Thèses sur le sujet « Computational Molecular Biology »

Numerous enzymatic reactions are controlled by the chemistry of metallic ions. This dissertation investigates the electronic properties of three transition metal (copper, chromium, and nickel) complexes and describes modeling studies performed on glutathione synthetase. (1) Copper nitrene complexes were computationally characterized, as these complexes have yet to be experimentally isolated. (2) Multireference calculations were carried out on a symmetric C2v chromium dimer derived from the crystal structure of the [(tBu3SiO)Cr(µ-OSitBu3)]2 complex. (3) The T-shaped geometry of a three-coordinate β-diketiminate nickel(I) complex with a CO ligand was compared and contrasted with isoelectronic and isosteric copper(II) complexes. (4) Glutathione synthetase (GS), an enzyme that belongs to the ATP-grasp superfamily, catalyzes the (Mg, ATP)-dependent biosynthesis of glutathione (GSH) from γ-glutamylcysteine and glycine. The free and reactant forms of human GS (wild-type and glycine mutants) were modeled computationally by employing molecular dynamics simulations, as these currently have not been structurally characterized.

L'objectiu del treball presentat en aquesta tesi va ser el desenvolupament i l'aplicació de metodologies computacionals que integren l’anàlisis de informació sobre seqüències proteiques, informació funcional i genòmica per a la reconstrucció, anotació i organització de proteomes complets, de manera que els resultats es poden comparar entre qualsevol nombre d'organismes amb genomes completament seqüenciats. Metodològicament, m'he centrat en la identificació de l'organització molecular dins d'un proteoma complet d'un organisme de referència i comparació amb proteomes d'altres organismes, en espacial, estructural i funcional, el teixit cel • lular de desenvolupament, o els nivells de la fisiologia. La metodologia es va aplicar per abordar la qüestió de la identificació de organismes model adequats per a estudiar diferents fenòmens biològics. Això es va fer mitjançant la comparació d’un conjunt de proteines involucrades en diferents fenòmens biològics en Saccharomyces cerevisiae i Homo sapiens amb els conjunts corresponents d'altres organismes amb genomes. La tesi conclou amb la presentació d'un servidor web, Homol-MetReS, en què s'implementa la metodologia. Homol-MetReS proporciona un entorn de codi obert a la comunitat científica en què es poden realitzar múltiples nivells de comparació i anàlisi de proteomes.
El objetivo del trabajo presentado en esta tesis fue el desarrollo y la aplicación de metodologías computacionales que integran el análisis de la secuencia y de la información funcional y genómica, con el objetivo de reconstruir, anotar y organizar proteomas completos, de tal manera que estos proteomas se puedan comparar entre cualquier número de organismos con genomas completamente secuenciados. Metodológicamente, I centrado en la identificación de organización molecular dentro de un proteoma completo de un organismo de referencia, vinculando cada proteína en que proteoma a las proteínas de otros organismos, de tal manera que cualquiera puede comparar los dos proteomas en espacial, estructural, funcional tejido, celular, el desarrollo o los niveles de la fisiología. La metodología se aplicó para abordar la cuestión de la identificación de organismos modelo adecuados para estudiar diferentes fenómenos biológicos. Esto se hizo comparando conjuntos de proteínas involucradas en diferentes fenómenos biológicos en Saccharomyces cerevisiae y Homo sapiens con los conjuntos correspondientes de otros organismos con genomas completamente secuenciados. La tesis concluye con la presentación de un servidor web, Homol-MetReS, en el que se implementa la metodología. Homol-MetReS proporciona un entorno de código abierto a la comunidad científica en la que se pueden realizar múltiples niveles de comparación y análisis de proteomas.
The aim of the work presented in this thesis was the development and application of computational methodologies that integrate sequence, functional, and genomic information to provide tools for the reconstruction, annotation and organization of complete proteomes in such a way that the results can be compared between any number of organisms with fully sequenced genomes. Methodologically, I focused on identifying molecular organization within a complete proteome of a reference organism and comparing with proteomes of other organisms at spatial, structural, functional, cellular tissue, development or physiology levels. The methodology was applied to address the issue of identifying appropriate model organisms to study different biological phenomena. This was done by comparing the protein sets involved in different biological phenomena in Saccharomyces cerevisiae and Homo sapiens. This thesis concludes by presenting a web server, Homol-MetReS, on which the methodology is implemented. It provides an open source environment to the scientific community on which they can perform multi-level comparison and analysis of proteomes.

Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2008.
Title from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Microbiology and Immunology Virology." Discipline: Microbiology and Immunology; Department/School: Medicine.

Because of its integration power, quantifying power, explanatory power and predictive power, mathematical and computational modeling is becoming an important tool to test and advance our understanding about cellular process in the post-genomic era. Iterative approach between modeling, making prediction and experimental testing might increase the rate of forming and testing hypotheses in Biology. mRNA decay is an ideal system to start knowledge based modeling. In the second chapter, I applied the computational modeling approach to test our understanding about normal mRNA turnover processes in yeast. The computational modeling reproduces experimental observations for the unstable MFA2 and stable PGK1 transcripts, suggesting we have a relatively robust understanding for the mRNA decay process in yeast. Subsequent analysis and a series of in silico experiments led to several important insights about this process, which are presented in the second chapter. In the last chapter, I extended this kind of computational analysis to nonsense mediated mRNA decay (NMD), which is a surveillance system all eukaryotic cells have to recognize and degrade mRNAs containing premature translation termination codons. Initial in silico analysis suggests the popular leaky surveillance model about NMD is inconsistent with previous observations. Further experimental analysis using PGK1 mRNA with a nonsense codon in four different positions revealed several new properties of NMD. First, regardless of the position of the nonsense codon, the entire observable population of transcripts is recognized as aberrant, which is different from the leaky surveillance model. Second, the rate of decapping is accelerated in a position dependent manner, although at all positions the dependence of decapping on deadenylation is removed. This provides a mechanistic explanation for the polarity in NMD wherein 5' nonsense codons exert larger effects than 3' nonsense codons. Third, NMD leads to enhanced deadenylation independent of the position of the nonsense codon. This multitude of changes in the metabolism of nonsense containing mRNAs suggests that these transcripts contain multiple alterations in mRNP structure and/or transcript localization. Based on these observations, I constructed a robust computational model that accurately describes the process of NMD and can serve as a predictive model for future work.

In this thesis I have studied the RNA molecule and its function and characteristics in the regulation of gene expression. I have focused on two events that are important for the regulation of the transcriptome: Translational regulation through micro RNAs; and RNA editing through adenosine deaminations.

Micro RNAs (miRNAs) are ~22 nucleotides long RNA molecules that by semi complementarity bind to untranslated regions of a target messenger RNA (mRNA). The interaction manifests through an RNA/protein complex and act mainly by repressing translation of the target mRNA. I have shown that a pre-cursor miRNA molecule have significantly different information content of sequential composition of the two arms of the pre-cursor hairpin. I have also shown that sequential composition differs between species.

Selective adenosine to inosine (A-to-I) RNA editing is a post-transcriptional process whereby highly specific adenosines in a (pre-)messenger transcript are deaminated to inosines. The deamination is carried out by the ADAR family of proteins and require a specific sequential and structural landscape for target recognition. Only a handful of messenger substrates have been found to be site selectively edited in mammals. Still, most of these editing events have an impact on neurotransmission in the brain.

In order to find novel substrates for A-to-I editing, an experimental setup was made to extract RNA targets of the ADAR2 enzyme. In concert with this experimental approach, I have constructed a computational screen to predict specific positions prone to A-to-I editing.

Further, I have analyzed editing in the mouse brain at four different developmental stages by 454 amplicon sequencing. With high resolution, I present data supporting a general developmental regulation of A-to-I editing. I also present data of coupled editing events on single RNA transcripts suggesting an A-to-I editing mechanism that involve ADAR dimers to act in concert. A different editing pattern is seen for the serotonin receptor 5-ht2c.

