This thesis is comprised of three parts. The first part describes a novel framework for computing importance measures on graph vertices. The concept of a D-spectrum is introduced, based on vertex ranks within certain chains of nested sub-graphs. We show that the D- spectrum integrates the degree distribution and coreness information of the graph as two particular such chains. We prove that these spectra are realized as fixed points of certain monotone and contractive SDSs we call t-systems. Finally, we give a vertex deletion algorithm that efficiently computes D-spectra, and we illustrate their correlation with stochastic SIR-processes on real world networks. The second part deals with the topology of the intersection nerve for a bi-secondary structure, and its singular homology. A bi-secondary structure R, is a combinatorial object that can be viewed as a collection of cycles (loops) of certain at most tetravalent planar graphs. Bi-secondary structures arise naturally in the study of RNA riboswitches - molecules that have an MFE binary structural degeneracy. We prove that this loop nerve complex has a euclidean 3-space embedding characterized solely by H2(R), its second homology group. We show that this group is the only non-trivial one in the sequence and furthermore it is free abelian. The third part further describes the features of the loop nerve. We identify certain disjoint objects in the structure of R which we call crossing components (CC). These are non-trivial connected components of a graph that captures a particular non-planar embedding of R. We show that each CC contributes a unique generator to H2(R) and thus the total number of these crossing components in fact equals the rank of the second homology group. / Doctor of Philosophy / This Thesis is divided into three parts. The first part describes a novel mathematical framework for decomposing a real world network into layers. A network is comprised of interconnected nodes and can model anything from transportation of goods to the way the internet is organized. Two key numbers describe the local and global features of a network: the number of neighbors, and the number of neighbors in a certain layer, a node has. Our work shows that there are other numbers in-between the two, that better characterize a node. We also give explicit means of computing them. Finally, we show that these numbers are connected to the way information spreads on the network, uncovering a relation between the network’s structure and dynamics on said network. The last two parts of the thesis have a common theme and study the same mathematical object. In the first part of the two, we provide a new model for the way riboswtiches organize themselves. Riboswitches, are RNA molecules within a cell, that can take two mutually opposite conformations, depending on what function they need to perform within said cell. They are important from an evolutionary standpoint and are actively studied within that context, usually being modeled as networks. Our model captures the shapes of the two possible conformations, and encodes it within a mathematical object called a topological space. Once this is done, we prove that certain numbers that are attached to all topological spaces carry specific values for riboswitches. Namely, we show that the shapes of the two possible conformations for a riboswich are always characterized by a single integer. In the last part of the Thesis we identify what exactly in the structure of riboswitches contributes to this number being large or small. We prove that the more tangled the two conformations are, the larger the number. We can thus conclude that this number is directly proportional to how complex the riboswitch is.

