Chronic wound healing is a staggering public health problem worldwide. It affects 6.5 million individuals in the U.S., including 1.3 million to 3 million having pressure ulcers (bedsores). As many as 10-15% of the 20 million indiviuals with diabetes in the U.S. are at risk of developing chronic ulcers. Ischemia, caused primarily by peripheral artery diseases, represents a major complicating factor in cutaneous wound healing. In this talk I will explain the wound healing process, which involves interactions among different types of cells and the extracellular matrix. I will describe pre-clinical experiments with ischemic wounds carried out in the Comprehensive Wound Center at OSU, and will present recent mathematical modeling results in a joint work with Chuan Xue and Chandan Sen
A number of human diseases do not have a known cause and lack effective treatment. One of such diseases is idiopathic pulmonary fibrosis (IPF), a progressive scarring disease of the lung without known cause or pharmacological treatment. To date, the only effective treatment is lung transplantation and the mean time of survival from diagnosis is 5 years. We have taken a systems- and discovery-based approach to identify key regulatory networks and targets in the lungs of people with this disorder and have correlated these network changes with progression of lung function testing abnormalities. We have taken advantage of the observation that in gene networks, microRNA are identified as potential regulators of hubs. We will explore the regulation of microRNAs in IPF and discuss how changes in these microRNA may serve as a key regulatory role in the human disease. In this presentation, we will also discuss scale free networks and provide new insights into the mechanisms and potential treatment of this disorder. Our goal is to create platform approaches that can be applied to human health and disease.
In his 1995 book, "River out of Eden", Richard Dawkins described R.A. Fisher as "the greatest of Darwin's successors". Fisher was a statistician whose work in agricultural science 75 years ago arguably led to the planet's ability to feed itself. He contributed in many fundamental ways to biometry and statistical inference. Two hundred years before Fisher, Thomas Bayes' work on probability was published, and that has led to statistical inference of a type that Fisher was never able to accept. Now we are faced with a Twenty-first Century with huge questions in Energy, Climate, Environment, Finance, Water, and (still) Food. Uncertainty abounds, and society's approach has been to collect more data. The challenge is to find the nuggets of knowledge in these increasingly massive datasets. In this talk, I shall show how Fisher and Bayes both contribute to Statistical Science's role in helping to answer these and many other questions.
Direct sensing of a physiological signal by a nascent RNA transcript has emerged recently as a common mechanism for regulation of gene expression in bacteria. RNAs of this type, termed "riboswitches," interact with the cognate regulatory signal. This interaction can modulate the structure of the nascent transcript, which in turn can determine whether the RNA folds into the helix of an intrinsic terminator, resulting in premature termination of transcription. Similar RNA rearrangements mediate translational regulation by sequestration of the ribosome binding site; in this case, regulation can occur by interaction of the effector with either the nascent RNA or the full-length transcript. We have identified several systems of this type, including the T box system, which monitors the charging ratio of a specific tRNA, the S box and SMK box systems, which respond to S-adenosylmethionine (SAM), and the L box system, which responds to lysine. Each class of riboswitch RNA recognizes its signal with high specificity and an affinity appropriate to the in vivo pools of the effector. Characterization of the RNA-effector interaction in these systems has provided new information about how different classes of effectors are recognized, and about the impact of these regulatory mechanisms on the cell.
The talk will begin with some general comments on the role of ecological theory and its history. I will argue that a key element of a successful theory in any discipline - understanding of how and why simple models differ from more complex models - is largely lacking in theoretical ecology. This has meant that many specific simplifications have often become fixtures of almost all models without any knowledge of the either the adequacy or consequences of these simplifications. Models of density dependence and competition are, in most cases, simplified representations of the interactions of consumers exploiting resources that limit population growth. However, the most commonly used models of both density dependence and competition have features that are inconsistent with the majority of plausible consumer-resource models. Some other issues dealing with the choice of variables in ecological models will be discussed.
Tuberculosis continues to cause the suffering and death of millions of people in the world each year. Growing numbers of multi drug- and extensively drug-resistant bacterial strains are contributing to the problem as well as coincident HIV infection and a vaccine with variable efficacy. New therapies and vaccines require a more complete and integrated knowledge of the host immune response to infection. During infection, M. tuberculosis bacilli traverse the lung airways and settle in the alveolar spaces where they encounter alveolar macrophages (AMF). The alveolus is a highly immune-regulated microenvironment and AMF contribute to this by displaying an anti-inflammatory phenotype also known as an "alternative activation state". This biological state allows AMF to effectively clear microbes and particles within the alveolus while minimizing collateral inflammatory damage, but on the other hand may be exploited by the host-adapted M. tuberculosis. Our ongoing studies are characterizing the unique interactions that occur between M. tuberculosis, macrophages and components of the innate immune system during lung infection, including aspects related to host susceptibility. Examples of the scientific platforms being used will be highlighted during this seminar.
During cytokinesis an actomyosin contractile ring assembles and constricts in coordination with mitosis to properly segregate genetic materials into two daughter cells. The molecular mechanism of contractile-ring assembly remains poorly understood and controversial. We test several assumptions of the two prevailing models for contractile-ring assembly during cytokinesis in the fission yeast Schizosaccharomyces pombe: the spot/leading cable model and the search, capture, pull, and release (SCPR) model. The two models differ in their predictions for the number of initiation sites of actin assembly and in the role of myosin-II. Monte Carlo simulations of the SCPR model require that the formin Cdc12p is present in >30 nodes from which actin filaments are nucleated and captured by myosin-II in neighboring nodes. The force produced by myosin motors pulls the nodes together to form a compact contractile ring. Live microscopy of cells expressing formin Cdc12p fluorescent fusion proteins shows that Cdc12p localizes to a broad band of 30 to 50 dynamic nodes, where actin filaments are nucleated in random directions. Perturbations of myosin-II motor activity demonstrated that it is required to condense the nodes into a contractile ring. Taken together, these data provide strong support for the stochastic SCPR model of contractile-ring formation in cytokinesis.
