Integrating glycolysis, citric acid cycle, pentose phosphate pathway, and fatty acid beta-oxidation into a single computational model.

The metabolic network of a living cell is highly intricate and involves complex interactions between various pathways. In this study, we propose a computational model that integrates glycolysis, the pentose phosphate pathway (PPP), the fatty acids beta-oxidation, and the tricarboxylic acid cycle (TCA cycle) using queueing theory. The model utilizes literature data on metabolite concentrations and enzyme kinetic constants to calculate the probabilities of individual reactions occurring on a microscopic scale, which can be viewed as the reaction rates on a macroscopic scale. However, it should be noted that the model has some limitations, including not accounting for all the reactions in which the metabolites are involved. Therefore, a genetic algorithm (GA) was used to estimate the impact of these external processes. Despite these limitations, our model achieved high accuracy and stability, providing real-time observation of changes in metabolite concentrations. This type of model can help in better understanding the mechanisms of biochemical reactions in cells, which can ultimately contribute to the prevention and treatment of aging, cancer, metabolic diseases, and neurodegenerative disorders.

Deep Reinforcement Learning for Articulatory Synthesis in a Vowel-to-Vowel Imitation Task.

Articulatory synthesis is one of the approaches used for modeling human speech production. In this study, we propose a model-based algorithm for learning the policy to control the vocal tract of the articulatory synthesizer in a vowel-to-vowel imitation task. Our method does not require external training data, since the policy is learned through interactions with the vocal tract model. To improve the sample efficiency of the learning, we trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, we extracted acoustic features using an acoustic word embedding (AWE) model. This model was trained to discriminate between different words and to enable compact encoding of acoustics while preserving contextual information of the input. Our preliminary experiments showed that introducing this AWE model was crucial to guide the policy toward a near-optimal solution. The acoustic embeddings, obtained using the proposed approach, were revealed to be useful when applied as inputs to the policy and the model of speech production dynamics.

Conversion of fat to cellular fuel-Fatty acids ß-oxidation model.

ß-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a ß-oxidation model of fatty acids based on queueing theory. It uses Michaelis-Menten enzyme kinetics, and literature data on metabolites' concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has been validated against experimental data. There are diseases that change the metabolism of fatty acids and the presented model can be used to understand the cause of these changes, analyze metabolites abnormalities, and determine the initial target of treatment.

Modeling an immune response to influenza A virus infection in alveolar epithelial cells.

Influenza A viruses (IAV) have been the cause of several influenza pandemics in history and are a significant threat for the next global pandemic. Hospitalized influenza patients often have excess interferon production and a dysregulated immune response to the IAV infection. Obtaining a better understanding of the mechanisms of IAV infection that induce these harmful effects would help drug developers and health professionals create more effective treatments for IAV infection and improve patient outcomes. IAV stimulates viral sensors and receptors expressed by alveolar epithelial cells, like RIG-I and toll-like receptor 3 (TLR3). These two pathways coordinate with one another to induce expression of type III interferons to combat the infection. Presented here is a queuing theory-based model of these pathways that was designed to analyze the timing and amount of interferons produced in response to IAV single stranded RNA and double-stranded RNA detection. The model accurately represents biological data showing the necessary coordination of the RIG-I and TLR3 pathways for effective interferon production. This model can serve as the framework for future studies of IAV infection and identify new targets for potential treatments.

Queueing theory model of mTOR complexes' impact on Akt-mediated adipocytes response to insulin.

A queueing theory based model of mTOR complexes impact on Akt-mediated cell response to insulin is presented in this paper. The model includes several aspects including the effect of insulin on the transport of glucose from the blood into the adipocytes with the participation of GLUT4, and the role of the GAPDH enzyme as a regulator of mTORC1 activity. A genetic algorithm was used to optimize the model parameters. It can be observed that mTORC1 activity is related to the amount of GLUT4 involved in glucose transport. The results show the relationship between the amount of GAPDH in the cell and mTORC1 activity. Moreover, obtained results suggest that mTORC1 inhibitors may be an effective agent in the fight against type 2 diabetes. However, these results are based on theoretical knowledge and appropriate experimental tests should be performed before making firm conclusions.