Theoretical molecular biologists try to understand the workings of cells through mathematics. Some theoreticians use systems of ordinary differential equations (ODEs) as the basis for mathematical modelling of molecular networks. This thesis develops algorithms for estimating molecular reaction rate constants within those mathematical models by fitting the models to experimental data. An additional step is taken to fit non-timecourse experimental data (e.g., transformations must be performed on the ODE solutions before the experimental and simulation data are similar, and therefore, comparable). VTDIRECT is used to perform (a deterministic direct search) global estimation and ODRPACK is used to perform (a trust region Levenberg-Marquardt based) local estimation of rate constants. One such transformation performed on the ODE solutions determines the value of the steady state of the ODE solutions. A new algorithm was developed that finds all steady state solutions of the ODE system given that the system has a special structure (e.g., the right hand sides of the ODEs are rational functions). Also, since the rate constants in the models cannot be negative and may have other restrictions on the values, ODRPACK was modified to address this problem of bound constraints. The new Fortran 95 version of ODRPACK is named ODRPACK95.
Ph. D.

Members of the ANL superfamily of enzymes are involved in primary and secondary metabolism throughout all domains of life and identify key pathways that contribute to essential physiological reactions as well as defense mechanisms to evade competition. Specifically, acetyl-CoA synthetases are directly involved in energy metabolism, while NonRibosoaml Peptide Synthetases and some Aryl-CoA Ligases produce secondary natural products that confer virulence for the producing organism. Due to the ANL superfamily's ubiquitous involvement in primary and secondary metabolism, gaining an understanding of how these enzymes work and identifying ways to regulate them could provide an alternative route for antibiotic targets. It is well documented that domain alternation is paramount for the ANL superfamily of enzymes including the adenylation and thioester-forming reactions of NRPS adenylation domains. This thesis utilizes structural and functional analysis in conjunction with computational methods to further our understanding of these unique enzymes.

In chapter 2 we present the structure of an adenylation:Peptidyl Carrier Protein di-omain NRPS from the cryptic PA1221 biosynthetic operon from Pseudomonas aeruginosa. The PA1221 structure is the second example of an adenylation:PCP in the PDB and validates the chimeric fusion interactions of EntE-B. The similar interacting regions are between the 2^nd PCP helix and a helix in the N-terminal subdomain of the adenylation domain as well as the loop connecting the longest β-strands of the C-terminal subdomains interacting with loop 1 of the PCP.

Chapter 3 presents the structure of an acetoacetatyl-CoA Synthetase that is a confirmed substrate for a protein acetyltransferase, PatA, for inactivation through acetylation of the catalytic A10 lysine. This Streptomyces lividans acetoacetyl-CoA synthetase is the first structure to fully resolve the loop connecting C-terminal extension helix to the C-terminal subdomain. The C-terminal extension is only present in ACS proteins revealing an interaction where the C-terminal extension stabilizes the dynamic P-loop in the adenylate forming conformation.

In chapter 4 we further explore the PA1221 operon by functionally identifying the substrate preference of PA1215, the hypothetical fatty-acyl-CoA Ligase, that is proposed to acylate the charge PCP of PA1221. We computationally validate the substrate preference with a homology model and AutoDock to gain insight into the proteins slow kinetics. We also provide further insight into the biochemistry of a subset of ANL superfamily members, the phenylacetic acid CoA ligases, involved in the utilization of aryl-carboxylic acids as a carbon source as well as the derivatization of penicillin. We analyze their unique dimeric structures identifying structural motifs that are contributed through the dimeric interface, but are otherwise located to different sides of the enzyme in a monomeric form.

