Seminars
General Birth-Death Processes: Probabilities, Inference, and Applications | |
| Date | Thursday May 24, 2012 at 10:00 AM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Forrest Crawford, Biomathematics Doctoral Student, Department of Biomathematics, UCLA |
| Sponsoring Dept | Biomathematics |
| Abstract | ABSTRACT: A birth-death process (BDP) is a continuous-time Markov chain that counts the number of particles in a system over time. Each particle can give birth to another particle or die with rates that depend on how many particles there are. BDPs are popular modeling tools in evolution, population biology, genetics, epidemiology, and ecology. Despite the widespread interest in BDPs, no efficient method exists to evaluate the finite-time transition probabilities in a process with arbitrary birth and death rates. Statistical inference of birth and death rates also remains largely limited to continuously-observed processes. The lack of progress in developing statistical tools for dealing with data from BDPs has hindered their adoption by applied researchers, and represents a major research frontier in statistical inference for stochastic processes. In this dissertation, I seek to fill this apparent void. First, I develop mathematical theory and computational tools for computing transition probabilities for general BDPs. Second, I develop algorithms for maximum likelihood estimation of rate parameters in discretely observed processes. Third, I derive probability distributions for characteristics of certain BDPs that are fundamental in macroevolutionary studies. In each case, I give practical applications of the methodology, and show how unsolved problems can be attacked using these techniques. |
| Flyer | crawford_forrest_defense_seminar2.pdf |
Genome-Based HIV-1 Incidence Assay with High Sensitivity and Specificity | |
| Date | Thursday May 17, 2012 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Ha Youn Lee, Ph.D., Associate Professor, Dept. of Molecular Microbiology, University of Southern California |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: To assess how many people have been recently infected in a given area is an important task in HIV-1/AIDS prevention. Accurately classifying recent or incident infections (e.g. within around the first year since transmission) from chronic infections enables one to track the epidemics, evaluate the impact of antiretroviral treatment, and assess the efficacy of HIV-1 prevention trials including vaccination, microbicides, and other types of interventions. Conventional serological testing is found to have a number of critical limitations which result in notable inaccuracy. In this talk, we turn to utilizing recent advances in understanding early HIV-1 infections and demonstrate that information derived from a set of HIV-1 envelope gene sequences obtained from a single blood sample can accurately distinguish incident infections from chronic ones. By analyzing previously published 5596 full envelope HIV-1 genes from 182 incident and 43 chronic subjects, we find that every incident case displays a robust signature, the presence of closely related strains, regardless of either single-variant or multi-variant transmission. We demonstrate that the sequence similarity used as a biomarker has high specificity and sensitivity over 95% and is not sensitive to viral and host specific factors including the clade of the viral strain, viral load, and the length and location of sequences in the HIV-1 envelope gene. The potency and accuracy of our sequencing-based HIV incidence assay is unprecedented and the assay holds great promise as a means of assessing the level of HIV-1 incidence from a single blood draw in cross-sectional blood surveys. |
| Flyer | lee_ha_youn_20120517.pdf |
CANCELLED - Network controls on diversity in human and ecological systems | |
| Date | Thursday April 05, 2012 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Evan Economo, Ph.D., Assistant Professor, Dept. of Ecology & Evolutionary Biology, Michigan Society of Fellows, Univ. of Michigan |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Both ecological and human systems are characterized by stunning patterns of diversity. Understanding biodiversity- the variation of genes, species, phenotypes and interactions within and across ecosystems, has long been a fascination of biologists. Humans have created worlds of diversity as well, in the foods we eat, the products we buy, and most of all in the ideas we think. Ecosystems are arranged spatially and connected in complex ways by dispersal of individual organisms, and humans communicate through interpersonal and technological networks. In this talk I explore the theoretical connections between these two types of systems by integrating network influence models in social science with coalescent theory in population genetics. I identify several general features of network structure that can amplify or depress diversity. In particular, the asymmetries of connections among nodes emerges as more important variation in the strength and topology of connectivity. With these theoretical perspectives, I analyze a parameterized coral reef network across the Pacific and show that certain nodes act as structural depressors due to their position in the network. Finally, I discuss how recent revolutions in information transmission may be changing the diversity, performance, and collective wisdom of human societies. Network controls on diversity in human and ecological systems. |
| Flyer | economo_evan_20120405_cancelled.pdf |
Stochastic nucleation and growth | |
