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.
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.
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.
- Egeblad, M., A. J. Ewald, et al. (2008). Visualizing stromal cell dynamics in different tumor microenvironments by spinning disk confocal microscopy. Dis. Model. Mech. 1:155-167. PMID: 1904807
- Ewald, A.J., A. Brenot, M. Duong, B.S. Chan & Z. Werb (2008). Collective epithelial migration and cell rearrangements drive mammary branching morphogenesis. Dev. Cell. 14:570-581. PMID: 18410732.
- Kouros-Mehr, H., S. K. Bechis, et al. (2008). GATA-3 links tumor differentiation and dissemination in a luminal breast cancer model. Cancer Cell. 13:141-52. PMID: 18242514.
- Lu, P. & Z. Werb (2008). Patterning mechanisms of branched organs. Science. 322:1506-1509. PMID: 19056977.
- Welm, B.E, G. J. P. Dijkgraaf, A. S. Bledau, A. L. Welm & Z. Werb (2008). Lentiviral transduction of stem cells for genetic analysis of mammary development and breast cancer. Cell Stem Cell. 2:90-102. PMID: 18371425.
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.