Il est essentiel pour chaque organisme d’avoir la possibilité de réguler ses fonctions afin de permettre sa survie et d’améliorer sa capacité de se reproduire en divers habitats. Avec l’information disponible, il semble que les organismes consacrent une partie assez importante de leur matériel génétique à des fonctions de régulation. On peut envisager que certains mécanismes de régulation ont persisté dans le temps parce qu’ils remplissent bien leurs rôles. Les premières études sur les procaryotes ont indiqué qu’il y avait peu de mécanismes de régulation exerçant le contrôle des gènes, mais il a été démontré par la suite qu’une variété de ces mécanismes est utilisée pour la régulation de gènes et d’opérons. En particulier, les opérons bactériens impliqués dans la biosynthèse des acides aminés, l’ARNt synthétase, la dégradation des acides aminés, les protéines ribosomales et l’ARN ribosomal font l’objet d’un contrôle par l’atténuation de la transcription. Ce mécanisme d’atténuation de la transcription diffère d’autres mécanismes pour la génération de deux structures différentes de l’ARNm, où l’une de ces structures réprime le gène en aval, et l’autre permet de continuer la transcription/traduction. Dans le cadre de cette recherche, nous nous sommes intéressé au mécanisme d’atténuation de la transcription chez les procaryotes où aucune molécule ne semble intervenir comme facteur de régulation, en me concentrant sur la régulation des opérons bactériens. Le but principal de ce travail est de présenter une nouvelle méthode de recherche des riborégulateurs qui combine la recherche traditionnelle des riborégulateurs avec la recherche structurale. En incorporant l’étude du repliement de l’ARNm, nous pouvons mieux identifier les atténuateurs répondant à ce type de mécanisme d’atténuation. Ce mémoire est divisé en quatre chapitres. Le premier chapitre présente une revue de la littérature sur l’ARN et un survol sur les mécanismes de régulation de l’expression génétique chez les procaryotes. Les chapitres 2 et 3 sont consacrés à la méthodologie utilisée dans cette recherche et à l’implémentation du logiciel TA-Search. Enfin, le chapitre 4 expose les conclusions et les applications potentielles de la méthode. / It is essential for each organism to have the possibility to regulate its functions to allow its survival and improve its capacity to reproduce in different environments. With the information available, it is apparent that most organisms dedicate an important piece of their genetic material to regulating functions. We could think that certain regulating mechanisms have most likely persisted over time because they fulfilled their roles. The first prokaryotes studies indicated that there are few regulating mechanisms that take control over genes, but it has been proven that a variety of these mechanisms are used in the regulation of genes and operons. In particular, the bacterial operons involved in the biosynthesis of amino acids, tRNA synthetase, the degradation of amino acids, the ribosomal proteins and RNA ribosomal could be controlled by transcription attenuation. This mechanism of regulation differs from others for the creation of two different structures of the mRNA where one of these structures represses the gene in 3’ and the other one allows the transcription/translation to continue. In this work, I’m interested in the mechanism of transcription attenuation in prokaryotes where no molecule appears to act as a regulatory factor. In particular, I’m interested in the regulation of bacterial operons. The principal goal of this work is to present a new method for detecting riboswitches that combines the traditional research of these elements with the structural research by incorporating the study of mRNA folding. This thesis is divided into four chapters. Chapter 1 is a review of the literature on RNA and an overview of the regulatory mechanism of gene expression in prokaryotes. Chapter 2 and 3 present the method developed for this work and its implementation in new software, TA-Search. Finally, Chapter 4 is dedicated to providing a discussion and conclusion for this work.

Phosphorylation is an important mechanism for regulation of protein function and aktivity. Tyrosine phosphorylation plays a critical role in signaling pathways. Aberrant tyrosine phosphorylation was observed in many cancer types. My work follows patological details of tyrosine phosphorylation sites of lung and colorectal cancers. Point of view includes aminoacid sequence, secondary structure, domain localization, expression, model organism ortholog occurrence. The project is based on analysis of literary informations and data from protein databases. There are no new phosphorylation sites in observed cancer types. Regular secondary structures, α-helices and β-sheets, are significantly phosphorylated in compare with loops. Annexin and Kinase domains are the most phosphorylated. Gene expression change of phosphorylated proteins occurs in observed cancer cells. Powered by TCPDF (www.tcpdf.org)

Coordination of transition metals to peptides, either through the incorporation of unnatural chelating groups or amino acid ligating side-chains, expands the utility of peptides for biological studies. The first part of this project describes induction of α-helical secondary structure in pentapeptides upon side-chain coordination of inert transition metal ions. The second part of this project describes the syntheses of biologically active peptide species that contain a macrobicyclic hexaamine ligand that can complex radioactive metal ions for diagnostic imaging purposes. / Short peptide sequences do not form thermodynamically stable α-helices in water. The capacity of two metal clips, cis-[Ru(NH3)4(solvent)2]2+ and cis [Pd(en)(solvent)2]2+ to induce α-helicity in peptides that are five amino acids long, Ac HARAH NH2 and Ac MARAM-NH2 has been explored. In all cases at pH < 5, the metal ions bind to the side-chains of amino acid residues at positions i, i+4 of the pentapeptides resulting in formation of bidentate macrocyclic species. Circular dichroism and 1H nuclear magnetic resonance data indicate that the metal complexes of Ac-MARAM-NH2 are highly α helical in water, and in the most spectacular case, coordination of Ac-MARAM-NH2 to cis-[Ru(NH3)4(solvent)2]2+ results in up to 80% α-helicity. In contrast, metal complexes of Ac-HARAH-NH2 exhibit significantly less α-helicity in water. / 64Cu-radiolabelled peptides have been investigated for their ability to target specific tissue or cell types. These peptides require a chelating group that binds copper ions strongly. Macrobicyclic hexaamine ligands, based on the compound commonly referred to as “sarcophagine”, have demonstrated extremely high stability under biological conditions. Here we describe the synthesis of diaminosarcophagine chelators with carboxylate groups for conjugation to peptides. These new chelators have been attached to the N-terminus or lysine side-chain of biologically-active peptides, including Tyr3 octreotate, Lys3-bombesin and an integrin targeting peptide. Spectroscopic and voltammetric studies of these species suggest that the conjugated sarcophagine group retains the high metal binding affinity and structural properties of the parent species, diaminosarcophagine. These are among the first sarcophagine-peptide compounds that have been properly characterised. The new sarcophagine-peptide conjugates can be easily radiolabelled with 64Cu2+ over a wide pH range at ambient temperature.