| Date | Thursday February 23, 2012 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Maria Rita D'Orsogna, Ph.D., Assistant Professor, Mathematics Department, CSU Northridge |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | The binding of individual components to form composite structures is a ubiquitous phenomenon within the sciences. Within heterogeneous nucleation, particles may be attracted to an initial exogenous site: the formation of droplets, aerosols and crystals usually begins around impurities or boundaries. Homogeneous nucleation on the other hand describes identical particles spontaneously clustering upon contact. Given their ubiquity in physics, chemistry and material sciences, nucleation and growth have been extensively studied in the past decades, often assuming infinitely large numbers of building blocks and unbounded cluster sizes. These assumptions also led to the use of mass-action, mean field descriptions such as the well known Becker Doering equations. In cellular biology, however, nucleation events often take place in confi ned spaces, with a fi nite number of components, so that discrete and stochastic effects must be taken into account. In this talk we examine finite sized homogeneous nucleation by considering a fully stochastic master equation, solved via Monte-Carlo simulations and via analytical insight. We find striking differences between the mean cluster sizes obtained from our discrete, stochastic treatment and those predicted by mean field treatments. We also consider heterogeneous nucleation stochastic treatments, first passage time results and possible applications to prion unfolding and clustering dynamics. |
| Flyer | maria_dorsogna_20120223.pdf |
THIS TALK HAS BEEN CANCELLED | |
| Date | Thursday December 01, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Eric Sobel, Ph.D., Adjunct Professor, Department of Human Genetics, UCLA |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | To be announced |
Hemodynamic Optimization Parameter Evaluation for Cardiac Resynchronization Therapy | |
| Date | Thursday November 17, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Gene A. Bornzin, Ph.D., Vice President, Fellow, Research, St. Jude Medical, Inc., Sylmar, California |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Cardiac Resynchronization Therapy (CRT) improves cardiac performance in advanced heart failure patients. Patients indicated for CRT have low ejection fraction and a wide electrocardiographic QRS. These patients have a mechanically dyssynchronous contraction of the ventricles that can be improved by providing cardiac stimulation of both the right and left ventricles using specialized cardiac pacemakers. CRT pacing therapy reduces cardiovascular related hospitalizations and improves survival while decreasing symptoms and improves exercise tolerance. About 200,000 patients benefit from CRT annually. This presentation will cover CRT therapy delivery including the basic pacemaker and defibrillation devices, implantation, and some technological challenges remaining. One challenging clinical objective is not just to provide therapy, but to optimize therapeutic benefit. Currently in select patients, pacemaker settings are adjusted to optimize hemodynamic performance using echo cardiography. This process takes place in the clinic and is extremely time consuming and expensive. Ideally, CRT pacemaker settings could be quickly and easily optimized for every patient during a routine office visit. Ultimately, in the not too distant future, the implanted CRT devices will actually measure cardiac performance with sensing systems. Feedback from the sensing system would then be used to adjust stimulation parameters to optimize cardiac performance. Completed research into methods that could be used to measure and optimize cardiac performance will be presented along with a discussion of ongoing clinical research. |
| Flyer | Bornzin_Gene_2011117.pdf |
A Parthian shot at neutrality: revisiting the neutrality assumption for tropical tree species | |
| Date | Thursday November 10, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Jerome Chave, Ph.D, Senior Researcher (Director) at National Center for Scientific Research, France |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Hubbell’s neutral theory of biodiversity challenges the classical niche-based view of ecological communities, where species attributes and environmental conditions jointly determine community composition. Functional equivalence among species, as assumed by neutral ecological theory, has been recurrently falsified, yet many patterns of tropical tree communities appear consistent with neutral predictions. This may mean that neutral theory is a good first-approximation theory or that species abundance data sets contain too little information to reject neutrality. Here we present a simple test of neutrality based on species abundance distributions in ecological communities. Based on this test, we show that deviations from neutrality are more frequent than previously thought in tropical forest trees, especially at small spatial scales. We then develop a nonneutral model that generalizes Hubbell?s dispersal-limited neutral model in a simple way by including one additional parameter of frequency dependence. We also develop a statistical method to infer the parameters of this model from empirical data by approximate Bayesian computation. In more than half of the permanent tree plots, we show that our new model fits the data better than does the neutral model. Finally, we discuss whether observed deviations from neutrality may be interpreted as the signature of environmental filtering on tropical tree species abundance distributions. This study is mostly based on F Jabot and J Chave, 2011, Analyzing Tropical Forest Tree Species Abundance Distributions Using a Nonneutral Model and through Approximate Bayesian Inference American Naturalist. with some background information on the biological question for the broader audience of a biomathematics seminar. |
