Dynamic flux distributions (unit: mmol/g DCW/h) in central metabolic pathways.

<p>The yellow filled cycles are intracellular metabolites; the blue filled cycles are substrates and extracellular metabolites (LAC: extracellular lactate, PYR: extracellular pyruvate, ACT: extracellular acetate); the dashed lines indicate inactive pathways; the green filled boxes are reactions listed in iSO783. All the abbreviations refer to iSO783 <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002376#pcbi.1002376-Schuetz1" target="_blank">[7]</a>.</p

Parameters estimated in the empirical Monod model.

<p>Parameters estimated in the empirical Monod model.</p

Monod model for growth kinetics.

<p>The green dots are the measurements, and the blue lines are the simulated growth by the empirical Monod model.</p

Experimentally observed and simulated isotopomer labeling patterns [M-57]⁺ in proteinogenic amino acids.

<p>The standard error for GC-MS measurement was below 0.02. <b>A1</b>: dynamic isotopomer simulation for glutamate from dFBA without considering reaction reversibility (dFBA w/o reversibility). <b>A2</b>: dynamic isotopomer simulation for glutamate from dFBA considering reaction reversibility (dFBA w/ reversibility). Bar plot: comparison of experimentally observed isotopomer labeling to simulated isotopomer labeling patterns of glutamate (<b>A1</b>: without considering reaction reversibility; <b>A2</b>: considering reaction reversibility). <b>B</b>: The model fitting of the isotopomer labeling data of five key amino acids (Ala, Gly, Ser, Asp, and Glu) at t = 24 and 30 h.</p

Flowchart of dFBA to decipher the dynamic metabolism of <i>S. oneidensis</i> MR-1.

<p>Flowchart of dFBA to decipher the dynamic metabolism of <i>S. oneidensis</i> MR-1.</p

Exchange coefficients for key metabolic pathways of MR-1.

<p>Exchange coefficients for key metabolic pathways of MR-1.</p

The role of the non-profit sector in the mental health care

This diploma thesis called "The role of the nonprofit sector in the mental health care" deals with problems expressed in the context of nongovernmental organizations that are regarded as a significant stakeholder very important actors in the issue of mental disease. Besides the state that creates predominantly political conception concerning mental health and also mostly ensures some constituents of mental health care (i.e. in the psychiatric asylums) there is also nonprofit sector playing an important role. We can observe its importance on the level of creation of the new mental health conceptions as well as on the level of response to the continuing process of deinstitutionalization of psychiatric care manifested in wider approach to mental disease and moreover its services with which the nonprofit sector completes and sometimes substitutes the role of the state. This thesis firstly deals with the mental disease as the integral part of the complex state of health of each of us, which importance is often underestimated and consequently insufficiently reflected in mental health policy in the ČR (and not only here) which should follow the world wide trend of finding out the negative consequences of mental diseases. After grounding the nonprofit sector in social and legal frame I continue in analyzing and..

Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming

<div><p>¹³C metabolic flux analysis (¹³C-MFA) has been widely used to measure <i>in vivo</i> enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux (<a href="http://mflux.org" target="_blank">http://mflux.org</a>) that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 ¹³C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on ¹³C-MFA are published for non-model species.</p></div

A universal central metabolic pathway for bacteria.

<p>The central carbon metabolic pathway is simplified into 29 fluxes, used as the outputs of our model.</p

Summary of root mean squared error (RMSE) from 20 case studies: averaged flux from ¹³C-MFA dataset, ML-only, and MFlux (ML + quadratic programming).

<p>The average RMSE is 7.7 from ML-only, and 5.6 from MFlux. Detailed information is in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004838#pcbi.1004838.s002" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004838#pcbi.1004838.s003" target="_blank">S2</a> Tables.</p