Interval and Possibilistic Methods for Constraint-Based Metabolic Models

This thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells. The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed in detail, network-based pathways analysis (NPA), metabolic flux analysis (MFA), and flux balance analysis (FBA). Four types of metabolic pathways are also compared to clarify the subtle differences among them. The second part is devoted to interval methods for constraint-based models. The first contribution is an interval approach to traditional MFA, particularly useful to estimate the metabolic fluxes under data scarcity (FS-MFA). These estimates provide insight on the internal state of cells, which determines the behaviour they exhibit at given conditions. The second contribution is a procedure for monitoring the metabolic fluxes during a cultivation process that uses FS-MFA to handle uncertainty. The third part of the document addresses the use of possibility theory. The main contribution is a possibilistic framework to (a) evaluate model and measurements consistency, and (b) perform flux estimations (Poss-MFA). It combines flexibility on the assumptions and computational efficiency. Poss-MFA is also applied to monitoring fluxes and metabolite concentrations during a cultivation, information of great use for fault-detection and control of industrial processes. Afterwards, the FBA problem is addressed.Llaneras Estrada, F. (2011). Interval and Possibilistic Methods for Constraint-Based Metabolic Models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10528Palanci

Which Metabolic Pathways Generate and Characterize the Flux Space? A Comparison among Elementary Modes, Extreme Pathways and Minimal Generators

Important efforts are being done to systematically identify the relevant pathways in a metabolic network. Unsurprisingly, there is not a unique set of network-based pathways to be tagged as relevant, and at least four related concepts have been proposed: extreme currents, elementary modes, extreme pathways, and minimal generators. Basically, there are two properties that these sets of pathways can hold: they can generate the flux space—if every feasible flux distribution can be represented as a nonnegative combination of flux through them—or they can comprise all the nondecomposable pathways in the network. The four concepts fulfill the first property, but only the elementary modes fulfill the second one. This subtle difference has been a source of errors and misunderstandings. This paper attempts to clarify the intricate relationship between the network-based pathways performing a comparison among them

Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity

<p>Abstract</p> <p>Background</p> <p>Constraint-based models enable structured cellular representations in which intracellular kinetics are circumvented. These models, combined with experimental data, are useful analytical tools to estimate the state exhibited (the phenotype) by the cells at given pseudo-steady conditions.</p> <p>Results</p> <p>In this contribution, a simplified constraint-based stoichiometric model of the metabolism of the yeast <it>Pichia pastoris</it>, a workhorse for heterologous protein expression, is validated against several experimental available datasets. Firstly, maximum theoretical growth yields are calculated and compared to the experimental ones. Secondly, possibility theory is applied to quantify the consistency between model and measurements. Finally, the biomass growth rate is excluded from the datasets and its prediction used to exemplify the capability of the model to calculate non-measured fluxes.</p> <p>Conclusions</p> <p>This contribution shows how a small-sized network can be assessed following a rational, quantitative procedure even when measurements are scarce and imprecise. This approach is particularly useful in lacking data scenarios.</p

A possibilistic framework for constraint-based metabolic flux analysis

<p>Abstract</p> <p>Background</p> <p>Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.</p> <p>Results</p> <p>Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data – even if those are scarce – to distinguish possible from impossible flux states in a gradual way.</p> <p>Conclusion</p> <p>We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.</p

Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory

Living cells can be modelled by successively imposing known constraints that limit their behaviour, such as mass balances, thermodynamic laws or enzyme capacities. The resulting constraint-based models enclose all the functional states that the modelled cells may exhibit. Then, predictions can be obtained from the models in two main ways: adding experimental data to determine the state of cells at given conditions (MFA) or invoking an assumption of evolved optimal behaviour (FBA). Both MFA and FBA predictions are typically performed at steady state. However, it is easy to take extracellular dynamics into account. This work explores the benefits of using possibility theory to get these dynamic predictions. It will be shown that the possibilistic methods (a) provide rich estimates for time-varying fluxes and metabolite concentrations, (b) account for uncertainty and data scarcity, and (c) give predictions relaxing the optimality assumption of FBA. On the other hand, these methods could serve as basis for monitoring and fault detection systems in industrial bioprocesses.This research has been partially supported by the Spanish Government MINECO (1st and 3rd authors are grateful to grant CICYT DPI2011-28112-C04-01, and A. Sala is grateful to grant DPI2011-27845-C02-01).Llaneras Estrada, F.; Sala, A.; Picó Marco, JA. (2012). Dynamic estimations of metabolic fluxes with constraint-based models and possibility theory. Journal of Process Control. 22(10):1946-1955. https://doi.org/10.1016/j.jprocont.2012.09.00119461955221

