Lapin Sergey


Jordan Culp and Allison Fisher


Xiongzhi Chen


Elissa
Schwartz






Elissa Schwartz



Kyle
Harrington


Prashanta Dutta


Todd Coffey


Lynn
Schreyer


Nikolaos Voulgarakis


Eric Lofgren


Daniel Farber


Title:
Circadian Clocks and Disease:
Opportunities for Dialogue between Math and Biology Abstract: Circadian (daily) rhythms are phylogenetically ancient, and present in nearly all organisms that have a life span greater than 24h. It has been hypothesized that circadian clocks impart adaptive fitness, making them a key component of life. In mammals, the master circadian clock is located in the small hypothalamic suprachiasmatic nucleus (SCN), comprised of nearly 10,000 independent oscillators that couple together to form a cohesive network clock. Disruption of this clock, especially by mistimed light exposure, leads to significant mental and physical health problems. However, the process by which oscillators become disrupted at a cellular level remain elusive. My lab explores how intact and robust circadian timing promotes resilience at various levels of biological organization, and how disrupting these rhythms leads to negative health outcomes. I will present data highlighting what is known about the structure and function of the SCN oscillator, and how disruption of this clock by light can affect health. It is hoped that this presentation can help form a basis for future indepth discussion and potential collaboration at the interface between math and biology in the context of circadian rhythms and health. Suggested Readings: Antle, M. C., Foley, D. K., Foley, N. C. & Silver, R. Gates and oscillators: a network model of the brain clock. J Biol Rhythms 18, 339–350 (2003). Yan, L. L. et al. Exploring spatiotemporal organization of SCN circuits. Cold Spring Harb Symp Quant Biol 72, 527–541 (2007). Karatsoreos, I. N. & McEwen, B. S. Psychobiological allostasis: resistance, resilience and vulnerability. Trends Cogn Sci (Regul Ed) – (2011). doi:10.1016/j.tics.2011.10.005 Pauls, S. D., Honma, K.I., Honma, S. & Silver, R. Deconstructing Circadian Rhythmicity with Models and Manipulations. Trends Neurosci 39, 405–419 (2016). 
Title:
Investigations into a model of Virus /
Immune System Dynamics Abstract: TBA 
Title:
Dosestructured population dynamics Abstract: Population response to environmental stimuli such as chemicals, heat, or radiation are so far mostly addressed through individualbased modeling approaches. I will describe an alternative approach where population states are structured on generalized dose, based on a kinematic approach that is a simple generalization of agestructuring. The resulting framework accommodates different memories of exposure as in recovery from toxic ambient conditions, differentiation between exogenous and endogenous sources of variation in population response, and quantification of acute or subacute effects on populations arising from lifehistory exposures to abiotic species. I will summarize three examples involving growth suppression in fish, inactivation of microorganisms with ultraviolet irradiation, and metabolic lag in bacterial growth. 
Title:
On a CrossDiffusion
System modeling Vegetation Spots and Strips in Arid Landscape Abstract: In this paper we study a model which describes the vegetation spots and strips in arid landscape. The mathematical mode consists of a crossdiffusion system with reaction sources. Global existence and uniqueness are proved. Some asymptotic behaviors of the solution will be discussed. Those asymptotic behaviors will demonstrate the vegetation patterns observed in the African drylands. 
Title:
Does spatial structure
mediate bacterial persistence in the presence of phage? Abstract: Phage (viruses that infect bacteria) produce a burst of progeny upon lysis of a bacterial cell, and the resulting rapid increase in phage density would seem to doom bacterial populations in some settings. Despite its initial promise, “phage therapy” (as an alternative to antibiotics) has not lived up to expectations. Lab experiments have shown longterm coexistence of bacteria and phage in liquid (chemostat) and spatial (biofilm) cultures. Additionally, surviving cell densities in biofilms are orders of magnitude larger than in liquid cultures. Is there something about spatial structure, per se, that protects cells, or do they derive their protection from mechanisms that only arise spatially? We study spatial agentbased models, as well as ordinary differential equations, to examine the possible effects that spatial structure could have on bacterial survival. Among the mechanisms that we consider are resource concentration, barriers (such as exopolysaccharides (EPS)), cellular debris, and cell signaling. We also propose a kind of “effective burst size” as an indication of the effect of spatial structure. This is joint work with Jim Bull, Kelly Christensen and Carly Scott. 