| Flyer | jerome_chave_20111110.pdf |
Using Genomes to Track the Evolution of Life on Earth and Beyond | |
| Date | Thursday November 03, 2011 at 3:00 PM |
| Location | Schoenberg Hall, Schoenberg Music Building |
| Speaker | James Lake, Ph.D., Faculty Research Lectureship, 11/3/11 |
| Sponsoring Dept | 111th UCLA Faculty Research Lectureship |
| Description | We are referring guests to the 111th UCLA Faculty Research Lecture being given by Prof. James Lake, Distinguished Professor of Molecular, Cell and Developmental Biology and Human Genetics. See above Sponsoring Department link. |
Modeling adventures in proteomics: statistical peptide identification from clustered tandem mass spectrometry data | |
| Date | Thursday October 27, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Vladimir Minin, Ph.D., Assistant Professor, Department of Statistics, University of Washington |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Tandem mass spectrometry experiments generate from thousands to millions of spectra. These spectra can be used to identify the presence of proteins in biological samples. In this work, we propose a new method to identify peptides, substrings of proteins, based on clustered tandem mass spectrometry data. In contrast to previously proposed approaches, which identify one representative spectrum for each cluster using traditional database searching algorithms, our method uses all available information to score all the spectra in a cluster against candidate peptides using Bayesian model selection. We illustrate the performance of our method by applying it to seven-standard-protein mixture data as well as to more complex mixture data from Francisella novicida and Saccharomyces cerevisiae. |
| Flyer | minin_vladimir_20111027.pdf |
The biofluiddynamics of fungal spore and genome dispersal | |
| Date | Thursday October 20, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Marcus Roper, Ph.D., Professor, Department of Mathematics, UCLA |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Fungi are the most diverse of all eukaryotic organisms and enjoy extraordinary ecological success as decomposers, pathogens and mutualists. I will discuss how solving hard physical problems of dispersing (i) spores and (ii) genomes may be a central part of their success in so many niches: #1. The forcibly launched spores of ascomycete fungi must eject through a boundary layer of nearly still air in order to reach dispersive air flows. Because of their microscopic size singly ejected spores are almost immediately brought to rest by fluid drag. However, by coordinating the ejection of thousands or hundreds of thousands of spores, fungi such as the devastating plant pathogen Sclerotinia sclerotiorum, are able to create a flow of air that carries spores across the boundary layer and around any intervening obstacles. #2. A growing filamentous fungi may harbor a diverse population of nuclei. Increasing evidence shows that this internal genetic flexibility is a motor for diversification, virulence, and the ability of fungi to utilize nutritionally complex substrates like plant cell walls. I’ll show that to maintain stable populations of different nuclei near the growing tips, ascomycete fungi must create internal flows over the entire of the colony. |
| Flyer | roper_marcus_20111020.pdf |
Spatially-explicit ecological dynamics in streams and rivers | |
| Date | Thursday October 13, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Kurt Anderson, Ph.D., Assistant Professor, Department of Biology, UC Riverside |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Many organisms disperse in media possessing a net unidirectional flow. The systems these organisms inhabit, exemplified by streams and rivers, are also characterized by a high degree of multi-scale spatial and temporal environmental variability. Most conceptual frameworks describing ecological organization in streams and rivers prominently feature both upstream-downstream linkages and variability that occurs across spatial and temporal scales. I will discuss modeling studies where I have explored how the spatial distribution of organisms results from multi-scale spatial variability in systems with directionally-biased dispersal. I will begin by discussing spatial scales that characterize population responses near equilibrium. Then, I will discuss transient and non-equilibrium dynamics using metrics that are independent of initial conditions – resilience, reactivity, and the amplification envelope – and relate them to the spatial scale of the population perturbation. Current work aims to extend previous themes to branching river networks and the surrounding landscape. I will conclude with implications for conservation of instream populations. |
| Flyer | anderson_kurt_20111013.pdf |
Scaling up the effects of physiological constraints from individuals to communities | |
| Date | Thursday October 06, 2011 at 4:00 PM |
| Location | 23-105 Center for the Heatlh Sciences (CHS) |
| Speaker | Samraat Pawar, Ph.D., Postdoctoral Fellow, Department of Biomathematics, UCLA |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Biodiversity patterns are generated by processes acting at multiple levels of biological organization, ranging from individual organisms to whole ecosystems consisting of multiple, interactions populations. Understanding the mapping between these levels is crucial for the development of a general theory of the generation and maintenance of biodiversity. I present a quantitative framework for predicting how individual-level physiological constraints in nature drive the dynamics and structure of multiple species communities. This theory accurately predicts the effects of organismal body size, habitat spatial dimensionality and environmental temperature on consumer-resource interactions, and scales up these interactions to the species interaction networks that drive dynamics of whole communities. Furthermore, it makes predictions that explain a number of observed features of existing biodiversity patterns, and can form a foundation for better predicting future changes in these patterns due to natural and anthropogenic changes in the environment. |