Queueing theory model of pentose phosphate pathway.

Due to its role in maintaining the proper functioning of the cell, the pentose phosphate pathway (PPP) is one of the most important metabolic pathways. It is responsible for regulating the concentration of simple sugars and provides precursors for the synthesis of amino acids and nucleotides. In addition, it plays a critical role in maintaining an adequate level of NADPH, which is necessary for the cell to fight oxidative stress. These reasons prompted the authors to develop a computational model, based on queueing theory, capable of simulating changes in PPP metabolites' concentrations. The model has been validated with empirical data from tumor cells. The obtained results prove the stability and accuracy of the model. By applying queueing theory, this model can be further expanded to include successive metabolic pathways. The use of the model may accelerate research on new drugs, reduce drug costs, and reduce the reliance on laboratory animals necessary for this type of research on which new methods are tested.

Queueing theory model of Krebs cycle.

MOTIVATION: Queueing theory can be effective in simulating biochemical reactions taking place in living cells, and the article paves a step toward development of a comprehensive model of cell metabolism. Such a model could help to accelerate and reduce costs for developing and testing investigational drugs reducing number of laboratory animals needed to evaluate drugs. RESULTS: The article presents a Krebs cycle model based on queueing theory. The model allows for tracking of metabolites concentration changes in real time. To validate the model, a drug-induced inhibition affecting activity of enzymes involved in Krebs cycle was simulated and compared with available experimental data. AVAILABILITYAND IMPLEMENTATION: The source code is freely available for download at https://github.com/UTP-WTIiE/KrebsCycleUsingQueueingTheory, implemented in C# supported in Linux or MS Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stochastic Simulation of Cellular Metabolism.

Increased technological methods have enabled the investigation of biology at nanoscale levels. Such systems require the use of computational methods to comprehend the complex interactions that occur. The dynamics of metabolic systems have been traditionally described utilizing differential equations without fully capturing the heterogeneity of biological systems. Stochastic modeling approaches have recently emerged with the capacity to incorporate the statistical properties of such systems. However, the processing of stochastic algorithms is a computationally intensive task with intrinsic limitations. Alternatively, the queueing theory approach, historically used in the evaluation of telecommunication networks, can significantly reduce the computational power required to generate simulated results while simultaneously reducing the expansion of errors. We present here the application of queueing theory to simulate stochastic metabolic networks with high efficiency. With the use of glycolysis as a well understood biological model, we demonstrate the power of the proposed modeling methods discussed herein. Furthermore, we describe the simulation and pharmacological inhibition of glycolysis to provide an example of modeling capabilities.

Modeling 3-D diffusion using queueing networks.

The objective of this paper is to propose and then validate a new method for simulating molecular diffusion in a 3-D environment. Diffusion governing principles of Brownian motion have been discovered by Einstein and Smoluchowski. In a classical approach, diffusion is modeled using partial differential equations. However, solving those, even using numerical methods is usually time consuming, particularly in the case of an inhomogeneous environment. In this paper, we propose to use queueing networks to model diffusion of molecules as governed by Fick's law. The proposed model has been validated using the Kolmogorov-Smirnov test to compare results obtained from a simulation with theoretical standard deviations resulting from Einstein-Smoluchowski's approach.

The mixed lineage kinase-3 inhibitor URMC-099 improves therapeutic outcomes for long-acting antiretroviral therapy.

During studies to extend the half-life of crystalline nanoformulated antiretroviral therapy (nanoART) the mixed lineage kinase-3 inhibitor URMC-099, developed as an adjunctive neuroprotective agent was shown to facilitate antiviral responses. Long-acting ritonavir-boosted atazanavir (nanoATV/r) nanoformulations co-administered with URMC-099 reduced viral load and the numbers of HIV-1 infected CD4+ T-cells in lymphoid tissues more than either drug alone in infected humanized NOD/SCID/IL2Rγc-/- mice. The drug effects were associated with sustained ART depots. Proteomics analyses demonstrated that the antiretroviral responses were linked to affected phagolysosomal storage pathways leading to sequestration of nanoATV/r in Rab-associated recycling and late endosomes; sites associated with viral maturation. URMC-099 administered with nanoATV induced a dose-dependent reduction in HIV-1p24 and reverse transcriptase activity. This drug combination offers a unique chemical marriage for cell-based viral clearance. From the Clinical Editor: Although successful in combating HIV-1 infection, the next improvement in antiretroviral therapy (nanoART) would be to devise long acting therapy, such as intra-cellular depots. In this report, the authors described the use of nanoformulated antiretroviral therapy given together with the mixed lineage kinase-3 inhibitor URMC-099, and showed that this combination not only prolonged drug half-life, but also had better efficacy. The findings are hoped to be translated into the clinical setting in the future.

