Structure and Microrheology of Complex Polymer Solutions: from Genome Organization to Active-Passive Mixtures

Polymers are intriguing physical systems whose complex properties are at the heart of how viscoelastic substances, materials which under strain manifest a behavior which is intermediate between a liquid and a solid, work. Understanding the properties of these materials is the main goal of the theoretical and computational tools of Polymer Physics. A particularly important, yet not fully understood, class of polymer materials is represented by concentrated solutions and melts of unknotted and unconcatenated ring polymers: in fact, at odds with the more familiar case of linear polymers which tend to become highly mixed and mutually penetrating, the presence of mutual avoidance and topological constraints (entanglements) between ring polymers force these chains to remain \u201cterritorial\u201d, i.e. each chain is virtually unmixed from the rest of the others. Because of this feature, solutions of ring polymers display unique material properties, in particular single chains tend to crumple into highly branched conformations and feature marked corrugated surfaces. Recently, it has been suggested that the spatial configurations of ring polymers in solution can be used as model systems for the organization of chromosome conformations during interphase, i.e. inside the nuclei of eukaryotic cells. This surprising analogy is built upon the claim that chromosomes undergo slow relaxation inside the nucleus which results in the spontaneous formation of so-called territories, regions of the nucleus which have a profound impact on crucial cellular functions such as gene expression and gene regulation. In this Thesis, we explore the analogy between ring polymers in solution, their large-scale crumpled 3d structure and interphase chromosomes by employing a combination of the theory of polymer solutions and numerical simulations. In more detail, we investigate primarily the following aspects: (a) the formation of ordered domains on a simple Ising-like toy model for crumpled polymers; (b) The analysis of the viscoelastic properties of model chromosome conformations whose stochastic motion is restricted by the presence of external constraints; (c) The discussion of the viscoelastic properties of solutions of active vs. non- active rings, where \u201dactive\u201d means that polymers are driven out-of-equilibrium by pumping energy inside the system

Probabilistic analysis of the human transcriptome with side information

Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.Comment: Doctoral thesis. 103 pages, 11 figure

Network and Algebraic Topology of Influenza Evolution

Evolution is a force that has molded human existence since its divergence from chimpanzees about 5.4 million years ago. In that same amount of time, an influenza virus, which replicates every six hours, would have undergone an equivalent number of generations over only a hundred years. The fast replication times of influenza, coupled with its high mutation rate, make the virus a perfect model to study real-time evolution at a mega-Darwin scale, more than a million times faster than human evolution. While recent developments in high-throughput sequencing provide an optimal opportunity to dissect their genetic evolution, a concurrent growth in computational tools is necessary to analyze the large influx of complex genomic data. In my thesis, I present novel computational methods to examine different aspects of influenza evolution. I first focus on seasonal influenza, particularly the problems that hamper public health initiatives to combat the virus. I introduce two new approaches: 1. The q2-coefficient, a method of quantifying pathogen surveillance, and 2. FluGraph, a technique that employs network topology to track the spread of seasonal influenza around the world. The second chapter of my thesis examines how mutations and reassortment combine to alter the course of influenza evolution towards pandemic formation. I highlight inherent deficiencies in the current phylogenetic paradigm for analyzing evolution and offer a novel methodology based on algebraic topology that comprehensively reconstructs both vertical and horizontal evolutionary events. I apply this method to viruses, with emphasis on influenza, but foresee broader application to cancer cells, bacteria, eukaryotes, and other taxa