Title:
Optimal Control Applied
to a Basic Model of Latent Cell Activation of HIV with a Focus on the
Comparison of Objective Functions Abstract: The main aim of this paper was to compare three objective functions for an optimal control problem of the HIV treatment. In the administration of the HAART regimen the amount of free viral particles and the amount of T cells in different time frame of the treatment was observed for each of these objective functions. The basic reproduction number of the model (state equations) were calculated. In this paper, the Pontryangin Maximum Principle is used to achieve the optimal dosage of drug administered according to each of these objective functions based on the basic model of latent cell activation. The forward backward sweep method is used to numerically solve the optimality system, i.e., forward in time for the state equations and backward in time for the adjoint equations using the initial conditions and final conditions(transversality conditions) respectively. 
Title:
A vector inhomogeneous
Poisson process with stochastic gain as a model of multiunit neural
activity Abstract: Poisson processes have been used as approximate models of neural spiking activity for a long time. The two main difficulties with this model has been estimating the timevarying rate from neural observations, and observations showing variance inconsistent with the Poisson assumption (typically higher than the mean, vs to equal according to Poisson). A recent enhancement of the model proposes that the Poisson process has a rate that is modulated by another random variable, reflecting large scale brain state: m(t)=g*f(t) with g ~ p(g) termed a gain random variable [Goris et.al ‘14]. The resultant doublystochastic process is essentially a continuous mixture distribution of Poissons weighted by the distribution p(g). A second enhancement, proposed by us, focuses the estimation on the cumulative count process rather than on the differential point process. That allowed for the cumulative raw rate function F(t) to be approximated well by a lowparameter estimator, which included nonuniform time sampling. It also provided the differential rate function as its derivative, f(t) = F'(t). It also allows for a natural extension to model the simultaneous activity of multiple neural units, as a vector Poisson process. The main drawback of this approach is that the cumulative Poisson process is heteroschedastic in time, albeit with a known variance function. This modification demonstrated remarkable explanatory power for a subset of neurons from the ferret primary auditory cortex. I will show some results from applications to neural data, and directions of current and future research. I am particularly interested in hearing about better estimators of the model process parameters. This is joint work with Stephen David and Zachary Schwartz (OHSU, Portland). 
Title:
Statistical
classification for cancer diagnosis Abstract: Noninvasive biomedical imaging technologies have been widely used in medical disciplines for interrogation of the pathophysiology and pathogenesis of diseases to access disease features repeatedly over time and often at multiple spatially interdependent units. We are interested in a liver cancer study with the objective of determining the effectiveness of using CT perfusion characteristics to identify and discriminate between regions of liver that contain malignant tissues from normal liver tissue. This is a classical classification problem where we want to identify which category/class a new observation belongs, on the basis of a training set of data with known classes. However, the CT imaging data provides new challenges because of its increasing size and structure complexity. To reduce model complexity and simplify the resulting inference, possible spatial correlation is often neglected. In this talk, I will introduce several classification approaches that are designed for implementing various correlation structure. The method offers maximal relative improvement in the presence of temporal sparsity wherein measurements are obtainable at only a few time points. 
Title:
Upgrade the Compartments
of Mixed Linear Model to Reduce Both False Positives and False
Negatives in Gene Mapping Abstract: Reporting false discoveries not only damages academic reputation, but also wastes resources by misleading followup studies. Population structure and kinship among individuals are two common factors that cause false positives in genomewide association studies (GWAS). The Mixed Linear Model (MLM) is an effective method to eliminate false positives. MLM fits both testing genetic marker and population structure as fixed effects and incorporates kinship to define the variance and covariance structures of the random individual genetic effect. Unfortunately, the confounding of testing genetic marker with population structure and kinship makes the statistical test on the genetic marker less powerful. Even when the genetic marker is a functional polymorphism, its effect is diluted by the covariate population structure and the kinship. The confounding effect is more problematic on traits that are associated with population structure such as fitness in natural populations or economic traits in cultivars. This presentation describes the principles and practices for developing new MLM methods to simultaneously reduce false positives and retain high statistical power. 
