Stochastic modeling and optimization of phage display

Phage display, SELEX and other methods of combinatorial chemistry have become very popular means of finding ligands with high affinities to given targets. Despite their success, they suffer from numerous sources of error and bias, such as very low initial concentrations of species, non-specific binding, and the sampling of only a tiny fraction of the library at the end of an experiment. To understand the interaction of these errors and to better devise molecular search strategies that take the errors into account, I devise and analyze a highly detailed model of phage display. The model is specifically designed to study the influence of the stochastic nature of each laboratory step. The model includes phage multivalency, multiple classes of targets, and solid-phase equilibrium and washing, yet it is amenable to analytic results and rapid computer simulation. With both analytic and simulation approaches, I: (1) describe the effects of target concentration, phage valency, degree of background binding and other laboratory parameters on the probabilities of phage binding and of being selected; (2) show the effects of an increasing selection stringency strategy and how it results in a tradeoff between rapid library enrichment and high probability of sampling the best ligands; and (3) show how the number of phage sampled for detailed study at the end of a search alters search success. The work concludes with several practical suggestions for the control of selection stringency.     

Reverse physiology: discovery of the novel neuropeptide, orphanin FQ/nociceptin

The mechanism for the formation of biomimetic model cell membranes consisting of bilayers composed of alkanethiols and phospholipids was probed with a kinetic study using surface plasmon resonance. The kinetics of formation of a monolayer of phospholipid from vesicles in solution onto a hydrophobic alkanethiol monolayer is described by a model that takes into account the lipid concentration, diffusion, and a surface reorganization rate constant. Monomer phospholipid apparently does not play a direct role in determining the kinetics of bilayer formation. Expressions for the limiting cases of this model describe the behavior of two distinct vesicle concentration conditions. At high concentrations of lipid vesicles the formation of the bilayer appears to be limited by the diffusion of vesicles to the surface; at lower concentrations of vesicles, the rate-limiting step is apparently the surface reorganization of lipid. This kinetic model can also be used to describe the formation of a biomimetic bilayer from an alkanethiol monolayer and cell membranes.     

A simplified modeling approach using microbial growth kinetics for predicting exposure concentrations of organic chemicals in treated wastewater effluents

Various mathematical relationships have been used to assess exposure concentrations of organic chemicals when emissions occur via wastewater treatment. These relationships range from a simple removal factor calculation to more sophisticated approaches using kinetic based mathematical models. While these existing approaches have been used by decision makers to screen new chemicals for exposure assessments, they all have limitations in the predictive capabilities. Thus, a simplified modeling approach grounded in sound scientific fundamentals that utilizes relatively easy to obtain input parameters is needed. In this paper a simplified modeling approach that utilizes microbial growth kinetics was developed for predicting effluent concentrations in secondary biological wastewater treatment systems. Receiving water predicted exposure concentrations (PEC) are assessed by using a dilution factor. One advantage of this approach is that it allows for wastewater treatment plant effluent concentrations, and therefore receiving water exposure levels, to be predicted with a minimum amount of experimental data. It also provides quantitative data that can be used to assess the relative biodegradability of different chemicals for use in regulatory and risk assessment activities.     

Learning to choose: Cognitive aging and strategy selection learning in decision making.

Decision makers often have to learn from experience. In these situations, people must use the available feedback to select the appropriate decision strategy. How does the ability to select decision strategies on the basis of experience change with age? We examined younger and older adults' strategy selection learning in a probabilistic inference task using a computational model of strategy selection learning. Older adults showed poorer decision performance compared with younger adults. In particular, older adults performed poorly in an environment favoring the use of a more cognitively demanding strategy. The results suggest that the impact of cognitive aging on strategy selection learning depends on the structure of the decision environment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stochastic simulation of ligand-receptor interaction