Finally, to help identify how the protein moves between the two productive conformations we subject members of the superfamily to computational dynamic simulations including Anisotropic Network Modeling, Interpolative Elastic Network Modeling, all-atom molecular dynamics, and analyze the output from these methods with Principal Component and Normal Mode Analysis. We developed a method to visualize a dynamic reaction coordinate through measuring the Conformation Determining Angle (defined by structural motifs that are present in superfamily members) and use this metric to interrogate all ANL superfamily member PDB entries for domain organization. Finally, we test our hypothesis that domain alternation proceeds through an extended, open conformation with structural comparisons and MD. Here we report functional and structural analysis of ANL superfamily members that are related through bacterial cell metabolism and natural product biosynthesis.

The Glucagon-like peptide 1 receptor (GLP-1R) belongs to the pharmaceutically important Class B family of G-protein coupled receptors (GPCRs) and its incretin peptide ligand GLP-1 analogs are adopted drugs for the treatment of type 2 diabetes (T2D). Despite remarkable anti-diabetic effects, Glucagon Like Peptide-1 (GLP-1) peptide-based drugs are limited by the need of injection or high cost oral formulation. On the other hand, developing non-peptide small molecule drugs targeting GLP-1R remains elusive likely due to the large nature of the orthosteric binding site on GLP-1R. A promising approach is to develop small molecule agonistic positive allosteric modulators (ago-PAMs) or positive allosteric modulators (PAMs) of GLP-1R by targeting the potential allosteric sites in the transmembrane (TM) domain of human GLP-1R.

As the first step of taking this approach, we constructed a three-dimensional structure model of the TM domain of human GLP-1R using homology modeling and conformational sampling techniques. Next, a potential allosteric binding site on the TM domain was predicted computationally. In silico screening of drug-like compounds against this predicted allosteric site has identified nine compounds as potential GLP-1R agonists. The independent agonistic activity of two compounds was subsequently confirmed using cyclic adenosine monophosphate (cAMP) response element (CRE)-based luciferase reporting system. One compound was also shown to stimulate insulin secretion through in vitro assay. In addition, this compound synergized with GLP-1 to activate human GLP-1R.

In 2017, the crystal structures of GLP-1R in its active state (PDB ID: 5VAI) became available. Hence, we have performed another round of in silico screening employing this structure. First, the potential ligand binding sites in 5VAI were identified using computational tools and in silico screening procedure as described above was carried out again. A new small 8 molecule with low molecular weight and logP was identified. In vitro studies of this compound confirmed that it acts as the ago-Positive Allosteric Modulator (PAM) of GLP-1R that improves GLP-1's affinity and efficacy towards GLP-1R. When used in combination with GLP-1, this compound improves insulin secretion than using GLP-1 alone. Site specific mutagenesis studies confirmed its binding site as predicted in the TM domain of GLP-1R.

Finally, this ago-PAM molecule was further optimized to improve its potency and specificity towards GLP-1R using structure-based optimization strategy and medicinal synthesis. The newly designed compound, whose molecular weight was less than the parental compound, was found to act as the PAM of GLP-1R and showed improvement in the specificity than the parental compound. Thus, this new compound could be further exploited in the drug development for T2D treatment.

