Increasing numbers of biochemical network models become available and reuse of these models is becoming more common. As a consequence, tools to compare models are needed. Comparison can be difficult because model builders often use different standards during reconstruction, metabolite formulas are not always indicated, IDs and names of metabolites are different, and models are stored in different formats (SBML, COBRA and others). Herein, a model comparison algorithm for SBML and COBRA format models is presented, called ModeRator. Precondition for correct matching of reactions is the comparison of the participating metabolites. ModeRator is based on the comparison of metabolite names as text strings. An automatic three level filtering approach is implemented in the software, which rejects pairs of potentially equal metabolites and builds an opinion about metabolite pairs with high similarity in metabolite names. ModeRator was applied to two test cases, comparing two models of each, E.coli and S.cerevisiae. Matches of the automatic mapping were manually inspected and compared with the automatic predictions. Automatic metabolite mapping of E.coli models (1314 and 1704 metabolites) comparing only identifiers revealed a high number of accordant metabolites. Both models originate from the same source (BioCyc database). No significant difference between automatic mapping and manual curation are observed. For the comparison of two S.cerevisiae models (679 and 1061 metabolites), three level filtration by metabolite name is used. The discrepancy between manual curated predictions and ModeRator predictions was 7%.
Abstract. An unstructured kinetic model was developed in this study for the batch production of bioethanol by the yeast Kluyveromyces marxianus DSM 5422 from the renewable sources of agricultural and food processing origin, such as whey permeate or inulin, which include the terms of both substrate and product inhibitions. Experimental data collected from multiple fermentations in bioreactors with three different initial concentrations for each substrate were used to estimate the parameters and to validate the proposed model. The growth of K.marxianus can be expressed by the Haldane-type extended Monod model in combination with the Jerusalimsky term for the non-competitive product inhibition and the Luedeking-Piret equation was adequate to describe the growth-associated formation of ethanol as the target product. The parameters in the models were estimated by minimizing mean-squared errors between the predictions of the models and the experimental data using the differential evolution (DE) algorithm and the L-BFGS-B nonlinear optimization code. In all cases, the model simulation matched well with the fermentation data being confirmed by the high R-squared values (0.984, 0.992 and 0.965 for WP, lactose and inulin, respectively). The kinetic models proposed here can be employed for the development and optimization of the bioethanol production processes from renewable resources.
Introduction
Insulin pump therapy represents an alternative to multiple daily injections and can improve glycemic control and quality of life (QoL) in Type 1 diabetes mellitus (T1DM) patients. We aimed to explore the differences and factors related to the T1DM-specific QoL of such patients in Latvia.
Design and methods
A mixed-method cross-sectional study on 87 adult T1DM patients included 20 pump users and 67 users of injections who participated in the quantitative part of the study; 8 pump users and 13 injection users participated in the qualitative part. Patients were invited to participate using a dedicated digital platform. Their QoL and self-management habits were assessed using specially developed questionnaires adapted to Latvian conditions. Multiple logistic regression models were built to investigate the association between social and self-management factors and patients’ QoL. In addition, qualitative analysis of answers was performed.
Results
Insulin pump users were younger, had higher incomes, and reported higher T1DM expenses than users of multiple daily injections. There were no differences in self-management between the groups; Total QoL differed at the 0.1 significance level. In fully adjusted multiple logistic regression models, the most important factor that increased Total QoL was lower T1DM-related expenses (odds ratio, OR 7.02 [95% confidence interval 1.29; 38.0]). Men and those with more years of living with T1DM had better QoL (OR 9.62 [2.20; 42.1] and OR 1.16 [1.05; 1.29], respectively), but the method of administration was not significantly associated with QoL (OR 7.38 [0.87; 62.9]). Qualitative data supported the results of quantitative analysis.
Conclusions
QoL was the main reason to use an insulin pump, while the expense was the main reason to avoid the use of it or to stop using it. Reimbursement policies thus should be considered to enable patients to choose the more convenient method for themselves.
Dynamic models give detailed information about the influence of many parameters on the behaviour of the biochemical process of interest. Parameter optimization of dynamic models is used in parameter estimation tasks and in design tasks
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