We have developed an algorithm for the stochastic simulation of ligand-receptor interactions based on 10(4)-10(5) fictitious binding sites. Reversible receptor binding was simulated by alternate random selection of sites, the first selection resulting in "occupation" if the selected site was "free," the second selection resulting in "dissociation" if the selected site was "occupied." We show that the mathematical formalism of mass action kinetics is predicted on purely statistical grounds. The model was extended by the introduction of two further selections, simulating a conformational change in the ligand-receptor complex ("receptor isomerization model"). All random selections were gauged separately by "probability barriers," taking the place of macroscopic kinetic rate constants. Simulation of gradual increases and gradual decreases of the fraction of occupied fictitious binding sites in the receptor isomerization model, using various combinations of "rate constants," resulted in biexponential time dependencies, in agreement with predictions from the integrated rate equations. Stochastic simulation of molecular processes is a powerful and versatile technique, providing the researcher with a means of studying mechanisms of increasing complexity.     

Steady-state cerebral glucose concentrations and transport in the human brain

Understanding the mechanism of brain glucose transport across the blood-brain barrier is of importance to understanding brain energy metabolism. The specific kinetics of glucose transport have been generally described using standard Michaelis-Menten kinetics. These models predict that the steady-state glucose concentration approaches an upper limit in the human brain when the plasma glucose level is well above the Michaelis-Menten constant for half-maximal transport, Kt. In experiments where steady-state plasma glucose content was varied from 4 to 30 mM, the brain glucose level was a linear function of plasma glucose concentration. At plasma concentrations nearing 30 mM, the brain glucose level approached 9 mM, which was significantly higher than predicted from the previously reported Kt of approximately 4 mM (p < 0.05). The high brain glucose concentration measured in the human brain suggests that ablumenal brain glucose may compete with lumenal glucose for transport. We developed a model based on a reversible Michaelis-Menten kinetic formulation of unidirectional transport rates. Fitting this model to brain glucose level as a function of plasma glucose level gave a substantially lower Kt of 0.6 +/- 2.0 mM, which was consistent with the previously reported millimolar Km of GLUT-1 in erythrocyte model systems. Previously reported and reanalyzed quantification provided consistent kinetic parameters. We conclude that cerebral glucose transport is most consistently described when using reversible Michaelis-Menten kinetics.     

Attention as inference: Selection is probabilistic; responses are all-or-none samples.

Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, “probability distributions”). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual presentation, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adsorption of Carbon Disulfide (CS2) in Water by Different Types of Activated Carbon—Equilibrium, Dynamics, and Mathematical Modeling

Adsorption equilibrium and kinetics of carbon disulfide in water by granular activated carbon (GAC), powdered activated carbon (PAC), and activated carbon fiber (ACF) were investigated and compared in an effort to elucidate the fundamentals for optimizing the control process design. It has been shown that the BET expression can satisfactorily describe the adsorption equilibrium of carbon disulfide (CS2) on GAC, PAC, and ACF and the corresponding kinetic experimental data properly correlated with the second-order kinetic model, which indicates that the CS2 adsorption is the rate-limiting step. A two-phase mathematical model was developed to simulate CS2 transfer in fixed-bed operation filled with the GAC, PAC, and ACF, and the equilibrium and kinetics information is subsequently used in the model to characterize the dynamics of adsorption. The model includes mechanisms such as axial dispersion, advection, liquid-to-solid mass transport, and intraparticle mass transport by pore and surface diffusion. It is manifested that the model was able to predict the dynamic breakthrough curve of CS2 in a fixed-bed adsorption column filled with GAC, PAC, and ACF at varied conditions (standard deviations for 1.5?cm/min is 12.13% and for 2.2?cm/min is 16.12%), based on BET-3 equilibrium and second-order kinetics, which indicates that the methodology proposed by this work could be employed for adsorbents selection, adsorption design, and process optimization for CS2 waste-water emission control.     