These results demonstrated that allosteric regulation exists in GLP-1R and can be exploited for developing small molecule agonists. The success of this work will help pave the way for small molecule drug discovery targeting other Class B GPCRs through allosteric regulations.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references.
An ability to control the degree of heterogeneity in cellular phenotypes may be important for cell populations to survive uncertain and ever-changing environments or make cell-fate decisions in response to external stimuli. Cells may control the degree of gene expression heterogeneity and ultimately levels of phenotypic heterogeneity by modulating promoter switching dynamics. In this thesis, I investigated various mechanisms by which heterogeneity in the expression of FLO 11 in S. cerevisiae could be generated and controlled. First, we show that two copies of the FLOJ1 locus in S. cerevisiae switch between a silenced and competent promoter state in a random and independent fashion, implying that the molecular event leading to the transition occurs in cis. Through further quantification of the effect of trans regulators on both the slow epigenetic transitions between a silenced and competent promoter state and the fast promoter transitions associated with conventional regulation of FLO11, we found different classes of regulators affect epigenetic, conventional, or both forms of regulation. Distributing kinetic control of epigenetic silencing and conventional gene activation offers cells flexibility in shaping the distribution of gene expression and phenotype within a population. Next, we demonstrate how multiple molecular events occurring at a gene's promoter could lead to an overall slow step in cis. At the FLO] 1 promoter, we show that at least two pathways that recruit histone deacetylases to the promoter and in vivo association between the region -1.2 kb from the ATG start site of the FLO11 ORF and the core promoter region are all required for a stable silenced state. To generate bimodal gene expression, the activator Msnlp forms an alternate looped conformation, where the core promoter associates with the non-coding RNA PWR1's promoter and terminator regions, located at -2.1 kb and -3.0 kb from the ATG start site of the FLO]1 ORF respectively. Formation of the active looped conformation is required for Msnlp's ability to stabilize the competent state without destabilizing the silenced state and generate a bimodal response. Our results support a model where multiple stochastic steps at the promoter are required to transition between the silenced and active states, leading to an overall slow step in cis. Finally, preliminary investigations of heterozygous diploids revealed possible transvection occurring at FLO] 1, where a silenced allele of FLO 11 appeared to transfer silencing factors to a desilenced FLO11 allele on the homologous chromosome. These observations suggest a new mechanism through which heterogeneity in FL011 expression could be further controlled, in addition to the molecular events at the FL011 promoter we elucidated previously.
by Leah M. Octavio.
Ph.D.

Thesis (Ph. D.)--University of New Mexico, 2001.
"July, 2001." Includes bibliographical references (leaves 112-121). Color figures, full content, and supplementary materials are available online via www.santafe.edu/p̃th/dss.

Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references.
The complex etiology of neurodegenerative diseases is not fully understood, and the characterization of cellular pathways that are dysfunctional in these diseases is key for therapeutic development. Chemical and genetic perturbagens can probe cellular pathways to shed insight about both disease etiology and potential therapeutic targets. We analyzed the functional effects of chemical perturbagens in neurodegenerative disease models as evidenced by changes in transcriptomic, metabolomic, epigenomic, and proteomic data ("multi-omics" data). Our studies revealed novel modes of action for small molecule compounds that promote survival in a model of Huntington's Disease, a fatal neurodegenerative disorder. Integration of our multi-omics data using an interpretable network approach revealed that the autophagy and bioenergetics cellular pathways are affected by different sets of compounds that promote survival. Using staining and western blot assays, we validated the effect on autophagy for one set of compounds and found that the compounds activate this pathway. Using a cellular bioenergetics assay, we found that a second set of compounds shifts the bioenergetic flux from mitochondrial respiration to glycolysis, validating our network results. In a second study related to Huntington's Disease, we analyzed the effects of two peripheral huntingtin gene silencing techniques in mouse liver. We show that the transcriptional and metabolomic changes associated with both genetic silencing methods converge on similar cellular pathways, such as the immune response and fatty acid metabolism. As a whole, this thesis presents new insights into the functional effects of perturbagens that could impact neurodegenerative disease pathology and drug discovery.
by Natasha L. Patel-Murray.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program

Molecular interactions between cellular components such as proteins and nucleic acids govern the fundamental processes of living systems. Technological advancements in the past decade have allowed the characterization of these molecular interactions at the single-molecule level with high temporal and spatial resolution. Simultaneously, progress in computer simulation has enabled theoretical research at the atomistic level, assisting in the interpretation of experimental results. This thesis combines single-molecule force spectroscopy and simulation to explore inter- and intra-molecular interactions. Specifically, we investigate the interaction between RecA and DNA to elucidate the underlying molecular mechanism of the DNA homologous recombination process. We also evaluate the stability of the von Willebrand Factor (vWF) A2 domain to determine the molecular origins of von Willebrand Diseases (vWD). This thesis also describes the development and application of a new single-molecule technique that combines the centrifuge force microscope (CFM) with DNA self-assembled mechanical switches to enable massively parallel repeating force measurements of molecular interactions.
Engineering and Applied Sciences - Applied Physics