| Flyer | pawar_samraat_20111006.pdf |
Using model-based methods to quantify exon-level gene expression from RNA-seq data | |
| Date | Thursday September 29, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Zhaohui Steve Qin, Ph.D, Associate Professor, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | RNA sequencing (RNA-seq) is a powerful new technology for mapping and quantifying transcriptome using ultra high-throughput next generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-seq, substantial biases, and uncertainty in short read alignment pose daunting challenges for data analysis. In particular, large base-specific variations and between-base correlations make simple approaches, such as those that use averaging to normalize RNA-seq data and quantify gene expressions, ineffective. In this study, we propose a model-based method to characterize base-level read coverage within each exon. The underlying expression level is included as a key parameter in this model. Since our method is capable of capturing local genomic features that affect read coverage profile throughout the exon, we are able to obtain improved quantification of the true underlying expression levels. |
| Flyer | zhaohui_steve_qin_20110929_updated.pdf |
Geometry and scaling in the vascular system: Theory vs MRI | |
| Date | Thursday September 01, 2011 at 4:00 PM |
| Location | AV-139 Center for the Health Sciences (CHS) |
| Speaker | Mitchell Johnson, Graduate Student, Department of Biomathematics, UCLA |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | The geometry of vascular systems determines the cost of energy delivery, and thus constrains the growth and metabolic rate of individuals, cells and tumors. We develop an algorithm and software package for automatically quantifying the geometry of vascular trees from radiography images (MR, CT, etc.). We use this technique to compare measurements for human arterial networks to theoretical predictions and previous measurements in humans, pigs, and rats, in order to address basic questions in the optimality of vessel network structure and metabolic scaling theory. Our goal is to develop high-throughput angiography, which would make it practical to gather data at a faster pace, and with higher precision, than is practical with existing methods. |
| Flyer | johnson_mitchell_seminar_20110901.pdf |
Joint Analysis of Longitudinal Measurements and Competing Risks Failure Time Data” | |
| Date | Monday August 29, 2011 at 2:00 PM |
| Location | 53-105 Center for the Health Sciences (CHS) |
| Speaker | Ning Li, Ph.D., Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center |
| Sponsoring Dept | UCLA Biomathematics |
| Flyer | ning_li_20110829.pdf |
Temperature Dependency of Energy and Mass Fluxes in Dynamic Energy Budget Theory. | |
| Date | Thursday May 26, 2011 at 4:00 PM |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Erik Muller, Ph.D., Associate Researcher, Marine Science Institute, Department of Ecology, Evolution and Marine Biology , UC Santa Barbara |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Dynamic Energy Budget (DEB) theory is a process based theory that describes the rates at which an organism acquires resources from the environment and subsequently utilizes the energy and nutrients therein for production and maintenance. The core model covers all life stages of heterotrophic organisms with just 3 state variables and 12 parameters. Despite its focus on processes, the theory currently describes the impact of temperature on the dynamics of energy acquisition and allocation in a purely descriptive manner. In order to improve realism in the representation of temperature effects in DEB theory, I am using formalism from complex network theory. Before presenting this work in detail, I will survey the implications of the theory for seemingly unrelated biological phenomena, such as body-size scaling relationships, symbiogenesis and toxic effects. |
| Flyer | muller_erik_20110526.pdf |
Statistical and Computational Methods for Ancestry Estimation and Variable Selection in Genome-Scale Datasets | |
| Date | Friday May 06, 2011 at 11:00 AM |
| Location | 14-214U Center for the Health Sciences |
| Speaker | David Alexander, UCLA Department of Biomathematics |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: As genotyping and sequencing technologies reach higher and higher throughput levels, genetic datasets are becoming ever larger, creating a growing need for highly efficient algorithms for routine analyses. Our work on the efficient individual ancestry estimation program ADMIXTURE has shown that easily-implemented and stable EM algorithms, widely believed to be a good choice for estimation in large datasets, can sometimes prove vastly inferior to more intricate coordinate- and block-relaxation approaches. Furthermore, our work on a novel quasi-Newton convergence acceleration procedure shows that the efficiency of existing iterative optimization algorithms can be greatly improved with no change to the statistical model and only minor implementation effort. For secondary analyses, computationally intensive methods are more tolerable. In this vein, we explore the application of some new developments in bootstrap aggregation and high-dimensional variable selection to genome-wide association, to see whether more complex models can be used to wring more information from previously studied association data. |
| Flyer | alexander_david_seminar.pdf |
