An information-flow-based model with dissipation, saturation and direction for active pathway inference

<p>Abstract</p> <p>Background</p> <p>Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.</p> <p>Results</p> <p>In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.</p> <p>Conclusions</p> <p>Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.</p

Dissecting flux balances to measure energetic costs in cell biology: techniques and challenges

Life is a nonequilibrium phenomenon: metabolism provides a continuous supply of energy that drives nearly all cellular processes. However, very little is known about how much energy different cellular processes use, i.e. their energetic costs. The most direct experimental measurements of these costs involve modulating the activity of cellular processes and determining the resulting changes in energetic fluxes. In this review, we present a flux balance framework to aid in the design and interpretation of such experiments, and discuss the challenges associated with measuring the relevant metabolic fluxes. We then describe selected techniques that enable measurement of these fluxes. Finally, we review prior experimental and theoretical work that has employed techniques from biochemistry and nonequilibrium physics to determine the energetic costs of cellular processes.Comment: 27 pages, 3 figure

Innovative Techniques of Neuromodulation and Neuromodeling Based on Focal Non-Invasive Transcranial Magnetic Stimulation for Neurological Disorders

This dissertation aims to develop alternative technology that improves the current range of application of transcranial magnetic stimulation (TMS), on a scale that would permit defining specific non-invasive treatments for Parkinson’s disease and other neurological disorders. This is accomplished through three specific objectives. 1) The design of a neurostimulation system that increases the focality in TMS to regions of narrow target areas and variable depths in the brain cortex. 2) The assessment of the feasibility of novel high-frequency neuromodulation techniques that would allow increasing the focality in deeper areas beyond the cortical surface. 3) The development of a computational model of the motor pathway that allows studying the underlying mechanisms that originate PD symptoms, and the effects of TMS for the development of new treatments. The results successfully demonstrated the feasibility of using the novel high-frequency neuromodulation technique as an effective manner to reduce the necessary current in TMS coils. This reduction, which reached an order of magnitude of 100 times compared to commercial TMS technology, made it possible to reduce the coil sizes, making them more focal to targets (in the order of a few millimeters square). Finally, our innovative oscillatory model of the motor pathway allowed us to conclude that an internal regulatory mechanism that we believe neurons activate in advanced PD stages seems to be the pathological response of some neural subpopulations to dopamine depletion, trying to compensate for the downstream effects in the system. We also found that such a mechanism seems to the burstiness in PD

Assessing Pesticide Reduction in Constructed Wetlands Using a Tanks-in-Series Model Within a Bayesian Framework

Frequent pesticide detection at toxic levels to test organisms in California\u27s Central Coast waterbodies has motivated regulators, resource agencies and end-users to investigate and adopt management practices and technologies to diminish agricultural chemicals entering receiving waters. Treatment wetlands are a technology of special interest because of their ability to simultaneously treat multiple pollutants commonly found in agricultural and urban runoff including nitrate, suspended sediment and pesticides. We sought evidence for transformation of three highly water soluble pesticides (diazinon, methomyl and acephate) in a full-scale constructed treatment wetland located at the base of the Salinas Valley. We pumped water into the wetland from a slough containing agricultural runoff. The pumping rate was set to achieve a four-day mean residence time, and outlet samples were collected four days after inlet samples. We developed a dynamic tanks-in-series model and fit it to pesticide concentration data from the wetland, using parameters for number of tanks in series, mean hydraulic residence time, pesticide decay, and two parameters for inlet concentrations outside of the sampling period. We used a Bayesian analytical approach to determine the 95% credible intervals (CI) and most likely values for the five model parameters, and developed inference for pesticide decay based on the CI for the decay rate parameter. The CIs for the three pesticide decay parameters were positive and did not span zero, supporting the postulate that the wetland removed these pesticides to some extent. CIs for first-order decay rates were 0.097-0.289 day-1 for diazinon, 0.068-0.232 day-1 for methomyl, and 0.068-0.246 day-1 for acephate. These intervals can be used in conjunction with simple decay models to optimize the design of wetlands and to estimate size requirements