Orientador: João Meidanis
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-08T13:34:53Z (GMT). No. of bitstreams: 1 Almeida_AndreAtanasioMaranhao_M.pdf: 3188069 bytes, checksum: b0743bc208c47e2d263f7d5503c22c07 (MD5) Previous issue date: 2007
Resumo: Nas últimas décadas presenciamos grandes avanços na biologia molecular que levaram ao acúmulo de um grande volume de dados acerca de moléculas, tais como DNAs e proteínas, essenciais para a vida e para seu entendimento.O estágio atual é de busca por ferramentas que permitam extrair informações com relevância biológica destes dados. Neste contexto, a comparação de genomas surge como uma das ferramentas e nesta categoria incluímos rearranjo de genomas. Em rearranjo, o genoma é representado por uma seqüência de blocos conservados e, dados dois genomas e um conjunto de operações, busca-se pela que transformem um genoma no outro. Em 1995, Hannenhallie Pevzner apresentaram o primeiro algoritmo polinomial para o problema da ordenação por reversões orientadas. Tal algoritmo executa em tempo O(n4) e foi o primeiro algoritmo polinomial para um modelo realístico de rearranjo de genomas. Desde então, surgiram algoritmos que apresentam desempenho assintoticamente melhor. O melhor deles, apresentado por Tannier e Sagot em 2004, é capaz de executar em tempo O (n(n log n)1/2). Há um algoritmo linear, desenvolvido por Bader e colegas[2], mas este capaz de determinar a seqüência de reversões, apenas calcula a distância. Motivado pela carência de uma derivação algébrica mais formal da teoria desenvolvida em rearranjo de genomas, desenvolvemos uma solução formal para o problema da distância de reversão com sinal. Utilizamos, em tal solução, um formalismo algébrico para rearranjo de genomas que relaciona a recente teoria de rearranjo de genomas ?basicamente fundamentada no trabalho de Hannenhalli e Pevzner ? e a teoria de grupos de permutação de uma nova forma. Pretendemos criar a base para grandes avanços na área através de um formalismo algébrico forte
Abstract: In the last decades we have seen a great progress in molecular biology. That lead to a large volume of data on molecules, DNA and proteins, essential for life.The current stage of research lies in the pursuit of tools to extract information with biological relevance from this data. In this context, comparison of genomes is an important tool and genome rearrangements is a way of doing that comparison. In rearrangement analysis the genome is represented by a sequence of conserved blocks. The aim is to ?nd a minimum sequence of operations that transform a genome into another given as input two genomes and a set of allowed operations. In 1995, Hannenhalli and Pevzner presented the ?rst polinomial algorithm for sorting signed permutations by reversals. This algorithm has complexity O(n4) in time and was the ?rst polinomial algorithm for a realistic model of genome rearrangement. Since then, new algorithms with better asintotic performance had appeared. The fastest algorithm, with complexity O(n?n logn), was developed byTannier and Sagot in 2004. Motivated by a lack of a more formal derivation in the genome rearrangement developed theory, we developed a formal solution for the signed reversal distance problem. We use an algebraic formalism that relates the recent genome rearrangement theory ? basically based on a work of Hannenhalli and Pevzner ? to permutation group theory in a new form. We intend to build a solid theoretical base for further advances in the area through strong algebraic formalism
Mestrado
Teoria da Computação
Mestre em Ciência da Computação

xi, 119 p. : ill. (some col.)
The evolutionary history of protein families can be difficult to study because necessary ancestral molecules are often unavailable for direct observation. As an alternative, the field of computational phylogenetics has developed statistical methods to infer the evolutionary relationships among extant molecular sequences and their ancestral sequences. Typically, the methods of computational phylogenetic inference and ancestral sequence reconstruction are combined with other non-computational techniques in a larger analysis pipeline to study the inferred forms and functions of ancient molecules. Two big problems surrounding this analysis pipeline are computational error and statistical uncertainty. In this dissertation, I use simulations and analysis of empirical systems to show that phylogenetic error can be reduced by using an alternative search heuristic. I then use similar methods to reveal the relationship between phylogenetic uncertainty and the accuracy of ancestral sequence reconstruction. Finally, I provide a case-study of a molecular machine in yeast, to demonstrate all stages of the analysis pipeline. This dissertation includes previously published co-authored material.
Committee in charge: John Conery， Chair； Daniel Lowd， Member； Sara Douglas， Member； Joseph W. Thornton， Outside Member