Aging has long been assumed to be a passive consequence of molecular wear and tear. But it's not so simple. Genetic studies have shown that the aging process, like everything else in biology, is under exquisite regulation, in this case, by a complex, multifaceted hormonal and transcriptional system that affects aging in many species, including humans. In 1993, we showed that changing a single gene in the small roundworm C. elegans can double its lifespan. This gene encodes an insulin/IGF-1 like receptor, which indicates that aging is regulated hormonally. By manipulating genes and cells, we have now been able to extend the lifespan and period of youthfulness of healthy, active C. elegans by six times. We have found that signals from the reproductive system and sensory neurons influence the lifespan of C. elegans, and these processes, too, may be evolutionarily conserved. These signals act, at least in part, to control the expression of a wide variety of subordinate genes, including metabolic, stress response, antimicrobial, and novel genes, whose activities act in a cumulative fashion to determine the lifespan of the animal. Some of these subordinate genes can also influence the progression of age-related disease, including cancer. In this way, this hormone system couples the natural aging process to age-related disease susceptibility.
Cellular DNA is a long, thread-like molecule with remarkably complex topology. Enzymes that manipulate the geometry and topology of cellular DNA perform many vital cellular processes (including segregation of daughter chromosomes, gene regulation, DNA repair, and generation of antibody diversity). Some enzymes pass DNA through itself via enzyme-bridged transient breaks in the DNA; other enzymes break the DNA apart and reconnect it to different ends. In the topological approach to enzymology, circular DNA is incubated with an enzyme, producing an enzyme signature in the form of DNA knots and links. By observing the changes in DNA geometry (supercoiling) and topology (knotting and linking) due to enzyme action, the enzyme binding and mechanism can often be characterized. This talk will discuss topological models for DNA strand passage and exchange, including the analysis of site-specific recombination experiments on circular DNA and the analysis of packing geometry of DNA in viral capsids.
Biomechanical imaging is a promising new technology that enables monitoring of and predicting disease progression and the identification of cancerous and fibrotic tissue. The dynamic data that is input for our work is movies of propagating or harmonic waves; the movies are created from sets of MR or sets of ultrasound data that is acquired while the tissue is moving in response to a pulse or an oscillating force. The main characteristics of the movies are: either (1) there is a wave propagating with a front; or (2) there is a traveling wave created by two sources oscillating at different but nearly the same frequencies; or (3) there is multifrequency harmonic oscillation.
We will briefly show some of our recent work in cancer identification created from data with the characteristics (1) or (2) above. The remaining talk will concentrate on the mathematical model, algorithms and reconstructions from movie data acquired when the tissue is undergoing response to a single or multifrequency harmonic oscillation. We discuss viscoelastic and elastic models, our current choice for viscoelastic model and its properties. We discuss approximations to the mathematical model, estimates of the error made by the approximation, the algorithms inspired by the full model and the approximate model and their stability and accuracy properties, why some biomechanical parameters cannot be reliably recovered, and current questions about biomechanical parameters that inspire our work. We present images created by our algorithms both from synthetic, in vivo and in vitro data.
We have focused on the cell behaviors underlying mammary development and during breast cancer tumor progression. We have taken a combined imaging, cell biological, genetic and pharmacological approach to determine the tissue transformations underlying branching morphogenesis and neoplastic progression, then to dissect the molecular regulation of these cell behaviors and interactions.
I will start this talk describing my experience designing 1) a microarray, 2) an experiment and 3) data analysis techniques for finding regions with different methyalation levels among human tissues. I will also describe how this work led to a new definition CpG islands that we applied to 30 different. Finally, I will describe a new theory for a role of epigenetics in evolution that suggests that genetic variants that do not change mean phenotype could change the variability of phenotype; and this could be mediated epigenetically. This inherited stochastic variation model would provide a mechanism to explain an epigenetic role of developmental biology in selectable phenotypic variation, as well as the largely unexplained heritable genetic variation underlying common complex disease. I will show some experimental results as proof of principle and describe some of the statistical issues involved in the data analysis and simulations supporting the new theory.
In 1975 when Fred Sanger was developing dideoxy termination DNA sequencing for which he was to receive his second Nobel Prize, he found Roger Staden who developed the first computer program to assemble longer DNA sequences from the reads. The reads were randomly located and oriented along the target DNA. Until recently all DNA sequence assembly programs were further sophisticated elaborations of Staden's original technique. They often consist of three major steps: compare all pairs of reads, find an approximate arrangement of the significant overlaps, and multiple alignment for this arrangement. Staden used a greedy version of this method. In 1995 an elegant and entirely new approach was proposed in which each read is broken down into shorter overlapping words, and then a certain graph is constructed so that Eulerian paths in this graph correspond to the target DNA sequence. This will show how this graph is constructed and give some examples of its operation. Today for new-generation sequencing, this Eulerian method is a method of choice.
To a mathematical biologist, the answer to both of these questions is (no surprise) 'of course'. The brain is after all an extremely complicated network with hundreds of billions of neurons interacting in highly nonlinear ways, generating complex firing patterns that depend nontrivially on parameters. How can one possibly understand mechanisms underlying those patterns, test hypotheses and interpret data without a computational model and mathematical analysis to understand the model? Many (most?) neuroscientists remain skeptical. How can such simple-minded equations possibly help explain something so complicated? Even if one accepts that the brain is simply a complicated network, then how can one construct a useful model when so little is known about the properties of individual neurons, how and which neurons communicate with each other, what the collective behavior of any neuronal system is, what the firing patterns mean, what any part of the brain does, etc... ?