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

Bioinformatics is a fast-developing field, which makes use of computational methods to analyse and structure biological data. An important branch of bioinformatics is structure and function prediction of proteins, which is often based on finding relationships to already characterized proteins. It is known that two proteins with very similar sequences also share the same 3D structure. However, there are many proteins with similar structures that have no clear sequence similarity, which make it difficult to find these relationships. In this thesis, two methods for annotating protein domains are presented, one aiming at assigning the correct domain family or families to a protein sequence, and the other aiming at fold recognition. Both methods use hidden Markov models (HMMs) to find related proteins, and they both exploit the fact that structure is more conserved than sequence, but in two different ways. Most of the research presented in the thesis focuses on the structure-anchored HMMs, saHMMs. For each domain family, an saHMM is constructed from a multiple structure alignment of carefully selected representative domains, the saHMM-members. These saHMM-members are collected in the so called "midnight ASTRAL set", and are chosen so that all saHMM-members within the same family have mutual sequence identities below a threshold of about 20%. In order to construct the midnight ASTRAL set and the saHMMs, a pipe-line of software tools are developed. The saHMMs are shown to be able to detect the correct family relationships at very high accuracy, and perform better than the standard tool Pfam in assigning the correct domain families to new domain sequences. We also introduce the FI-score, which is used to measure the performance of the saHMMs, in order to select the optimal model for each domain family. The saHMMs are made available for searching through the FISH server, and can be used for assigning family relationships to protein sequences. The other approach presented in the thesis is secondary structure HMMs (ssHMMs). These HMMs are designed to use both the sequence and the predicted secondary structure of a query protein when scoring it against the model. A rigorous benchmark is used, which shows that HMMs made from multiple sequences result in better fold recognition than those based on single sequences. Adding secondary structure information to the HMMs improves the ability of fold recognition further, both when using true and predicted secondary structures for the query sequence. / Bioinformatik är ett område där datavetenskapliga och statistiska metoder används för att analysera och strukturera biologiska data. Ett viktigt område inom bioinformatiken försöker förutsäga vilken tredimensionell struktur och funktion ett protein har, utifrån dess aminosyrasekvens och/eller likheter med andra, redan karaktäriserade, proteiner. Det är känt att två proteiner med likande aminosyrasekvenser också har liknande tredimensionella strukturer. Att två proteiner har liknande strukturer behöver dock inte betyda att deras sekvenser är lika, vilket kan göra det svårt att hitta strukturella likheter utifrån ett proteins aminosyrasekvens. Den här avhandlingen beskriver två metoder för att hitta likheter mellan proteiner, den ena med fokus på att bestämma vilken familj av proteindomäner, med känd 3D-struktur, en given sekvens tillhör, medan den andra försöker förutsäga ett proteins veckning, d.v.s. ge en grov bild av proteinets struktur. Båda metoderna använder s.k. dolda Markov modeller (hidden Markov models, HMMer), en statistisk metod som bland annat kan användas för att beskriva proteinfamiljer. Med hjälp en HMM kan man förutsäga om en viss proteinsekvens tillhör den familj modellen representerar. Båda metoderna använder också strukturinformation för att öka modellernas förmåga att känna igen besläktade sekvenser, men på olika sätt. Det mesta av arbetet i avhandlingen handlar om strukturellt förankrade HMMer (structure-anchored HMMs, saHMMer). För att bygga saHMMerna används strukturbaserade sekvensöverlagringar, vilka genereras utifrån hur proteindomänerna kan läggas på varandra i rymden, snarare än utifrån vilka aminosyror som ingår i deras sekvenser. I varje proteinfamilj används bara ett särskilt, representativt urval av domäner. Dessa är valda så att då sekvenserna jämförs parvis, finns det inget par inom familjen med högre sekvensidentitet än ca 20%. Detta urval görs för att få så stor spridning som möjligt på sekvenserna inom familjen. En programvaruserie har utvecklats för att välja ut representanter för varje familj och sedan bygga saHMMer baserade på dessa. Det visar sig att saHMMerna kan hitta rätt familj till en hög andel av de testade sekvenserna, med nästan inga fel. De är också bättre än den ofta använda metoden Pfam på att hitta rätt familj till helt nya proteinsekvenser. saHMMerna finns tillgängliga genom FISH-servern, vilken alla kan använda via Internet för att hitta vilken familj ett intressant protein kan tillhöra. Den andra metoden som presenteras i avhandlingen är sekundärstruktur-HMMer, ssHMMer, vilka är byggda från vanliga multipla sekvensöverlagringar, men också från information om vilka sekundärstrukturer proteinsekvenserna i familjen har. När en proteinsekvens jämförs med ssHMMen används en förutsägelse om sekundärstrukturen, och den beräknade sannolikheten att sekvensen tillhör familjen kommer att baseras både på sekvensen av aminosyror och på sekundärstrukturen. Vid en jämförelse visar det sig att HMMer baserade på flera sekvenser är bättre än sådana baserade på endast en sekvens, när det gäller att hitta rätt veckning för en proteinsekvens. HMMerna blir ännu bättre om man också tar hänsyn till sekundärstrukturen, både då den riktiga sekundärstrukturen används och då man använder en teoretiskt förutsagd. / Jeanette Hargbo.

