Program
Poster Session B
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Poster session B is generously sponsored by Taylor and Francis.
The poster sessions will take place in the lobby of the Woodward building. Poster session A will be held from 5.30pm-7pm on Monday, July 27. Poster session B will be held from 5.30pm-6.45pm on Wednesday, July 29. Refreshments will be served. |
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| PS28B | Aidoo, Anthony | |
|---|---|---|
| Eastern Connecticut State University | ||
| Prevalence of Aquatic Insects and Arsenic Concentration Determine the Distribution of Mycobacterium Ulcerans Infection | ||
| Mycobacterium ulcerans (MU), a pathogenic bacterium that causes dermal ulcers known as ”buruli ulcers” (BU), is fast becoming a debilitating affliction in many countries worldwide. The incidence of BU is not limited solely to tropical environments. In fact, occurrence of BU has been documented in subtropical and temperate regions. A modified SIR model is used to explain the transmission of Mycobacterium Ulcerans (MU) and it’s dependence on arsenic environments. Some studies have suggested that arsenic plays a major role in the spread and prevalence of Buruli Ulcer (BU). In addition, it has been hypothesized that a vector in the form of a water-bug plays a key role in the epidemiology of BU. We develop an epidemiological model based on these assumptions for the dynamics and prevalence of BU and show that arsenic positively induces the growth and spread of MU. | ||
| Coauthor(s): Bonsu Osei | ||
| PS23B | Cazelles, Bernard | |
| ENS-CNRS | ||
| Intrinsic mechanisms can generate chaotic dynamics with uniform phase in influenza epidemics: from theory to observations. | ||
| In temperate zones and during inter-pandemic periods, the dynamics of human Influenza virus lead to annual epidemics of variable amplitude caused by alternating types and subtypes of the virus. These recurrent epidemics in temperate areas are characterized by : relatively constant phase and highly variable amplitude. We show that a minimal SIRS model including key processes of influenza transmission and evolution lead to chaotic dynamics with uniform phase (known as UPCA). These theoretical results are confirmed by attractor reconstruction and statistical inference. Influenza epidemics peaks variability is currently explained by punctuated evolution of influenza main antigen, higher peaks reflecting higher antigenic changes. Our results suggest that non-linear dynamics alone without perturbation induced by sudden antigenic change can explain the observed pattern of influenza recurrence. Higher epidemic peaks should not be taken as a signature of punctually large antigenic changes as they can result from intrinsic mechanism. If our intrinsic view is correct, care should be taken in interpreting the cause of influenza epidemics variability. | ||
| Coauthor(s): Sébastien Ballesteros, Anton Camacho | ||
| PS01B | Lai, Pik-Yin | |
| Dept. of Physics, National Central University | ||
| Effect of coupling strength on the beating rate in excitable cardiac cell culture | ||
| Variation in heart beating rates during the development of cell connections and synchronized beating had been observed in experiment[1]. This phenomenon can be reproduced in a model of coupled excitable FitzHugh Nagumo elements in the presence of noise. In some coupling regimes, the beating rate can be enhanced up to 60% in our simulations, as observed in experiments. The variation of the beating rate can be explained qualitatively by Kramers’ escape rate in a single potential well model. [1] "Synchronization in growing heterogeneous media", W. Chen, S. C. Cheng, E. Avalos, O. Drugova, G. Osipov, Pik-Yin Lai, and C. K. Chan, Europhys. Lett. 86, 18001(2009). | ||
| Coauthor(s): Wei-Yin. Chiang, C.K. Chan | ||
| PS02B | Lai, Tan Lei Tanny | |
| A*STAR Institute of High Performance Computing | ||
| Mathematical modelling of lamellipodial dynamics | ||
| We describe a mathematical model of lamellipodial protrusion and retraction and their role in sensing the rigidity of the substrate. Several experiments have attempted to elucidate the mechanism and determined the periodicity of lamellipodium protrusions and retractions. Our goal is to use simple principles in cell mechanics and cell biology to derive a mathematical model to study how the periodicity of the protrusion and retraction arises and how it depends on, for example, the stiffness of the substrate. The variables in the model include (i) the positions of the barbed (fast polymerizing) and pointed (slow polymerizing) ends of actin filaments, (ii) the integrin density and (iii) the myosin density at the leading edge of the lamellipodium. Actin has been shown to polymerize at different rates at the barbed and pointed ends, thus the velocity of the two ends were dictated by the de/polymerization rates as well as the forces exerted by myosin and integrin on the actin filaments. The tensile strength of an actin filament was also taken into consideration, with the actin filament breaking off when the total force exerted on the actin filament exceeds a critical value. Integrin activation has been shown to result in the activation of myosin light chain kinase via the activation of Rho. Rate of change of myosin densities were thus allowed to increase with the amount of integrin present in the neighbourhood, with a time delay to account for propagation of the signal. Experiments have shown the existence of stretch-activated proteins bound to integrins which unfold to expose phosphorylation sites when stretched, allowing other proteins to assemble at the integrins to stabilize the focal contact. This is modeled by ensuring that adhesion strengthens only when the stretch exceeds a threshold. In addition, we also allow for integrin initiation when myosin starts to exert a force on the actin, therefore pulling on the leading edge of the lamellipodium. Solutions of our model are consistent with the periodic behavior observed in the experiments. We also show quantitatively how the period of the lamellipodial protrusions and retractions depends on the degree of integrin stabilization contributed by the stretch-activated proteins, the force-induced activation of integrin and the tensile strength of the actin network. Also, high tensile strength caused the system to become unstable while a low tensile strength eliminated periodicity. Periodicity was abolished when the amplitude of the force-induced activation of integrin was reduced. We also discuss how these results can be validated in further experiments. | ||
| Coauthor(s): Keng-Hwee Chiam | ||
| PS03B | Latulippe, Joe | |
| Cal Poly Pomona | ||
| A Phenomenological Model for Mixed Responses in Neurons | ||
| Mammalian sensory system neurons exhibit many different stimulus-induced responses. These response patterns can often be characterized as On, Off, or Mixed, where the Mixed response is a combination of the On and Off patterns. A single cell model that can reproduce these ubiquitous stimulus-response patterns is developed and examined using leading order analyses and averaging. The model consists of four coupled first-order nonlinear differential equations describing the dynamics of the membrane potential of the cell. The fast and slow dynamics of the model are explored and pieced together to describe the overall cycle of the solution patterns. For a certain class of inputs, this analysis is then used to determine appropriate durations that can guarantee Mixed-responses. By changing the fast subsystem structure, many different types of responses can also be reproduced. | ||
| Coauthor(s): Mark Pernarowski | ||
| PS04B | Leblanc, Alain | |
| University of Rochester Medical Center | ||
| DEDiscover - Software for Modeling Biological System via Differential Equations | ||
| Mathematical modeling is playing an increasingly important role in biological systems research. Effective application of such models to biological systems requires the development of tools that are accessible to biologists, mathematical modelers, and statisticians, and that implement computationally efficient and scientifically valid algorithms. Implementation of these tools involves significant user-interface, algorithmic, and computational challenges. Our team has created the DEDiscover software application as a user friendly and cross platform software tool that facilitates development, exploration, and application of systems of differential equation to biological systems. Within its clear workflow-based user interface, DEDiscover allows model development and exploration via simulation, for both ordinary and delay differential equation based models. Further, DEDiscover permits fitting these same models to observed data, providing confidence intervals in addition to the parameter estimates themselves. Further, DEDiscover allows storage and visualization of multiple sets of parameter values for each model, permitting visual comparison alternative parameter settings, including “pre-specified” verse “estimated” parameter values. For maximum flexibility, DEDiscover allows the user to select from a variety of differential equation solvers, numerical maximization algorithms, statistical estimation techniques, and confidence interval calculation methods. DEDiscover’s modular design makes it easy to add additional computational methods. DEDiscover also includes several popular models and example data for host-virus interaction of HIV and influenza, allowing users to immediately begin experimenting with these models. In this poster, we describe the design and features of DEDiscover and demonstrate its application to influenza models and to observed data from influenza laboratory experiments. DEDiscover is funded by NIAID/NIH grant NO1 AI50020 “Center for Biodefense Immune Modeling”. Keywords: Biological System, ordinary differential equation (ODE), delay differential equation (DDE), model fitting, parameter estimation, simulation, Software. | ||
| Coauthor(s): Hulin Wu, Gregory R. Warnes, Hongyu Miao, Alain Leblanc, Canglin Wu | ||
| PS05B | Leviyang, Sivan | |
| Georgetown University | ||
| A General, Coalescent-Based Approach to the Analysis of Genetic Statistics used in Population Structure Inference | ||