In this talk, I will give examples in which issues raised in the study of specific neuronal systems, computational modeling and mathematical analysis have all benefited from each other. In particular, I will describe work on Parkinsonian rhythms generated in the basal ganglia, sensory processing in the insect's antennal lobe and models for working memory.
Information technology has enabled collection of massive amounts of data in science, engineering, social science, finance and beyond. Extracting useful information from massive and high-dimensional data is the focus of today's statistical research and practice. After broad success of statistical machine learning on prediction through regularization, interpretability is gaining attention and sparsity is being used as its proxy. With the virtues of both regularization and sparsity, sparse modeling methods (e.g. Lasso) has attracted much attention for theoretial research and for data modeling.
In this talk, I would like to discuss both theory and pratcice of sparse modeling. First, I will present some recent theoretical results on bounding L2-estimation error (when p>>n) for a class of M-estimation methods with decomposable penalities. As special cases, our results cover Lasso, L1-penalized GLMs, grouped Lasso, and low-rank sparse matrix estimation. Second, I will present on-going research with the Gallant Lab at Berkeley on understanding visual pathway. In particular, sparse models (linear, non-linear, and graphical) have been built to relate natural images to fMRI responses in human primary visual cortex area V1. Issues of model validation will be discussed.
Genome-wide surveys have suggested that genetic variation affecting the regulation of mRNA expression, processing, and translation predominates over those that directly alter the amino acid sequence of encoded proteins. While the latter are easy to spot with use of extensive sequencing, regulatory variants often remain hidden. We have developed a comprehensive approach to detecting such regulatory variants, unexpectedly finding that many key genes involved in disease and drug therapy carry frequent regulatory variants. In parallel, others have pursued genome-wide association studies (GWAS), finding indications of numerous disease risk genes, but the overwhelming majority of the genetic risk remains unknown. Our research program therefore is beginning to address the question as to why the underlying genetic factors remain uncertain. One hypothesis is that regulatory variants could play a key role, but to account for disease risk we must search for frequent alleles that can fill the gap. For such variants to reach high frequency, positive selection during evolution is likely to play a role. In this seminar I will discuss why GWAS may have missed such genes/alleles, and what our approach should be to discover the main disease risk alleles, with an eye on the nexus between evolution, wellness, fitness, and disease.
Increasingly, biomedical researchers need to build functional models from images (MRI, CT, EM, etc.). The "pipeline" for building such models includes image analysis (segmentation, registration, filtering), geometric modeling (surface and volume mesh generation), simulation (FE, FD, BE, linear and non-linear solves, etc.), visualization (scalars, vectors, tensors, etc) and evaluation (uncertainty, error, etc.).
I will present research challenges and software tools for image-based biomedical modeling, simulation and visualization and discuss their application for solving important research and clinical problems in neuroscience, cardiology, and genetics.
I will review the phylogeny reconstruction problem, mostly for mutation data, discuss what is expected from phylogeny reconstruction methods and how do they live up to the expectation. I will discuss the amount of information needed for every reconstruction method, and also the amount of information needed for particular methods.
Phylogenetics is the area of research concerned with finding the genetic relationship between species. The relationship can be represented by a phylogenetic tree, which is a simple, connected, acyclic graph equipped with some statistical information. This furnishes a certain polynomial map and we are interested in polynomials, called phylogenetic invariants, which vanish for every choice of model parameters. The set of phylogenetic invariants forms a certain algebraic object and we want to compute this object explicitly. One of the reasons that we want an explicit description of these polynomials is because it is claimed by Casanellas and Fernandez-Sanchez that using the entire set of phylogenetic invariants is an efficient phylogenetic reconstruction method. More importantly, phylogenetic invariants were used by Allman and Rhodes to study the problem of identifiability of tree topology for a number of phylogenetic models. In other words, given a distribution of observations that a certain model predicts, is it possible to uniquely determine all the parameters of the model? It is an important question since, if a tree is not uniquely determined by an expected joint distribution, then we cannot use that model for inference.
This presentation will explore in some detail group-based models and their invariants. (The content is drawn from the joint work with Sonja Petrović, UIC , firstname.lastname@example.org)
Under certain conditions, predation acts as a selective pressure that drives prey adaptation. In response, the predator can evolve counter-defenses to increase the likelihood of successful attack. Investment in such traits is often costly, so that there is a trade-off between trait investment and reproductive ability. There is some evidence that cost, at least for the prey, can vary with changes in the environment such as low resource availability. For our investigation, we assume that competition for resources is most likely to occur at high prey densities. The result is that as prey density increases, so does the cost for prey defense. Quantitative trait models are employed to examine the stability and dynamics of the system. We find that variable cost of prey defense tends to stabilize the system when the rate of prey evolution is either very fast or very slow.
Spatial segregation among life cycle stages has been observed in many stage-structured species, both in homogeneous and heterogeneous environments. We investigate density dependent dispersal of life cycle stages as a mechanism responsible for this separation by using stage-structured, integrodifference equation (IDE) models that incorporate density dependent dispersal kernels. After investigating mechanisms that can lead to spatial patterns in two dimensional Juvenile-Adult IDE models, we construct spatial models to describe the population dynamics of the flour beetle species T. castaneum, T. confusum and T. brevicornis and use them to assess density dependent dispersal mechanisms that are able to explain spatial formations observed in these species.