Nipah belongs to the family of paramyxoviruses that cause numerous fatal diseases in humans and farm animals. There are no FDA approved drugs for Nipah or any of the paramyxoviruses. Designing antiviral therapies that are more resistant to viral mutations require understanding of molecular details underlying infection. This dissertation focuses on obtaining molecular insights into the very first step of infection by Nipah. Such details, in fact, remain unknown for all paramyxoviruses. Infection begins with the allosteric stimulation of Nipah virus host binding protein by host cell receptors. Understanding molecular details of this stimulation process have been challenging mainly because, just as in many eukaryotic proteins, including GPCRs, PDZ domains and T-cell receptors, host receptors induce only minor structural changes (< 2 Å) and, consequently, thermal fluctuations or dynamics play a key role. This work utilizes a powerful molecular dynamics based approach, which yields information on both structure and dynamics, laying the foundation for its future applications to other paramyxoviruses. It proposes a new model for the initial phase of stimulation of Nipah’s host binding protein, and in general, highlights that (a) interfacial waters can play a crucial role in the inception and propagation of allosteric signals; (b) extensive inter-domain rearrangements can be triggered by minor changes in the structures of individual domains; and (c) mutations in dynamically stimulated proteins can induce non-local changes that spread across entire domains.

Proteins and enzymes are large and complex biological molecules, characterized by unique three-dimensional structure are highly flexible and dynamic nature. Thorough understanding of protein and enzyme function requires studying of their conformational flexibility, because important physiological processes, such as ligand binding and catalysis rely on an enzyme’s dynamic nature and their ability to adopt a variety of conformational states. Computational methods are widely applied in studying enzymes and proteins structure and function providing a detailed atomistic-level of resolution data about the dynamics and catalytic processes, mechanisms in biomolecules, therefore even more nowadays a term ‘computational enzymology’ has emerged. Experimental methods often have difficulty in predicting dynamic motions of proteins. Computational simulations techniques, such as Molecular Dynamics simulations, have proven successful in simulating the conformational flexibility of proteins in studying structure-function relationships. Additionally, the binding events between two molecules, e.g. an enzyme and its substrate, can be computationally predicted with molecular docking methods. Enzymes are proteins that catalyse almost all biochemical reactions and metabolic processes in all organisms. In order to study the conformational flexibility of proteins we apply molecular dynamics simulations, and in order to simulate their reaction mechanisms we apply quantum mechanical simulations. Quantum mechanical simulations can also be used to predict the electronic structure of organic compounds, by calculating their electronic structures we perform orbital analyses and predict their optical properties. The results gained from our computational simulations can give new insights into explanation of experimental findings and data and can inspire and guide further experiments.

Thesis (Ph.D.)--Indiana University, School of Informatics and Computing, 2009.
Title from PDF t.p. (viewed on Jul 22, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7299. Adviser: Haixu Tang.

Thesis (M. S.)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Bader, David; Committee Co-Chair: Heitsch, Christine; Committee Member: Harvey, Stephen; Committee Member: Vuduc, Richard.