Title:
Lagrangian Mesh Modeling
Viscoelasticity Abstract: We describe an immersed boundary Lagrangian mesh method for modeling complex fluids where the fluid viscoelasticity is represented by a discrete network of Maxwell elements. The rheological properties of the Lagrangian mesh fluid are compared with an OldroydB pde model for complex fluids. We show simulation results from immersed boundary models for peristalsis and sperm motility in Lagrangian mesh and OldroydB fluids. 
Title:
Mathematical models for the
Trojan YChromosome eradication strategy of an invasive species Abstract: The Trojan YChromosome (TYC) strategy, a genetic biocontrol method, has been proposed to eliminate invasive species by introducing sexreversed trojan females. Because constant introduction of the trojans for all time is not possible in practice, there arises the question: What happens if this injection is stopped after some time? Can the invasive species recover? To answer that question, we study this strategy through deterministic and stochastic models. Our results show that: (1) with the inclusion of an Allee effect, the number of the invasive females is not required to be very low when this injection is stopped, and the remaining trojan population is sufficient to induce extinction of the invasive females; (2) incorporating diffusive spatial spread does not produce a Turing instability, which would have suggested that the TYC eradication strategy might be only partially effective; (3) the probability distribution and expectation of the extinction time of invasive females are heavily shaped by the initial conditions and the model parameters; (4) elevating the constant number of the trojan females being introduced into the population will lead to a decrease in the expected extinction time for wildtype females, as opposed to an increase in the extinction probability within an application time. 
Title::
Mathematical Modeling and
Parameter Estimation of Dynamical Cell Signaling Pathways in Fibroblasts Abstract: In many types of tumors, stromal fibroblasts become ‘activated’ and express a number of contractile proteins, particularly αsmooth muscle actin (SMA), known as myofibroblasts. The differentiation of fibroblast to myofibroblast may be induced by TGFβSMAD and SDF1CXCR4 pathways, producing an activated, myofibroblastrich stromal microenvironment through secretion of various cytokines and growth factors. The myofibroblastrich stromal microenvironment of different human cancers is associated with an increased risk of invasion and metastasis and a poor clinical prognosis. We developed an ODE model of the differentiation of fibroblast to myofibroblasts. We adapted a Bayesian inference and MCMC method for ODE systems to estimate model parameters values by using experimental data from a set of cell culture experiments. We show that the ODE model gives qualitative agreement with experimental results over a wide range of initial TGFβ initial conditions. 
Title:: An empirically based mathematical model for the potential role of masting by introduced bamboos in North American deer mice population irruptions Abstract: The ongoing naturalization of frost/shade tolerant Asian bamboos in North America may cause adverse environmental consequences involving introduced bamboos, native rodents and ultimately humans. A specific concern is that the eventual masting by an abundant bamboo within Pacific Northwest coniferous forests could produce a temporary glut of food capable of driving a population irruption of a common native seed predator, the deer mouse (Peromyscus maniculatus), a hantavirus carrier. To address this concern, we conducted feeding trials for deer mice with bamboo and native seeds. Adult deer mice consumed bamboo seeds as readily as they consumed native seeds, and females produced a median litter of 4 pups on a bamboo diet. We used our empirical results to parameterize a modified RosenzweigMacArthur consumerresource mathematical model to project the populationlevel response of deer mice to a hypothetical pulsed supply of bamboo seeds. The qualitative dynamics of the model, a system of nonlinear ordinary differential equations, predicts rodent population irruptions and declines similar to reported cycles involving Asian and South American rodents but unprecedented in North American deer mice. This is joint work with Melissa Smith and Richard Mack. 
Title:: Finding the Best Classification Rates for Arabic Sign Language Data using Data Analysis Methods Abstract: In this talk, we identify several types of methods for finding the classification rates for Arabic sign language data (training and testing data), and these data (feature vectors) are taken and extracted from a published paper: (Recognition of Arabic Sign Language Alphabet using Polynomial Classifiers, Khaled Assaleh and M. AlRousan, EURASIP JASP 2005:13 (2005) 21362145. DOI: 10.1155/ASP.2005.2136)). These data represent images that were collected from 30 deaf participants who had to wear colored gloves and then perform their own Arabic sign gestures. We are only using 10 letters (classes) out of the 30 alphabets in Arabic sign language that can be performed in 42 gestures. Firstly, we begin with visualizing the three classes of data in twodimensional plot using the Principle Component Analysis (PCA). Moreover, we start using linear classifier to generate linear discriminant functions using the pseudo inverse method in order to find the classification rates for both data types: training and testing data. Then, we classify data using neural networks (NN) model, and we implement the kmeans algorithm to find the classification rates for our data. Finally, we compare the different types of methods with each other to find the best method for achieving excellent classification rates among the other methods. 