The goal of this talk is to describe the analysis of a specific aspect of transient dynamics not covered by previous theory. The question addressed is whether one component of a perturbed solution to a system of differential equations can overtake the corresponding component of a reference solution as both converge to a stable node at the origin, given that the perturbed solution was initially farther away and that both solutions are nonnegative for all time. We call this phenomenon tolerance, for its relation to a biological effect.
Using geometric arguments it is shown that tolerance will exist in generic linear systems with a complete set of eigenvectors and in excitable nonlinear systems. A notion of inhibition is also defined that may constrain the regions in phase space where the possibility of tolerance arises in general systems. However, these general existence theorems do not yield an assessment of tolerance for specific initial conditions. To address that issue, some analytical tools were developed to determine if particular perturbed and reference solution initial conditions will exhibit tolerance.
Thyroid hormone regulation is a classic example of biological feedback control, and thyroid disorders such as hypothyroidism affect more than 300 million people worldwide. We developed a physiologically based, ordinary differential equation model of the human hypothalamic-pituitary-thyroid axis, in order to address several clinical applications. The model is broken into two major components-- the thyroid and brain submodels, each quantified from human clinical data. We combined these two submodels to form a complete closed loop model, which we validated using additional independent clinical data. Using the closed-loop model, we address several applications in replacement thyroid hormone (L-T4) bioequivalence (equivalence between different brands/preparations of L-T4), circadian rhythms, and thyroid cancer.
While alternans in a single cardiac cell appears through a simple period-doubling bifurcation, in extended tissue the exact nature of the bifurcation is unclear. In particular, the phase of alternans can exhibit wave-like spatial dependence, either stationary or travelling, which is known as discordant alternans. We study these phenomena in simple cardiac models through a modulation equation proposed by Echebarria-Karma. We perform a bifurcation analysis for their modulation equation. We also find that for some extreme range of parameters, there are chaotic solutions. Chaotic waves in recent years have been regarded to be closely related to dreadful cardiac arrhythmia. Proceeding work illustrates some chaotic phenomena in two- or three-dimensional space, for instance spiral and scroll waves. We show the existence of chaotic waves in one dimension, which may provide a different mechanism accounting for the instabilities in cardiac dynamics.
The transcription factor NF-kB is critical to the control of responses to cellular stress, inter- and intracellular signaling, cell growth, survival and apoptosis. At rest, NF-kB is sequestered by its inhibitor IkB in the cytoplasm. Upon stimulation, such as tumor necrosis factor $\alpha$ (TNF$\alpha$), NF-kB gets released from IkB and translocates to the nucleus and regulates genes transcription, including regulating transcription of gene IkB. Then the newly synthesized IkB, on the other hand, removes NF-kB from the nucleus. Hence, NF-kB and IkB form a negative feedback loop. Negative feedback loop is often associated to oscillations. Indeed, oscillations of the concentration of nuclear NF-kB has been observed both at population and single cell levels by Hoffmann et al. and Nelson et al. respectively. Ashall et al. recently reported that different frequencies of the oscillations leads to different gene expression. It has been reported in many works that NF-kB signaling pathway may interact with many other signaling pathways, including P53 signaling pathway. So it is important to understand that how the frequencies of NF-kB oscillations may be influenced by its interacting signals. However, the existence and mechanism of those potential interactions are not clear. In this talk, I study this issue by considering the pathway subjected to two types of putative signals: sinusoid and pulsate signals. A rich variety of nonlinear dynamics can be observed. In addition, we consider possible cell-cell communication by secretion of TNF$\alpha$.
The 2D Boussinesq system is potentially relevant to the study of atmospheric and oceanographic turbulence, as well as other astrophysical situations where rotation and stratification play a dominant role. In fluid mechanics, the 2D Boussinesq system is commonly used in the field of buoyancy-driven flow. It describes the motion of incompressible inhomogeneous viscous fluid subject to convective heat transfer under the influence of gravitational force. It is well-known that the 2D Boussinesq equations are closely related to 3D Euler or Navier-Stokes equations for incompressible flow, and it shares a similar vortex stretching effect as that in the 3D incompressible flow. In fact, in vortex formulation, the 2D inviscid Boussinesq equations are formally identical to the 3D incompressible Euler equations for axisymmetric swirling flow. Therefore, the qualitative behaviors of the solutions to the two systems are expected to be identical. Better understanding of the 2D Boussinesq system will undoubtedly shed light on the understanding of 3D flows. In this talk, I will discuss some recent results concerning global existence, uniqueness and asymptotic behavior of classical solutions to initial boundary value problems for 2D Boussinesq equations with partial viscosity terms on bounded domains for large initial data.
We develop mathematical models that describe the interaction between the immune cells and the Human Immunodeficiency Virus (HIV). First, we consider a model that includes drug treatment whose efficacy determines the prognosis of the disease in terms of the parameters that describe the threshold and actual number of virions produced. We shall determine the model efficacy and show that when the efficacy is below the model efficacy, the CD4 T cell count decreases to a low level that cannot sustain an effective immune response. On the other hand, at drug efficacy levels greater than or equal to the model efficacy, the CD4 T cell count increases to levels sufficient to support an effective immune response but this state is unstable and a small residual infection remains in the body which is suppressed by continuously taking the medication. Secondly, we investigate the interaction between two infectious HIV strains and show how this affects disease progression.
Antibiotic resistant organisms (ARO) pose an increasing serious threat in hospitals. Factors which contribute to the spread of ARO in hospitals are poor immune system of most patients, close living quarters, and contact with health care workers (HCWs). One of the most life threatening ARO is methicillin-resistant staphylococcus aureus (MRSA).