The genomes of diverse organisms are predicted to contain 20 – 30% membrane protein encoding genes and more than half of all therapeutics target membrane proteins. However, only 2% of crystal structures deposited in the protein data bank represent integral membrane proteins. This reflects the difficulties in studying them using standard biochemical and crystallographic methods. The first problem frequently encountered when investigating membrane proteins is their low natural abundance, which is insufficient for biochemical and structural studies. The aim of my thesis was to provide a simple method to improve the production of recombinant proteins. One of the most commonly used methods to increase protein yields is codon optimization of the entire coding sequence. However, our data show that subtle synonymous codon substitutions in the 5’ region can be more efficient. This is consistent with the view that protein yields under normal conditions are more dependent on translation initiation than elongation. mRNA secondary structures around the 5’ region are in large part responsible for this effect although rare codons, as well as other factors, also contribute. We developed a PCR based method to optimize the 5’ region for increased protein production in Escherichia coli. For those proteins produced in sufficient quantities several additional hurdles remain before high quality crystals can be obtained. A second aim of my thesis work was to provide a simple method for topology mapping membrane proteins. A topology map provides information about the orientation of transmembrane regions and the location of protein domains in relation to the membrane, which can give information on structure-function relationships. To this end we explored the split-GFP system in which GFP is split between the 10th and 11th β-strands. This results in one large and one small fragment, both of which are non-fluorescent but can re-anneal and regain fluorescence if localized to the same cellular compartment. Fusing the 11th β-strand to the termini of a protein of interest and expressing it, followed by expression of the detector fragment in the cytosol, allows determination of the topology of inner membrane proteins. Using this strategy the topology of three model proteins was correctly determined. We believe that this system could be used to predict the topology of a large number of additional proteins, especially single-spanning inner membrane proteins in E. coli. The methods for efficient protein production and topology mapping engineered during my thesis work are simple and cost-efficient and may be very valuable in future studies of membrane proteins. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.</p>

Complex organization is found in many biological systems. For example, biopolymers could possess very hierarchic structure, which provides their functional peculiarity. Understating such, complex organization allows describing biological phenomena and predicting molecule functions. Besides, we can try to characterize the specific phenomenon by some probabilistic quantities (variances, means, etc), assuming the primary biopolymer structure to be randomly formed according to some statistical distribution. Such a formulation is oriented toward evolutionary problems.Artificially constructed biological network is another common object of statistical physics with rich functional properties. A behavior of cells is a consequence of complex interactions between its numerous components, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond and to adapt to changing environment. Recent theoretical advances allow us to describe cellular network structure using graph concepts to reveal the principal organizational features shared with numerous non-biological networks.The aim of this thesis is to develop bunch of methods for studying statistical and dynamic objects of complex architecture and, in particular, scale-free structures, which have no characteristic spatial and/or time scale. For such systems, the use of standard mathematical methods, relying on the average behavior of the whole system, is often incorrect or useless, while a detailed many-body description is almost hopeless because of the combinatorial complexity of the problem. Here we focus on two problems.The first part addresses to statistical analysis of random biopolymers. Apart from the evolutionary context, our studies cover more general problems of planar topology appeared in description of various systems, ranging from gauge theory to biophysics. We investigate analytically and numerically a phase transition of a generic planar matching problem, from the regime, where almost all the vertices are paired, to the situation, where a finite fraction of them remains unmatched.The second part of this work focus on statistical properties of networks. We demonstrate the possibility to define co-expression gene clusters within a network context from their specific motif distribution signatures. We also show how a method based on the shortest path function (SPF) can be applied to gene interactions sub-networks of co-expression gene clusters, to efficiently predict novel regulatory transcription factors (TFs). The biological significance of this method by applying it on groups of genes with a shared regulatory locus, found by genetic genomics, is presented. Finally, we discuss formation of stable patters of motifs in networks under selective evolution in context of creation of islands of "superfamilies".

