The Graph, Geometry and Symmetries of the Genetic Code with Hamming Metric
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
On the Circuit Diameter Conjecture
On the Circuit Diameter Conjecture Steffen Borgwardt1, Tamon Stephen2, and Timothy Yusun2 1 University of Colorado Denver [email protected] 2 Simon Fraser University {tamon,tyusun}@sfu.ca Abstract. From the point of view of optimization, a critical issue is relating the combina- torial diameter of a polyhedron to its number of facets f and dimension d. In the seminal paper of Klee and Walkup [KW67], the Hirsch conjecture of an upper bound of f − d was shown to be equivalent to several seemingly simpler statements, and was disproved for unbounded polyhedra through the construction of a particular 4-dimensional polyhedron U4 with 8 facets. The Hirsch bound for bounded polyhedra was only recently disproved by Santos [San12]. We consider analogous properties for a variant of the combinatorial diameter called the circuit diameter. In this variant, the walks are built from the circuit directions of the poly- hedron, which are the minimal non-trivial solutions to the system defining the polyhedron. We are able to prove that circuit variants of the so-called non-revisiting conjecture and d-step conjecture both imply the circuit analogue of the Hirsch conjecture. For the equiva- lences in [KW67], the wedge construction was a fundamental proof technique. We exhibit why it is not available in the circuit setting, and what are the implications of losing it as a tool. Further, we show the circuit analogue of the non-revisiting conjecture implies a linear bound on the circuit diameter of all unbounded polyhedra – in contrast to what is known for the combinatorial diameter. Finally, we give two proofs of a circuit version of the 4-step conjecture. -
7 LATTICE POINTS and LATTICE POLYTOPES Alexander Barvinok
7 LATTICE POINTS AND LATTICE POLYTOPES Alexander Barvinok INTRODUCTION Lattice polytopes arise naturally in algebraic geometry, analysis, combinatorics, computer science, number theory, optimization, probability and representation the- ory. They possess a rich structure arising from the interaction of algebraic, convex, analytic, and combinatorial properties. In this chapter, we concentrate on the the- ory of lattice polytopes and only sketch their numerous applications. We briefly discuss their role in optimization and polyhedral combinatorics (Section 7.1). In Section 7.2 we discuss the decision problem, the problem of finding whether a given polytope contains a lattice point. In Section 7.3 we address the counting problem, the problem of counting all lattice points in a given polytope. The asymptotic problem (Section 7.4) explores the behavior of the number of lattice points in a varying polytope (for example, if a dilation is applied to the polytope). Finally, in Section 7.5 we discuss problems with quantifiers. These problems are natural generalizations of the decision and counting problems. Whenever appropriate we address algorithmic issues. For general references in the area of computational complexity/algorithms see [AB09]. We summarize the computational complexity status of our problems in Table 7.0.1. TABLE 7.0.1 Computational complexity of basic problems. PROBLEM NAME BOUNDED DIMENSION UNBOUNDED DIMENSION Decision problem polynomial NP-hard Counting problem polynomial #P-hard Asymptotic problem polynomial #P-hard∗ Problems with quantifiers unknown; polynomial for ∀∃ ∗∗ NP-hard ∗ in bounded codimension, reduces polynomially to volume computation ∗∗ with no quantifier alternation, polynomial time 7.1 INTEGRAL POLYTOPES IN POLYHEDRAL COMBINATORICS We describe some combinatorial and computational properties of integral polytopes. -
Dissertation