Decision Tree Modeling Using Integrated Multilevel Stochastic Networks

Decision trees (DTs) have proven to be valuable tools for decision making. The common approach for using DTs is calculating the expected value (EV) based on single-number estimates, but the single-number EV method has limited the DTs’ real-life applications to a narrow scope of decision problems. This paper introduces the stochastic multilevel decision tree (MLDT) modeling approach, which is useful for analyzing decision problems characterized by uncertainty and complexity. The MLDT’s advantages are shown through a computer simulation program: the Decision Support Simulation System (DSSS). The DSSS allows users to model probabilistic linear graph networks and provides a hierarchical modeling method for modeling decision trees to present uncertainties more accurately. It consists of three modules: tree analysis networks (TANs), the shortest and longest path dynamic programming analysis network, and cost time analysis networks. The paper only discusses the TAN module by presenting the MLDT concept under the TAN of the DSSS computer application. The content of the paper includes the modeling approach, its advantages, and examples that can be used in modeling stochastic trees. The DT-DSSS was verified by conducting several tests and validated by using it extensively for undergraduate courses in civil engineering at the University of Calgary for the last two academic years.     

Design Model for COD Removal in Constructed Wetlands Based on Biofilm Activity

Current design criteria for free-water-surface constructed wetlands are based on either empirical relationships or first-order reaction rates and do not emphasize the microbial activity. This study was conducted to evaluate the role of biofilm bacteria present in free-water-surface constructed wetland beds in the removal of organic matter. A kinetic model incorporating the biofilm kinetics and dispersion number was proposed to predict chemical oxygen demand removal in free-water-surface constructed wetlands. The model parameters were determined from laboratory experiments and from the literature. The proposed kinetic model satisfactorily predicted chemical oxygen demand removal efficiencies in a pilot-scale constructed wetland unit located in the tropics. A design chart and a design example based on the kinetic model are given.     

An adaptive approach to human decision making: Learning theory, decision theory, and human performance.

Describes a general model of decision rule learning, the rule competition model, composed of 2 parts: an adaptive network model that describes how individuals learn to predict the payoffs produced by applying each decision rule for any given situation and a hill-climbing model that describes how individuals learn to fine tune each rule by adjusting its parameters. The model was tested and compared with other models in 3 experiments on probabilistic categorization. The 1st experiment was designed to test the adaptive network model using a probability learning task, the 2nd was designed to test the parameter search process using a criterion learning task, and the 3rd was designed to test both parts of the model simultaneously by using a task that required learning both category and cutoff criteria. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Influence of fluorochrome labeling density on the photobleaching kinetics of fluorescein in microscopy

The objective of this study was to identify, through kinetic analysis of individual elementary reactions, the conditions under which a simple first-order photobleaching kinetic model is sufficient for quantitative fluorescence measurements, and those under which more complex photobleaching kinetics must be considered. Three model systems of various fluorophore densities and distributions were employed to verify the kinetic analysis. The results showed that the photobleaching kinetics of free fluorescein at concentrations lower than 5 microM corresponded closely to a single exponential function and therefore involved predominantly simple unimolecular or pseudounimolecular photochemical reactions. When fluorescein was bound to polyvinyl alcohol (PVA) molecules, the photobleaching kinetics of the densely labeled PVA deviated more from a single-exponential function than sparsely labeled PVA. When fluorescein was bound to a DNA probe, the photobleaching kinetics were more complex and deviated significantly from a single-exponential function, due to one or more bimolecular processes with apparent concentration-dependent photobleaching rate constants. The practical applications of time-integrated fluorescence emission are discussed in the context of simple and complex photobleaching kinetics.     

Stochastic Field Models for Aircraft Jet Engine Applications

This paper addresses the stochastic modeling for advanced jet engine applications and also presents an overview of the basic theoretical aspects of different useful stochastic field models and then illustrates their application to practical problems. It is shown that stochastic fields adequately capture key random aspects of engine environment and behavior. The illustrated applications address stochastic modeling issues that are typical for engine structural analysis, such as time-varying operating speed conditions, manufacturing geometry deviations, and engine vibration, that produce highly nonlinear responses. This paper discusses stochastic modeling for two situations often met in practice: (1) stochastic process∕field models with known statistics; and (2) stochastic process∕field models with unknown statistics. Illustrative examples are used to highlight features of different stochastic field modeling techniques.