Bayesian Nonparametric Inference of Effective Population Trajectories with Gaussian Processes | |
| Date | Monday January 31, 2011 at 4:00 PM |
| Location | 23-105 Center for the Health Sciences (CHS) |
| Speaker | Vladimir Minin, Ph.D, Assistant Professor, Department of Statistics, University of Washington |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Changes in population size influence genetic diversity of the population and, as a result, leave imprints in genomes of individuals in the population. We are interested in an inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic processes that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that times at which genealogical lineages coalesce contain all information about population size dynamics. Viewing these coalescent times as a point process, estimation of population size trajectories is equivalent to estimating a conditional intensity of the coalescent point process. Therefore, our inverse problem is very similar to estimation of an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to nonparametric estimation of population size dynamics under the coalescent. We validate our method using simulated and real data and compare our Gaussian process approach to competing Gaussian Markov random field smoothing and change-point model methods. |
| Description | Host: Marc Suchard, M.D., Ph.D. and Elliot Landaw, M.D., Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) |
| Flyer | minin_vladimir_20110203.pdf |
A Tale of Two Competitions: Microscopic and Macroscopic | |
| Date | Friday January 14, 2011 at 11:00 AM |
| Location | 53-105 Center for the Health Sciences |
| Speaker | Royce Zia, Ph.D., Professor Emeritus, Physics Department, Virginia Tech |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Ubiquitous in nature, competition occurs from the microscopic to the global scales, in the arenas of biology, sociology, ecology, etc. I will present recent studies of models of two very different systems. One concerns the process of translation in protein synthesis, in which many mRNA’s compete for a finite pool of ribosomes. The model here involves the purely mathematical process of totally asymmetric simple exclusion (TASEP), as we study multiple open TASEP’s competing for a single reservoir of particles. The second model system is also simple: population dynamics of many species, competing pairwise, under well-mixed conditions. In particular, I will introduce a remarkable recent discovery - coined “survival of the weakest” - in 3 cyclically competing species. Other interesting behavior, though not cast in such counter intuitive terms, are also displayed in the 4 species system. General properties of S>4 systems with *arbitrary* pairwise interactions will also be presented. |
| Flyer | zia_royce_20110114.pdf |
Modeling Synaptic Transmission from a Molecular Level | |
| Date | Friday January 14, 2011 at 4:00 PM |
| Location | RESCHEDULED FROM 1/13/11: 33-105 Center for the Health Sciences (CHS) |
| Speaker | David Holcman, Ph.D., Director of Interdisciplinary Research, Department of Biology and Mathematics, Ecole Normale Superieu |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Neuronal transmission relies on synaptic microcontacts between neurons where thousands of scaffolding molecules are clustered. The geometry of the synapse as well as the distribution of these molecules are shaping the neuronal response, fundamental for both information processing and storage. In this talk, I will present some of our recent attempts using Brownian simulations, stochastic and PDE analysis combined with experimental physiological data to study the synaptic current in normal and pathological conditions. To estimate this current, we model glutamate diffusion inside the synaptic cleft. By computing the mean and the variance, we obtain the precise dependency of the post synaptic current as a function of the synaptic parameters. We use this analysis to study a recent experiments on the Cx30 KO Mice and show how glial cells can significantly affect synaptic transmission. |
| Description | Host: Tom Chou, Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) |
| Flyer | holcman_david_20110113.pdf |
Advances in Pedigree Analysis: Hardy-Weinberg Equilibrium, Strain Imputation, and Maternal Effects | |
| Date | Friday December 10, 2010 at 10:00 AM |
| Location | AV-139 CHS |
| Speaker | Jin Zhou, Graduate Student, Department of Biomathematics, UCLA |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Genetic studies usually gather participants in one of the three ways: random samples, cases and controls, and pedigrees. Pedigree analysis is computationally demanding and, with the passing of HIPAA, pedigrees are difficult to collect. For these reasons researchers currently favor cases and controls and random samples over pedigrees. However, pedigrees take advantage of familial relationships and vertical inheritance patterns that can avoid some of the confounding and variance inflation that arise when population substructure is present. Pedigree analysis can also test for both linkage and association. In this dissertation I expand the utility of pedigree analysis in three ways: (a) Hardy-Weinberg testing for pedigrees, (b) association testing using imputed strain origins in animal crosses with inbred strains, and (c) testing for prenatal effects using variance component models. Doctoral Committee:. Dr. Kenneth Lange, Dr. Janet Sinsheimer, Dr. Elliot M. Landaw, Dr. Christina Palmer |
| Flyer | zhou_jin_seminar.pdf |
The Continuum of Modeling and Simulation in Basic and Health Research and Therapeutic Discovery and Development: A Personal Overview with Examples | |
| Date | Thursday December 02, 2010 at 4:00 PM |
| Location | 13-105 Center for the Health Sciences (CHS) |