Dissertation submitted to the Combined Faculty of Natural Sciences and Mathematics of the Ruperto-Carola University of Heidelberg, Germany for the degree of Doctor of Natural Sciences Presented by M.Sc. Matilde Bertolini born in Parma, Italy Oral examination: 02/03/2021 Profiling interactions of proximal nascent chains reveals a general co-translational mechanism of protein complex assembly Referees: Prof. Dr. Bernd Bukau Prof. Dr. Caludio Joazeiro Preface Contributions The experiments and analyses presented in this Thesis were conducted by myself unless otherwise indicated, under the supervision of Dr. Günter Kramer and Prof. Dr. Bernd Bukau. The Disome Selective Profiling (DiSP) technology was developed in collaboration with my colleague Kai Fenzl, with whom I also generated initial datasets of human HEK293-T and U2OS cells. Dr. Ilia Kats developed important bioinformatics tools for the analysis of DiSP data, including RiboSeqTools and the sigmoid fitting algorithm. He also offered great input on statistical analyses. Dr. Frank Tippmann performed analysis of crystal structures. Table of Contents TABLE OF CONTENTS List of Figures ......................................................................................................V List of Tables ...................................................................................................... VII List of Equations .............................................................................................. VIII Abbreviations .................................................................................................... -
Phylogeny Recapitulates Learning: Self-Optimization of Genetic Code
bioRxiv preprint doi: https://doi.org/10.1101/260877; this version posted February 6, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Phylogeny Recapitulates Learning: Self-Optimization of Genetic Code Oliver Attie1, Brian Sulkow1, Chong Di1, and Wei-Gang Qiu1,2,3,* 1Department of Biological Sciences, Hunter College & 2Graduate Center, City University of New York; 3Department of Physiology and Biophysics & Institute for Computational Biomedicine, Weil Cornell Medical College, New York, New York, USA Emails: Oliver Attie [email protected]; Brian Sulkow [email protected]; Chong Di [email protected]; *Correspondence: [email protected] Abstract Learning algorithms have been proposed as a non-selective mechanism capable of creating complex adaptive systems in life. Evolutionary learning however has not been demonstrated to be a plausible cause for the origin of a specific molecular system. Here we show that genetic codes as optimal as the Standard Genetic Code (SGC) emerge readily by following a molecular analog of the Hebb’s rule (“neurons fire together, wire together”). Specifically, error-minimizing genetic codes are obtained by maximizing the number of physio-chemically similar amino acids assigned to evolutionarily similar codons. Formulating genetic code as a Traveling Salesman Problem (TSP) with amino acids as “cities” and codons as “tour positions” and implemented with a Hopfield neural network, the unsupervised learning algorithm efficiently finds an abundance of genetic codes that are more error- minimizing than SGC. -
15 BASIC PROPERTIES of CONVEX POLYTOPES Martin Henk, J¨Urgenrichter-Gebert, and G¨Unterm
15 BASIC PROPERTIES OF CONVEX POLYTOPES Martin Henk, J¨urgenRichter-Gebert, and G¨unterM. Ziegler INTRODUCTION Convex polytopes are fundamental geometric objects that have been investigated since antiquity. The beauty of their theory is nowadays complemented by their im- portance for many other mathematical subjects, ranging from integration theory, algebraic topology, and algebraic geometry to linear and combinatorial optimiza- tion. In this chapter we try to give a short introduction, provide a sketch of \what polytopes look like" and \how they behave," with many explicit examples, and briefly state some main results (where further details are given in subsequent chap- ters of this Handbook). We concentrate on two main topics: • Combinatorial properties: faces (vertices, edges, . , facets) of polytopes and their relations, with special treatments of the classes of low-dimensional poly- topes and of polytopes \with few vertices;" • Geometric properties: volume and surface area, mixed volumes, and quer- massintegrals, including explicit formulas for the cases of the regular simplices, cubes, and cross-polytopes. We refer to Gr¨unbaum [Gr¨u67]for a comprehensive view of polytope theory, and to Ziegler [Zie95] respectively to Gruber [Gru07] and Schneider [Sch14] for detailed treatments of the combinatorial and of the convex geometric aspects of polytope theory. 