| Populations are often divided into subpopulations. Biologists use various statistics formed from genetic data to infer migration rates between subpopulations. Inference with such genetic statistics is difficult because little is known about their sampling distributions. Further, what little is known is tied to some specific model of migration patterns between the subpopulations. The statistic Fst serves as an example of this situation. The sampling distribution of Fst is not known and most of what is known about the distribution comes from simulation in which an island model or stepping stone model is assumed. In this talk we consider genetic statistics under a certain class of evolutionary models that we refer to as G/KC models. We show that in a large population limit, the island and 2-d stepping stone models are special cases of G/KC models. We then study the behavior of Fst under an arbitrary G/KC model and derive formulas that describe the distribution of Fst in the large sample setting. In this way we are able to study Fst in a general setting and free our analysis from a specific migration model such as the island model. Our approach is general and can be applied to other statistics such as heterozygosity measures. Our analysis uses coalescent based methods. | ||
| PS06B | Li, Ting | |
| University of Washington | ||
| The vitality model: A way to understand population survival | ||
| A four-parameter model describing mortality as the first passage of an abstract measure of survival capacity, vitality, is developed and used to explore four classic problems in biodemography: 1) medfly demographic paradox, 2) effect of diet restriction on longevity, 3) effect of early growth rate on later life survival and 4) mortality plateaus. The model quantifies the sources of mortality in these classical problems into vitality-dependent and -independent parts and characterizes the vitality-dependent part in terms of initial and evolving heterogeneities. In general, it provides an accessible tool to decompose any survival curves into four pieces: intrinsic mortality related to the senescence rate, extrinsic mortality related to the accidental rate as well as two sources of heterogeneities among a population. Through the lens of the partition, we are able to better understand the underlining biological and ecological mechanisms that shape the survival curves. | ||
| PS07B | Li, Yongfeng | |
| IMA, University of MInnesota | ||
| GK model of open signaling cascade | ||
| Cell signaling is achieved by reversible phosphorylation-dephosphorylation reaction cascades in proteins. A single reversible phosphorylation--dephosphorylation reaction model is given by Goldbeter and Koshland(GK) based on Michaelis--Menten kinetics. It is well known that the necessary condition for the bistability in the biological systems is positive feedback or double negative feedback,. In this work, a GK-type model is proposed to couple N reaction cycles through forward activation. Without additional feedback, a bistable-like behavior is observed when the cascade length N becomes large. Some detailed analysis on this model is provided. | ||
| Coauthor(s): Jeyaraman, Srividhya | ||
| PS08B | Lind, Christine | |
| University of Washington | ||
| A Simple Chemical Kinetic Model for Facilitated Diffusion | ||
| We present a simple chemical kinetic model for facilitated diffusion of oxygen by a carrier molecule such as hemoglobin or myoglobin. This simple model mathematically illuminates the cause of the enhancement of the transport by the carrier molecules. Results from the simple model are compared to previous experimental and mathematical results. | ||
| Coauthor(s): Hong Qian | ||
| PS09B | Loladze, Irakli | |
| University of Nebraska - Lincoln | ||
| Molecular, Evolutionary, and Geophysical origins of Redfield Ratio N:P~16 in Oceans | ||
| Among the biosphere’s largest patterns is atomic nitrogen:phosphorus ratio (N:P) ~ 16 found throughout deep ocean; though N:P of individual phytoplankton species ranges from 6 to 60, the average N:P of plankton is also ~16. Discovered empirically by Redfield 75 years ago, this pattern is central to carbon sequestration models and marine biogeochemical cycling. However, the rationale behind N:P~16 is not known. Here, we show that Redfield stoichiometry follows from fundamental molecular values such as N in amino acids, N and P in nucleotides; in nutrient replete conditions N:P~16 reflects biochemically optimal RNA:protein. If nutrients are limiting, our nonlinear dynamic model shows how environment and evolution affect Redfield ratio. Finally, we show the role of Coriolis effect on the maintenance Redfield ratio over geological times around 16. Our work shows that Redfield N:P originates on a molecular scale while evolutionary forces coupled with Earth’s rotation amplify the pattern to the global scale. | ||
| Coauthor(s): Simon Levin | ||
| PS10B | Masso, Majid | |
| George Mason University | ||
| Modeling HIV-1 protease functional consequences upon mutation: structure based prediction of enzymatic activity change and inhibitor susceptibility | ||
| We report on a novel approach, combining a knowledge-based statistical contact potential with machine learning tools, for developing accurate predictive models of HIV-1 protease (PR) functional changes due to single or multiple amino acid replacements. Motivation stems from the nature of structure-function relationships, suggesting that accurate in silico evaluation of HIV-1 PR structural changes upon mutation correlate with experimentally measured changes in activity and susceptibility to inhibitors. Delaunay tessellation, a well-established computational geometry procedure, is applied to a diverse collection of protein structures in order to derive a four-body statistical potential. This potential is then used to empirically calculate residue compatibility scores at all HIV-1 PR positions. A subsequently formulated computational mutagenesis methodology quantifies the relative environmental change from wild type at every position in an HIV-1 PR mutant, which provide the components of a feature vector representation for the mutant. For activity change, a dataset of 536 HIV-1 PR single missense mutants, experimentally categorized by R. Swanstrom et al. based on their degree of pol processing capability relative to the wild type protein, is used to train and evaluate the performance of neural network, decision tree, support vector machine, and random forest supervised classification predictive models. Multiple datasets are prepared in the case of HIV-1 PR inhibitor susceptibility, each based on the experimentally measured degree of resistance of HIV-1 PR mutants to seven commercially available protease inhibitor medications. The HIV-1 PR mutants were isolated and sequenced from patients enrolled in clinical trials and consist of single as well as multiple residue replacements, and fold levels of resistance are available from the online Stanford University HIV Drug Resistance Database. Individual regression (actual fold level values) and supervised classification (resistance categories) predictive mo dels are trained for each protease inhibitor. | ||
| PS11B | Matrajt, Laura | |
| University of Washington and Fred Hutchinson Cancer Research Center | ||
| One Versus Two Doses: Best Vaccination Strategies for Pandemic Influenza | ||
| Vaccination remains one of the most effective interventions against pandemic influenza. Most of the current influenza A (H5N1) vaccines require two doses: a prime and a boost at least 3 weeks later. A possible novel influenza A (H1N1) vaccine is likely to require two doses as well. We developed a mathematical model to evaluate two strategies for optimally allocating limited vaccine supplies to determine if it is better to only prime a larger number of people or to vaccinate half as many with the full two doses. We aim to evaluate the effect of key parameters on the final illness attack rates (defined as the percentage of the population who became ill). We performed a thorough search in the parameter space for the following parameters: vaccination date, primary response level (defined as the percentage of the full vaccine efficacy that the vaccine will attain after the prime), vaccination coverage, concavity of the vaccine efficacies as functions of time, and the basic reproductive number R0. We compared final illness attack rates if full pre-pandemic vaccination (two doses) is used compared with reactive mass vaccination (after the beginning of the epidemic) with a single dose. There is a threshold in the values of R0: below which the strategy of fewer vaccinees/two doses gives lower final attack rates; above it, vaccinating more people with one dose is better. Though the threshold depends on all the parameters considered, the primary response levels are key. Our model differs from previous work in that it includes the primary response levels, and it models the vaccine efficacies dynamically. In the event of a vaccine shortage, our results could provide valuable insight for allocating limited resources. The model also highlights the need for better a understanding of the kinetics and better estimates of the vaccine efficacies. | ||
| Coauthor(s): Ira M. Longini Jr. | ||
| PS13B | Miron, Rachelle | |
| University of Ottawa | ||
| Modelling imperfect adherence to HIV induction therapy | ||
| Induction-maintenance therapy is a treatment regime where patients are prescribed an intense course of treatment for a short period of time (the induction phase), followed by a simplified long-term regimen (maintenance). In this paper, we investigate, using mathematical modelling, the effect of imperfect adherence during the inductive phase. We address the following research questions: 1. Can we determine the maximal length of a drug holiday and the number of subsequent doses that must be taken to avoid resistance? 2. How many drug holidays can be taken during the induction phase? 3. Does the length of the induction period depend on the drug regimen? We show that, for a 180 day therapeutic program, a patient can take several drug holidays, but then has to follow each drug holiday with a strict, but fairly straightforward, drug-taking regimen. Since the results are dependent upon the drug regimen, we calculated the length and number of drug holidays for all fifteen protease-sparing triple-drug cocktails that have been approved by the US Food and Drug Administration. Our theoretical predictions are in line with recent results from pilot studies of short-cycle treatment interruption strategies and may be useful in guiding the design of future clinical trials. | ||
| PS14B | Mondaini, Rubem | |
| Federal University of Rio de Janeiro | ||
| A global optimization analysis of protein structure | ||
