Both Ligand- and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Protein–Coupled Receptor Signaling

G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β2-adrenergic and α2A-adrenergic receptor systems

A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation

Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and propose a nonparametric Bayesian approach to infer rat hippocampal population codes during spatial navigation. To tackle the model selection problem, we leverage a nonparametric Bayesian model. Specifically, to analyze rat hippocampal ensemble spiking activity, we apply a hierarchical Dirichlet process-hidden Markov model (HDP-HMM) using two Bayesian inference methods, one based on Markov chain Monte Carlo (MCMC) and the other based on variational Bayes (VB). We demonstrate the effectiveness of our Bayesian approaches on recordings from a freely-behaving rat navigating in an open field environment. We find that MCMC-based inference with Hamiltonian Monte Carlo (HMC) hyperparameter sampling is flexible and efficient, and outperforms VB and MCMC approaches with hyperparameters set by empirical Bayes

Civil RICO Reform: The Gatekeeper Concept

Since coming into vogue in the mid-1980s, civil RICO has often been criticized and targeted for reform. Critics claim that civil RICO is too broad because it potentially applies to all commercial transactions.More specifically, opponents claim that RICO\u27s inclusion of mail and wire fraud as predicate acts unjustly subjects all legitimate businesses to liability.For example, Representative Rick Boucher, sponsor of the 1989 RICO reform legislation, has stated: Fraud allegations are commonly made in contract situations, and all that is needed to convert a simple contract dispute into a civil RICO case is the allegation that there was a contract and the additional allegation that either the mails or the telephones were used more than once in either forming or breaching the contract. Such criticism has led to numerous attempts by courts and legislators to curtail civil RICO. For the most part, these efforts have sought to emasculate civil RICO rather than to rectify isolated problems of abuse or over breadth

Lipid Raft-Mediated Regulation of G-Protein Coupled Receptor Signaling by Ligands which Influence Receptor Dimerization: A Computational Study

G-protein coupled receptors (GPCRs) are the largest family of cell surface receptors; they activate heterotrimeric G-proteins in response to ligand stimulation. Although many GPCRs have been shown to form homo- and/or heterodimers on the cell membrane, the purpose of this dimerization is not known. Recent research has shown that receptor dimerization may have a role in organization of receptors on the cell surface. In addition, microdomains on the cell membrane termed lipid rafts have been shown to play a role in GPCR localization. Using a combination of stochastic (Monte Carlo) and deterministic modeling, we propose a novel mechanism for lipid raft partitioning of GPCRs based on reversible dimerization of receptors and then demonstrate that such localization can affect GPCR signaling. Modeling results are consistent with a variety of experimental data indicating that lipid rafts have a role in amplification or attenuation of G-protein signaling. Thus our work suggests a new mechanism by which dimerization-inducing or inhibiting characteristics of ligands can influence GPCR signaling by controlling receptor organization on the cell membrane

A multifaceted approach to modeling the immune response in tuberculosis

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self‐organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host–pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479–489 DOI: 10.1002/wsbm.131 For further resources related to this article, please visit the WIREs websitePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86857/1/131_ftp.pd

Quantitative inference of cellular parameters from microfluidic cell culture systems

Microfluidic cell culture systems offer a convenient way to measure cell biophysical parameters in conditions close to the physiological environment. We demonstrate the application of a mathematical model describing the spatial distribution of nutrient and growth factor concentrations in inferring cellular oxygen uptake rates from experimental measurements. We use experimental measurements of oxygen concentrations in a poly(dimethylsiloxane) (PDMS) microreactor culturing human hepatocellular liver carcinoma cells (HepG2) to infer quantitative information on cellular oxygen uptake rates. We use a novel microchannel design to avoid the parameter correlation problem associated with simultaneous cellular uptake and diffusion of oxygen through the PDMS surface. We find that the cellular uptake of oxygen is dependent on the cell density and can be modeled using a logistic term in the Michaelis–Menten equation. Our results are significant not only for the development of novel assays to quantitatively infer cell response to stimuli, but also for the development, design, and optimization of novel in vitro systems for drug discovery and tissue engineering. Biotechnol. Bioeng. 2009;103: 966–974. © 2009 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63052/1/22334_ftp.pd

