applications / In this PhD work, we proposed to model metabolism. Our focus was to develop generic models, that are not specific to one organism or condition, but are instead based on general assumptions that we tried to validate using data from the literature.We first present TOTORO that uses a qualitative measurement of concentrations in two steady-states to infer the reaction changes that lead to differences in metabolite pools in both conditions.TOTORO enumerates all sub-(hyper)graphs that represent a sufficient explanation for the observed differences in concentrations. We exploit a dataset of Yeast (Saccharomyces cerevisiae) exposed to cadmium and show that we manage to retrieve the known pathways used by the organisms. We then address the same issue, but using a constraint-based programming framework, called KOTOURA, that allows to infer more quantitatively the reaction changes during the perturbed state. We use in this case exact concentration measurements and the stoichiometric matrix, and show on simulated datasets that the overall variations of reaction fluxes can be captured by our formulation.Finally, we propose MULTIPUS, a method to infer microbial communities and metabolic roads to produce specific target compounds from a set of defined substrates. We use in this case a weighted directed hypergraph. We apply MULTIPUS to the production of antibiotics using a consortium composed of an archae and an actinobacteria and show hat their metabolic capacities are complementary. We then infer for another community the excretion of an inhibitory product (acetate) by a 1,3-propanediol (PDO) producer and its consumption by a methanogene archae

Thesis (PhD (Chemical Engineering))--Cape Peninsula University of Technology, 2019 / Simultaneous nitrification and aerobic denitrification (SNaD) is a preferred method for single stage total nitrogen (TN) removal, which was recently proposed to improve wastewater treatment plant design. However, SNaD processes are prone to inhibition by toxicant loading with free cyanide (CN-) possessing the highest inhibitory effect on such processes, rendering these processes ineffective. Despite the best efforts of regulators to limit toxicant disposal into municipal wastewater sewage systems (MWSSs), free cyanide (CN-) still enters MWSSs through various pathways; hence, it has been suggested that CN- resistant or tolerant microorganisms be utilized for processes such as SNaD. To mitigate toxicant loading, organisms in SNaD have been observed to adopt a multiphase growth strategy to sequentially degrade CN- during primary growth and subsequently degrade TN during the secondary growth phase. However, CN- degrading microorganisms are not widely used for SNaD in MWSSs due to the inadequate application of suitable microorganisms (Chromobacterium violaceum, Pseudomonas aeruginosa, Thiobacillus denitrificans, Rhodospirillum palustris, Klebsiella pneumoniae, and Alcaligenes faecalis) commonly used in single-stage SNaD. The use of CN- degrading or resistant microorganisms for SNaD is a cost-effective method compared to the use of other methods of CN- removal prior to TN removal, as they involve multi-stage systems (as currently observed in MWSSs). The use of CN- degrading microorganisms, particularly when used as a consortium, presents a promising and sustainable resolution to mitigate inhibitory effects of CN- in SNaD. However, SNaD is known to be completely inhibited by CN- thus it is imperative to also study some thermodynamic parameters of SNaD under high CN- conditions to see the feasibility of the process. The Gibbs free energy is significant to understand the feasibility of SNaD, it is also vital to study Gibbs free energy to determine whether or not the biological reaction is plausible. The relationship between the rate of nitrification and Gibbs free energy was also investigated. The attained results showed that up to 37.55 mg CN-/L did not have an effect on SNaD. The consortia degraded CN- and achieved SNaD, with degradation efficiency of 92.9 and 97.7% while the degradation rate of 0.0234 and 0.139 mg/L/hr for ammonium-nitrogen (NH4-N) and CN- respectively. Moreover, all the free Gibbs energy was describing the individual processes were found to be negative, with the lowest Gibbs free energy being -756.4 and -1830.9 Kcal/mol for nitritation and nitratation in the first 48 h of the biological, reaction respectively. Additionally, a linear relationship between the rate of NH4-N and nitrite-nitrogen (NO2-N) degradation with their respective Gibbs free energy was observed. Linear model was also used to predict the relationship between NH4-N, NO2-N degradation and Gibbs free energy. These results obtained showed a good correlation between the models and the experimental data with correlation efficiency being 0.94 and 0.93 for nitritation, and nitratation, respectively. From the results found it can be deduced that SNaD is plausible under high cyanide conditions when cyanide degrading or tolerant microorganisms are employed. This can be a sustainable solution to SNaD inhibition by CN- compounds during wastewater treatment. Furthermore, a single strain was purified from the consortium and identified as Acinetobacter courvalinii. This bacterial strain was found to be able to perform sequential CN- degradation, and SNaD; an ability associated with multiphase growth strategy of the microorganism when provided with multiple nitrogenous sources, i.e. CN- and TN. The effect of CN- on nitrification and aerobic denitrification including enzyme expression, activity and protein functionality of Acinetobacter courvalinii was investigated. It was found that CN- concentration of up to 5.8 mg CN-/L did not affect the growth of Acinetobacter courvalinii. In cultures whereby the A. courvalinii isolate was used, degradation rates of CN- and NH4-N were found to be 2.2 mg CN-/L/h and 0.40 mg NH4-N/L/h, respectively. Moreover, the effect of CN- on NH4-N, nitrate-nitrogen (NO3-N) and NO2-N oxidizing enzymes was investigated, with findings indicating CN- did not affect the expression and activity of ammonia monooxygenase (AMO), but affected the activity of nitrate reductase (NaR) and nitrite reductase (NiR). Nevertheless, a slow decrease in NO2-N was observed after the addition of CN- thus confirming the activity of NaR and the activation of the denitrification pathway by the CN-. Moreover, five models’ (Monod, Moser, Rate law, Haldane, and Andrew’s model) ability to predict SNaD under CN- conditions, indicated that only Rate law, Haldane and Andrew’s models, were suited to predict both SNaD and CN- degradation. Due to low degradation rates of NH4-N and CN-, optimization of SNaD was essential. Therefore, response surface methodology was used to optimize the SNaD under CN- conditions. The physiological parameters that were considered for optimization were temperature and pH; with the result showing that the optimum for pH and temperature was 6.5 and 36.5oC respectively, with NH4-N and CN- degradation efficiency of 50 and 80.2%, respectively. Furthermore, the degradation kinetics of NH4-N and CN- were also studied under the optimum conditions in batch culture reactors, and the results showed that up to 70.6% and 97.3% of NH4-N and CN- were simultaneously degraded with degradation rates of 0.66 and 0.41 mg/L/h, respectively. The predictive ability of RSM was further compared with cybernetic models, and cybernetic models were found to better predict SNaD under CN- conditions. These results exhibited a promising solution in the management of inhibition effected of CN- towards SNaD at an industrial scale.