Pak-Wing Fok

Simulation of a pulsatile non-Newtonian flow past a stenosed 2D artery with atherosclerosis

Atherosclerotic plaque can cause severe stenosis in the artery lumen. Blood flow through a substantially narrowed artery may have different flow characteristics and produce different forces acting on the plaque surface and artery wall. The disturbed flow and force fields in the lumen may have serious implications on vascular endothelial cells, smooth muscle cells, and circulating blood cells. In this work a simplified model is used to simulate a pulsatile non-Newtonian blood flow past a stenosed artery caused by atherosclerotic plaques of different severity. The focus is on a systematic parameter study of the effects of plaque size/geometry, flow Reynolds number, shear-rate dependent viscosity and flow pulsatility on the fluid wall shear stress and its gradient, fluid wall normal stress, and flow shear rate. The computational results obtained from this idealized model may shed light on the flow and force characteristics of more realistic blood flow through an atherosclerotic vessel.

Dynamic boundaries in asymmetric exclusion processes.

We investigate the dynamics of a one-dimensional asymmetric exclusion process with Langmuir kinetics and a fluctuating wall. At the left-hand boundary, particles are injected onto the lattice; from there, the particles hop to the right. Along the lattice, particles can adsorb or desorb, and the right-hand boundary is defined by a wall particle. The confining wall particle has intrinsic forward and backward hopping, a net leftward drift, and cannot desorb. Performing Monte Carlo simulations and using a moving-frame finite segment approach coupled to mean field theory, we find the parameter regimes in which the wall acquires a steady-state position. In other regimes, the wall will either drift to the left and fall off the lattice at the injection site, or drift indefinitely to the right. Our results are discussed in the context of nonequilibrium phases of the system, fluctuating boundary layers, and particle densities in the laboratory frame versus the frame of the fluctuating wall.

Charge-transport-mediated recruitment of DNA repair enzymes

Damaged or mismatched bases in DNA can be repaired by base excision repair enzymes (BER) that replace the defective base. Although the detailed molecular structures of many BER enzymes are known, how they colocalize to lesions remains unclear. One hypothesis involves charge transport (CT) along DNA [Yavin et al., Proc. Natl. Acad. Sci. U.S.A. 102, 3546 (2005)]. In this CT mechanism, electrons are released by recently adsorbed BER enzymes and travel along the DNA. The electrons can scatter (by heterogeneities along the DNA) back to the enzyme, destabilizing and knocking it off the DNA, or they can be absorbed by nearby lesions and guanine radicals. We develop a stochastic model to describe the electron dynamics and compute probabilities of electron capture by guanine radicals and repair enzymes. We also calculate first passage times of electron return and ensemble average these results over guanine radical distributions. Our statistical results provide the rules that enable us to perform implicit-electron Monte Carlo simulations of repair enzyme binding and redistribution near lesions. When lesions are electron absorbing, we show that the CT mechanism suppresses wasteful buildup of enzymes along intact portions of the DNA, maximizing enzyme concentration near lesions.

Mathematical model of intimal thickening in atherosclerosis: Vessel stenosis as a free boundary problem

Atherosclerosis is an inflammatory disease of the artery characterized by an expansion of the intimal region. Intimal thickening is usually attributed to the migration of smooth muscle cells (SMCs) from the surrounding media and proliferation of SMCs already present in the intima. Intimal expansion can give rise to dangerous events such as stenosis (leading to stroke) or plaque rupture (leading to myocardial infarction). In this paper we propose and study a mathematical model of intimal thickening, posed as a free boundary problem. Intimal thickening is driven by damage to the endothelium, resulting in the release of cytokines and migration of SMCs. By coupling a boundary value problem for cytokine concentration to an evolution law for the intimal area, we reduce the problem to a single nonlinear differential equation for the luminal radius. We analyze the steady states, perform a bifurcation analysis and compare model solutions to data from rabbits whose iliac arteries are subject to a balloon pullback injury. In order to obtain a favorable fit, we find that migrating SMCs must enter the intima very slowly compared to cells in dermal wounds. This cell behavior is indicative of a weak inflammatory response which is consistent with atherosclerosis being a chronic inflammatory disease.

Accelerated Search Kinetics Mediated by Redox Reactions of DNA Repair Enzymes

A charge transport (CT) mechanism has been proposed in several articles to explain the localization of base excision repair (BER) enzymes to lesions on DNA. The CT mechanism relies on redox reactions of iron-sulfur cofactors that modify the enzymes binding affinity. These redox reactions are mediated by the DNA strand and involve the exchange of electrons between BER enzymes along DNA. We propose a mathematical model that incorporates enzyme binding/unbinding, electron transport, and enzyme diffusion along DNA. Analysis of our model within a range of parameter values suggests that the redox reactions can increase desorption of BER enzymes not already bound to lesions, allowing the enzymes to be recycled--thus accelerating the overall search process. This acceleration mechanism is most effective when enzyme copy numbers and enzyme diffusivity along the DNA are small. Under such conditions, we find that CT BER enzymes find their targets more quickly than simple passive enzymes that simply attach to the DNA without desorbing.