Title:: Laminar Development of the Primary Visual Cortex Abstract: In this talk, we will introduce the architecture of the visual system in higher order primates and cats. Through activitydependent plasticity mechanisms, the left and right eye streams segregate in the cortex in a stripelike manner, resulting in a pattern called an ocular dominance map. We introduce a mathematical model to study how such a neural wiring pattern emerges and extend it to consider the joint development of the ocular dominance map with another feature of the visual system, the cytochrome oxidase (CO) blobs, which appear in the center of the ocular dominance stripes. Since cortex is in fact comprised of layers, we introduce a simple laminar model and perform a stability analysis of the wiring pattern. This intricate biological structure (ocular dominance stripes with 'blobs' periodically distributed in their centers) can be understood as occurring due to two Turing instabilities combined with the firstorder dynamics of the system. We show recent numerical simulations showing how monocular deprivation during development can dramatically alter the ocular dominance pattern, while leaving the CO blob distribution nearly unaltered. 
Title:: Directed evolution of phage lysins: using mathematical models to explore feasibility/design of new antibacterial drugs Abstract: Motivated by a mounting tide of drug resistant bacteria, the search for new antibacterial agents is embracing technologies that lie outside traditional bounds. One promising source of compounds is the lysins encoded by bacteriophages (viruses that infect and kill bacteria). These enzymes degrade the bacterial cell wall from the inside, leading to rupture of the cell and subsequent dispersal of phage progeny. Lysins have evolved to not kill cells from the outside, thus preserving future hosts, but recent lab work has shown that they can be engineered to kill from without. Developing lysins that have desirable properties for therapeutic use is complicated by the fact that the molecular basis of improvement is not yet understood. A possible way forward is provided by directed evolution. Here we propose lab protocols that involve the coculturing of two bacterial species–one producing a toxin/lysin that kills the other, leading to selective pressure for improved function of the toxin. We use mathematical models and simulations to explore the feasibility of this directed evolution and offer insights into various protocols. 
Title:: Improved Models of Equine Infectious Anemia Abstract: We present an improved model of equine infectious anemia. The compartmental model introduced by Schwartz et al. (2013) is modified to take into account the saturating effects of viral stimulation of macrophage infection and cytotoxic Tlymphocyte production. The formula for the basic reproduction number of the infection is not changed by the new terms in the model, but these do expand the domain of one form of infected steady state (the boundary equilibrium) and change the character of a second form of infected steady state (the endemic equilibrium). Computation of bifurcation diagrams provides a simple way to look at the effects of the new terms. The Matcont package for Matlab computes both bifurcation diagrams and eigenvalues, the latter providing important information about the stability of steady states associated with the EIAV infection. This is joint work with Elissa Schwartz. 
Title:: Mathematical modeling of sperm and cilia motility Abstract: The motility of sperm flagella and cilia are based on a common physiological structure capable of generating a wide range of dynamical behavior. We describe a fluidmechanical model for sperm and cilia coupling the internal force generation of dynein molecular motors through the passive elastic axonemal structure with the external fluid mechanics. As shown in numerical simulations for motile sperm, the model's flagellar waveform depends strongly on viscosity as well as dynein strength. 
Title:: Stochastic Ordering of Epidemics Abstract: A lesserknown method, known as stochastic comparison, has been developed for analyzing infectious epidemics. The method is powerful since it removes restrictive model assumptions commonly used in modeling epidemics processes. The tradeoff, however, is that such a comparison approach often leads to extracting qualitative structural features, rather than quantitative information. In this talk, I will discuss fundamental ideas of stochastic orders (a deep theory in its own right) in the context of analysis of classical epidemic models, for which strong model assumptions are significantly relaxed. 
Title:: The Behavior of a Population InteractionDiffusion Equation in its Subcritical Regime Abstract: A model interactiondiffusion equation for population density originally analyzed by Wollkind et al. (1994, SIAM Review 36, pp. 176214) through terms of thirdorder in its supercritical parameter range is extended through terms of fifthorder to examine the behavior in its subcritical regime. It is shown that under the proper conditions the two subcritical cases behave in exactly the same manner as the two supercritical ones unlike the outcome for the truncated system. Further there also exists a region of metastability allowing for the possibility of population outbreaks. These results are then used to offer an explanation for the occurrence of isolated vegetative patches and sparse homogeneous distributions in the relevant ecological parameter range where there is subcriticality for a plantground water model system as opposed to periodic patterns and dense homogeneous distributions occurring in its supercritical regime. This is joint work with Mitchell G. Davis. 
