Article Database

Appraisals of robotic locomotor exoskeletons for gait: focus group insights from potential users with spinal cord injuries

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Purpose: To describe appraisals of robotic exoskeletons for locomotion by potential users with spinal cord injuries, their perceptions of device benefits and limitations, and recommendations for manufacturers and therapists regarding device use.

Thermodynamic analysis of biodegradation pathways

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Microorganisms provide a wealth of biodegradative potential in the reduction and elimination of xenobiotic compounds in the environment. One useful metric to evaluate potential biodegradation pathways is thermodynamic feasibility. However, experimental data for the thermodynamic properties of xenobiotics is scarce. The present work uses a group contribution method to study the thermodynamic properties of the University of Minnesota Biocatalysis/Biodegradation Database. The Gibbs free energies of formation and reaction are estimated for 914 compounds (81%) and 902 reactions (75%), respectively, in the database. The reactions are classified based on the minimum and maximum Gibbs free energy values, which accounts for uncertainty in the free energy estimates and a feasible concentration range relevant to biodegradation. Using the free energy estimates, the cumulative free energy change of 89 biodegradation pathways (51%) in the database could be estimated. A comparison of the likelihood of the biotransformation rules in the Pathway Prediction System and their thermodynamic feasibility was then carried out. This analysis revealed that when evaluating the feasibility of biodegradation pathways, it is important to consider the thermodynamic topology of the reactions in the context of the complete pathway. Group contribution is shown to be a viable tool for estimating, a priori, the thermodynamic feasibility and the relative likelihood of alternative biodegradation reactions. This work offers a useful tool to a broad range of researchers interested in estimating the feasibility of the reactions in existing or novel biodegradation pathways.

Computational framework for predictive biodegradation

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In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

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Inferring relevant control mechanisms for Interleukin-12 signaling in naive CD4+ T cells

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Interleukin-12 (IL-12) is a key cytokine involved in shaping the cell-mediated immunity to intracellular pathogens. IL-12 initiates a cellular response through the IL-12 signaling pathway, a member of the Janus kinase/signal transducer and activator of transcription (JAK/STAT) family of signaling networks. The JAK/STAT pathway includes several regulatory elements; however, the dynamics of these mechanisms are not fully understood. Therefore, the objective of this study was to infer the relative importance of regulatory mechanisms that modulate the activation of STAT4 in naïve CD4(+) T cells. Dynamic changes in protein expression and activity were measured using flow cytometry and these data were used to calibrate a mathematical model of IL-12 signaling. An empirical Bayesian approach was used to infer the relative strengths of the different regulatory mechanisms in the system. The model predicted that IL-12 receptor expression is regulated by a dynamic, autonomous program that was independent of STAT4 activation. In summary, a mathematical model of the canonical IL-12 signaling pathway used in conjunction with a Bayesian framework provided high-confidence predictions of the system-specific control mechanisms from the available experimental observations.

A two-compartment model of VEGF distribution in the mouse

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Pharmacokinetics and pharmacodynamics of VEGF-neutralizing agents

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Timescale analysis of rule-based biochemical reaction networks

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The flow of information within a cell is governed by a series of protein–protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed on reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor–ligand binding model and a rule‐based model of interleukin‐12 (IL‐12) signaling in naïve CD4+ T cells. The IL‐12 signaling pathway includes multiple protein–protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based on the available data. The analysis correctly predicted that reactions associated with Janus Kinase 2 and Tyrosine Kinase 2 binding to their corresponding receptor exist at a pseudo‐equilibrium. By contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL‐12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank‐ and flux‐based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule‐based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics.

Predicting the effects of anti-angiogenic agents targeting specific VEGF isoforms

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Effect of tumor microenvironment on tumor VEGF during anti- VEGF treatment: systems biology predictions

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Background: Vascular endothelial growth factor (VEGF) is known to be a potent promoter of angiogenesis under both physiological and pathological conditions. Given its role in regulating tumor vascularization, VEGF has been targeted in various cancer treatments, and anti-VEGF therapy has been used clinically for treatment of several types of cancer. Systems biology approaches, particularly computational models, provide insight into the complexity of tumor angiogenesis. These models complement experimental studies and aid in the development of effective therapies targeting angiogenesis.

Methods: We developed an experiment-based, molecular-detailed compartment model of VEGF kinetics and transport to investigate the distribution of two major VEGF isoforms (VEGF121 and VEGF165) in the body. The model is applied to predict the dynamics of tumor VEGF and, importantly, to gain insight into how tumor VEGF responds to an intravenous injection of an anti-VEGF agent.

Results: The model predicts that free VEGF in the tumor interstitium is seven to 13 times higher than plasma VEGF and is predominantly in the form of VEGF121 (>70%), predictions that are validated by experimental data. The model also predicts that tumor VEGF can increase or decrease with anti-VEGF treatment depending on tumor microenvironment, pointing to the importance of personalized medicine.

Conclusions: This computational study suggests that the rate of VEGF secretion by tumor cells may serve as a biomarker to predict the patient population that is likely to respond to anti-VEGF treatment. Thus, the model predictions have important clinical relevance and may aid clinicians and clinical researchers seeking interpretation of pharmacokinetic and pharmacodynamic observations and optimization of anti-VEGF therapies.