| 1. |
- Eriksson, Olivia, 1971-
(author)
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Simplicity within Complexity : Understanding dynamics of cellular networks by model reduction
- 2008
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Doctoral thesis (other academic/artistic)abstract
- Cellular networks composed of interactions between genes, proteins and metabolites, determines the behavioural repertoire of the cell. Recent developments in high-throughput experimental techniques and computational methods allow static descriptions of these networks on a genome scale. There are also several dynamical mathematical models characterizing small subnetworks of the cell such as a signaling cascade or cell division. These networks exhibit a considerable complexity, and mathematical analysis are therefore essential in order to uncover the underlying dynamical core driving the systems. A core description can reveal the relative functional contributions of the various molecular interactions and goes to the heart of what kind of computations biological circuits perform. Partially successful methodologies toward this end includes bifurcation analysis, which only considers a small number of dimensions, and large-scale computer simulations. In this thesis we explore a third route utilizing the inherent biological structure and dynamics of the network as a tool for model simplification. Using the well studied cell cycle, as a model system, we observe that the this network can be divided into dynamical modules displaying a switch-like behaviour. This allows a transformation into a piecewise linear system with delay, the subsequent use of tools from linear systems theory and finally a core dynamical description. Analytical expressions capturing important cell cycle features such as cell mass, as well as necessary constraints for cell cycle oscillations, are thereby retrieved. Finally we use the dynamical core together with large-scale simulations in order to study the balance between robustness and sensitivity. It appears that biological features such as switches, modularity and robustness provide a means to reformulate intractable mathematical problems into solvable ones, as biology appears to suggest a path of simplicity within the realm of mathematical complexity.
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| 2. |
- Granseth, Erik, 1978-
(author)
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Structure, prediction, evolution and genome wide studies of membrane proteins
- 2007
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Doctoral thesis (other academic/artistic)abstract
- α-helical membrane proteins constitute 20-30% of all proteins in a cell and are involved in many essential cellular functions. The structure is only known for a few hundred of them, which makes structural models important. The most common structural model of a membrane protein is the topology which is a two-dimensional representation of the structure. This thesis is focused on three different aspects of membrane protein structure: improving structural predictions of membrane proteins, improving the level of detail of structural models and the concept of dual topology. It is possible to improve topology models of membrane proteins by including experimental information in computer predictions. This was first performed in Escherichia coli and, by using homology, it was possible to extend the results to 225 prokaryotic organisms. The improved models covered ~80% of the membrane proteins in E. coli and ~30% of other prokaryotic organisms. However, the traditional topology concept is sometimes too simple for complex membrane protein structures, which create a need for more detailed structural models. We created two new machine learning methods, one that predicts more structural features of membrane proteins and one that predicts the distance to the membrane centre for the amino acids. These methods improve the level of detail of the structural models. The final topic of this thesis is dual topology and membrane protein evolution. We have studied a class of membrane proteins that are suggested to insert either way into the membrane, i.e. have a dual topology. These protein families might explain the frequent occurrence of internal symmetry in membrane protein structures.
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| 3. |
- Ekman, Diana, 1977-
(author)
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Domain rearrangement and creation in protein evolution
- 2008
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Doctoral thesis (other academic/artistic)abstract
- Proteins are composed of domains, recurrent protein fragments with distinct structure, function and evolutionary history. Some domains exist only as single domain proteins, however, a majority of them are also combined with other domains. Domain rearrangements are important in the evolution of new proteins as new functionalities can arise in a single evolutionary event. In addition, the domain repertoire can be expanded through mutations of existing domains and de novo creation. The processes of domain rearrangement and creation have been the focus of this thesis.According to our estimates about 65% of the eukaryotic and 40% of the prokaryotic proteins are of multidomain type. We found that insertion of a single domain at the N- or C-terminus was the most common event in the creation of novel multidomain architectures. However, domain repeats deviate from this pattern and are often expanded through duplications of several domains. Next, by mapping domain combinations onto an evolutionary tree we estimated that roughly one domain architecture has been created per million years, with the highest rates in metazoa. Much of this so called explosion of new architectures in metazoa seems to be explained by a set of domains amenable to exon shuffling. In contrast to domain architectures, most known domain families evolved early. However, many proteins have incomplete domain coverage, and could hence contain de novo created domains. In Saccharomyces cerevisiae, however, species specific sequences constitute only a minor fraction of the proteome, and are often short, disordered sequences located at the protein termini.