Integrating mitosis, toxicity, and transgene expression in a telecommunications packet-switched network model of lipoplex-mediated gene delivery.

Gene delivery systems transport exogenous genetic information to cells or biological systems with the potential to directly alter endogenous gene expression and behavior with applications in functional genomics, tissue engineering, medical devices, and gene therapy. Nonviral systems offer advantages over viral systems because of their low immunogenicity, inexpensive synthesis, and easy modification but suffer from lower transfection levels. The representation of gene transfer using models offers perspective and interpretation of complex cellular mechanisms,including nonviral gene delivery where exact mechanisms are unknown. Here, we introduce a novel telecommunications model of the nonviral gene delivery process in which the delivery of the gene to a cell is synonymous with delivery of a packet of information to a destination computer within a packet-switched computer network. Such a model uses nodes and layers to simplify the complexity of modeling the transfection process and to overcome several challenges of existing models. These challenges include a limited scope and limited time frame, which often does not incorporate biological effects known to affect transfection. The telecommunication model was constructed in MATLAB to model lipoplex delivery of the gene encoding the green fluorescent protein to HeLa cells. Mitosis and toxicity events were included in the model resulting in simulation outputs of nuclear internalization and transfection efficiency that correlated with experimental data. A priori predictions based on model sensitivity analysis suggest that increasing endosomal escape and decreasing lysosomal degradation, protein degradation, and GFP-induced toxicity can improve transfection efficiency by three-fold. Application of the telecommunications model to nonviral gene delivery offers insight into the development of new gene delivery systems with therapeutically relevant transfection levels.

Endosomal trafficking of nanoformulated antiretroviral therapy facilitates drug particle carriage and HIV clearance.

UNLABELLED: Limitations of antiretroviral therapy (ART) include poor patient adherence, drug toxicities, viral resistance, and failure to penetrate viral reservoirs. Recent developments in nanoformulated ART (nanoART) could overcome such limitations. To this end, we now report a novel effect of nanoART that facilitates drug depots within intracellular compartments at or adjacent to the sites of the viral replication cycle. Poloxamer 407-coated nanocrystals containing the protease inhibitor atazanavir (ATV) were prepared by high-pressure homogenization. These drug particles readily accumulated in human monocyte-derived macrophages (MDM). NanoATV concentrations were ∼1,000 times higher in cells than those that could be achieved by the native drug. ATV particles in late and recycling endosome compartments were seen following pulldown by immunoaffinity chromatography with Rab-specific antibodies conjugated to magnetic beads. Confocal microscopy provided cross validation by immunofluorescent staining of the compartments. Mathematical modeling validated drug-endosomal interactions. Measures of reverse transcriptase activity and HIV-1 p24 levels in culture media and cells showed that such endosomal drug concentrations enhanced antiviral responses up to 1,000-fold. We conclude that late and recycling endosomes can serve as depots for nanoATV. The colocalization of nanoATV at endosomal sites of viral assembly and its slow release sped antiretroviral activities. Long-acting nanoART can serve as a drug carrier in both cells and subcellular compartments and, as such, can facilitate viral clearance. IMPORTANCE: The need for long-acting ART is significant and highlighted by limitations in drug access, toxicity, adherence, and reservoir penetrance. We propose that targeting nanoformulated drugs to infected tissues, cells, and subcellular sites of viral replication may improve clinical outcomes. Endosomes are sites for human immunodeficiency virus assembly, and increasing ART concentrations in such sites enhances viral clearance. The current work uncovers a new mechanism by which nanoART can enhance viral clearance over native drug formulations.