15.1 COMBINATORIAL STRUCTURE GLOSSARY d V-polytope: The convex hull of a finite set X = fx1; : : : ; xng of points in R , n n X i X P = conv(X) := λix λ1; : : : ; λn ≥ 0; λi = 1 : i=1 i=1 H-polytope: The solution set of a finite system of linear inequalities, d T P = P (A; b) := x 2 R j ai x ≤ bi for 1 ≤ i ≤ m ; with the extra condition that the set of solutions is bounded, that is, such that m×d there is a constant N such that jjxjj ≤ N holds for all x 2 P . -
Effect of Correlated TRAN Abundances on Translation Errors and Evolution of Codon Usage Bias
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Faculty Publications and Other Works -- Ecology and Evolutionary Biology Ecology and Evolutionary Biology 9-2010 Effect of Correlated TRAN Abundances on Translation Errors and Evolution of Codon Usage Bias Premal Shah University of Tennessee - Knoxville Michael A. Gilchrist Follow this and additional works at: https://trace.tennessee.edu/utk_ecolpubs Part of the Ecology and Evolutionary Biology Commons Recommended Citation Shah, Premal and Gilchrist, Michael A., "Effect of Correlated TRAN Abundances on Translation Errors and Evolution of Codon Usage Bias" (2010). Faculty Publications and Other Works -- Ecology and Evolutionary Biology. https://trace.tennessee.edu/utk_ecolpubs/37 This Article is brought to you for free and open access by the Ecology and Evolutionary Biology at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Faculty Publications and Other Works -- Ecology and Evolutionary Biology by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Effect of Correlated tRNA Abundances on Translation Errors and Evolution of Codon Usage Bias Premal Shah1,2*, Michael A. Gilchrist1,2 1 Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America, 2 National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, United States of America Abstract Despite the fact that tRNA abundances are thought to play a major role in determining translation error rates, their distribution across the genetic code and the resulting implications have received little attention. In general, studies of codon usage bias (CUB) assume that codons with higher tRNA abundance have lower missense error rates. -
Separators of Simple Polytopes
Fachbereich Mathematik und Informatik der Freien Universit¨atBerlin Separators of simple polytopes A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Natural Sciences to the Department of Department of Mathematics and Computer Science by Lauri Loiskekoski Berlin 2017 Supervisor: Prof. Dr. G¨unter M. Ziegler Second Examiner: Prof. Dr. Volker Kaibel Date of defense: 21.12.2017 Acknowledgements First and foremost I would like to thank my supervisor G¨unter M. Ziegler for introducing me to this fascinating topic and always having time to discuss. You always knew the right references and had good ideas what to look at. I would like to thank Hao Chen, Arnau Padrol and Raman Sanyal for help- ing me get started on research and and answering my questions on separators and polytopes. I would like to thank my friends and colleague at the villa, in particular Francesco Grande, Katy Beeler, Albert Haase, Tobias Friedl, Philip Brinkmann, Nevena Pali´c,Jean-Philippe Labb´e,Giulia Codenotti, Jorge Alberto Olarte, Hannah Sch¨aferSj¨oberg and Anna Maria Hartkopf. Thanks for navigating the bureaucracy go to Elke Pose. Thanks to Johanna Steinmeyer for the TikZ pictures. I wish to thank the Berlin Mathematical School for funding my research and European Research Council and Osaka University for funding my travels. Finally, I would like to thank my friends in Finland, especially the people of Rissittely for answering all the unconventional questions I have had about academia and life in general. iii iv Contents Acknowledgements . iii 1 Introduction 1 2 Graphs and Polytopes 3 2.1 Polytopes . -