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| 4. |
- Light, Sara, 1975-
(author)
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Investigations into the evolution of biological networks
- 2006
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Doctoral thesis (other academic/artistic)abstract
- Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory.The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks.This thesis describes three investigations into the evolution of metabolic and protein-protein interaction networks. We present a comparative study of the importance of retrograde evolution, the scenario that pathways assemble backward compared to the direction of the pathway, and patchwork evolution, where enzymes evolve from a broad to narrow substrate specificity. Shifting focus toward network topology, a suggested mechanism for the evolution of biological networks, preferential attachment, is investigated in the context of metabolism. Early in the investigation of biological networks it seemed clear that the networks often display a particular, 'scale-free', topology. This topology is characterized by many nodes with few interaction partners and a few nodes (hubs) with a large number of interaction partners. While the second paper describes the evidence for preferential attachment in metabolic networks, the final paper describes the characteristics of the hubs in the physical interaction network of S. cerevisiae.
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| 5. |
- Ohlson, Tomas, 1977-
(author)
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The use of evolutionary information in protein alignments and homology identification
- 2006
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Doctoral thesis (other academic/artistic)abstract
- For the vast majority of proteins no experimental information about the three-dimensional structure is known, but only its sequence. Therefore, the easiest way to obtain some understanding of the structure and function of these proteins is by relating them to well studied proteins. This can be done by searching for homologous proteins. It is easy to identify a homologous sequence if the sequence identity is above 30%. However, if the sequence identity drops below 30% then more sophisticated methods have to be used. These methods often use evolutionary information about the sequences, which makes it possible to identify homologous sequences with a low sequence identity.In order to build a three--dimensional model from the sequence based on a protein structure the two sequences have to be aligned. Here the aligned residues serve as a first approximation of the structure.This thesis focuses on the development of fold recognition and alignment methods based on evolutionary information. The use of evolutionary information for both query and target proteins was shown to improve both recognition and alignments. In a benchmark of profile--profile methods it was shown that the probabilistic methods were best, although the difference between several of the methods was quite small once optimal gap-penalties were used. An artificial neural network based alignment method ProfNet was shown to be at least as good as the best profile--profile method, and by adding information from a self-organising map and predicted secondary structure we were able to further improve ProfNet.
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| 6. |
- Viklund, Håkan, 1974-
(author)
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Formalizing life : Towards an improved understanding of the sequence-structure relationship in alpha-helical transmembrane proteins
- 2007
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Doctoral thesis (other academic/artistic)abstract
- Genes coding for alpha-helical transmembrane proteins constitute roughly 25% of the total number of genes in a typical organism. As these proteins are vital parts of many biological processes, an improved understanding of them is important for achieving a better understanding of the mechanisms that constitute life.All proteins consist of an amino acid sequence that fold into a three-dimensional structure in order to perform its biological function. The work presented in this thesis is directed towards improving the understanding of the relationship between sequence and structure for alpha-helical transmembrane proteins. Specifically, five original methods for predicting the topology of alpha-helical transmembrane proteins have been developed: PRO-TMHMM, PRODIV-TMHMM, OCTOPUS, Toppred III and SCAMPI. A general conclusion from these studies is that approaches that use multiple sequence information achive the best prediction accuracy. Further, the properties of reentrant regions have been studied, both with respect to sequence and structure. One result of this study is an improved definition of the topological grammar of transmembrane proteins, which is used in OCTOPUS and shown to further improve topology prediction. Finally, Z-coordinates, an alternative system for representation of topological information for transmembrane proteins that is based on distance to the membrane center has been introduced, and a method for predicting Z-coordinates from amino acid sequence, Z-PRED, has been developed.
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