| In order to provide a biosystems approach to protein structure, we analyse the relation between the set of bond angles (amide in-plane bond angles and those from alpha-carbon geometry) and the set of dihedral angles. The present study is part of a research program aiming at the formulation of a general optimization problem and it is based on the small perturbations of these angles as the fundamental variables. The possible protein architectures are considered as deformations of a network where the regularity of structure formation and the reduction in the number of necessary variables are due to the identification of regular space curves through specified atom sites. An useful information coming from the analysis of atom sites as Steiner point positions and its relation to the problem of energy minimization is also introduced here and the assumption of evenly spaced points in 3D Euclidean Space is also seen to be enough to understand the present observed structures of the literature. | ||
| PS15B | Morishita, Yoshihiro | |
| Kyushu university | ||
| Quantification of deformation: Estimation of the spatio-temporal pattern of volumetric growth rate in chick wing development | ||
| Morphogenesis is achieved through volumetric growth of tissue at a rate varying over space and time. The volumetric growth rate of each piece of tissue reflects the behaviors of constituent cells, such as cell proliferation, cell death, cell growth and deformation. Hence clarifying the spatio-temporal pattern of volumetric growth accurately is a key to bridge between cell behaviors and morphogenesis of a tissue or an organ. Fate map data specifying the correspondence of cell positions between different stages provide potentially important information on the volumetric growth rate. However, their resolution on space and time are often limited, and they include unavoidable noises. In this paper, we propose a new method to estimate the spatio-temporal pattern of volumetric growth rate from fate map data. The method complements the data defects by using a mathematical model for cell trajectory dynamics and a statistical method (AIC). We apply the method to fate map data along the proximo-distal axis on chick wing development, and find that the volumetric growth pattern is biphasic: it is spatially uniform in earlier stages (until stage 23), but in later stages the volumetric growth occurs about four times as fast as in the distal region (within about 100 from the limb tip) than in the proximal region. The quantitative volumetric growth pattern will be a good indicator when we discuss the morphological anomalies of mutants and between-species differences of development, and in addition, when we quantitatively evaluate results obtained from mathematical model to describe organ growth and deformation. | ||
| Coauthor(s): Yoh Iwasa | ||
| PS16B | Morrison, Jennifer | |
| UBC | ||
| Quantifying transient directed motion in single particle tracks using a hidden Markov model | ||
| Single Particle Tracking (SPT) is a widely used biophysical technique whereby a biomembrane component is fluorescently or optically tagged and its trajectory is observed. Analysis of SPT data has led to investigations of the different modes of particle motion, the binding kinetics of specific proteins and the underlying structure of the plasma membrane. Quantification of a protein’s interactions with the cytoskeleton and identification of directed motion provides insight into the spatiotemporal organization of the signaling pathways involved. I will present a method to identify transient directed motion in tracks of membrane-bound proteins using a hidden Markov model. As shown with simulated data, we can accurately estimate the diffusion coefficients and directed velocities of a particle in each state as well as the transition rates between each state. I will also discuss our attempts at analyzing particle tracks on T Cells using this method. | ||
| Coauthor(s): Daniel Coombs, Raibatak Das | ||
| PS18B | Musielak, Magdalena | |
| The George Washington University | ||
| Nutrient Transport and Acquisition by Diatom Chains in a Moving Fluid | ||
| The role of fluid motion in delivery of nutrients to phytoplankton cells is a fundamental question in biological and chemical oceanography. Experimental data to test the contribution of advection to nutrient acquisition by phytoplankton are scarce, mainly because of the inability to visualize, record and thus imitate fluid motions in the vicinities of cells in natural flows. Steady flows, most often used in laboratory experiments, produce spatially uniform shear, and fail to capture the diffusion of momentum and vorticity, the essence of turbulence. Thus, numerical modelling plays an important role in the study of effects of fluid motion on diffusive and advective nutrient fluxes. We use the immersed boundary method to model the interaction of rigid and flexible diatom chains with the surrounding fluid and nutrients. We examine this interaction in two nutrient regimes. We also vary the length and flexibility of chains, as whether chains are straight or bent, rigid or flexible will affect their behavior in the flow and hence their nutrient fluxes. The results of numerical experiments suggest that stiff chains consume more nutrients than solitary cells. Stiff chains also experience larger nutrient fluxes compared to flexible chains, and the nutrient uptake per cell increases with increasing stiffness of the chain, suggesting a major advantage of silica frustules in diatoms. | ||
| Coauthor(s): Lisa Fauci, Lee Karp-Boss, Peter Jumars | ||
| PS19B | Naqib, Faisal | |
| McGill University | ||
| Mathematical modeling of intracellular signal transduction involved in neural plasticity | ||
| Facilitation of sensory neuron synapses in Aplysia californica is induced by the neurotransmitter serotonin (5-HT). This strengthening of synaptic transmission can be mediated by the activation of protein kinase C (PKC), which regulates transmitter release, ion channel function, cytoskeletal rearrangement and gene expression. PKC activation is sensitive to the method of 5-HT application. Spaced application of 5-HT (five 5-min applications of 5-HT with an inter application interval of 15-min) strongly desensitizes PKC activation, while prolonged application of 5-HT leads only to weak desensitization of PKC. Inhibition of protein kinase A (PKA) results in a decrease of PKC desensitization for both spaced and massed application of 5-HT. In contrast, applying a protein translation inhibitor drastically increases the desensitization seen after massed application of 5-HT, but decreases desensitization during spaced application of 5-HT. We present a mathematical model describing how the molecular components of this signaling pathway are connected and regulated in order to elicit the previously described dynamics. This model contains two proteins differentially produced by massed or spaced training as well as a PKA-dependent cycling step. The model is capable of reproducing experimental observations with high accuracy and makes several predictions on the mechanism of desensitization as well as its long-time behavior. | ||
| Coauthor(s): Christopher C. Pack, Wayne S. Sossin | ||
| PS20B | Osborne, James | |
| University of Oxford | ||
| The effects of geometry and seeding strategies on tissues grown in a bioreactor. | ||
| Multiphase modelling is a natural framework for studying many biological systems, such as tissue growth. Biological tissue can be grown external to the body in a perfusion bioreactor, a device to replicate the in vivo environment. The bioreactor system comprises a cell-seeded porous scaffold, which is placed within a culture medium filled cylinder and a flow is driven across the scaffold allowing the mechanical stimulation of cells via pressure and/or fluid shear. The stimuli affects whether the cells proliferate or deposit Extra Cellular Matrix (ECM). Using the multiphase framework, different phases represent the constituents of the system, e.g. the scaffold, ECM, cells, interstitial fluid and culture medium. The resulting model comprises non-standard mixed systems of non-linear Partial Differential Equations (PDEs). For example, multiphase models of the perfusion bioreactor consist of: (i) viscous fluid flow equations for each phase; (ii) hyperbolic PDEs for mass conservation; and (iii) elliptic or parabolic PDEs for nutrient concentrations. Analytical progress with such systems is usually only possible if additional model assumptions, such as radial symmetry or small aspect ratio of the bioreactor, are made. The numerical solution of these equations presents numerous challenges: the numerical methods for solving fluid flow equations and hyperbolic PDEs are notoriously prone to complications such as instability and large computational time. Advanced numerical algorithms are therefore required in order to guarantee an accurate and efficient solution. In this talk we utilise a numerical and computational framework based upon the Galerkin finite element method to present numerical solution of the coupled systems of parabolic, elliptic and hyperbolic PDEs described above in two or more dimensions. This enables us to investigate the effect of interactions between constitutive phases in the tissue model. In particular we investigate the two-dimensional structure of tissue constructs grown in a perfusion bioreactor, external to the body, under varying growth stimuli, showing: (i) when and where dimensional simplifications, such as the long wavelength limit, are appropriate; and (ii) how different seeding strategies affect the final composition of the resulting tissue constructs. This work is of both theoretical and practical interest utilising advanced numerical methods to create biologically relevant conclusions. | ||
| Coauthor(s): Reuben O'Dea, Jonathan Whiteley, Helen Byrne, Sarah Waters | ||
| PS21B | Polidan, Elizabeth | |
| Loyola Marymount University | ||
| Modeling the Energy Budget of the Spider Argiope trifasciata | ||