Three Questions on Graphs of Polytopes
Three questions on graphs of polytopes Guillermo Pineda-Villavicencio Federation University Australia G. Pineda-Villavicencio (FedUni) Mar 18 1 / 30 Outline 1 A polytope as a combinatorial object 2 First question: Reconstruction of polytopes 3 Second question: Connectivity of cubical polytopes 4 Third question: Linkedness of cubical polytopes G. Pineda-Villavicencio (FedUni) Mar 18 2 / 30 A polytope as a combinatorial object 2 3 4 5 6 7 1 3 5 7 0 1 4 5 2 3 6 7 0 2 4 6 0 1 2 3 6 7 5 7 4 5 1 5 6 7 3 7 4 6 0 4 1 3 0 1 2 6 2 3 0 2 0 1 4 5 5 7 4 1 6 3 0 2 G. Pineda-Villavicencio (FedUni) Mar 18 3 / 30 Reconstruction of polytopes (Dolittle, Nevo, Ugon & Yost) The k-skeleton of a polytope is the set of all its faces of dimension ≤ k. k-skeleton reconstruction: Given the k-skeleton of a polytope, can the face lattice of the polytope be completed? 4 5 6 7 1 3 5 7 0 1 4 5 2 3 6 7 0 2 4 6 0 1 2 3 5 7 4 5 1 5 6 7 3 7 4 6 0 4 1 3 0 1 2 6 2 3 0 2 5 7 4 1 6 3 0 2 G. Pineda-Villavicencio (FedUni) Mar 18 4 / 30 For d ≥ 4 there are pairs of d-polytopes with isomorphic (d − 3)-skeleta: a bipyramid over a (d − 1)-simplex and, a pyramid over the (d − 1)-bipyramid over a (d − 2)-simplex. -
The RNA Code and Protein Synthesis
Downloaded from symposium.cshlp.org on January 18, 2014 - Published by Cold Spring Harbor Laboratory Press The RNA Code and Protein Synthesis M. Nirenberg, T. Caskey, R. Marshall, et al. Cold Spring Harb Symp Quant Biol 1966 31: 11-24 Access the most recent version at doi:10.1101/SQB.1966.031.01.008 References This article cites 37 articles, 24 of which can be accessed free at: http://symposium.cshlp.org/content/31/11.refs.html Article cited in: http://symposium.cshlp.org/content/31/11#related-urls Email alerting Receive free email alerts when new articles cite this article - sign up in service the box at the top right corner of the article or click here To subscribe to Cold Spring Harbor Symposia on Quantitative Biology go to: http://symposium.cshlp.org/subscriptions Copyright © 1966 Cold Spring Harbor Laboratory Press Downloaded from symposium.cshlp.org on January 18, 2014 - Published by Cold Spring Harbor Laboratory Press The RNA Code and Protein Synthesis M. NIRENBERG, T. CASKEY, R. MARSHALL, R. BRIMACOMBE, D. KELLOGG, B. DOCTOIr D. HATFIELD, J. LEVIN, F. ROTTMAN, S. PESTKA, M. WILCOX, AND F. ANDERSON Laboratory of Biochemical Genetics, National Heart Institute, National Institutes of Health, Bethesda, Maryland and t Division of Biochemistry, Walter Reed Army Institute of Research, Walter Reed Army Medical Center, Washington, D.C. Many properties of the RNA code which were TABLE 1. CHARACTERISTICS OF AA-sRI~A BINDING TO RIBOSOMES discussed at the 1963 Cold Spring Harbor meeting were based on information obtained with randomly C14-Phe-sRNA bound to ribo- ordered synthetic polynucleotides. -
Combinatorial Aspects of Convex Polytopes Margaret M
Combinatorial Aspects of Convex Polytopes Margaret M. Bayer1 Department of Mathematics University of Kansas Carl W. Lee2 Department of Mathematics University of Kentucky August 1, 1991 Chapter for Handbook on Convex Geometry P. Gruber and J. Wills, Editors 1Supported in part by NSF grant DMS-8801078. 2Supported in part by NSF grant DMS-8802933, by NSA grant MDA904-89-H-2038, and by DIMACS (Center for Discrete Mathematics and Theoretical Computer Science), a National Science Foundation Science and Technology Center, NSF-STC88-09648. 1 Definitions and Fundamental Results 3 1.