In this talk, we will introduce a new mathematical model which focuses on the evolution of two MRSA bacterial strains: drug- resistant and non-drug resistant within population of patients and HCWs in a single hospital. We will introduce two important quantities: the threshold at which time the drug treatment is administered to patients, and time duration of drug-treatment. We will investigate the role of the amount, threshold and time duration of drug treatment on reducing the non-resistant bacteria in patients.
Simulations of the model show that as the amount of drug given to the patient is increased the drug-resistant bacteria significantly decreases, and as the treatment period is increased from one week to two weeks, the drug-resistant bacteria also decreases. Furthermore, we will demonstrate that the choice of the threshold has little influence on the outcome level of drug-resistant bacteria.
Bayesian hierarchical modeling pervades modern statistical research. In this talk, I will discuss two examples of these models in molecular evolution. The first describes the problem of non-vertical evolution and how to adequately model this process in a formal statistical framework. In particular, I will describe methods to hierarchically model branch length and topological incongruence between vertical trees inferred from multiple loci. After this, I will describe a hierarchical model to infer the population dynamic characteristics of large North American mammal populations.
Sequence variations altering protein function are a fundamental driving force in evolution. While the rapid proliferation of whole-genome sequence data should provide unprecedented insight into the evolution of protein function, especially in bacterial organisms for which thousands of complete genome sequences will soon be available, there are practical obstacles to achieving this potential. New methods to assess the functional similarity between proteins are needed to overcome these obstacles. We have evaluated an orthology-based method to group bacterial proteins based on likely similarity in biochemical function. The foundation of this method involves using the occurrence of multiple homologous proteins in a single microbial organism as evidence of functional diversification among those homologs. The resulting groups of functionally similar proteins are called Classes of Reciprocal Sequence Homologs (CRSHs). Different CRSHs vary tremendously in their degree of sequence conservation in widely diverged organisms (ranging from 25-70% identity). However, once this variation is taken into account, a simple model using only the mean evolutionary distance between pairs of microbial organisms accounts for the vast majority of the sequence differences within each CRSH. The likely functional similarity of the proteins in each CRSH is also supported by preservation of gene neighborhood in remotely related microbial organisms, which in turn is strongly correlated with transcriptional co-regulation in the model bacterium E. coli. Furthermore, a CRSH-based metric achieves 30% accuracy in predicting manually validated physical inter-protein interactions in E. coli. A webserver at www.orthology.org provides access to the CRSHs along with related quality-control, gene-neighborhood, and annotation information.
Phylogenetic methods are useful for finding the origin and tracing the character evolution of infectious disease. Here I will present case studies from H5N1 bird flu and H1N1 swine flu, where non-parametric maximum parsimony methods have been used to determine the geographic spread of bird flu and the geographic and animal host origins of the 2009 H1N1 swine flu.
Chronic wounds represent a major public health problem affecting 6.5 million people in the United States. Ischemia, primarily caused by peripheral artery diseases, represents a major complicating factor in cutaneous wound healing. In this talk, we present a mathematical model of ischemic dermal wounds. The model consists of a coupled system of partial differential equations in the partially healed region, with the wound boundary as a free boundary. The extracellular matrix (ECM) is assumed to be viscoelastic, and the free boundary moves with the velocity of the ECM at the boundary. The model equations involve the concentration of oxygen, PDGF and VEGF, the densities of macrophages, fibroblasts, capillary tips and sprouts, and the density and velocity of the ECM. Simulations of the model demonstrate how ischemic conditions may limit macrophage recruitment to the wound-site and impair wound closure. The results are in general agreement with experimental findings.
Ovarian cancers remain difficult to treat due to the emergence of drug resistance, which may be conferred in part by the expression of anti-apoptotic members of the Bcl-family of proteins. ABT-737 is a recently developed small molecule inhibitor of these proteins, currently in stages I/II of clinical trial. In recent experiments, ABT-737 co-administered with Carboplatin, a Pt-based chemotherapeutic drug used to treat ovarian carcinomas, was found to act in a synergistic manner on cancer cells in vitro. Here we develop a mathematical model to investigate the molecular basis of this synergism. The model is built up of two modules, simulating treatment by each compound as a single agent, and is calibrated versus in vitro cell growth inhibition data. These two components are then integrated to represent combination therapy. Numerical simulations indicate that Carboplatin sensitizes the cells to ABT-737 therapy, due to a diminished ability of cells to withstand DNA damage under lowered Bcl-xL levels. The model predicts the existence of a threshold, so that if intracellular Bcl-xL falls below this, cells with relatively low DNA damage are unable to evade apoptosis. Further, simulations indicate that co-treatment and post-treatment with ABT-737 is an optimal strategy to exploit the synergism of the two drugs. Pre-treatment however displays poor results in comparison, due to their proposed mechanism of action. Such modeling, if developed in conjunction with experimentation, can thus have far reaching effects in the field of anti-cancer drug development.
The ability of cells to sense and respond to mechanical forces, i.e. mechanotransduction, plays an important role in many biological processes and in several disease pathologies. Although the exact mechanisms by which cells sense and respond to mechanical force is not known, force transmission via cytoskeletal networks likely plays a key role. Our laboratory is specifically investigating how cytoskeletal mechanics and networks influence the injury, inflammation and repair of lung epithelial cells during the acute respiratory distress syndrome (ARDS). ARDS is a devastating disorder in which bacterial/viral infections (e.g. pneumonia or H1N1 flu) cause cellular damage, lung inflammation and multisystem organ failure. Although mechanical ventilation is required for survival, these ventilators often exacerbate the existing lung injury leading to high mortality rates (~30%). One source of injury during ventilation is the microbubble flows generated during cyclic airway closure and reopening. In addition to causing cell necrosis and barrier disruption, the mechanical forces generated by microbubbles also result in the up-regulation of inflammatory pathways. Since preventing microbubbles in a clinical setting is difficult, we are investigating an alternative approach to preventing injury in which changes in cytoskeletal mechanics/structure are used to mitigate the mechanotransduction processes responsible for inflammatory signaling. We are particularly interested in how changes in cytoskeletal networks may be used to mitigate the activation of Nf-kB pathways, secretion of pro-inflammatory cytokines and expression of microRNAs that regulate inflammation. In addition to in-vitro experiments, we have also developed a novel multi-scale mathematical model of force transmission and cell deformation to identify how specific changes in cytoskeletal structure influence injury patterns. Our combined computational-experimental approach has lead to a better understanding about how changes in cytoskeletal structure may be used to mitigate the mechanotransduction processes responsible for lung injury and inflammation during ARDS.