| Dynamic energy budgets model how individuals consume and utilize energy for growth, reproduction, and survival. These models make testable predictions about how an individual will function in a given environment, help explain observed behaviors, and link processes at the individual level (molecular, cellular, and/or organism level) to processes at the population and ecosystem levels. Spiders are a major component of many ecosystems, and modeling their energy budgets will aid in understanding this important predator. In this presentation, we develop a prototype mathematical model for orb-weaving spider energy budgets. The energy budget of any organism is expressed, at a basic level, as incoming energy minus outgoing energy. Outgoing energy is partitioned between three basic functions: growth, maintenance, and reproduction. This preliminary model focuses on the change in mass over time with the overall goal of maximizing reproduction. We have simulated a number of models with varying approaches to the costs and benefits of web building and maintenance. Validation experiments are planned for the lab as well as the field. Future enhancements to the model, adding complexity, are also planned. | ||
| Coauthor(s): Ben Fitzpatrick, Martin Ramirez | ||
| PS22B | Qian, Jenny | |
| Genome Sciences Centre, British Columbia Cancer Research Centre | ||
| Computational Methods for Detecting Novel Isoforms from De novo Transcriptome Assembly | ||
| De novo transcriptome assembly with ABySS (Assembly By Short Sequences) provides a unique window for further discovery of novel transcripts. As demonstrated recently, assembly data harbours valuable information for identifying small-scale sequence variations such as Single Nucleotide Polymorphism (SNPs), as well as larger-scale structural variations such as novel isoforms. The ABySS assembler uses de Bruijn graphs in a distributed representation, which makes assembling large genomes manageable. The assembly process can be divided into two stages: 1) At the single end tag (SET) stage, read sub-sequences of length k (k-mers) are extended one base at a time. Two sequences are joined to form longer sequences (contigs) when they share a unique (k-1) overlap. 2) At the paired end tag (PET) stage, SET contigs are merged into longer contigs only if sufficiently many paired reads support it. At the SET stage, there may be cases where two contigs cannot be unambiguously joined, namely, if two parallel contigs share the same k-1 overlaps. In the context of a transcriptome assembly, these parallel contigs imply the presence of isoforms, and the shorter one is of length 2(k-1). We call the latter a junction contig since it contains no additional sequence other than the (k-1)-base overlaps with its two neighbors. Junction contigs can be used to identify one of 4 possible novel events: unannotated skipped exon(s), retained intron(s), other alternative splicing, or additional exon(s). The detection mechanism of these events can be summarized as follows: 1 Align the junction contigs (ungapped) to both the reference transcriptome and the genome, and parse the output, to establish the association between junction contigs and known annotations. 2 Align the junction contigs (gapped) to PET contigs, and parse the output, to establish the association between junction contigs and their corresponding PET contigs. 3 Based on the associations established in steps 1 and 2, identify the novel events. We applied this classification scheme to the transcriptome assembly of a follicular lymphoma tumor sample. Our preliminary results show that, out of a complete set of 1859 junction contigs, 324 (17.8%) align to the transcriptome reference only, 660 (35.5%) do not align, and 126 (6.8%) align to the genome reference only. Further categorization of junction contigs in each pool allows us to identify and characterize novel isoforms from this transcriptome assembly in a systematic way. | ||
| Coauthor(s): Shaun Jackman, Cydney Nielsen, Marco Marra, Steven JM Jones, İnanç Birol | ||
| PS24B | Renardy, Yuriko | |
| Virginia Tech | ||
| Field-induced motion of a ferrofluid droplet: a testbed for treatment of retinal detachment | ||
| Recent developments in the synthesis and characterization of ferrofluids are motivated by biomedical applications, where the treatment of retinal detachment is one example. A small amount of ferrofluid is injected into the vitreous cavity of the eye and guided by a permanent magnet inserted outside the scleral wall of the eye. The drop travels toward the side of the eye, until it can seal a retinal hole. The time taken for the drop to migrate is an important quantity which needs to be predicted, and which must be relatively short for the feasibility of this procedure. The motion of a hydrophobic ferrofluid droplet under an externally applied magnetic field is investigated numerically. A viscous medium models the vitreous material. The time taken by the droplet to travel through the medium and the deformations in drop shape are investigated and found to compare well with an experimental study on a simplified model. | ||
| Coauthor(s): S. Afkhami, J. S. Riffle, T. G. St. Pierre, M. Renardy | ||
| PS25B | Robertson-Tessi, Mark | |
| University of Arizona | ||
| A Mathematical Model of Tumor-Immune System Interactions Following Chemotherapy | ||
| A mathematical model of tumor-immune interactions is presented. The model accounts for tumor-induced immunosuppessive effects, such as increases in TGF-beta and the population of regulatory T cells. In the phase immediately following cytoreductive treatment, the initial state of the immune system is primed for a larger tumor; cytokine concentrations and immune cell populations then undergo a transient decay to equilibrate with the new, lower tumor burden. The dynamic interplay between immunoresponsive and immunosuppressive forces during this transient period is simulated numerically, both for single and multiple cycles of chemotherapy. The model is used to probe the optimization of treatments to maximize immune system efficacy, and to shed light on which suppressive effects are important at different phases of tumor growth and treatment. | ||
| PS26B | Rodriguez, Kenny | |
| Loyola Marymount University | ||
| Does Cin5p Control The Early Transcriptional Response To Cold Shock In Saccharomyces cerevisiae? | ||
| Budding yeast, Saccharomyces cerevisiae, respond to temperature changes by activating or repressing the transcription of genes. While the response to heat has been well studied, little is known about which transcription factors regulate the response to cold. Bioinformatic analysis suggested that Cin5p was a candidate regulator, so a cold shock experiment was performed on the yeast deletion strain BY4741Δcin5. Early log phase cells grown at 30°C were shifted to 13°C for one hour and then recovered at 30°C for an additional hour. Cells were harvested at six time points, and the expression ratios of the genes compared to the initial time point at 30°C were measured using DNA Microarrays. Wild type microarray data from an identical cold shock experiment were compared to the Δcin5 dataset. Although most gene expression changes observed in the Δcin5 strain were similar to those seen in wild type yeast, 16% of the known Cin5p target genes had different expression profiles in the two strains. Cin5p target genes showing expression changes in the deletion strain suggests cross-talk by other related transcription factors or by compensation by the other members of the YAP-family. The Δcin5 strain was not impaired for growth at cold temperatures either on sold media or in liquid culture. However the Cin5p over-expression strain was impaired at cold, suggesting that Cin5p’s role in regulating the response to cold is more complex than originally hypothesized. | ||
| Coauthor(s): Kevin Entzminger | ||
| PS27B | Roman, Theodore | |
| Case Western Reserve University | ||
| In Silico Modeling of the Wnt Signaling Pathway | ||
| The Wnt signaling pathway plays important roles in multicellular development and human cancers. A better understanding of the components and interactions of this signaling pathway will enable better modeling of these processes, also potentially resulting in the development of novel medical treatments for various cancers. The Wnt pathway is better understood as a network of interactions rather than a linear progression of events. While some of the key players in this pathway and their interactions are well characterized, knowledge of many components of the pathway is still limited. Building on this existing knowledge of Wnt signaling, we aim to construct a comprehensive model of the Wnt signaling network in order to illuminate potentially novel pathway regulators and network motifs. Compared to existing models, our model will be more comprehensive because we will curate and integrate multiple types of data from multiple sources including: gene-expression data (e.g. microarrays and RNAi screens), protein-DNA binding data (e.g. transcription factor binding motifs), high-throughput protein-protein interaction data (e.g. affinity purification with mass spectrometry), and low throughput information from the literature. Literature references provided us with a well-annotated core of Wnt pathway interactors. Our own affinity purification-mass spectrometry (AP-MS) data and other published protein-protein interaction (PPI) data has provided us with a list of potential Wnt pathway interactors. We have developed an in silico model of the pathway from the core to enrich our understanding of these potential Wnt pathway interactors. This model consists of three layers: the core set of seed proteins--first-level proteins; all direct interactors with the core--second-level proteins; and all direct interactors with the second-level proteins. We scored the non-core nodes of the network based on the length of the shortest path to the core, degree of connectivity to the core, and promiscuity. To ensure our scoring did not occur simply due to chance, we compared the Wnt modeled network to a collection of randomized networks, which were generated by preserving the topology and degree distribution of the original human PPI network. In our scheme, a high score for a node indicates that the node (protein) is highly connected to only the known Wnt signaling proteins, and therefore may play a role in Wnt signaling. This new in silico method of building a select set of candidate proteins to be investigated in the wet lab will result in a more focused, more efficient experimental investigation of potential Wnt signaling proteins. | ||
| Coauthor(s): Alex Galante, Sudipto Saha, Mehmet Koyuturk, Rob Ewing | ||
| PS29B | Sadeghi, Sara | |
| Graduate student | ||
| Length Dependent Force Characteristics of Coiled-Coils | ||