| Speaker | Paolo Vicini, Ph.D., Research Fellow, Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and Development, San Diego |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Modeling and simulation approaches are increasingly used in drug discovery and development and are becoming an important part of experimental design and data analysis. “Model-based drug development” is fulfilling the promise of deploying integrated fit-for-purpose, predictive and robust models of pharmacokinetics (“what the body does to the drug”) and pharmacodynamics (“what the drug does to the body”) to support the design and analysis of discovery and development experiments and trials. The same promise can be brought to bear in academic basic and therapeutic research and requires close integration of biologists and modelers to achieve the desired results. It can be argued that the ever-increasing body of knowledge about physiology and the physiological response to drugs is motivating the use of computer models that are increasingly realistic and mechanistically plausible, thus motivating the intersection of integrated biology, physiology and pharmacology (“systems pharmacology”). In this talk, I will use practical examples and specific case studies from my own experience as a modeling and simulation scientist to support the usefulness and impact of these approaches in hypothesis testing and decision making. |
| Description | Host: Elliot Landaw, M.D., Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) If you would like to meet with this seminar speaker, please contact us. |
| Flyer | vicini_paolo_20101202.pdf |
Resolving Cooperative Ca2+ Binding Kinetics Using Compartmental Modeling | |
| Date | Thursday November 18, 2010 at 4:00 PM |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Guido Faas, Ph.D., Assistant Researcher, Department of Neurology, UCLA David Geffen School of Medicine |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: To get better understanding of fast Ca2+ signaling we want to quantify the competition between the Ca2+-binding proteins (CBPs) for Ca2+. Therefore, we developed a method to determine the Ca2+ binding kinetics of CBPs. When determining the kinetic properties of a CBP, one is faced with two major obstacles. First, the rate constants of Ca2+ binding to CBPs are too fast to be determined with conventional techniques. Second, many CBPs bind Ca2+ in a cooperative manner which is difficult to resolve under non-equilibrium conditions. So far, quantification of cooperative binding has only been resolved under steady-state conditions, but a full understanding of Ca2+ binding to CBPs necessitates a solution for cooperative binding under dynamically changing conditions. In this talk I will discuss a newly developed in-vitro technique that overcomes the problem of speed. To analyze the acquired data, we used a simple mathematical model comprised of ODEs to simulate all Ca2+ binding reactions in a reaction chamber. The experimental data were fitted by the model using a quasi-bootstrapped method to reliably determine the rate constants of Ca2+ binding to the CBPs. To resolve the cooperative nature of the binding under non-equilibrium conditions, we developed a new model for cooperative binding that was able to resolve for the first time the kinetics of a cooperative Ca2+ binding process. I will discuss how changing the rate constants underlying cooperativity gives rise to functionally important frequency tuning of CBPs by ambient Ca2+ levels. Our results have provided novel insights into the regulation of cellular Ca2+ signaling. |
| Description | Host: Elliot Landaw, M.D., Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) |
| Flyer | faas_guido_20101118.pdf |
Advanced Theoretical Models for Analyzing Single Molecule Force Measurements | |
| Date | Thursday November 04, 2010 at 4:00 PM |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Gaurav Arya, Ph.D., Assistant Professor, Department of NanoEngineering, University of California, San Diego |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Dynamic single molecule force spectroscopy provides a powerful approach for probing the underlying energy landscape that governs how molecules fold into complex 3D architectures, bind to each other, and undergo conformational transitions. These sophisticated experiments operate by imposing gradually increasing forces on single molecules (or complexes) and recording their force-extension behavior until eventual rupture. An outstanding question in this field is how to recover the intrinsic energy landscape of the molecule from such force measurements. In this talk I will describe the development of new theoretical models[1],[2] for extracting the height and location of activation energy barriers and intrinsic transition rates from single-molecule force measurements. The models go beyond the current state-of-the-art by accounting for both the finite stiffness of the pulling device and the non-linear stretching of the molecular handles often used for connecting the molecule of interest to the device. ___ [1] Maitra and Arya, PRL, 104, 108301, 2010 [2] Maitra and Arya, PCCP, in press, 2010 |
| Description | Host: Elliot Landaw, M.D., Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) |
| Flyer | arya_gaurav_20101104-1.pdf |
Methods for Detecting Interactions in High-throughput Genetic Data | |
| Date | Monday November 01, 2010 at 11:00 AM to 12:00 PM |
| Location | Gonda Center Conference Room - Room 1357 |
| Speaker | Alison Motsinger, Ph.D., Assistant Professor, Department of Statistics, North Carolina State University |
| Sponsoring Dept | UCLA Human Genetics |