Abstract: In a honey bee swarm, the actions of thousands of worker bees dynamically combine in several social decision-making processes that serve the colony as a whole. During "social foraging", the colony optimally allocates foragers according to the relative profitability of forage sites. A homeless swarm performs "nest-site selection" by having a small subset of the bees quickly search for, and agree on, the best new home it can find. After agreement, all the bees in the swarm take flight to migrate to their new home. Flight guidance occurs via "leader" bees streaking fast through the swarm in the direction of the new nest site and "followers" orienting their flight in the direction of the leaders and chasing them. In the first part of the talk, these three honeybee distributed decision making processes are overviewed and progress on their modeling and analysis is summarized. The second part of the talk focuses in more detail on midge swarms. Individual midge motion dynamics, sensing abilities, and flight rules are represented with a dynamical model. The sensing accuracy and flight rule are adjusted so that the model produces trajectory behavior, and velocity, speed, and acceleration distributions, that are remarkably similar to those found in midge swarm experiments. Mathematical cohesiveness analysis of the validated swarm model shows that the distances between the midges' positions and the swarm position centroid, and the midges' velocities and the swarm velocity centroid, are ultimately bounded (i.e., eventually satisfy a bound expressed in terms of individual midge parameters). Likewise, the swarm position and velocity centroids are shown to be ultimately bounded. Apparently, this is the first time that a validated swarm model has yielded itself to analytical studies.
Spectral clustering algorithms have shown promising results in statistics, bioinformatics, genetic study, machine learning, and other scientific fields. These spectral algorithms cluster observations (of size n) into groups by investigating eigenvectors of an affinity matrix or its corresponding Laplacian matrix, both of which are size of n by n. However, the computation involved in eigen-decompostion of an n by n matrix is expensive or even infeasible when the sample size is large. To overcome the computation hurdle, subsampling techniques, such as Nystrom extension, have been used in approximating eigenvectors of large matrices.
In this talk, we discuss the statistical properties of such approximation and their influence on the accuracy of various spectral clustering algorithms. We found that the perturbation of spectrum due to subsampling could lead to large discrepancy among clustering results based on different subsamples. In order to provide accurate and stable clustering results for large datasets, we propose a method to combine multiple sub-samples using data spectroscopic clustering and the Nystrom extension. In addition, we propose a sparse approximation of the eigenvectors to further speed up the computation. Simulation and experiments on real data sets showed that this multi-sample approach is fast and accuracy.
Present docking methodologies simulate only one single ligand at a time during docking process. In reality, the molecular recognition process always involves multiple molecular species. Typical protein-ligand interactions are, for example, substrate and cofactor in catalytic cycle; metal ion coordination together with ligand(s); and ligand binding with water molecules. In order to simulate the real molecular binding processes, we propose a novel multiple ligand simultaneous docking (MLSD) strategy which can deal with all the above processes, vastly improving docking sampling and binding free energy scoring. The work also compares two search strategies: Lamarckian Genetic Algorithm and Particle Swarm Optimization, which have respective advantages depending on the specific systems. The methodology proves robust through systematic testing against several diverse model systems: E. coli PNP complex with two substrates, SHP2NSH2 complex with two peptides and Bcl-xL complex with ABT-737 fragments. In all cases, the final correct docking poses and relative binding free energies were obtained. In PNP case, the simulations also capture the binding intermediates and reveal the binding dynamics during the recognition processes, which is consistent with the proposed enzymatic mechanism. In the other two cases, conventional single ligand docking fails due to energetic and dynamic coupling among ligands, whereas MLSD results in the correct binding modes. These three cases also represent potential applications in the areas of exploring enzymatic mechanism, interpreting noisy X-ray crystallographic maps, and aiding fragment-based drug design, respectively.
One of the major challenges facing researchers studying complex biological systems is integration of data from omics platforms. Omic-scale data include DNA variations, transcriptom profiles, and RAomics. Selection of an appropriate approach for a data integration task is problem dependent, primarily dictated by the information contained in the data. In situations where modeling of multiple raw data sets jointly might be extremely challenging due to their vast differences, rankings from each data set would provide a commonality based on which results could be integrated. Because the underlying spaces of genes (elements) from which each ranked list come from are likely to be different, taking the underlying spaces into consideration is paramount, as failure to do so would lead to inefficient use of data and might render biases and/or sub-optimal results. However, this important aspect is usually overlooked in the literature on rank-based integration methods for omic-scale data. Nevertheless, although no assumptions about the underlying spaces are explicitly stated, carefully dissections of the algorithms reveal implicit assumptions about the spaces regardless of whether such assumptions are valid for a particular integration problem. In this talk, I will discuss a number of space oriented methods, including Markov chain based heuristic algorithms and optimization based cross entropy Monte Carlo methods for integrating ranking data. Examples will be shown to dissect the methods and to demonstrate the effects of assumptions about the underlying spaces.