Thermal Management Using Liquid-vapor Phase Change in Nanochannels

Superior wettability of porous medium marks their potential to be used in the field of thermal management employing phase-change heat transfer. Comprehending the phenomena of wicking and liquid-vapor phase-change in micro/nano structured surfaces are key aspects towards advancing heat transfer solutions. In this work, fundamental understanding of droplet wicking, thin-film evaporation, and their subsequent application of heat-flux removal for cooling technology is first reported. The latter part of the dissertation is related to the disjoining pressure driven flow of nanoscale liquid film and liquid-vapor phase change in nano confinement. First, experimental and numerical investigation of droplet wicking in ∼728 nm height cross-connected buried SiO2 nanochannels, with micropores of diameter ∼2 μm at each intersection, is accomplished. The micropores allow water from a droplet placed on the surface to wick into the channels as well as allow thin-film evaporation from a meniscus. Experimental data in wicking-dominant regime are found to be in good agreement with analytical models and can be used to predict the wicking distance evolution in such nanochannels. Later, numerical technique of computational fluid dynamics (CFD) is employed to understand the dynamics of evaporating menisci in nanochannels and micropores. Evaporation flux at the meniscus interface of channels/pores is estimated over time. Local contact line regions are found to form underneath the pores when the meniscus recedes in the channels, thus rapidly enhancing evaporation flux as a power-law function of time. Temporal variation of wicking flux velocity and pressure gradient in the nanochannels is also independently computed, from which the viscous resistance variation is estimated and compared to the theoretical prediction. Further, to comprehend the effect of high-temperature on droplet spreading and evaporation over the nanochannels sample, experiments are conducted on a heated surface at temperatures ranging from 35°C to 90°C. Evaporation flux from the nanochannels/micropores is estimated from the droplet experiments but is also independently confirmed via an independent set of experiments where water is continuously fed to the sample through a microtube so that it matches the evaporation rate. Heat flux as high as ∼294 W/cm2 is achieved from channels and pores. The experimental findings are applied to evaluate the use of porous nanochannel geometry in spray cooling application and is found to be capable of passively dissipating high heat fluxes up to ∼77 W/cm2 at temperatures below nucleation, thus highlighting the thermal management potential of the fabricated geometry. Next, the porous nanochannels device capable to dissipate high heat flux is employed to regulate the temperature of a commercial PV panel by numerically integrating the device on the back face of the panel. The spatial and temporal variation of the PV surface temperature is obtained by solving the energy balance equation numerically and the extent of cooling and the resulting enhancement in the electrical power output is studied in detail. The nanochannels device is found to reduce the PV surface temperature significantly with an average cooling of 31.5°C. Additionally, the enhancement in the electrical power output by ~33% and the reduction in the response time to 1/8th demonstrating the porous nanochannels as an efficient thermal management device. In the later part of the work, an expression is developed for the disjoining pressure in a water film as a function of distance from the surface from prior experimental findings, which is key to understand water transport and liquid-vapor phase change in nanoscale confinement. The expression is implemented in a commercial CFD solver and the disjoining pressure effect on water wicking in nanochannels of height varying from 59 nm to 1 micron is simulated. The simulation results are in excellent agreement with experimental data, thus demonstrating and validating that near-surface molecular interactions can be integrated in continuum numerical simulations. Following the implementation, transpiration process and the passive water transport in trees of over a height of 100 m is simulated by using a domain comprising of nanopore connected to a tube with a ground-based water tank, thus mimicking the stomata-xylem-soil pathway in trees. In addition, the implementation of disjoining pressure in CFD simulation enabled the study of homogeneous bubble nucleation in nanochannel filled with liquid water. The bubble nucleation temperature was found to be ~125°C which closely matches with the experimental observation (~123°C) providing the evidence on incorporation of the disjoining pressure term to account for the effect of nanoscale confinement. By means of nucleation simulation, lesser-known parameters of homogeneous nucleation including the heat-flux supplied, the liquid film thickness underneath the bubble, etc. are identified which otherwise would been challenging to achieve experimentally

Computational modeling of the pharmacokinetics and pharmacodynamics of selected xenobiotics

2016 Fall.Includes bibliographical references.The determination of important endpoints in toxicology and pharmacology continues to involve the acquisition of large amounts of data through resource-intensive experimental studies involving a large number of resources. Because of this, only a small fraction of chemicals in the environment and marketplace can reasonably be evaluated for safety, and many promising drug candidates must be eliminated from consideration based on inadequate evaluation. Promisingly, advances in biologically-based computational models are beginning to allow researchers to estimate these endpoints and make useful extrapolations using a limited set of experimental data. The work described in this dissertation examined how computational models can provide meaningful insight and quantitation of important pharmacological and toxicological endpoints related to toxicity and pharmacological efficacy. To this end, physiologically-based pharmacokinetic and pharmacodynamic models were developed and applied for several pharmaceutical agents and environmental toxicants to predict significant, and diverse, biological endpoints. First, physiologically-based modeling allowed for the evaluation of various dosing regimens of rifapentine, a drug that is showing great promise for the treatment of tuberculosis, by comparing lung-specific concentration predictions to experimentally-derived thresholds for antibacterial activity. Second, physiologically-based pharmacokinetic modeling, coupled with Bayesian inference, was used as part of a methodology to characterize genetic differences in acetaminophen pharmacokinetics and also to help clinicians predict an ingested dose of this drug under overdose conditions. Third, a methodology for using physiologically-based pharmacokinetic modeling to predict health-based cognitive endpoints was demonstrated for chronic exposure to chlorpyrifos, an organophosphorus insecticide. The environmental public health indicators derived from this work allowed for biomarkers of exposure to be used to predict neurobehavioral changes following long-term exposure to this chemical. Finally, computational modeling was used to develop a mechanistically-plausible pharmacodynamic model for hepatoprotective and pro-inflammatory events to relate trichloroethylene dosing conditions to observed pathologies associated with auto-immune hepatitis