| Abstract | The explosion of genetic information over the last decade presents an analytical challenge for genetic association studies. As the number of genetic variables examined per individual increases, both variable selection and statistical modeling tasks must be performed during analysis. While these tasks could be performed separately, coupling them is necessary to select meaningful variables that effectively model the data. This challenge is heightened due to the complex nature of the phenotypes under study and the complex underlying genetic etiologies. To address this problem, a number of novel methods have been developed. In the current study, we compare the performance of six analytical approaches to detect both main effects and gene-gene interactions in a range of genetic models. Multifactor dimensionality reduction, grammatical evolution neural networks, random forests, focused interaction testing framework, step-wise logistic regression, and explicit logistic regression were compared. As one might expect, the relative success of each method is context dependent. This study demonstrates the strengths and weaknesses of each method and illustrates the importance of continued methods development. LITERATURE: 1.A comparison of analytical methods for genetic association studies.Motsinger-Reif AA, Reif DM, Fanelli TJ, Ritchie MD.Genet Epidemiol. 2008 Dec;32(8):767-78 |
| Description | Contact & Intro: Marc Suchard, ext. 57442 & msuchard@ucla.edu |
Artificial Life Approaches for Understanding and Applying Biological Concepts | |
| Date | Thursday October 28, 2010 at 4:00 PM |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Reiji Suzuki, Ph.D., Associate Professor, Department of Complex Systems Science, Graduate School of Information Science, Nagoya University, Japan |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | ABSTRACT: Artificial Life (ALife) is an interdisciplinary field of research that aims to understand and apply emergent dynamics of biological systems by synthesizing life-like behaviors using artificial media. In this seminar, I will talk about the following two topics aiming at understanding or applying biological concepts: 1. [Coevolution of learning and niche construction] In the standard view of the modern evolutionary synthesis, organisms are basically regarded as passively evolving entities based on selection and mutations. However, there are other ways, based on ecological activities, for modifying the selection pressure. One is for individuals to change their own phenotype, called learning or phenotypic plasticity, and the other is to change their environmental condition, called niche construction. To better understand the effects of mutual interactions between these mechanisms on evolution, we constructed a simple individual-based model in which individuals can perform both a niche construction of their shared environmental factor and modification of their phenotype through their lifetime learning. This can lead to unusual dynamics, including a cyclic coevolution of genes for learning and niche construction when the temporal locality of these ecological processes is low. 2. [Adaptive walk on fitness soundscape] We propose a new kind of interactive evolutionary computation (IEC) for musical works, which is inspired by a biological metaphor -- “adaptive walk on fitness landscapes”. This system enables a user to explore its favorite musical works by walking through a virtual landscape of sounds called a fitness soundscape. This is a virtual two-dimensional plane that represents the genetic space of possible musical works. Several sound sources are placed near corresponding genotype positions, each specifying the kind of sounds and its relative location from the genotype. By using the human abilities for localization and selective listening of sounds, the user can repeatedly walk toward the direction from which more favorite sounds come. This virtual environment can be realized by a multiple speaker system or a headphone creating “surrounded sound”. We report on the basic concept of the system and several prototypes for PC and iPhone (iSoundScape, available on AppStore for free). We also talk about an extended version of iSoundScape that features various birdsongs from California. |
| Description | Host: Elliot Landaw, M.D., Ph.D. To receive e-mail seminar notices, contact David Tomita (dtomita@biomath.ucla.edu) |
Unidentifiable models and the search for identifiable parameter combinations | |
| Date | Thursday October 21, 2010 at 4:00pm |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Nikki Meshkat, Graduate Student, UCLA Mathematics |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Parameter identifiability analysis for dynamic system ODE models concerns finding which unknown parameters can be quantified from given input-output data. Unidentifiable models are those which have parameters that can take on an infinite number of values and yet result in identical input-output data, thus making parameter estimation impossible for such parameters. This can be remedied by finding algebraic combinations of parameters that take on a unique or finite number of values, which are then used as candidates to reparameterize the model, rendering it identifiable. This talk will explore the differential algebra approach to structural identifiability and an algorithm for finding globally identifiable parameter combinations of nonlinear ODE models using Gröbner bases. |
| Flyer | Seminar flyer (pdf) |
Information Geometry: Applications of General Relativity to Cardiology, Biostatistics and Neuroscience | |
| Date | Thursday October 14, 2010 at 4:00 PM |
| Location | 13-105 Center for the Health Sciences (CHS) |