Validation of a FBA model for Pichia pastoris in chemostat cultures

Background: Constraint-based metabolic models and flux balance analysis (FBA) have been extensively used in the last years to investigate the behavior of cells and also as basis for different industrial applications. In this context, this work provides a validation of a small-sized FBA model of the yeast Pichia pastoris. Our main objective is testing how accurate is the hypothesis of maximum growth to predict the behavior of P. pastoris in a range of experimental environments. Results: A constraint-based model of P. pastoris was previously validated using metabolic flux analysis (MFA). In this paper we have verified the model ability to predict the cells behavior in different conditions without introducing measurements, experimental parameters, or any additional constraint, just by assuming that cells will make the best use of the available resources to maximize its growth. In particular, we have tested FBA model ability to: (a) predict growth yields over single substrates (glucose, glycerol, and methanol); (b) predict growth rate, substrate uptakes, respiration rates, and by-product formation in scenarios where different substrates are available (glucose, glycerol, methanol, or mixes of methanol and glycerol); (c) predict the different behaviors of P. pastoris cultures in aerobic and hypoxic conditions for each single substrate. In every case, experimental data from literature are used as validation. Conclusions: We conclude that our predictions based on growth maximisation are reasonably accurate, but still far from perfect. The deviations are significant in scenarios where P. pastoris grows on methanol, suggesting that the hypothesis of maximum growth could be not dominating in these situations. However, predictions are much better when glycerol or glucose are used as substrates. In these scenarios, even if our FBA model is small and imposes a strong assumption regarding how cells will regulate their metabolic fluxes, it provides reasonably good predictions in terms of growth, substrate preference, product formation, and respiration ratesThis research has been partially supported by the Spanish Government (cicyt: DPI 2011-28112-C04-01, DPI 2013-46982-C2-2-R) and Biopolis S.L. (R.C.055/12). Yeimy Morales is grateful for the BR Grant of the University of Girona (BR2012/26). The authors are grateful to the company Biopolis S.L. for his support to this research.Morales, Y.; Tortajada, M.; Picó Marco, JA.; Vehi, J.; Llaneras, F. (2014). Validation of a FBA model for Pichia pastoris in chemostat cultures. BMC Systems Biology. 8:1-17. https://doi.org/10.1186/s12918-014-0142-yS1178Macauley-Patrick S, Fazenda ML, McNeil B, Harvey LM: Heterologous protein production using the Pichia pastoris expression system. 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PFA toolbox: a MATLAB tool for Metabolic Flux Analysis

Background: Metabolic Flux Analysis (MFA) is a methodology that has been successfully applied to estimate metabolic fluxes in living cells. However, traditional frameworks based on this approach have some limitations, particularly when measurements are scarce and imprecise. This is very common in industrial environments. The PFA Toolbox can be used to face those scenarios. Results: Here we present the PFA (Possibilistic Flux Analysis) Toolbox for MATLAB, which simplifies the use of Interval and Possibilistic Metabolic Flux Analysis. The main features of the PFA Toolbox are the following: (a) It provides reliable MFA estimations in scenarios where only a few fluxes can be measured or those available are imprecise. (b) It provides tools to easily plot the results as interval estimates or flux distributions. (c) It is composed of simple functions that MATLAB users can apply in flexible ways. (d) It includes a Graphical User Interface (GUI), which provides a visual representation of the measurements and their uncertainty. (e) It can use stoichiometric models in COBRA format. In addition, the PFA Toolbox includes a User s Guide with a thorough description of its functions and several examples. Conclusions: The PFA Toolbox for MATLAB is a freely available Toolbox that is able to perform Interval and Possibilistic MFA estimations.This research has been partially supported by the Spanish Government (FEDER-CICYT: DPI 2014-55276-C5-1-R). Yeimy Morales is grateful for the BR Grants of the University of Girona (BR2012/26). Gabriel Bosque Chacon is recipient of a doctoral fellowship from the Spanish Government (BES-2012-053772).Morales, Y.; Bosque Chacón, G.; Vehi, J.; Picó Marco, JA.; Llaneras, F. (2016). PFA toolbox: a MATLAB tool for Metabolic Flux Analysis. 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Interval and possibilistic methods for constraint-based metabolic models, PhD Thesis. Universidad Politécnica de Valencia: Departamento de Ingeniería de Sistemas y Automática; 2011.Llaneras F, Picó J. An interval approach for dealing with flux distributions and elementary modes activity patterns. J Theor Biol. 2007;246(2):290–308.Llaneras F, Sala A, Picó J. A possibilistic framework for constraint-based metabolic flux analysis. BMC Syst Biol. 2009;3(1):79.Tortajada M, Llaneras F, Picó J. Validation of a constraint-based model of Pichia pastoris metabolism under data scarcity. BMC Syst Biol. 2010;4(1):115.Llaneras F, Picó J. A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient. BMC Bioinformatics. 2007;8(1):421.Iyer VV, Ovacik MA, Androulakis IP, Roth CM, Ierapetritou MG. Transcriptional and metabolic flux profiling of triadimefon effects on cultured hepatocytes. 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Barriers to linkage to care in hepatitis C patients with substance use disorders and dual diagnoses, despite centralized management

