| Workshop on Persistent Homology for Biosciences October 18, 2014, East Lansing, USA Titles and Abstracts |
Structures on spaces of barcodes Gunnar Carlsson (Department of Mathematics, Stanford University) Abstract:Persistence barcodes are useful invariants describing finite metric spaces from many different sources. In many situations, it is useful to impose mathematical structures (metrics, coordinates) on the set of barcodes so that one can understand the relationships between pairs of barcodes. We will talk about these structures, and suggest ways they can be used to get a better handle on multidimensional persistence. Topological analysis of symmetric matrices Chad Giusti (Department of Mathematics, University of Pennsylvania) Abstract: A common method in population analysis is to summarize relationships of elements in the population by some sort of symmetric (dis)similarity measure matrix. However, the observed values from which these measures are computed are rarely the "true" underlying state variables, but rather are seen through the filter of an unknown nonlinear (but monotonic) transformation. Even simple nonlinearities can badly deform the spectrum of a matrix, destroying signatures of structure that are provided by the usual linear algebra-based tools (such as PCA), or even creating the illusion of structure where none exists. To address this issue, we describe a novel combinatorial object, the Order Canonical Form (OCF), which encodes only information invariant in a symmetric matrix under monotonic transformation. The OCF retains a great deal of information about the structure of the original population of variables, including robust signatures of random and geometric organizations that can be extracted through persistent homology. As an application, we study neural activity from the rat hippocampus under a variety of conditions (including REM sleep) and find strong evidence of geometric structure at the level of network-organization. This is joint work with Vladimir Itskov and Carina Curto. Detection and Assembling of Protein Structure Components using Geometric and Topological Modeling for Cryo-EM Density Images Jing He (Department of Computer Science, Old Dominion University) Abstract: Electron cryo-microscopy (cryo-EM) technique has improved dramatically over the last twenty years. It is becoming a powerful technique to derive the near atomic structures of large molecular complexes such as viruses, membrane protein complexes, cytoskeleton fibers and ribosomes. Many of the cryo-EM density images have been derived to medium resolutions between 5 and 10Å. In order to interpret the protein structures from such images, an interdisciplinary approach is needed. We have combined image processing with geometrical modeling to detect protein secondary structure components such as alpha-helices and beta-strands. The detected components need to be assembled to form a native topology. I will discuss how the analysis of geometric shape and surface is applied to advance the pattern recognition problem. A constrained dynamic programming algorithm to assemble the structure components will be discussed. Protein topology and compressibility Vidit Nanda (Department of Mathematics, University of Pennsylvania) Abstract: A standard question in contemporary proteomics asks which properties of proteins may be directly inferred from their molecular structure. Using only X-Ray crystallography data (of the type which is cataloged in the Protein Data Bank), I will outline a method which accurately estimates the compressibility of a given protein. The method involves imposing a filtered simplicial structure around the atom centers, computing various algebraic-topological invariants, and some rudimentary statistical techniques. This is joint work with Marcio Gameiro, Yasu Hiraoka, Shunsuke Izumi, Miro Kramar and Konstantin Mischaikow. Persistent homology of time-delay embeddings Jose Perea (Department of Mathematics, Duke University) Abstract: We present in this talk a theoretical framework for studying the persistent homology of point-clouds from time-delay (or sliding window) embeddings. We will show that maximum 1-d persistence yields a suitable measure of periodicity at the signal level, and present theorems which relate the resulting diagrams to the choices of window size, embedding dimension and field of coefficients. We will also demonstrate how this methodology has been successfully applied to the study of periodicity on time series from gene expression data. Bottlenecks of 3D Domains Yiying Tong (Computer Science and Engineering, Michigan State University) Abstract: We present a method for computing bottleneck loops - a set of surface loops that describe the narrowing of the volumes inside/outside of the surface and extend the notion of surface homology and homotopy loops. The intuition behind their definition is that such a loop represents the region where an offset of the original surface would get pinched. Our generalized loops naturally include the usual 2g handles/tunnels computed based on the topology of the genus-g surface, but also include loops that identify chokepoints or bottlenecks, i.e., boundaries of small membranes separating the inside or outside volume of the surface into disconnected regions. Based on persistent homology theory, our definition builds on a measure to topological structures, thus providing resilience to noise and a well-defined way to determine topological feature size. Analyzing biological data via topological terrain metaphors Yusu Wang (Computer Science and Engineering, Ohio State University) Abstract: I will talk about the use of topological terrain metaphors for (biological) data visualization and analysis. I will in praticular describe two software we developed: \emph{Denali}, a generic tool for visualizing tree-like structures (such as clustering trees) using topological terrain metaphors, as well as \emph{Ayla}, a specialized visual analytic tool for exploring molecular simulation data. This is joint work with J. Eldridge, W. Harvey, M. Belkin, T.-P. Bremer, C. Li, I. Park, V. Pascucci and O. Ruebel. Persistent homology analysis of biomolecular structure and function Kelin Xia (Department of Mathematics, Michigan State University) Abstract: Proteins are the most important biomolecules for living organisms. The understanding of protein structure and function is one of the most challenging tasks in biological science. In the present work, persistent homology is introduced for extracting molecular topological fingerprints (MTFs) based on the persistence of molecular topological invariants. Persistent homology is utilized both as a qualitative tool, namely, for characterization, identification and classification and as a quantitative tool, i.e., for mathematical modeling, analysis and physical prediction of protein point cloud and cryo-EM data. MTFs are employed to reveal the topology-function relationship of proteins. Applications are considered to protein folding stability, protein thermal fluctuation, protein electrostatic analysis, protein solvation prediction, and cryo-EM structural determination. This is a joint work with Guo-Wei Wei. |