| Speaker | Nicholas Wisniewski, Ph.D., Postgraduate Researcher, UCLA Department of Medicine |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | The method of applying differential geometry to physical problems has led to a revolutionary century in science. The idea that emerges from the relativistic principles of metric invariance and coordinate covariance is that objects formulated geometrically correspond directly to invariant physical quantities, the objective quantities we are interested in measuring and modeling. At the same time, the simultaneous development of information theory in virtually every field of the natural sciences began to form another useful representation of dynamical, interacting systems in terms of probability theory, especially through the concepts of information and entropy. This is viewed by many as an equally large triumph for science, as it aims to reduce our models from particle, field, neural, cellular, molecular, genetic, atomic, and nuclear representations down to a representation in terms of only the dynamics of information and entropy. Through the work of Rao, Chentsov, and Amari, we have now reached a unification of the two major scientific philosophies of the 20th century, a field known as Information Geometry. This talk will introduce the concepts of geometry through physics, where geometric objects can be intuitively connected to physical observables. The ideas of information theory will then be presented, along with the unification to Information Geometry. Applications of the geometry to cardiac electrophysiological modeling and neuroimaging via DT-MRI will be presented in detail. Applications to dynamical inference and reasoning in artificial intelligence and theoretical neuroscience will be touched upon. New elementary hypothesis testing procedures, such as a “geometric t-test” and “geometric ANOVA” will be presented. Finally, the geometric representation of the EM algorithm will be outlined, and its usage in neural networks and machine learning with applications to neuroimaging techniques such as image registration and image segmentation will be discussed. |
| Flyer | Seminar flyer (pdf) |
Optimization in High-Dimensions: Acceleration, Annealing, and GPUs | |
| Date | Thursday October 07, 2010 at 4:00 PM |
| Location | 43-105 Center for the Health Sciences (CHS) |
| Speaker | Kenneth Lange, Ph.D., Professor, UCLA Departments of Biomathematics, Human Genetics and Statistics |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | Modern high-throughput datasets from data mining, genomics, and imaging demand high-dimensional models with ten to hundreds of thousands of parameters. These problems challenge classical methods of optimization such as Newton’s method and Fisher’s scoring algorithm that store, compute, and invert large Hessian or information matrices. If parameter constraints and parameter bounds intrude, then the algorithms require further modication. Although numerical analysts have devised various remedies and safeguards, these all come at a cost of greater implementation complexity. In this talk, I first describe the minorization-maximization (MM) principle. In many examples it is possible to construct a surrogate optimization function with parameters separated. Optimizing such a surrogate function avoids large matrices and drives the objective function in the right direction. If the surrogate function does not approximate the objective function well, then overall convergence is slow. Two devices can accelerate convergence. On the software side, one can apply a generic quasi-Newton scheme. On the hardware side, one can turn to parallel computing on inexpensive graphics processing units (GPUs). Both offer one to two orders of magnitude improvement over naive algorithms. I will illustrate the potential of both approaches by a variety of biomedical examples. Time permitting, I will also present a few variations of deterministic annealing that tend to avoid inferior modes and the dominant mode in multimodal statistical problems. The annealing algorithms are simple variations of MM algorithms |
| Description | Note the location is 43-105 CHS - the fourth floor lecture hall. |
| Flyer | Seminar flyer (pdf) |
Virtual Surgery: Scientific Computing in Real Time | |
| Date | Thursday September 30, 2010 at 4:00pm |
| Location | 13-105 Center for the Health Sciences (CHS) |
| Speaker | Joseph Teran, PhD, Assistant Professor, UCLA Department of Mathematics |
| Sponsoring Dept | UCLA Biomathematics |
| Abstract | As a general rule, scientific computing for solid and fluid mechanics is regarded an offline task, often requiring days of CPU time to complete. However, it is now evident that future microprocessors will be highly parallel, incorporating a large number of cores with multi-threading and vector processing capabilities. This revolution in architecture will afford future chips the computational capacity found in today’s massive clusters. Unfortunately, realization of this potential revolution in computing power is contingent upon the ability of numerical algorithms to successfully leverage the raw capacity of these parallel multiprocessors. This task is non-trivial given the nascent state of the architecture. Although the computing environment will resemble traditional high-performance computing, multi- core hardware will be sufficiently different to prevent simple porting of existing techniques from parallel computing. Novel approaches are needed that leverage the mathematical nuances of the various governing equations to meet the memory and scalability constraints of the hardware. I will discuss ongoing challenges developing such techniques and the potentially revolutionary applications they |
| Flyer | Seminar flyer (pdf) |
F-Statistics: A Methodology for Learning about History of Many Populations | |
| Date | Monday September 13, 2010 at 11:00 am |
| Location | Neuroscience Research Building Auditorium |
| Speaker | Nick Patterson, PhD, Broad Institute of MIT & Harvard |
| Sponsoring Dept | UCLA Human Genetics |
| Description | http://www.genetics.ucla.edu/speakers/ Host: Marc Suchard, ext. 57442 msuchard@ucla.edu |