Title:: Inferring the cell differentiation trajectory from singlecell gene expression data Abstract: Cells differentiate at different speeds: even when collected at the same time point, cells may be at different stages of the differentiation process, exhibiting different gene expression profiles. Additionally, single cells are often a mixture of multiple subtypes, sometimes including previous unknown subtypes and subtypes that were sampled due to imperfection of the experimental procedure. We aim to infer the cell differentiation trajectory from singlecell gene expression data, while accounting for potentially multiple paths and possibly unknown and unintended subtypes. We cluster cells by their gene expression profiles into `super cells’ and infer a causal graph among the super cells. The causal graph corresponds to the differentiation process at a coarse level. Individual cells are placed along this process depending on their distance from the nearest super cells. I will illustrate this idea with examples and explain the current development in methods and algorithms for this inference. 
Title:: Introduction to differential equations perturbed by random noise Abstract: This will be a introductory talk on differential equations with a forcing term that is random, in contrast to the classical case. One needs only knowledge from elementary analysis (e.g. RiemannStieltjes integral) to understand the talk content. I will discuss the motivation to add the ``noise'' to a differential equation that models natural phenomenon, including the differential equations in biology, how to make sense of the ``noise'' mathematically, basic stochastic calculus, and some of my naive (failed) attempts to prove the wellposedness of differential equations in biology with noise. 
Title:: Statistical Analysis of Complex Data Objects Abstract: Noninvasive medical imaging tools such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and ultrasound (US) have provided significant assistance to disease diagnosis and treatment monitoring. In the meantime, they also bring exciting challenges in statistical analysis since the data collected by those imaging tools are not only increasing in size, but also in its complexity. In this talk, I will introduce several complex data objects including treestructured data, functional data, and imaging data. These complex data objects often lie in nonEuclidean space and traditional statistical tasks such as regression, classification and hypothesis testing become rather difficult. I will talk about the most current techniques for handling these complex objects and the related applications. An important lesson is that analysis in the space of data objects can reveal much deeper scientific insights than the simple analysis of summary statistics. 
Title:: Using compartmental and agentbased modeling to understand the 2009 H1N1 influenza outbreak in Pullman, WA Abstract: Knowledge of mechanisms of infection in vulnerable populations is needed in order to prepare for future outbreaks. Here, using compartmental and agentbased modeling with a unique dataset collected during the 2009 outbreak of influenza A (H1N1)pdm09 in Pullman, we studied H1N1 infection dynamics in a rural university environment. Specifically, we evaluated mechanisms of infection, we estimated infection parameters, and we predicted the number of symptomatic individuals that would have resulted given a variety of plausible scenarios. Our findings are relevant for future influenza epidemics in similar settings. 
Title:: Application of Topology to Data Analysis Abstract: In the era of Big Data scientists face the challenging task of the analysis of data. Ideally, they would like to extract some qualitative signal from their data in the hope to better understand the underlying biological processes. In recent years, tools from persistent homology  a subfield of algebraic topology  have been successfully applied in a number of applications. The general idea is that geometric structure found in data may inform us about biological functionality. The key benefits of using topological features to describe data include coordinatefree description of shape, robustness in the presence of noise and invariance under many transformations, as well as highly compressed representations of structures. In this presentation I will review the fundamental tenets of persistent homology and its application to problems from natural sciences as well as available open source computational tools. 
Title:: Invariant signal processing in auditory biological systems Abstract: The sense of hearing is an elaborate perceptual process. Sounds reaching our ears vary in multiple features: pitch, intensity, rate. Yet when we parse speech, our comprehension is little affected by the vast variety of ways in which a single phrase can be uttered. This amazing ability to extract relevant information from wildly varying sensory signals is also ubiquitous in other sensory modalities, and is by no means restricted only to human speech. Even though the effect itself is well characterized, we do not understand the approaches used by different neural systems to achieve such performance. In an ongoing project, we are testing the hypothesis that broadly invariant signal processing is achieved through various combinations of locally invariant elements. The main questions we would like to address are: 1. What are the characteristics of locallyinvariant units in auditory pathways? 2. How are biological locallyinvariant units combined to form globally invariant processors? 3. What are the appropriate mathematical structures with which to address and model these sensory processes? The mathematical aspects of the research involve an interesting combination of probability theory (a must in the study of biological sensory systems) and group theory, needed to characterize invariants and symmetries. The combination defines the concepts of a probabilistic symmetry, and expands the scope of probabilities on group structures, originally introduced by Grenander. 
