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Given a manifold with a string structure, we construct a spinor bundle on its loop space. Our construction is in analogy with the usual construction of a spinor bundle on a spin manifold, but necessarily makes use of tools from infinite dimensional geometry. We equip this spinor bundle on loop space with an action of a bundle of Clifford algebras. Given two smooth loops in our string manifold that share a segment, we can construct a third loop by deleting this segment. If this third loop is smooth, then we say that the original pair of loops is a pair of compatible loops. It is well-known that this operation of fusing compatible loops is important if one wants to understand the geometry of a manifold through its loop space. In this work, we explain in detail how the spinor bundle on loop space behaves with respect to fusion of compatible loops. To wit, we construct a family of fusion isomorphisms indexed by pairs of compatible loops in our string manifold. Each of these fusion isomorphisms is an isomorphism from the relative tensor product of the fibres of the spinor bundle over its index pair of compatible loops to the fibre over the loop that is the result of fusing the index pair. The construction of a spinor bundle on loop space equipped with a fusion product as above was proposed by Stolz and Teichner with the goal of studying the Dirac operator on loop space". Our construction combines facets of the theory of bimodules for von Neumann algebras, infinite dimensional manifolds, and Lie groups and their representations. We moreover place our spinor bundle on loop space in the context of bundle gerbes and bundle gerbe modules.
Abstract
With the advent of molecular genetic methods, an increasing number of morphologically cryptic taxa has been discovered. The majority of them, however, remains formally undescribed and without a proper name although their importance in ecology and evolution is increasingly being acknowledged. Despite suggestions to complement traditional descriptions with genetic characters, the taxonomic community appears to be reluctant to adopt this proposition. As an incentive, we introduce QUIDDICH, a tool for the QUick IDentification of DIgnostic CHaracters, which automatically scans a DNA or amino acid alignment for those columns that allow to distinguish taxa and classifies them into four different types of diagnostic characters. QUIDDICH is a system‐independent, fast and user‐friendly tool that requires few manual steps and provides a comprehensive output, which can be included in formal taxonomic descriptions. Thus, cryptic taxa do not have to remain in taxonomic crypsis and, bearing a proper name, can readily be included in biodiversity assessments and ecological and evolutionary analyses. QUIDDICH can be obtained from the comprehensive R archive network (CRAN, https://cran.r-project.org/package=quiddich).
Mathematical phylogenetics provides the theoretical framework for the reconstruction and analysis of phylogenetic trees and networks. The underlying theory is based on various mathematical disciplines, ranging from graph theory to probability theory.
In this thesis, we take a mostly combinatorial and graph-theoretical position and study different problems concerning phylogenetic trees and networks.
We start by considering phylogenetic diversity indices that rank species for conservation. Two such indices for rooted trees are the Fair Proportion index and the Equal Splits index, and we analyze how different they can be from each other and under which circumstances they coincide. Moreover, we define and investigate analogues of these indices for unrooted trees.
Subsequently, we study the Shapley value of unrooted trees, another popular phylogenetic diversity index. We show that it may fail as a prioritization criterion in biodiversity conservation and is outcompeted by an existing greedy approach. Afterwards, we leave the biodiversity setting and consider the Shapley value as a tree reconstruction tool. Here, we show that non-isomorphic trees may have permutation-equivalent Shapley transformation matrices and identical Shapley values, implying that the Shapley value cannot reliably be employed in tree reconstruction.
In addition to phylogenetic diversity indices, another class of indices frequently discussed in mathematical phylogenetics, is the class of balance indices. In this thesis, we study one of the oldest and most popular of them, namely the Colless index for rooted binary trees. We focus on its extremal values and analyze both its maximum and minimum values as well as the trees that achieve them.
Having analyzed various questions regarding phylogenetic trees, we finally turn to phylogenetic networks. We focus on a certain class of phylogenetic networks, namely tree-based networks, and consider this class both in a rooted and in an unrooted setting.
First, we prove the existence of a rooted non-binary universal tree-based network with n leaves for all positive integers n, that is, we show that there exists a rooted non-binary tree-based network with $n$ leaves that has every non-binary phylogenetic tree on the same leaf set as a base tree.
Finally, we study unrooted tree-based networks and introduce a class of networks that are necessarily tree-based, namely edge-based networks. We show that edge-based networks are closely related to a family of graphs in classical graph theory, so-called generalized series-parallel graphs, and explore this relationship in full detail.
In summary, we add new insights into existing concepts in mathematical phylogenetics, answer open questions in the literature, and introduce new concepts and approaches. In doing so, we make a small but relevant contribution to current research in mathematical phylogenetics.
Liver diseases are important causes of morbidity and mortality worldwide. The aim of
this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key
pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional
state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from
subjects with six different liver pathologies and from control livers. ANOVA was employed to
obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database)
was used to predict target genes. A miRNA–gene differential regulatory (MGDR) network was
constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment
analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated
Discovery (DAVID). We identified important DEMs common and specific to the different patient
groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless
the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were
deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs
comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in
regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog
(PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel
panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These
miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases.