| Coiled-coil domains within and between proteins play important structural roles in biology. They consist of two or more α-helices that form a superhelical structure due to packing of the hydrophobic residues that pattern each helix. A recent continuum model[1] showed that the correspondence between the chirality of the pack to that of the underlying hydrophobic pattern comes about because of the internal deformation energy associated with each helix in forming the superhelix. We have developed a coarse-grained atomistic model for coiled-coils that includes the competition between the hydrophobic energy that drives folding and the cost due to deforming each helix. The model exhibits a structural transition from a non coiled-coil to coiled-coil state as the contribution from the deformation energy changes. We explore the force-extension properties of these model coiled-coils as a function helix length and find that shorter coils unfold at lower force than longer ones, with the required unfolding forces eventually becoming length independent. S. Neukirch, A. Goriely, A.C. Hausrath, PRL(100), 038105(2008) | ||
| Coauthor(s): Eldon Emberly | ||
| PS30B | Saeki, Koichi | |
| Kyushu University | ||
| Advantage of having regulatory T cells requires localized suppression of immune reactions | ||
| The negative selection of immature T cells in the thymus cannot eliminate every self-reactive T cells that have a potential to cause autoimmune diseases. To prevent autoimmune diseases, regulatory T cells suppress the activity of self-reactive T cells, but they also interrupt normal immune reactions against foreign antigens. We discuss the advantage of having regulatory T cells by considering the ability of coping with foreign antigens and the harm of autoimmunity. We modeled the process of negative selection and the differentiation of T cells as follows, immature T cells reactive to abundant self antigens are completely eliminated, those reactive to rare self antigen will become regulatory T cells, and those that fail to interact with the antigens to which they are reactive will become conventional T cells. In this model, some self-reactive T cells can escape the negative selection during the limited training period and become to conventional T cells. Analysis suggests that producing regulatory T cells can be beneficial if the body is composed of many compartments and regulatory T cells suppress the immune reactions only within the same compartment (localized suppression). This result indicates that regulatory T cells should stop circulating once they are activated or suppress other T cells only on the same antigen presenting cells. | ||
| Coauthor(s): Yoh Iwasa | ||
| PS31B | Samii, Laleh | |
| Simon Fraser University | ||
| “Studying the biased motion and motor properties of molecular spiders on a 1D track” | ||
| The purpose of our work is to understand how “molecular spiders” function. Molecular spiders are synthetic molecular walkers where each leg consists of a deoxyribozyme, a single-stranded DNA catalyst. The deoxyribozyme leg interacts with its substrate, a partially complementary ssDNA, through binding and cleavage. Experimental studies suggest the motion of the spider is biased towards uncleaved substrates (Pei et al., 2005). Inspired by experimental results for molecular spiders, we perform Monte Carlo simulation studies of a bipedal spider moving with an inchworm (IW) or a hand-over-hand (HOH) stepping mechanism on a 1D lattice. Each lattice site represents the substrate to be cleaved and can be in one of two states: cleaved or uncleaved. Binding, unbinding and substrate cleavage by the enzyme are controlled by rate constants in a simple kinetic model for the interaction of a deoxyribozyme leg with its substrate. The transitions between these biochemical states are Markovian processes and use the Gillespie algorithm to numerically simulate our kinetic model. This algorithm determines which transition in our kinetic model is going to take place first and how long this transition takes. We focus on the following central issues for the molecular spider: (a) the biased motion of IW and HOH spiders; (b) the effect of track properties on biased motion; (c) the memory effect (role of conversion of substrate to product by cleavage); (d) the processivity (number of cleavage events before spider detachment); (e) the mechanochemical coupling of IW and HOH spiders; and (f) the force-velocity relationship for molecular spiders. Our investigation shows that the biased motion of spiders depends on track properties as well as on stepping mechanisms. In addition, a “memory effect” or cleavage of the substrate plays a significant role in the biased motion of spiders. | ||
| Coauthor(s): Laleh Samii, Heiner Linke, Martin J. Zuckermann,Nancy R. Forde | ||
| PS32B | Shah, Pinal | |
| Benedictine University | ||
| The Dynamics of One-Predator Two-Prey Models for Integrated Pest Management | ||
| We present several variations of one-predator two-prey models for integrated pest management. Features of these models include stage structure for the predator species and one of the prey species and a birth pulse (rather than continuous growth) for the stage structured prey species. We demonstrate the existence of total pest eradication solutions and permanent solutions. We also investigate the effects of model parameters through the analysis of bifurcation diagrams. | ||
| PS33B | Shayganmanesh, Ahmad | |
| School of Mathematics,Iran university of science and technology ,P.O.Box ,16844,Narmak,Tehran,Iran | ||
| Thermal Convection instability of Fluid Layer in Cylindrical Geometry | ||
| This Paper considers the effect of a Perturbed Wall in regard to a horizental fluid layer in which the lower regid surface is of the form z=(ε^2)g(s),s=εr,in axisymmetric cylindrical polar coordinates. The boundary conditions at s=0 for linear amplitude equations are found and it is shown that the distributions of convection cells concentrated near the center. Keywords:Thermal convection;Perturbed Wall;Inner solution; | ||
| PS34B | Simon, Misha | |
| University of Washington | ||
| A comparison between volumetric and localized spatial analysis techniques for assessing model parameters | ||
| Gliomas are the most common type of primary malignant brain tumor, and are characterized by their aggressive growth and uniformly fatal prognosis. Grade IV gliomas, referred to as glioblastoma multiforme (GBM), grow and progress to fatality rapidly, despite aggressive therapy. Because of their diffuse and invasive nature, current medical imaging techniques only capture a fraction of the entire region of malignancy, but mathematical modeling utilizing spatial and temporal information from these images can paint a more informative picture of GBM proliferation and invasion dynamics. Models for GBM aggressiveness focusing on the dynamics the diffuse invasion (net dispersal rate D) and proliferation (net rate ρ) of malignant cells have been shown to accurately reflect the disease progression as seen on medical imaging (Harpold). Swanson et al (Harpold) have demonstrated a methodology for estimating model parameters using a combination of volumetrically-assessed growth velocity and aggressiveness (D/ρ) to accurately model and predict GBM growth and invasion in individual patients. Patient-specific rates of invasion, proliferation and radial growth velocity are assessed and calculated from tumor volumes obtained from MRI scans taken at different time points prior to treatment. This current model parameter estimation methodology relies on gross changes in tumor volume for calculating radial velocity, but for patients with tumors abutting anatomical barriers in the brain or diffusing in non-spherical growth patterns, the use of localized spatial analysis can specifically tailor the model parameter estimates, thus improving the model predictions. To perform the localized spatial analysis, two pre-treatment images for a patient are translated and rotated into spatial correspondence and a minimum Euclidean distance metric is used to assess rates of invasion, velocity and aggressiveness ratio (D/ρ). We can also generate a distribution of velocities of advancement of the imageable tumor front and compare the model parameter estimates for the patient-specific rates of radial growth and biological aggressiveness. The volumetric velocity represents a composite average of the observed range and distribution of velocities and D/ρ produced by the localized spatial analysis method, across various MRI imaging techniques. A quantitative comparison between volumetric techniques and spatially-resolved techniques for model parameter estimation will be provided in this presentation. Analysis reveals that patient-specific predictions of the disease progression will benefit from this detailed anatomical analysis. | ||
| Coauthor(s): Russell Rockne, Rita Sodt, Kristin R. Swanson | ||
| PS35B | Singh, Taruna | |
| Case Western Reserve University | ||
| Mathematical Analysis of Cognitive Output of Alzheimer's Disease | ||
| Statistical measures of graph and network theory have been shown to correlate with a group’s level of dementia. This study follows this idea by calculating the Euclidean Distance and Maximum Difference Distances between Normal, Mild Cognitive Impairment, and Alzheimer's Disease, for both Animal and Vegetable Tests of Category Fluency. Both measures of distance show that Transition Matrices consisting of connections between the ten most common nodes preserve differences in cognitive output. An exponential decay in patient response rate (responses per fifteen second bins) encourages further study to determine if Markov properties are present in patient responses. We are currently pursuing the idea of preferential attachment as it relates to normal and Alzheimer’s patients. | ||
| Coauthor(s): Taruna Singh, David Meyer, Jason Messer, Peter J Thomas, Wojbor A Woyczynski, Alan J Lerner | ||
| PS36B | Smith, Ryne | |
| University of St. Thomas | ||
| A Continuum-Discrete Hybrid Model for the Movement of Single Cells | ||
