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.

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.

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.

Modeling of an immune response: Queuing network analysis of the impact of zinc and cadmium on macrophage activation.

Chronic obstructive pulmonary disease is characterized by progressive, irreversible airflow obstruction resulting from an abnormal inflammatory response to noxious gases and particles. Alveolar macrophages rely on the transcription factors, nuclear factor κB and mitogen-activated protein kinase, among others, to facilitate the production of inflammatory mediators designed to help rid the lung of foreign pathogens and noxious stimuli. Building a kinetic model using queuing networks, provides a quantitative approach incorporating an initial number of individual molecules along with rates of the reactions in any given pathway. Accordingly, this model has been shown useful to model cell behavior including signal transduction, transcription, and metabolic pathways. The aim of this study was to determine whether a queuing theory model that involves lipopolysaccharide-mediated macrophage activation in tandem with changes in intracellular Cd and zinc (Zn) content or a lack thereof, would be useful to predict their impact on immune activation. We then validate our model with biologic cytokine output from human macrophages relative to the timing of innate immune activation. We believe that our results further prove the validity of the queuing theory approach to model intracellular molecular signaling and postulate that it can be useful to predict additional cell signaling pathways and the corresponding biological outcomes.

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.

Identifying Intracellular pDNA Losses From a Model of Nonviral Gene Delivery.

Nonviral gene delivery systems are a type of nanocommunication system that transmit plasmid packets (i.e., pDNA packets) that are programmed at the nanoscale to biological systems at the microscopic cellular level. This engineered nanocommunication system suffers large pDNA losses during transmission of the genetically encoded information, preventing its use in biotechnological and medical applications. The pDNA losses largely remain uncharacterized, and the ramifications of reducing pDNA loss from newly designed gene delivery systems remain difficult to predict. Here, the pDNA losses during primary and secondary transmission chains were identified utilizing a MATLAB model employing queuing theory simulating delivery of pEGFPLuc transgene to HeLa cells carried by Lipofectamine 2000 nonviral DNA carrier. Minimizing pDNA loss during endosomal escape of the primary transmission process results in increased number of pDNA in the nucleus with increased transfection, but with increased probability of cell death. The number of pDNA copies in the nucleus and the amount of time the pDNAs are in the nucleus directly correlates to improved transfection efficiency. During secondary transmission, pDNAs are degraded during distribution to daughter cells. Reducing pDNA losses improves transfection, but a balance in quantity of nuclear pDNA, mitosis, and toxicity must be considered in order to achieve therapeutically relevant transfection levels.

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.

A novel method for simulating insulin mediated GLUT4 translocation.

Glucose transport in humans is a vital process which is tightly regulated by the endocrine system. Specifically, the insulin hormone triggers a cascade of intracellular signals in target cells mediating the uptake of glucose. Insulin signaling triggers cellular relocalization of the glucose transporter protein GLUT4 to the cell surface, which is primarily responsible for regulated glucose import. Pathology associated with the disruption of this pathway can lead to metabolic disorders, such as type II diabetes mellitus, characterized by the failure of cells to appropriately uptake glucose from the blood. We describe a novel simulation tool of the insulin intracellular response, incorporating the latest findings regarding As160 and GEF interactions. The simulation tool differs from previous computational approaches which employ algebraic or differential equations; instead, the tool incorporates statistical variations of kinetic constants and initial molecular concentrations which more accurately mimic the intracellular environment. Using this approach, we successfully recapitulate observed in vitro insulin responses, plus the effects of Wortmannin-like inhibition of the pathway. The developed tool provides insight into transient changes in molecule concentrations throughout the insulin signaling pathway, and may be employed to identify or evaluate potentially critical components of this pathway, including those associated with insulin resistance. In the future, this highly tractable platform may be useful for simulating other complex cell signaling pathways. Biotechnol. Bioeng. 2014;111: 2454-2465. © 2014 Wiley Periodicals, Inc.