Title: Mathematical Modeling of Cholera Epidemics Abstract: Cholera is a severe waterborne infectious disease caused by a bacterium Vibrio cholerae. This disease strikes hardest in underdeveloped regions that lack proper hygiene and sanitation and have limited access to clean water and other resources. Today, cholera remains a major public health threat. In this talk, I will present some recent work in mathematical modeling of cholera. First, a brief introduction to infectious disease modeling will be given. Secondly, we will focus on some recent developments in cholera modeling. Particularly, we will talk about endemic stability, spatial spread, cholera traveling waves and disease threshold dynamics by using ODE and PDE models. 
Title: Analysis of the Transmission of Malaria Parasite Abstract: In this work, we developed a mathematical model of malaria transmission using ordinary differential equations for the spread of malaria in human and mosquito populations. Particularly, the complex disease transmission pathways are modeled by incorporating not only the recovered humans but also the infected persons return to the susceptible class. Through a rigorous equilibrium analysis, we found that the model can exhibit three equilibria: diseasefree equilibrium, mosquitofree equilibrium and endemic equilibrium. The basic reproduction number, R0, is calculated using next generation matrix method. With the derived R0, we analyzed the local dynamics and the disease threshold of malaria infection. Specifically, local stability of the endemic equilibrium is verified using center manifold theory. The result shows that the disease invades the population if R0 is greater than unity and dies out if R0 is less than unity. Additionally, numerical simulation is carried out on the set of values complied for areas of low and high transmission to explore possible behaviors of the model using the baseline parameter values. 
Title: A Look at Multiplicity through Misclassification Abstract: Multiplicity in large scale studies using, for example, microarray genomic data and functional neuroimaging data (fMRI), has been an intensively researched topic in recent years. One option often used by researchers is a “top rtable”, which involves ranking the hypotheses in some order (pvalues or test statistics) and reporting the top r results. This has immediate practical applications as what we have is a list of “interesting” results that are worth following up, irrespective of the actual pvalue (adjusted or not). In this manuscript we take another look at multiplicity using top tables. Our approach is intended to be a compromise between theory and practice. We look at the relationship between the probability of correct classification, which we call rpower (the units picked in the topr table do indeed come from the alternative), and the value of r. We analytically define rpower in terms of order statistics and quantify the probability of correct classification. We use numerical integration to calculate rpower as a function of effect size, δ; the number of hypotheses tested, N; the number of hypotheses coming from the null, k; and r. We show that rpower is positively related to effect size, and negatively related to k/N. The relationship to r depends upon whether r < k. There are two possible uses of our results: based on a prechosen rpower we can calculate r and decide on the number of hypotheses to be followed up or if r is calculated using some other criterion we can use our method to calculate rpower in that context. We illustrate these ideas using examples from microarrays and functional magnetic resonance imaging data. Coauthored by: Nicole A. Lazar Department of Statistics University of Georgia Athens, GA, 30602 email:nlazar@stat.uga.edu Alan Genz Department of Mathematics Washington State University, Pullman, WA, 99163 email:genz@math.wsu.edu 
Title: New modeling tools for scaling forest dynamics within the hierarchical patchdynamics framework Abstract: The forested ecosystem is a complex adaptive system having a complicated hierarchical structure. In this presentation the framework of complex adaptive systems is employed to understand and predict how natural and anthropogenic disturbances occurring at different scales propagate through the forested ecosystems and affect forest structure and dynamics. The original Matreshka modeling framework considers vegetation dynamics as the results of vegetation processes at several hierarchical scales driven by natural and anthropogenic disturbances of different magnitude. The particular processes include growth of individual trees, dynamics of trees within the stand, forest stand mosaic, and changes of the collection of forest stands of different forest types at the landscape level. Recently we have developed models including the Crown Plastic SORTIE, LES, and PPA to address the scaling of vegetation dynamics from the individual to the stand level. All these models employ individual tree plasticity as a crucial factor for forest selforganization. We have also developed a Markov chain model of forest stand dynamics. To parameterize and validate models we have broadly employed data of the USDA Forest Inventory and Analysis Program (FIA data) and of the Quebec forest inventory program. We have also developed an original remote sensing technique to parameterize the spatial component of our individual based models. These new modeling tools will be useful for understanding how climatic changes and different forest management practices can affect forest dynamics and carbon footprint. 