| The movement of individual cells is vital in various biological processes including immune response, embryonic development, and the spread of cancer. Since both intracellular biochemistry and cell mechanics affect single cell motility, and therefore these biological processes, it is important to understand cell motility from both of these viewpoints. In this poster, we present a continuum model of the signal transduction pathway that affects the chemotaxis, (Bagorda et al., 2006), i.e. movement in respone to a chemical signal, of the cell type Dictyostelium discoidium (Dd), and we couple this model to a discrete model of cell mechanics. The governing equations for the intracellular biochemisty are solved using the finite element method, and the underlying triangular element mesh is used as framework for the series of nodes and springs that are used to calculate the displacments and force distribution within the cell. This continuous-discrete hybrid model provides a streamlined and effective computational tool that allows us to investigate the interplay between intracellular biochemistry and cell mechanics. While our preliminary simulations focus on understanding the experiimentally-observed oscillatory “C-to-spot” formation of the contraction-inducing intracellular species myosin (Koehl and McNalley, 2002), the goal of the model is to better understand the fundamental biochemical and mechanical processes that are necessary to observe extension of the leading edge of Dd and the contraction of its rear, both of which occur in a highly coordinated periodic manner. A. Bagorda, V.A. Mihaylov, and C.A. Parent. Chemotaxis: moving forward and holding on to the past. Thrombosis and Haemostasis, 95:12–21, 2006. G. Koehl and J.G. McNally. Myosin II redistribution during rear retraction and the role of filament assembly and disassembly. Cell Biology International, 26:287–296, 2002. | ||
| Coauthor(s): Ryan Maciej, Magdalena Stolarska | ||
| PS37B | Smith, Wendy | |
| Case Western Reserve University | ||
| Precision of burst timing in conditional pacemakers of the pre-Botzinger complex studied in silico. | ||
| Maintaining a stable respiratory rhythm is a crucial function of the human brainstem area known as the preBoetzinger Complex (pBC). Breakdown of reliable rhythmic behavior at the level of single nerve cells in this complex might play a role in certain breathing disorders. For example, failure of the immature circuit to sustain a regular rhythm may contribute to apnea of prematurity in preterm infants and may be a contributing factor to sudden infant death syndrome in fullterm infants. It is well established that individual nerve cells may produce highly unreliable patterns of action potential timing in response to some patterns of input, particularly constant DC current injection. Despite sources of variability such as irregular synaptic inputs it has been observed that adding a small amplitude fluctuating current to a given DC current injection can dramatically increase the reliability and precision of the response in a wide variety of cells. We investigated the temporal precision of burst-like activity onset for conditional pacemaker cells (using the NEURON simulation environment) driven by constant, sinusoidal and representative endogenous synaptic drive currents. | ||
| Coauthor(s): Timothy S. Anderson, Christopher G. Wilson, Kenneth A. Loparo and Peter J. Thomas | ||
| PS38B | Sodt, Rita | |
| University of Washington | ||
| Simulation of Anisotropic Growth of Gliomas Using Diffusion Tensor Imaging | ||
| Gliomas are highly invasive brain tumors that account for nearly half of all primary brain tumors. Since current medical imaging techniques only detect a portion of the cancerous cells comprising these lesions, a reaction-diffusion model was developed to explore the extent of the tumor invasion below the threshold of detection of imaging and to predict glioma growth that can be tailored to an individual patient\\\\\\\'s tumor (Harpold et al, 2007). This computational model is based on two key elements: net rates of cell proliferation and cell diffusion. The diffusion coefficient is a function of the spatial variable that differentiates regions of grey and white matter to reflect the fact that glioma cells migrate faster in white matter than in grey matter (Swanson et al, 2000). In previous model implementations, cell diffusion was assumed to be constant and isotropic. However, it is commonly accepted that glioma cells migrate preferentially along the direction of white matter tracts, which are organized in myelinated axonal fibres that cause diffusion to be fastest parallel to, and slowest perpendicular to, the fibre tract (Sadlbauer et al, 2009). Additionally, the neuronal cell density in grey matter is higher than that in white matter, providing a faster route for glioma cell motility in white matter. To model anisotropic glioma cell migration in a 3D virtual brain, we use diffusion tensor imaging (DTI), a type of magnetic resonance image (MRI) that quantifies the directional orientation of white matter tracts. Thus, the DTI provides a tensor at each spatial location in the brain indicating the magnitude and direction that glioma cells tend to migrate. Patient-specific tissue classification maps are used in coordination with the DTI atlas to show how well the DTI based model predicts observed tumor growth. We also illustrate that anatomy and spatial heterogeneity can dictate the benefit of anisotropic simulations over isotropic simulations. I will compare the results of our isotropic and anisotropic simulations to quantify the overall differences in growth patterns between the two approaches and further, compare with the observed growth in individual patients to determine which technique more accurately predicts the growth of gliomas in vivo. A. Stadlbauer, et al. Detection of tumour invasion into the pyramidal tract in glioma patients with sensorimotor deficits by correlation of F-fluoroethyl-L-tyrosine PET and magnetic resonance diffusion tensor imaging. Acta Nueroch, 2009 H. L. P. Harpold, et al. The evolution of mathematical modeling of glioma growth and invasion. J Neuropath and Exp Neurol, 66(1):1-9, 2007 K. R. Swanson, et al. A Quantitative Model for Differential Motility of Gliomas in Grey and White Matter. Cell Prolif, 33: 317-329, 2000 S. Jbabdi, et al. Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Mag Res in Med, 54:616–624, 2005 | ||
| Coauthor(s): Russ Rockne, Kristin Swanson, Ira Kalet | ||
| PS39B | Sperber, Anthony | |
| Case Western Reserve University | ||
| Hemodynamics, Cerebral Blood Flow, and the BOLD Signal | ||
| Oxygen delivery to the brain continues to be the focus of active research due to its importance in clinical applications and to the lack of understanding of the mechanisms binding together neuronal activity, energy metabolism, and cerebral blood flow. A better understanding of the interrelation between these phenomena is especially important for the interpretation of functional imaging modalities that are sensitive to blood oxygenation. In particular, BOLD fMRI images are often interpreted directly as maps of brain activity, while the reality may be more subtle. In this work, standard BOLD fMRI models such as the balloon model are revisited and details concerning the oxygen binding of hemoglobin are added. Based on the refined model, the possibility of extracting information concerning the blood oxygenation levels from the time-dependent BOLD signal is investigated by using statistical sampling methods such as particle filtering. | ||
| Coauthor(s): Nicholas Lorenzo, Daniela Calvetti, Erkki Somersalo | ||
| PS41B | Statz, Jonathan | |
| New College of Florida | ||
| Analysis of Stimulus Responses in Biochemical Chain Reactions Involving Feedback and Feedforward Loops | ||
| Finite nonlinear difference equations can be used to study responses of a multi-step biochemical reaction chain to a stimulus at any step of the chain. This reaction chain can be thought of as signaling pathway activation at the receptor level by a stimulus, such as drugs, and the subsequent changes in the protein levels at each step in the reaction chain as responses. In this study, we developed two mathematical models for two hypothetical multi-step reaction chains involving loops to study transient and long term behaviors of these systems as we go down the chain. The first model is for a reaction chain with a negative feedforward loop, and the second one is for a negative feedback loop. Although both of the models have the same steady state equations and values, we saw that negative feedforward and negative feedback loops can produce significantly different behaviors. The former can bring the system into oscillations with various periods if the loop isstrong enough as the length of the reaction chain increases, whereas the latter is not capable of producing oscillations and more complicated dynamics. We also observed that the negative feedforward loop can produce chaotic behavior. Even though our models are simplified assuming that rate constants at each reaction step after receptor activation are equal, our model for the negative feedforward loop can still produce rich dynamics which have to be achieved if one or more of our assumptions are relaxed. | ||
| Coauthor(s): Necmettin Yildirim | ||
| PS42B | Szeto, Mindy | |
| University of Washington | ||
| Anatomic Variation in Quantitative Measures of Glioma Aggressiveness | ||
| Gliomas, the most common primary brain tumors, are extremely aggressive and uniformly fatal, recurring inevitably despite treatment by surgical resection, radiation therapy, and chemotherapy. This is especially true of high-grade, rapidly growing glioblastoma multiforme (GBM), which account for nearly half of all gliomas. Current medical imaging techniques are unable to assess the full extent of diffuse glioma invasion, and its dynamics in vivo remain unclear. Mathematical modeling is therefore an ideal approach to enhancing the diagnosis and treatment of GBM. Using a model for glioma growth that has been shown to have prognostic significance as well as spatial accuracy in predicting disease distribution, progression, and recurrence, GBM aggressiveness can be described in terms of the diffuse invasion (net dispersal rate D) and proliferation (net rate ρ) of malignant cells. We investigate the influence of anatomical location on GBM growth kinetics as quantified by the model parameters and the radial velocity of tumor expansion. Routine magnetic resonance imaging (MRI) data from 140 newly diagnosed GBM patients was reviewed to categorically assess each tumor’s spatial relationship to central brain structures. Patient-specific model parameters and velocity of tumor expansion were calculated from tumor volumes and radii obtained via image analysis. Statistical results across all spatio-anatomic classifications showed no significant differences in range or variability for the distributions of D, ρ, D/ρ, or velocity, implying that the biological aggressiveness of GBM is independent of anatomic location. This is a direct contradiction to previous studies that have proposed a link between anatomical location and aggressive glioma behavior associated with poor patient survival. The observation that heterogeneity in aggressiveness does not appear to be biased as much as expected across spatial locations encourages future consideration of the effects of anatomic barriers and relative white matter location on differential growth patterns. | ||