Hepatitis C virus; Dual diagnosis; Substance use disorderVirus de l'Hepatitis C; Diagnòstic dual; Trastorn per consum de substànciesVirus de la Hepatitis C; Diagnóstico dual; Trastorno por consumo de sustanciasBackground: Hepatitis C virus (HCV) management is a challenge in patients with substance use disorder (SUD). This study aimed to describe an HCV screening and linkage to care program in SUD patients, and analyze the characteristics of this population in relation to HCV infection, particularly the impact of psychiatric comorbidities (dual diagnosis). Methods: This study was a prospective clinical cohort study using a collaborative, multidisciplinary model to offer HCV care (screening, diagnosis, and therapy) to individuals with SUD attending a dedicated hospital clinic. The characteristics of the participants, prevalence of HCV infection, percentage who started therapy, and adherence to treatment were compared according to the patients’ consumption characteristics and presence of dual diagnosis. HCV screening, diagnosis, treatment initiation, and sustained virologic response were analyzed. Results: 528 individuals attended the center (November 2018–June 2019) and 401 (76%) accepted screening. In total, 112 (28%) were anti-HCV-positive and 42 (10%) had detectable HCV RNA, but only 20 of the latter started HCV therapy. Among the 253 (63%) patients with a dual diagnosis, there were no differences in HCV infection prevalence versus patients with SUD alone (p = 0.28). Dual diagnosis did not lead to a higher risk of HCV infection or interfere with linkage to care or treatment. Conclusion: This study found a high prevalence of dual diagnosis and HCV infection in SUD patients, but dual diagnosis was not associated with an increased risk of acquiring HCV or more complex access to care. Despite use of a multidisciplinary management approach, considerable barriers to HCV care remain in this population that would need more specific focus.This work was supported by AbbVie

A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient

<p>Abstract</p> <p>Background</p> <p>An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties.</p> <p>Results</p> <p>We propose a procedure to estimate the evolution of the metabolic fluxes that is structured as follows: 1) measure the concentrations of extracellular species and biomass, 2) convert this data to measured fluxes and 3) estimate the non-measured fluxes using the Flux Spectrum Approach, a variant of Metabolic Flux Analysis that overcomes the difficulties mentioned above without assuming optimal behaviour. We apply the procedure to a real problem taken from the literature: estimate the metabolic fluxes during a cultivation of CHO cells in batch mode. We show that it provides a reliable and rich estimation of the non-measured fluxes, thanks to considering measurements uncertainty and reversibility constraints. We also demonstrate that this procedure can estimate the non-measured fluxes even when there is a lack of measurable species. In addition, it offers a new method to deal with inconsistency.</p> <p>Conclusion</p> <p>This work introduces a procedure to estimate time-varying metabolic fluxes that copes with the insufficiency of measured species and with its intrinsic uncertainty. The procedure can be used as an off-line analysis of previously collected data, providing an insight into the dynamic behaviour of the organism. It can be also profitable to the on-line monitoring of a running process, mitigating the traditional lack of reliable on-line sensors in industrial environments.</p

A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient-1

<p><b>Copyright information:</b></p><p>Taken from "A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient"</p><p>http://www.biomedcentral.com/1471-2105/8/421</p><p>BMC Bioinformatics 2007;8():421-421.</p><p>Published online 30 Oct 2007</p><p>PMCID:PMC2212668.</p><p></p>ing the derivative or using a dynamic observer). Finally, the calculated fluxes may be filtered to get a smooth signal. Each step is conditioned by the operation mode (on-line or off-line)