Trade of cattle between farms forms a complex trade network. We investigate partitions of this network for cattle trade in Germany. These partitions are groups of farms with similar properties and they are inferred directly from the trade pattern between farms. We make use of a rather new method known as stochastic block modeling (SBM) in order to divide the network into smaller units. SBM turns out to outperform the more established community detection method in the context of disease control in terms of trade restriction. Moreover, SBM is also superior to geographical based trade restrictions and could be a promising approach for disease control.
A common task in natural sciences is to
describe, characterize, and infer relations between discrete
objects. A set of relations E on a set of objects V can
naturally be expressed as a graph G = (V, E). It is
therefore often convenient to formalize problems in natural
sciences as graph theoretical problems.
In this thesis we will examine a number of problems found in
life sciences in particular, and show how to use graph theoretical
concepts to formalize and solve the presented problems. The
content of the thesis is a collection of papers all
solving separate problems that are relevant to biology
or biochemistry.
The first paper examines problems found in self-assembling
protein design. Designing polypeptides, composed of concatenated
coiled coil units, to fold into polyhedra turns out
to be intimately related to the concept of 1-face embeddings in
graph topology. We show that 1-face embeddings can be
canonicalized in linear time and present algorithms to enumerate
pairwise non-isomorphic 1-face embeddings in orientable surfaces.
The second and third paper examine problems found in evolutionary
biology. In particular, they focus on
inferring gene and species trees directly from sequence data
without any a priori knowledge of the trees topology. The second
paper characterize when gene trees can be inferred from
estimates of orthology, paralogy and xenology relations when only
partial information is available. Using this characterization an
algorithm is presented that constructs a gene tree consistent
with the estimates in polynomial time, if one exists. The
shown algorithm is used to experimentally show that gene trees
can be accurately inferred even in the case that only 20$\%$ of
the relations are known. The third paper explores how to
reconcile a gene tree with a species tree in a biologically
feasible way, when the events of the gene tree are known.
Biologically feasible reconciliations are characterized using
only the topology of the gene and species tree. Using this
characterization an algorithm is shown that constructs a
biologically feasible reconciliation in polynomial time, if one
exists.
The fourth and fifth paper are concerned with with the analysis
of automatically generated reaction networks. The fourth paper
introduces an algorithm to predict thermodynamic properties of
compounds in a chemistry. The algorithm is based on
the well known group contribution methods and will automatically
infer functional groups based on common structural motifs found
in a set of sampled compounds. It is shown experimentally that
the algorithm can be used to accurately
predict a variety of molecular properties such as normal boiling
point, Gibbs free energy, and the minimum free energy of RNA
secondary structures. The fifth and final paper presents a
framework to track atoms through reaction networks generated by a
graph grammar. Using concepts found in semigroup theory, the
paper defines the characteristic monoid of a reaction network. It
goes on to show how natural subsystems of a reaction network organically
emerge from the right Cayley graph of said monoid. The
applicability of the framework is proven by applying it to the
design of isotopic labeling experiments as well as to the
analysis of the TCA cycle.
Twisted topological K-theory is a twisted version of topological K-theory in the sense of twisted generalized cohomology theories. It was pioneered by Donavan and Karoubi in 1970 where they used bundles of central simple graded algebras to model twists of K-theory. By the end of the last century physicists realised that D-brane charges in the field of string theory may be studied in terms of twisted K-theory. This rekindled interest in the topic lead to a wave of new models for the twists and new ways to realize the respective twisted K-theory groups. The state-of-the-art models today use bundles of projective unitary operators on separable Hilbert spaces as twists and K-groups are modeled by homotopy classes of sections of certain bundles of Fredholm operators. From a physics perspective these treatments are not optimal yet: they are intrinsically infinite-dimensional and these models do not immediately allow the inclusion of differential data like forms and connections.
In this thesis we introduce the 2-stack of k-algebra gerbes. Objects, 1-morphisms and 2-morphisms consist of finite-dimensional geometric data simultaneously generalizing bundle gerbes and bundles of central simple graded k-algebras for k either the field of real numbers or the field of complex numbers. We construct an explicit isomorphism from equivalence classes of k-algebra gerbes over a space X to the full set of twists of real K-theory and complex K-theory respectively. Further, we model relative twisted K-groups for compact spaces X and closed subspaces Y twisted by algebra gerbes. These groups are modeled directly in terms of 1-morphisms and 2-morphisms of algebra gerbes over X. We exhibit a relation to the K-groups introduced by Donavan and Karoubi and we translate their fundamental isomorphism -- an isomorphism relating K-groups over Thom spaces with K-groups twisted by Clifford algebra bundles -- to the new setting. With the help of this fundamental isomorphism we construct an explicit Thom isomorphism and explicit pushforward homomorphisms for smooth maps between compact manifolds, without requiring these maps to be K-oriented. Further -- in order to treat K-groups for non-torsion twists -- we implement a geometric cocycle model, inspired by a related geometric cycle model developed by Baum and Douglas for K-homology in 1982, and construct an assembly map for this model.