| Coauthor(s): Russ Rockne, Kristin Swanson | ||
| PS43B | Takase, Mitsuo | |
| LINFOPS Inc. | ||
| Neural network and diffusion immune model with a delay for cytotoxic action and local ignition mechanism for cancer and immune system interaction | ||
| There is a large similarity between the immune system and neural networks. A numerical interaction total model between a cancer mass and the immune system with neural network part and diffusive recurrent parts is shown. The model can be thought as a total model for the simulation of situations and the analysis of the behaviors of cases in interaction not only between a cancer mass and the immune system, but also between an infectious pathogen and the immune system. Elements considered of the immune system in this model are Th cell (helper T cell), Tc cell (cytotoxic T cell) and IL2. It is assumed that there is only one cancer mass in a body. As a vector is input to the synapses of a neuron in a neural network, in this interaction model between a cancer mass and the immune system, the contact process of a cancer cell and a T cell is necessary, so the calculation of the cancer cell density distribution and the distributions of Th cell density and Tc cell density including those of activated Tc and Th cells is necessary. From these conditions, this model consists of the following three parts which affect each other simultaneously in the process of the stimulation. (A) Neural network model part where pattern matching and memorization are performed. (B)Sink-source model part to calculate Tc cell and Th cell density distributions (C)Sink-source model part to calculate cancer cell density distribution λTc ＞1 where λTc is the proliferation rate of Tc cells and an eigen value of feedback loop in the part (B) of this simulation model is necessary to be kept for a while to control Th cell and Tc cell density levels as a function of the model for complete recovery from cancer diseases and the states to ignite the immune system locally. Activated T cells produce IL2, and IL2 makes activated T cells proliferate and produce T cells with the same high affinity receptors. So IL2 forms mutually excitatory network like neural networks with mutual connections. This can cause a local ignition of the immune system. | ||
| PS44B | Tang, Terry | |
| University of Lethbridge | ||
| Delay stochastic simulation of a complete eukaryotic gene expression | ||
| There are several steps in gene expression: transcription, splicing, mRNA transport, and translation. Though many stochastic models have been developed to simulate these steps, the big number of the stochastic equations had been a draw back. With the recently proposed delay model, however, this number has been greatly reduced. By applying stochastic simulations to the delay model, we study the rate of protein synthesis at a given time and the rate distribution. The effect that various parameters (especially delays) have on the protein levels as well as the protein production fluctuations and their magnitudes are also shown. | ||
| PS45B | Tchuenche, Michel | |
| University of Guelph | ||
| HIV/AIDS model assessing the effects of gender-inequality affecting women in African heterosexual settings | ||
| A sex-structured model for heterosexual transmission of HIV/AIDS for addressing the epidemic as a gender-based issue in African heterosexual settings is presented. The epidemic threshold and equilibria for the model are determined and stabilities are investigated. Comprehensive qualitative analysis of the model including positivity and boundedness of solutions, as well as persistence are carried out. The epidemic threshold for the model is computed and used to assess the effects of gender-inequality affecting women in heterosexual settings. The obtained gender-inequality-induced reproductive number is greater than the reproductive number in the absence of gender-inequality suggesting that gender-inequality affecting women in heterosexual settings enlarges the HIV/AIDS epidemic. Numerical simulations are carried out using demographic and epidemiological parameters for Zimbabwe and the obtained results confirm that gender-inequality increases HIV/AIDS prevalence in heterosexual settings. We conclude from the study that gender-inequality affecting women among heterosexuals should be properly addressed for the effective control of the HIV/AIDS epidemic. | ||
| Coauthor(s): Z. Mukandavire, N. Malunguza, C. Chiyaka, G. Musuka | ||
| PS46B | Tebaldi, Claudio | |
| Politecnico di Torino | ||
| Synchronized Chaos in Lotka-Volterra Systems with Adaptive Competition | ||
| A general N-species Lotka-Volterra system is considered for which, in absence of interactions, each species is governed by a logistic equation. Interactions among species take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the model is governed by a system of N^2 non-linear ordinary differential equations. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of reduced models of four equations, where N appears as a parameter, which are proven to account for existence and stability of the equilibria. Reduced models are found effective also in describing the transitions to time-dependent regimes. Such regimes exhibit remarkable properties of synchronization in the cases of both periodic and chaotic behavior, with multiplicity of attractors. In systems with few species, increasing the adaptation characteristic time, the strange attractors merge and synchronization is lost. On the contrary in larger communities multiplicity of attractors and synchronization persist also for very large adaptation time. | ||
| PS48B | Thomason, Sarah | |
| Murray State University | ||
| Evaluation of Microsatellites in Ambystoma tigrinum nebulosum | ||
| Phenotypic plasticity is the ability of a trait to change in response to an environmental cue. Salamanders are known to exhibit phenotypic plasticity in the form of facultative paedomorphosis, producing a paedomorphic (aquatic) or a metamorphic (terrestrial) body morphology, which provides a unique vertebrate model for understanding the evolution of phenotypic plasticity. Previous research has revealed the mechanisms that produce this polymorphism; however, little is known about the evolutionary mechanisms that maintain it. By studying the fitness consequences of facultative paedomorphosis, we can better understand the evolution of this polymorphism. We have proposed using nuclear markers to assign parentage and to create a pedigree within a closed population of tiger salamanders as a way of measuring fitness differences among morphs. As a first step, we evaluated polymorphism using previously designed Ambystoma microsatellite markers in spotted salamanders (Ambystoma maculatum). Tissue samples of 55 salamanders were collected from a local population and DNA was amplified using PCR to assess microsatellite variability. In this ongoing study, nine loci have been successfully amplified, six of which are polymorphic and will be used to determine relatedness in this population. The results of this study will eventually be applied to a population of facultatively paedomorphic tiger salamanders to better understand the evolution of phenotypic plasticity. | ||
| Coauthor(s): Sarah Farmer, Emily Croteau, Howard Whiteman | ||
| PS49B | Toth, Damon | |
| University of Utah | ||
| Investigating Causes of Seasonality of Respiratory Syncytial Virus | ||
| Children’s hospitals around the world see seasonal outbreaks of Respiratory Syncytial Virus (RSV), which is a major cause of lower respiratory tract disease in infants. We attempt to explain the form of seasonality seen in outbreaks of RSV in Utah and other temperate locations using a differential equation model. The model incorporates yearly fluctuation of a transmission parameter that causes a parametric resonance effect and a period-doubling bifurcation that could explain the biennial patterns suggested by outbreak data. | ||
| PS50B | Tsai, Hsiu-Ting | |
| School of Nursing, Chung Shan Medical University, Taichung, Taiwan | ||
| Glutathione S-transferase Gene Polymorphisms Increase Susceptibility of Hepatocellular Carcinoma among Taiwanese | ||
| BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most frequent malignant neoplasms worldwide and is the second leading cause of cancer death in Taiwan. Genetic polymorphism has been reported as a factor to increase the susceptibility of hepatocellular carcinoma. Glutathione S-transferases theta (GSTT1) and mu (GSTM1) play essential roles in detoxification of ingested xenobiotics and modulation of the susceptibility of gene-related cancer. The aim of this study was to estimate the roles of these two gene polymorphisms on hepatocellular carcinoma risk and clinicopathological status in Taiwanese. METHODS: Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) was used to measure gene polymorphisms in 102 patients with hepatocellular carcinoma and 386 healthy controls. RESULTS: Both gene polymorphisms were not associated with the clinical pathological status of hepatocellular carcinoma and serum expression of liver-related clinical pathological markers. No relationship between GSTM1 gene polymorphism and HCC susceptibility was found. However, in the younger group, age ≤ 56 years old, individuals with GSTT1 present genotype had 2.77 fold risk (95% CI: 1.09-7.09) to have hepatocellular carcinoma compared to null variant after adjustment for other confounders. CONCLUSIONS: GSTT1 present gene polymorphism is considered associated with hepatocellular carcinoma susceptibility in Taiwanese with age ≤ 56 years old. | ||
| Coauthor(s): Hsiu-Ting Tsai, Long-Yau Lin, Po-Hui Wang, Shun-Fa Yang | ||
| PS51B | Uribe, Jasmin | |
| University of Arizona | ||
| Modeling Transcriptional Regulation | ||