<p>In summary there currently exist techniques to discover miRNA however both require many calculations to be performed during the identification limiting their use at a genomic level. Machine learning techniques are currently providing the best results by combining a number of calculated and statistically derived features to identify miRNA candidates, however almost all of these still include computationally intensive secondary-structure calculations. It is the aim of this project to produce a miRNA identification process that minimises and simplifies the number of computational elements required during the identification process.</p>

Il est essentiel pour chaque organisme d’avoir la possibilité de réguler ses fonctions afin de permettre sa survie et d’améliorer sa capacité de se reproduire en divers habitats. Avec l’information disponible, il semble que les organismes consacrent une partie assez importante de leur matériel génétique à des fonctions de régulation. On peut envisager que certains mécanismes de régulation ont persisté dans le temps parce qu’ils remplissent bien leurs rôles. Les premières études sur les procaryotes ont indiqué qu’il y avait peu de mécanismes de régulation exerçant le contrôle des gènes, mais il a été démontré par la suite qu’une variété de ces mécanismes est utilisée pour la régulation de gènes et d’opérons. En particulier, les opérons bactériens impliqués dans la biosynthèse des acides aminés, l’ARNt synthétase, la dégradation des acides aminés, les protéines ribosomales et l’ARN ribosomal font l’objet d’un contrôle par l’atténuation de la transcription. Ce mécanisme d’atténuation de la transcription diffère d’autres mécanismes pour la génération de deux structures différentes de l’ARNm, où l’une de ces structures réprime le gène en aval, et l’autre permet de continuer la transcription/traduction. Dans le cadre de cette recherche, nous nous sommes intéressé au mécanisme d’atténuation de la transcription chez les procaryotes où aucune molécule ne semble intervenir comme facteur de régulation, en me concentrant sur la régulation des opérons bactériens. Le but principal de ce travail est de présenter une nouvelle méthode de recherche des riborégulateurs qui combine la recherche traditionnelle des riborégulateurs avec la recherche structurale. En incorporant l’étude du repliement de l’ARNm, nous pouvons mieux identifier les atténuateurs répondant à ce type de mécanisme d’atténuation. Ce mémoire est divisé en quatre chapitres. Le premier chapitre présente une revue de la littérature sur l’ARN et un survol sur les mécanismes de régulation de l’expression génétique chez les procaryotes. Les chapitres 2 et 3 sont consacrés à la méthodologie utilisée dans cette recherche et à l’implémentation du logiciel TA-Search. Enfin, le chapitre 4 expose les conclusions et les applications potentielles de la méthode. / It is essential for each organism to have the possibility to regulate its functions to allow its survival and improve its capacity to reproduce in different environments. With the information available, it is apparent that most organisms dedicate an important piece of their genetic material to regulating functions. We could think that certain regulating mechanisms have most likely persisted over time because they fulfilled their roles. The first prokaryotes studies indicated that there are few regulating mechanisms that take control over genes, but it has been proven that a variety of these mechanisms are used in the regulation of genes and operons. In particular, the bacterial operons involved in the biosynthesis of amino acids, tRNA synthetase, the degradation of amino acids, the ribosomal proteins and RNA ribosomal could be controlled by transcription attenuation. This mechanism of regulation differs from others for the creation of two different structures of the mRNA where one of these structures represses the gene in 3’ and the other one allows the transcription/translation to continue. In this work, I’m interested in the mechanism of transcription attenuation in prokaryotes where no molecule appears to act as a regulatory factor. In particular, I’m interested in the regulation of bacterial operons. The principal goal of this work is to present a new method for detecting riboswitches that combines the traditional research of these elements with the structural research by incorporating the study of mRNA folding. This thesis is divided into four chapters. Chapter 1 is a review of the literature on RNA and an overview of the regulatory mechanism of gene expression in prokaryotes. Chapter 2 and 3 present the method developed for this work and its implementation in new software, TA-Search. Finally, Chapter 4 is dedicated to providing a discussion and conclusion for this work.