In silico evaluation and exploration of antibiotic tuberculosis treatment regimens

Abstract Background Improvement in tuberculosis treatment regimens requires selection of antibiotics and dosing schedules from a large design space of possibilities. Incomplete knowledge of antibiotic and host immune dynamics in tuberculosis granulomas impacts clinical trial design and success, and variations among clinical trials hamper side-by-side comparison of regimens. Our objective is to systematically evaluate the efficacy of isoniazid and rifampin regimens, and identify modifications to these antibiotics that improve treatment outcomes. Results We pair a spatio-temporal computational model of host immunity with pharmacokinetic and pharmacodynamic data on isoniazid and rifampin. The model is calibrated to plasma pharmacokinetic and granuloma bacterial load data from non-human primate models of tuberculosis and to tissue and granuloma measurements of isoniazid and rifampin in rabbit granulomas. We predict the efficacy of regimens containing different doses and frequencies of isoniazid and rifampin. We predict impacts of pharmacokinetic/pharmacodynamic modifications on antibiotic efficacy. We demonstrate that suboptimal antibiotic concentrations within granulomas lead to poor performance of intermittent regimens compared to daily regimens. Improvements from dose and frequency changes are limited by inherent antibiotic properties, and we propose that changes in intracellular accumulation ratios and antimicrobial activity would lead to the most significant improvements in treatment outcomes. Results suggest that an increased risk of drug resistance in fully intermittent as compared to daily regimens arises from higher bacterial population levels early during treatment. Conclusions Our systems pharmacology approach complements efforts to accelerate tuberculosis therapeutic development.http://deepblue.lib.umich.edu/bitstream/2027.42/116019/1/12918_2015_Article_221.pd

114 Years and Counting – An Updated History of the University of South Dakota Sanford School of Medicine

If those who founded the USD Sanford School of Medicine in 1907 were to drop in on the school today, what would they find most surprising? Would the time travelers marvel at the modern teaching facilities, our amazing ability to cure previously deadly diseases, or would they be puzzled by the complexities of medical practice in the modern era of technology and regulation? The first century of the medical school’s existence brought immense change as the school evolved. As advances in medical science and medical practice accelerated, the school kept pace. The first century resulted in a well-established medical school that provided excellent education and supplied an outstanding medical workforce for South Dakota. Now more than a dozen years into its second century, the school has moved squarely into the national spotlight. As the following article details, the school has become an award-winning leader in curricular innovation, setting the standard for longitudinally integrated learning. New rural-based programs have drawn students to the wonderful small towns of South Dakota for a deep and meaningful educational experience. Basic science research has blossomed, bringing important discoveries and opening opportunities for students. New residency programs have been developed to train graduating students. The school has deepened its commitment to serve all the diverse communities of the state. Most recently, a new focus on kindness in medicine has emerged. Yet if visitors from 1907 found much to surprise them, there is much that would seem unchanged. The school still needs, receives and is very grateful for the support it receives from the physicians, healthcare institutions, leaders, and communities of South Dakota. Faculty and students are still known for their integrity, skill and hard work. The unwavering focus on educational excellence and providing a physician workforce for South Dakota continues. The advances of today are built on the foundations of yesterday. The pace of change is always increasing. Imagine the possibilities

Carbon-13 and silicon-29 NMR spectra of some [tris(trimethylsilyl)methyl]-substituted silanes and related compounds

Fourier Transform 13C and 29Si NMR spectra are reported for series of [tris-(trimethylsilyl)methyl] dimethylsilanes, [tris(trimethylsilyl)methyl] diphenylsilanes, and for related compounds. Analysis of chemical shift values indicates that such information can be useful in determining the structure in these highly hindered, but structurally similar compounds. In particular, the carbon and silicon atoms of the trimethylsilyl groups absorb in a narrow shift range, while other carbon and silicon atoms more characteristically reflect the substitution patterns in these compounds.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23894/1/0000133.pd