On the relationship between entropy, demand uncertainty, and expected loss

We analyze the effect of demand uncertainty, as measured by entropy, on expected costs in a stochastic inventory model. Existing models studying demand variability’s impact use either stochastic ordering techniques or use variance as a measure of uncertainty. Due to both axiomatic appeal and recent use of entropy in the operations management literature, this paper develops entropy’s use as a demand uncertainty measure. Our key contribution is an insightful proof quantifying how costs are non-increasing when entropy is reduced.

Reconstruction of potential energy profiles from multiple rupture time distributions

We explore the mathematical and numerical aspects of reconstructing a potential energy profile of a molecular bond from its rupture time distribution. While reliable reconstruction of gross attributes, such as the height and the width of an energy barrier, can be easily extracted from a single first passage time (FPT) distribution, the reconstruction of finer structure is ill-conditioned. More careful analysis shows the existence of optimal bond potential amplitudes (represented by an effective Peclet number) and initial bond configurations that yield the most efficient numerical reconstruction of simple potentials. Furthermore, we show that reconstruction of more complex potentials containing multiple minima can be achieved by simultaneously using two or more measured FPT distributions, obtained under different physical conditions. For example, by changing the effective potential energy surface by known amounts, additional measured FPT distributions improve the reconstruction. We demonstrate the possibility of reconstructing potentials with multiple minima, motivate heuristic rules-of-thumb for optimizing the reconstruction, and discuss further applications and extensions.

A Linearly Fourth Order Multirate Runge---Kutta Method with Error Control

To integrate large systems of locally coupled ordinary differential equations with disparate timescales, we present a multirate method with error control that is based on the Cash–Karp Runge–Kutta formula. The order of multirate methods often depends on interpolating certain solution components with a polynomial of sufficiently high degree. By using cubic interpolants and analyzing the method applied to a simple test equation, we show that our method is fourth order linearly accurate overall. Furthermore, the size of the region of absolute stability is increased when taking many “micro-steps” within a “macro-step.” Finally, we demonstrate our method on three simple test problems to confirm fourth order convergence.

Optimal auto-regulation to minimize first-passage time variability in protein level

The timing of cellular events is inherently random because of the probabilistic nature of gene expression. Yet cells manage to have precise timing of important events. Here, we study how gene expression could possibly be regulated to precisely schedule timing of an event around a given time. Event timing is modeled as the first-passage time (FPT) for a proteins level to cross a critical threshold. Considering auto-regulation as a possible regulatory mechanism, we investigate what form of auto-regulation would lead to minimum stochasticity in FPT around a fixed time. We formulate a stochastic gene expression model and show that under certain assumptions, it reduces to a birth-death process. Our results show that when the death rate is zero, the objective is best achieved when all of the birth rates are equal. On the contrary, when the death rate is non-zero, the optimal birth rates are not equal. In terms of the gene expression model, these results illustrate that when protein does not degrade, stochasticity in FPT around a given time is minimized when there is no auto-regulation of its expression. However, when the protein degrades, some form of auto-regulation is required to achieve this. These results are consistent with experimental findings for the lysis time stochasticity in λ phage.

Bayesian Uncertainty Quantification for Bond Energies and Mobilities Using Path Integral Analysis.

Dynamic single-molecule force spectroscopy is often used to distort bonds. The resulting responses, in the form of rupture forces, work applied, and trajectories of displacements, are used to reconstruct bond potentials. Such approaches often rely on simple parameterizations of one-dimensional bond potentials, assumptions on equilibrium starting states, and/or large amounts of trajectory data. Parametric approaches typically fail at inferring complicated bond potentials with multiple minima, while piecewise estimation may not guarantee smooth results with the appropriate behavior at large distances. Existing techniques, particularly those based on work theorems, also do not address spatial variations in the diffusivity that may arise from spatially inhomogeneous coupling to other degrees of freedom in the macromolecule. To address these challenges, we develop a comprehensive empirical Bayesian approach that incorporates data and regularization terms directly into a path integral. All experimental and statistical parameters in our method are estimated directly from the data. Upon testing our method on simulated data, our regularized approach requires less data and allows simultaneous inference of both complex bond potentials and diffusivity profiles. Crucially, we show that the accuracy of the reconstructed bond potential is sensitive to the spatially varying diffusivity and accurate reconstruction can be expected only when both are simultaneously inferred. Moreover, after providing a means for self-consistently choosing regularization parameters from data, we derive posterior probability distributions, allowing for uncertainty quantification.