Current Challenges in Modeling Cellular Metabolism

Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results

3D Organization of Eukaryotic and Prokaryotic Genomes

There is a complex mutual interplay between three-dimensional (3D) genome organization and cellular activities in bacteria and eukaryotes. The aim of this thesis is to investigate such structure-function relationships. A main part of this thesis deals with the study of the three-dimensional genome organization using novel techniques for detecting genome-wide contacts using next-generation sequencing. These so called chromatin conformation capture-based methods, such as 5C and Hi-C, give deep insights into the architecture of the genome inside the nucleus, even on a small scale. We shed light on the question how the vastly increasing Hi-C data can generate new insights about the way the genome is organized in 3D. To this end, we first present the typical Hi-C data processing workflow to obtain Hi-C contact maps and show potential pitfalls in the interpretation of such contact maps using our own data pipeline and publicly available Hi-C data sets. Subsequently, we focus on approaches to modeling 3D genome organization based on contact maps. In this context, a computational tool was developed which interactively visualizes contact maps alongside complementary genomic data tracks. Inspired by machine learning with the help of probabilistic graphical models, we developed a tool that detects the compartmentalization structure within contact maps on multiple scales. In a further project, we propose and test one possible mechanism for the observed compartmentalization within contact maps of genomes across multiple species: Dynamic formation of loops within domains. In the context of 3D organization of bacterial chromosomes, we present the first direct evidence for global restructuring by long-range interactions of a DNA binding protein. Using Hi-C and live cell imaging of DNA loci, we show that the DNA binding protein Rok forms insulator-like complexes looping the B. subtilis genome over large distances. This biological mechanism agrees with our model based on dynamic formation of loops affecting domain formation in eukaryotic genomes. We further investigate the spatial segregation of the E. coli chromosome during cell division. In particular, we are interested in the positioning of the chromosomal replication origin region based on its interaction with the protein complex MukBEF. We tackle the problem using a combined approach of stochastic and polymer simulations. Last but not least, we develop a completely new methodology to analyze single molecule localization microscopy images based on topological data analysis. By using this new approach in the analysis of irradiated cells, we are able to show that the topology of repair foci can be categorized depending the distance to heterochromatin

Evolution of overlapping reading frames in virus genomes

Viruses are formidable pathogens that represent the majority of biological entities in our planet, and their genomes are a source of interesting enigmas. One feature in which virus genomes are usually rich, is the presence of overlapping reading frames (OvRFs) — portions of the genome where the same nucleotide sequence encodes more than one protein. OvRFs are hypothesized to be used by viruses to encode proteins more compactly and to regulate transcription. In addition, OvRFs might be a source of gene novelty, facilitating the creation of new open reading frames (ORF) within the transcriptional context of existing ones. To characterize the distribution OvRFs in viruses, I analyzed 12,609 reference genomes from the NCBI virus database and discovered that, while the number of OvRFs increases the genome length, the overlapping regions tend to be shorter in longer genomes. I also demonstrated that dif- ferent frameshifts have distinct patterns in OvRFs. For example, +2 frameshifts are predominantly found in dsDNA viruses, whereas +0 frameshifts in RNA viruses tend to involve longer overlaps, which may increase the selective burden of the same nucleotide positions within codons. Further, I retrieved n = 8, 586 protein-coding sequences from n = 1, 224 reference genomes, and used an alignment-free method to cluster these sequences within virus families. I used these clusters to develop a new network-based representation of the distribution of OvRFs, which provides a means of visualizing and analyzing these genome features for each virus family. I also used these net- works to generate a high-level visualization of how overlapping genes are distributed among virus genomes in the same family. Evolution in overlapping genes is complicated because the effect of a nucleotide substitution has multiple contexts. To unravel the effects of OvRFs on virus evolution, I developed HexSE, a simulation model of nucleotide sequence evolution along a phylogeny that tracks the substitution rates at every nucleotide site. In HexSE, I implemented a customized data structure to efficiently track the substitution rates at every nucleotide site. These rates are determined by the stationary nucleotide frequencies, transition bias, and the distribution of selection biases (dN and dS) in the respective reading frames. Next, I compared HexSE simulations under varying settings to an alignment of actual hepatitis B virus (HBV) genomes, which revealed consistent drops in synonymous substitution rates (dS) in association with overlapping regions of an ORF. This thesis explores the cryptic information contained in viral genomes to help explain the evolutionary processes that shape them. In particular, understanding the impact of OvRFs on the evolution of virus genomes will provide us with crucial pieces of a significant puzzle — under- standing the origin of new genes in virus genomes, and thereby virus diversity

Aggregation of biological knowledge for immunological and virological applications

Ph.DDOCTOR OF PHILOSOPH