| Many fundamental questions in biology boil down to the relationship between genotype and phenotype. We are working towards a computational model of this relationship, starting with an arbitrary cis-regulatory genotype, and generating a computational phenotype in terms of transcription factor protein concentrations over time. The ultimate goal is to simulate the evolution of this “toy” gene regulatory network. As a component of this larger goal, our model mechanistically simulates transcriptional regulation through interacting transcription factors (TFs) that bind and unbind to available portions of DNA. We must track TF binding configurations for all nucleosome-free regions. This requires the storage of a huge number of potential configurations. This project presents a statistical approach, using a Boltzmann Chain, combined with a thermodynamic framework and dynamic programming to overcome this challenge. | ||
| Coauthor(s): Alex Lancaster, Grant Peterson, Mark Siegal, Joanna Masel | ||
| PS52B | Voorhees, Burton | |
| Athabasca University | ||
| Prudent vs. Preemptory Strategies in Risk/Reward | ||
| We consider four possible strategies for response to risk/reward situations in which there is the choice of immediate action on the appearance of a risk or reward, or of pausing briefly to assess the situation to determine if the appearance and the actuality coincide. Equations are derived and solved for the time evolution of populations practicing each strategy. The strategies are compared and conditions under which each is preferred are determined. We find that specific conditions must be satisfied if any mixed strategy is to survive. | ||
| PS53B | Woubeshet, Bethy | |
| Loyola Marymount University | ||
| Orb Web Geometry and Heavy Metal Bioaccumulation in Garden Spiders (Argiope sp.) | ||
| Webs are a major element in the energy economy of spiders. The sticky silk of orb web capture spirals is costly to produce and is a limiting factor in web building (Venner et. al., 2001). Consequently, spiders in poor condition may be forced to modify aspects of their web geometry (Blackledge et. al. 2002). In this study, we are examining whether individual garden spiders (Argiope sp.) modify their webs as a consequence of the bioaccumulation of toxic heavy metals. We report here on the perfection of digital orb web photography and the interactive generation of web measurements from digital images using software. Film-based orb web photography has involved coating the web surface with a reflective powder (e.g. cornstarch) and illumination of this surface with a flash unit casting light parallel to the web plane. Using orb webs of several species, we found that this approach works well for digitally-based orb web photography, especially if a black background isprovided and both camera and web are shaded with an umbrella. To calibrate subsequent measurements, an object of known size (fiducial) was placed in or on each web prior to photography. For image analysis, we created command codes in the MATLAB development environment to interactively generate the following web parameters: surface area; number of radii; number of spirals; inter-spiral distances; and inter-radii angles. We are have collected web images from spiders whose heavy metal contents will also be determined, enabling us to detect significant relationships between metal concentrations and any web parameter metric(s). | ||
| PS55B | Yang, Hyun | |
| UNICAMP | ||
| Modelling the interaction between Mycobacterium tuberculosis and the immune system | ||
| We present a mathematical model that describes the interaction between Mycobacterium tuberculosis and the immune response against it. One of the characteristics of Mycobacterium infection is the replication of the bacteria inside alveolar macrophages, which shows that tuberculosis is the prototype of infection that requires a cellular immune response for its control. In the majority of cases the immune response results in the suppression of mycobacterial infection, but does not completely eradicate it. From the model we assess the effects of the granuloma formation during the initial phase of infection, which can predict future reactivation of tuberculosis. | ||
| PS56B | Yang, Scott Cheng-Hisn | |
| Simon Fraser University | ||
| Modelling DNA replication: solution to the random-completion problem | ||
| DNA replication in Xenopus embryos starts at random positions along the genome and at random times during S phase. This spatiotemporal randomness implies fluctuations in the completion times and can lead to cell death if replication takes longer than the cell cycle time (approximately 25 min). Surprisingly, while the typical completion time (approximately 20 min) is close to the cell cycle time, replication failure occurs only about 1 in 300 times. These observations raise an interesting question, known as the “random-completion problem”: How is replication timing accurately controlled despite the stochasticity? We use a nucleation-and-growth model and extreme-value statistics to address quantitatively the random-completion problem. We first show that Xenopus embryos solve the problem by using a large reservoir of potential replication origins that are increasingly likely to initiate, a situation that leads to robust control of replication timing. We also show that variations in the spatial distribution of origins have minimal effect on the accurate control of replication times. Finally, we show that replication in Xenopus minimizes, approximately, the number of proteins required for DNA synthesis. | ||
| PS57B | Yokoyama, Atsushi | |
| Ritsumeikan University, Japan/UBC, Canada | ||
| Synchrony and Rhythmogenesis in Diffusely Distributed Endocrine Neurons by a Diffusive Autocrine Regulator | ||
| Reproduction in mammals is controlled by the pulsatile release of gonadotropin-releasing hormone (GnRH) from the hypothalamus with a species-specific period of about one hour. Revealing the mechanism underlying the origin of this rhythm has profound consequences in treating developmental and reproductive diseases and in improving human health. Although numerous experimental models have been developed including cultured hypothalamic tissues, placode-derived GnRH neurons, and GT1 cell lines, no well-accepted explanation has been found. About 800~2000 GnRH neurons participate in the pulse generation. Their cell bodies are distributed in a scattered manner in designated areas of the hypothalamus. Synchronization between these neurons is absolutely necessary for the generation of the hourly pulses of GnRH. How could scattered neurons synchronize their activities remains an unresolved puzzle. Experiments in the GT1 cells in culture revealed that GnRH neurons express GnRH receptors that allow GnRH to regulate its own secretion through an autocrine effect. GnRH-binding to its receptors on the GnRH neurons triggers the activation of three types of G-proteins of which two activates and one inhibits GnRH secretion (Krsmanovic et al, 2003, PNAS 100:2969). Based on this mechanism, GnRH secreted by GnRH neurons serve as a diffusive mediator as well as an autocrine regulator. A mathematical model is developed (Khadra-Li, 2006, Biophys. J. 91:74) and its robustness and potential applicability to GnRH neurons in vivo is investigated (Li-Khadra, 2008, BMB 70:2103). In this poster, we will introduce the key experimental and modeling results on this system including more recent results that are not yet published. These include the extension of the previous models to the case of diffusely distributed GnRH neurons coupled by a diffusive GnRH signal. Based on these results, one plau! sible explanation for why GnRH neurons are distributed in a scattered way is proposed. | ||
| Coauthor(s): Patrick Flectcher, Yue-Xian Li | ||
| PS58B | Yoon, Jeong-Mi | |
| University of Houston-Downtown | ||
| A Canonical Correspondence Analysis for representing the relationship between xylem fluid-feeding insect populations and their environmental variables involved with Pierce's Disease in Vineyards of Texas | ||
| Pierce’s disease (PD) of grapes is caused by Xylella fastidiosa (Xf), a plant pathogen that multiplies inside the xylem (water-conducting vessels) leading to blockage, drought symptoms and death. Sharpshooter insects, which feed on xylem sap, transfer the bacterium from plant to plant. PD has had negative effects on the California wine industry and is a serious concern for the growing Texas wine industry. As such, the USDA has funded a large study of the spatial and temporal distribution of insect species around Texas through the Texas PD Research and Education Program. The resulting data base of insect trapping numbers from vineyards includes thousands of rows of data and valuable environmental variables. The objective of this study was to analyze the optimal representation of the association between the vineyard sites, the sharpshooter frequencies and the related the environmental variables using a multivariate analysis called to Canonical Correspondence Analysis (CCA) using statistical software, XLSTAT. This presentation will focus on the qualitative analysis of the representation of the CCA plots based on the principal eigenvalues and the weighted correlation coefficients between ordination axes and environmental variables. Our data set consists of three dominant insects’ frequencies, environmental variables (i.e. elevation, precipitation, cold hardiness and eco-regions) at 40 different vineyard sites simultaneously. The insect frequencies were obtained by dividing the insect counts by the trapped days at each site and averaging it by using the total trapped samples at each site. This analysis suggests some novel ecological associations about Texas sharpshooter species. This research is a part of the ‘Interdisciplinary Training for Undergraduates in Biological and Mathematical Sciences (UBM)’which is funded by NSF. | ||
| Coauthor(s): Lisa Morano, Mitchell Forrest, Isabelle Lauziere, Ali Abedi, Danil Safin, Audrey Gonzales | ||
| PS59B | Yuen, William | |
| Harvard University | ||
| Statistical Inference of Intercellular Interactions in Endothelial Sprouting | ||
| Endothelial sprouting is the first step in angiogenesis, where new blood vessels form from pre-existing vessels. The understanding of the decision process involved in endothelial sprouting is critical in predicting and modulating vascular pattern. In this study, the population statistics of 3D in vitro human microvascular endothelial sprouting are analyzed. Spatial and density distributions of endothelial sprouts suggest that highly concentrated sprout density cannot be accounted for by matrix spatial heterogeneity or intrinsic cell heterogeneity. Rather, the analysis suggests a strong presence of intercellular interactions between endothelial cells in the forms of both activation and inhibition that evidently require Notch signaling. | ||
| Coauthor(s): William Yuen, David Mooney |