Title: Diagnostic capacity and vaccine response in space and time Abstract: In this presentation, I will talk about diagnostic capacity and vaccine response related to risk by using rabies in Tanzania as an example. Ebola virus disease will be mentioned at the end. 
Title: Vegetative Rhombic Pattern Formation Driven by Root Suction for an InteractionDiffusion PlantGround Water Model System in an Arid Flat Environment Abstract: A rhombic planform nonlinear crossdiffusive instability analysis is applied to a particular interactiondiffusion plantground water model system in an arid flat environment. This model contains a plant root suction effect as a crossdiffusion term in the ground water equation. In addition a thresholddependent paradigm that differs from the usually employed implicit zerothreshold methodology is introduced to interpret stable rhombic patterns. These patterns are driven by root suction since the plant equation does not yield the required positive feedback necessary for the generation of standard Turingtype selfdiffusive instabilities. The results of that analysis can be represented by plots in a root suction coefficient versus rainfall rate dimensionless parameter space. From those plots regions corresponding to bare ground and vegetative patterns consisting of isolated patches, rhombic arrays of pseudo spots or gaps separated by an intermediate rectangular state, and homogeneous distributions from low to high density may be identified in this parameter space. Then, a morphological sequence of stable vegetative states is produced upon traversing an experimentallydetermined root suction characteristic curve as a function of rainfall through these regions. Finally, that predicted sequence along a rainfall gradient is compared with observational evidence relevant to the occurrence of leopard bush, pearled bush, or labyrinthine tiger bush vegetative patterns, used to motivate an aridity classification scheme, and placed in the context of some recent biological nonlinear pattern formation studies. Coauthored by: Inthira Chaiya*, Richard A. Cangelosi$, Bonni J. KealyDichone$, Chontita Rattanakul* * Department of Mathematics, Faculty of Science, Mahidol University, Rama 6 Road, Bangkok 10400, Thailand. Inthira Chaiya is supported by the Royal Golden Jubilee Ph.D. Program (PHD/0191/2553). $ Department of Mathematics, Gonzaga University, 502 E. Boone Avenue MSC 2615, Spokane, WA 99258, USA 
Title: Introduction to Galton Watson Processes and Coalescence Problems Abstract: We introduce Galton watson branching processes. We review basic limit theorems. Then we talk about coalesence problems for such processes. We give some basic results and some applications. We outline some interesting open problems. 
Title: Maximum Likelihood Estimation of Species Richness via Generalized Linear Mixed Models Abstract: Understanding biological diversity in plant and animal communities is fundamental to understanding community structure and health. Ecologists view species diversity as a function of two components: evenness and richness. Evenness refers to the uniformity of abundance across all species in a region, while richness refers to the total number of unique species present. Estimators of species richness abound in the statistical literature, including one or two by the likes of Bradley Efron (1975, with R. Thisted) and R. A. Fisher (1943, with A. S. Corbet and C. B. Williams). The more commonly used estimators, with simplifying assumptions, include Good’s (1953) estimator, Darroch’s (1958) estimator where he assumed a zero truncated Poisson model with constant rate parameter, the jackknife estimator of Burnham and Overton (1978) and the nonparametric coverage estimators of Chao (1984) and Chao and Lee (1990). In addition, there are approaches using rarefaction, but these will not be discussed in the talk. Suppose a sample of n individuals produces Yi individuals from the ith species (i = 1, 2, ..., R), where R represents the total number of species present in the sampled region or sampling frame. It is commonly assumed that counts of this nature follow a Poisson distribution with parameters λi (i = 1, 2, ..., R). However, the ith species appears in the sample if and only if Yi > 0. Thus, the complete set of species is unobservable, and so R is unknown and must be estimated. Under these conditions, the distribution of the observed counts follows a zero truncated Poisson with parameters λi (i = 1, 2, ..., r), where r (≤ R) represents the total number of observed species. Models for estimating species richness are generally based on simplifying assumptions (e.g., λi=λ for all i as in the case of Darroch’s estimator). Community ecologists have long known that populations are generally composed of a few species with very high abundances as well as a relatively large number of species with very few individuals. This heterogeneity in abundances requires a more complex model structure (e.g., λi ≠λ), but the added complexity has made computation of model parameters difficult. In this talk I will propose an estimator of species richness based on a generalized linear mixed model (GLMM) framework in which the Yi (i = 1, 2, ..., R) are assumed to follow a Poisson distribution with parameters λi arising from a distribution with unknown parameters. The estimators based on the use of both continuous and discrete mixing distributions will be discussed. The results of a Monte Carlo simulation comparing the most commonly used estimators of species richness and the GLMM estimators will be presented. 