Budding yeast "Saccharomyces cerevisiae" is a model system for studying cell polarization, a fundamental symmetry breaking process underlying cell physiology.In this talk, I will introduce mathematical models for the establishment and maintenance of yeast cell polarization induced by mating pheromone. Simulation results including cell morphological changes will be presented and compared to experimental data. Roles of cell membrane dynamics such as endocytosis, exocytosis and the roles the microdomains on cell membrane (lipid raft) will also be discussed.
Across all domains of life, gene expression is tightly regulated in response to changing cellular and environmental conditions. These signals are often detected by macromolecular sensors which respond through structural rearrangements, effectively "flipping the switch" to initiate or terminate a molecular cascade of events that result in a regulatory outcome.
TRAP (trp RNA-binding Attenuation Protein) forms oligomeric protein rings that can bind to cellular tryptophan (Trp). Once bound to Trp, TRAP becomes activated for binding to a conserved RNA sequence in the 5'-leader region of the trp operon, whose genes encodes a number of enzymes involved in the biosynthesis of the amino acid. Binding of TRAP to RNA prevents transcription of the trp operon, lowering Trp production. In the absence of Trp, TRAP cannot bind RNA, and the 5' leader adopts a conformation that allows for transcription of the gene and increased production of Trp.
Anti-TRAP is an oligomeric protein that can bind to Trp-activated TRAP and prevent it from binding RNA. Anti-TRAP production in cells is also responsive to cellular levels of Trp. Anti-TRAP is found to equilibrate between different oligomeric states: trimers (AT3) and dodecamers (AT12). It is the trimeric form of the protein, AT3, which can bind and inhibits TRAP. The equilibrium between AT3 and AT12 is determined by concentration and pH. The pH dependence of the equilibrium between "active" AT3 and "inactive" AT12 hints to a new mechanism for responding to environmental changes.
Biophysical studies (spectroscopic, thermodynamic, kinetic) of the process and effect of ligand binding to allosteric gene-regulatory macromolecules provide unique as well as universal insights into the role of structure and dynamics in the regulation of gene expression by small molecule ligands.
Valbuzzi A, Yanofsky C. Inhibition of the B. subtilis regulatory protein TRAP by the TRAP-inhibitory protein, AT. Science 2001; 293 (5537): 2057-9. PMID: 11557884 | DOI: 10.1126/science.1062187
Gollnick P, Babitzke P, Antson A, Yanofsky C. Complexity in regulation of tryptophan biosynthesis in Bacillus subtilis. Annu Rev Genet 2005; 39: 47-68. PMID: 16285852 | DOI: 10.1146/annurev.genet.39.073003.093745
Shevtsov MB, Chen Y, Gollnick P, Antson AA. Crystal structure of Bacillus subtilis anti-TRAP protein, an antagonist of TRAP/RNA interaction. Proc Natl Acad Sci U S A 2005; 102 (49): 17600-5. PMID: 16306262 | DOI: 10.1073/pnas.0508728102
McElroy CA, Manfredo A, Gollnick P, Foster MP. Thermodynamics of tryptophan-mediated activation of the trp RNA-binding attenuation protein. Biochemistry 2006; 45 (25): 7844-53. PMID: 16784236 | DOI: 10.1021/bi0526074
The variety of possible gene regulatory network topologies that exist for even a small number of network components is exceedingly large; for example, in systems consisting of two transcription factor-coding genes and their associated proteins, and considering only the most vital elementary biochemical processes, there exists over 40,000 possible networks (as defined by functionally unique sets of reactions). Understanding the dynamical behavior and stability of large classes of biological networks such as these remains a difficult problem, made particularly important as a result of the role played by network bistability in cellular differentiation, cell cycle control, viral reproduction, and other essential biological functions. To begin to address this challenge, we conduct a comprehensive survey of all two-component networks using the parameter-free Chemical Reaction Network Theory and find that ~45% (>18,000) have the capacity for bistability for some set of parameter values, including eight networks lacking the cooperativity typically found in bistable systems. These results illustrate the surprising ease with which switch-like behavior can arise in even simple biochemical networks, suggesting a large number of previously unknown bistable network topologies and the potential for novel synthetic gene circuit designs.
Waterborne diseases remain a serious concern in public health. For example, one of the U.N. Millenium Development Goals is to halve the number of individuals without access to safe drinking water by 2015. Despite their importance, relatively little mathematical work has been done on modeling waterborne diseases. I will present a simple extension of classical "SIR" models to include a waterborne compartment, and then discuss a number of open problems stemming from this work.
Many statistical methods have already been developed to select genes that are differentially expressed under different conditions or in different populations. Often the selected genes are subsequently examined for over representation in known pathways, thus implicating activation of the pathway as relevant in explaining the observed differences. Potential pathways are typically not incorporated into the initial discovery process for fear of biasing discovery toward what is already known.
Here we show how covariance can be used to exploit pathway structure without biasing selection in favor of known pathways. Starting with a simple model for differences of expression in a paired-subject experiment, we show that, for large, highly coordinated gene networks, the eigenvectors of the covariance matrix may contain substantial information about which genes are relevant to the differential processes. A similar type covariance structure is identified for gene expression at different epochs in the reproductive cycle of rainbow trout, and a robust method for feature selection, called SCOOP, is developed to select genes that naturally describe reproductive processes and the suggest implication of genes with previously unknown function.
This is joint work with Yushi Liu and Bill Hayton.
I will introduce some mathematical models for cell motion and model based inverse problems for extracting information from single cell movement. Inverse problems in the context of cell motility is a novel approach we have been developing. These models define mathematical diagnostic techniques for the analysis of single cell movements.