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 Faces : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.3 Polarity and Duality : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.4 Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 Shellings 4 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.2 Euler's Relation : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.3 Line Shellings : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.4 Shellable Simplicial Complexes : : : : : : : : : : : : : : : : : : : 5 2.5 The Dehn-Sommerville Equations : : : : : : : : : : : : : : : : : : 6 2.6 Completely Unimodal Numberings and Orientations : : : : : : : 7 2.7 The Upper Bound Theorem : : : : : : : : : : : : : : : : : : : : : 8 2.8 The Lower Bound Theorem : : : : : : : : : : : : : : : : : : : : : 9 2.9 Constructions Using Shellings : : : : : : : : : : : : : -
A Numerical Representation and Classification of Codons To
bioRxiv preprint doi: https://doi.org/10.1101/2020.03.02.971036; this version posted March 3, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. A Numerical Representation and Classication of Codons to Investigate Codon Alternation Patterns during Genetic Mutations on Disease Pathogenesis Antara Senguptaa, Pabitra Pal Choudhuryd, Subhadip Chakrabortyb,e, Swarup Royc,∗∗, Jayanta Kumar Dasd,∗, Ditipriya Mallicke, Siddhartha S Janae aDepartment of Master of Computer Applications, MCKV Institute of Engineering, Liluah, India bDepartment of Botany, Nabadwip Vidyasagar College, Nabadwip, India cDepartment of Computer Applications,Sikkim University, Gangtok, Sikkim, India dApplied Statistical Unit, Indian Statistical Institute, Kolkata, India eSchool of Biological Sciences, Indian Association for the Cultivation of Science, Kolkata, India 1. Introduction Genes are the functional units of heredity [4]. It is mainly responsible for the structural and functional changes and for the variation in organisms which could be good or bad. DNA (Deoxyribose Nucleic Acid) sequences build the genes of organisms which in turn encode for particular protein us- ing codon. Any uctuation in this sequence (codons), for example, mishaps during DNA transcription, might lead to a change in the genetic code which alter the protein synthesis. This change is called mutation. Mutation diers from Single Nucleotide Polymorphism(SNP) in many ways [23]. For instance, occurrence of mutation in a population should be less than 1% whereas SNP occurs with greater than 1%. Mutation always occurs in diseased group whereas SNP is occurs in both diseased and control population. Mutation is responsible for some disease phenotype but SNP may or may not be as- sociated with disease phenotype. -
Gauss-Bonnet for Multi-Linear Valuations
GAUSS-BONNET FOR MULTI-LINEAR VALUATIONS OLIVER KNILL Abstract. We prove Gauss-Bonnet and Poincar´e-Hopfformulas for multi- linear valuations on finite simple graphs G = (V; E) and answer affirmatively a conjecture of Gr¨unbaum from 1970 by constructing higher order Dehn- Sommerville valuations which vanish for all d-graphs without boundary. A first example of a higher degree valuations was introduced by Wu in 1959. P dim(x) It is the Wu characteristic !(G) = x\y6=; σ(x)σ(y) with σ(x) = (−1) which sums over all ordered intersecting pairs of complete subgraphs of a finite simple graph G. It more generally defines an intersection number !(A; B) = P x\y6=; σ(x)σ(y), where x ⊂ A; y ⊂ B are the simplices in two subgraphs A; B of a given graph. The self intersection number !(G) is a higher order Euler P characteristic. The later is the linear valuation χ(G) = x σ(x) which sums over all complete subgraphs of G. We prove that all these characteristics share the multiplicative property of Euler characteristic: for any pair G; H of finite simple graphs, we have !(G × H) = !(G)!(H) so that all Wu characteristics like Euler characteristic are multiplicative on the Stanley-Reisner ring. The Wu characteristics are invariant under Barycentric refinements and are so com- binatorial invariants in the terminology of Bott. By constructing a curvature P K : V ! R satisfying Gauss-Bonnet !(G) = a K(a), where a runs over all vertices we prove !(G) = χ(G) − χ(δ(G)) which holds for any d-graph G with boundary δG.