Title: Global wellposedness and asymptotic behavior of solutions to a reactionconvectiondiffusion cholera epidemic model Abstract: We study the initial boundary value problem of the SIRSB PDE model for cholera dynamics with advection and diffusion. We obtain the local wellposedness result by relying on the theory of cooperative dynamics system. Via a priori estimates making use of the special structure of the system and continuation of local theory argument, we show that in fact it is globally wellposed. Finally, we show the local asymptotic stability of the solutions considering different values of the basic reproduction number using some spectral analysis. This is a collaboration work with Xueying Wang. 
Title: Identifying the Conditions under which Antibodies Protect Against Infection by Equine Infectious Anemia Virus Abstract: The ability to predict the conditions under which antibodies protect against viral infection would transform our approach to vaccine development. A more complete understanding is needed of antibody protection against lentivirus infection, as well as the role of mutation in resistance to an antibody vaccine. Recently, an example of antibodymediated vaccine protection has been shown via passive transfer of neutralizing antibodies before equine infectious anemia virus (EIAV) infection of horses with severe combined immunodeficiency (SCID). Viral dynamic modeling of antibody protection from EIAV infection in SCID horses may lead to insights into the mechanisms of control of infection by antibody vaccination. In this work, such a model is constructed in conjunction with data from EIAV infection of SCID horses to gain insights into multiple strain competition in the presence of antibody control. Conditions are determined under which wildtype infection is eradicated with the antibody vaccine. In addition, a threestrain competition model is considered in which a second mutant strain may coexist with the first mutant strain. The conditions that permit viral escape by the mutant strains are determined, as are the effects of variation in the model parameters. This work extends the current understanding of competition and antibody control in lentiviral infection, which may provide insights into the development of vaccines that stimulate the immune system to control infection effectively. 
Title: Mathematical Modeling of Integrin Dynamics in Cell Motility: From Stochasticity to Sensitivity Abstract: A cell’s ability to move to the correct location at the correct time is vital for maintenance of homeostasis; improper movement is often indicative of a pathogenic phenotype. As such, it is critical to understand the molecular phenomena of motility. A key step in the process of cell motility is the development of focal adhesions, which are protein complexes involving cytoskeletal elements, membrane bound proteins, and extracellular matrix components. A fundamental component of a focal adhesion is the transmembrane receptor protein, integrin, that links the actin cytoskeleton to extracellular matrix proteins. Here we develop and analyze a stochastic model of a nascent focal adhesion. The model captures the dynamics of the rate reactions over time between extracellular ligand molecules, intracellular adhesion proteins called talin, and integrins. To better inform our model conclusions, we discuss results from a variety of sensitivity analysis techniques for both deterministic and stochastic models. 
Title: Mathematical Modeling of the Dynamics of the TGFbeta and SDf1 Signaling Pathways Abstract: An important aspect of mathematical systems biology is modeling the dynamics of biochemical networks where molecules are the nodes and the molecular interactions are the edges. It is an extremely complex system if you are trying to include every single related component. Sometimes lots of substrates influence distant enzymes in the network. This can make the network more complicated. Including everything that researchers in biology believe to be important can lead to systems of hundreds or thousands of ODEs with many unknown parameter values. In this talk, I will introduce a reduced mathematical model that incorporates the crosstalk between breast tumor cells and their environments and predicts how the process affects the tumor progress. We focus on two autocrine signaling loops mediated by TGFbeta and SDF1 and show how the components in these pathways regulate the differentiation of fibroblasts into myofibroblasts and affect cell growth and proliferation. 