Cells are living organisms, and it is natural to think that behavioral or shape changes of a cell bear information about the underlying mechanisms that generate these changes. Reading the cell motion, namely, understanding these underlying biophysical and mechanochemical mechanisms of behavioral and shape changes is of paramount importance. This is analogous to the examination of patients, analysis of their symptoms, behavioral changes by physicians for possible causes behind their pathologies. The mathematical models we developed play the role of physicians or physical exams in diagnosis of the pathologies for cells instead of human subjects. Thus, the technique transforms the simplest physical information, i.e. cell position, into practical and clinically usable form.
It is well known that mammals and other animals cycle between periods of sleep and wakefulness. Several recent studies have analyzed the durations of sleep and wake periods (or bouts) in adult mammals and found that wake bouts follow a power law distribution, while the durations of sleep bouts are characterized by an exponential distribution. Motivated by these studies and recent models of sleep-wake regulation, we investigate how the structure of such a neural network might contribute to the observed dynamics. In particular, we consider simple stochastic processes on different random networks and present some preliminary results relating the structure of the network with the distribution of event durations on the network.
In microscopic systems formed by living cells, the small numbers of some reactant molecules can result in dynamical behavior that is discrete and stochastic rather than continuous and deterministic. An analysis tool that respects these dynamical characteristics is the stochastic simulation algorithm (SSA), which applies to well-stirred chemically reacting systems. However, cells are hardly homogeneous! Spatio-temporal gradients and patterns play an important role in many biochemical processes. In this lecture we report on recent progress in the development of methods for spatial stochastic and multiscale simulation, and outline some of the many interesting complications that arise in the modeling and simulation of spatially inhomogeneous biochemical systems.
In this talk we shall present several mathematical models arising from the competition of phytoplankton species for nutrients and light.
While search is ongoing for a safe and effective HIV vaccine, encouraging data from animal studies have ignited interest in antiretroviral (ARV) pre-exposure prophylaxis (PrEP) as a strategy to prevent HIV infection. Several clinical trials of PrEP are underway. Though crucial for investigating the safety and efficacy of PrEP in humans, the clinical trials cannot address the population-level and/or long-term impact of PrEP including consequences of resistance. There is widespread concern, however, about the potential emergence and spread of HIV drug resistance arising from PrEP roll-out, particularly in resource-constrained settings, where antiretroviral treatment options are limited. This concern is amplified by the possibility that the same antiretroviral drugs will be used for both treatment and PrEP. A more practical and immediate means of addressing these issues is needed to provide initial insight. Mathematical modeling is a powerful research tool that has provided important insights about the dynamics of HIV infection at both the individual and population level. We therefore developed a detailed mathematical model, incorporating heterogeneity in age, gender, sexual activity, HIV infection status, stage of disease, PrEP coverage/discontinuation, and HIV drug susceptibility, to analyze the potential impact of orally administered PrEP on HIV prevention and HIV drug resistance outcomes, through simulation of different PrEP implementation scenarios in a sub-Saharan epidemic. In this work we have identified the main drivers of HIV prevention and drug resistance from PrEP as well as critical gaps in knowledge. Our findings are important for PrEP research and PrEP preparedness.
Dr. Abbas did her residency training in internal medicine at SUNY Downstate Medical Center. She did her fellowship training in infectious diseases and obtained a Master's degree in biomedical informatics at the University of Pittsburgh, where she became interested in mathematical and computational infectious disease epidemiology. She is presently an Assistant Professor of Medicine at the Cleveland Clinic Lerner College of Medicine and Staff Physician at the Cleveland Clinic Foundation. Her current research is focused on modeling the impact of antiretrovirals on the spread of HIV.
The writhe number is a geometric measure of self-entanglement which measures non-planarity of a spatial conformation. It has important applications to biopolymers including DNA and RNA. To apply this measure to study complex non-circular and even branched polymers, the writhe number can be extended to edge-oriented finite spatial graphs. In addition, a new writhe additivity formula is presented for structures constrained to separate topological domains.
Writhe-based shape descriptors can be applied to describe RNA structures by producing a set of feature vectors, and using clustering techniques for RNA shape classification. Among tRNA structures, a differentiation between thermophilic and non-thermophilic species is observed, and also, a clear distinction between tRNA and diverse ribozymes is produced.
To better understand the intricate architecture of three-dimensional RNAs, we analyze currently solved RNA junctions in terms of basepair interactions and three-dimensional configurations. Nine broad 4-way junction families are identified according to coaxial stacking patterns and helical configurations. Analysis reveals also a number of highly conserved basepair interaction patterns and novel tertiary motifs.
The neuropeptide orexin/hypocretin is essential for normal consolidation of sleep/wake behavior, and disruption of the orexin system is associated with the sleep disorder narcolepsy. Recent experimental work has characterized elements of orexin neuron electrophysiology and state-dependent behavior, however, many questions, particularly questions of dynamics, can be difficult to address in an experimental setting. I will discuss several modeling approaches, spanning multiple scales, which we have undertaken to investigate the intrinsic dynamics of these neurons and their role in sleep/wake regulation.
My work centres on understanding how small scale interactions within a cell can lead to large scale organization. In this talk, I will discuss recent work concerning stable segregation of Par proteins at the one cell stage of the C. elegans embryo. Experimental work has determined that Par proteins interact by mutual phosphorylation, which was thought to be sufficient for stable segregation. However, mathematical modelling suggests that higher order complex formation, such as dimerization, is required. Experimental tests of the mathematical model are consistent with model predictions and the model is able to explain previously puzzling observations. I will conclude by placing these results in the larger context of my work and discussing how they can help us better understand stable protein asymmetry in other cell types.