Mis-folded proteins and their associated aggregates are a contributing factor in some human diseases. In this study we used the protein lysozyme as a model to define aggregation structures under denaturing conditions. Sasahara et al. (2007), Frare et al. (2009, 2006), and Rubin et al. (2008) observed conditions where heat denatured lysozyme formed fibril structures that were observed to be 8-17 nanometers in diameter under the electron microscope. Even though the crystal structure of lysozyme is known, the denatured form of this protein is still unknown. Therefore, we used Rosetta++ protein folding and blind docking software to create in silico models of the protein at denaturing temperatures and subsequently docked them into aggregates. Here we compare those structures and select forms consistent with the fibril structure from the previous papers. The next step is to be able to use the predicted models of the fibrilar forms of denatured lysozyme to help us understand the exact conformation of fibril structures. This will let us confirm the docking interactions during the fibril aggregation process. The ultimate goal is to use the validated denatured structures to model interactions with heat shock proteins during the dis-aggregation process.

L'objectiu d'aquesta tesi és desenvolupar i implementar un conjunt d'eines de mineria de dades per ajudar en la reconstrucció de circuits biològics a través de l'anàlisi i la integració de grans conjunts de dades biològiques. Aquests circuits són importants perquè regulen tots els processos que controlen la vida i la salut dels organismes. El treball principal de la tesis es centra en l'anàlisi de les dades bibliòmiques desenvolupant-se dues eines, Biblio-MetReS per la reconstrucció de xarxes de PPIs i la identificació dels processos en què intervenen aquestes xarxes, i CheNER per la identificació de noms de compostos químics. L'eina final desenvolupada es centra en la integració de mètodes per a l'anàlisi estructural i modelització de proteïnes amb mètodes d'acoblament per a la predicció de complexos físics de proteïna-proteïna.
El objetivo de esta tesis es desarrollar e implementar un conjunto de herramientas de minería de datos para ayudar en la reconstrucción de circuitos biológicos a través del análisis y la integración de grandes conjuntos de datos biológicos. Estos circuitos son importantes porque regulan todos los procesos que controlan la vida y la salud de los organismos. El trabajo principal de la tesis se centra en el análisis de los datos bibliómicos, desarrollándose con este fin dos herramientas diferentes, Biblio-MetReS para la reconstrucción de redes PPIs y la identificación de los procesos en que intervienen estas redes, y CheNER para la identificación de nombres de compuestos químicos. La herramienta final que he desarrollado se centra en la integración de métodos para el análisis estructural y modelado de proteínas con métodos de acoplamiento para la predicción de complejos físicos de proteína-proteína.
The aim of this thesis is the development and implementation of a set of data mining tools to aid in the reconstruction of biological circuits through analysis and integration of large biological datasets. These circuits are important because they regulate and maintain life and health in organisms. The main part of the thesis is focused on analyzing bibliomic data for which I develop two tools, Biblio-MetReS for the reconstruction of PPIs networks and to identify the processes in which the networks are involved, and CheNER for the identification of chemical compounds names. The final tool developed focuses on the integration of methods for structural analysis and modeling of proteins with docking methods for prediction of native protein-protein physical complexes.

Understanding protein-ligand interactions is highly important in drug development. In the present work the objective is to comprehend the link between structure and function using molecular modelling. Specifically, this thesis has been focused on implementation of receptor flexibility in molecular docking and studying structure-activity relationships of potassium ion channels and their blockers. In ligand docking simulations protein motion and heterogeneity of structural waters are approximated using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid-based lookup table of interaction energies are tested. Two weighted average methods permit consistent and accurate ligand docking using a single grid representation of the target protein structures. Quaternary ammonium ions (QAIs) are well known K+ channel blockers. Conformations around C–N bonds at the quaternary centre in tetraalkylammonium ions in water solution are investigated using quantum mechanical methods. Relative solvation free energies of QAIs are further estimated from molecular dynamics simulations. The torsion barrier for a two-step interconversion between the conformations D2d and S4 is calculated to be 9.5 kcal mol–1. Furthermore D2d is found to be more stable than the S4 conformation which is in agreement with experimental studies. External QAI binding to the K+ channel KcsA is also studied. Computer simulations and relative binding free energies of the KcsA complexes with QAIs are calculated. This is done with the molecular dynamics free energy perturbation approach together with automated ligand docking. In agreement with experiment, the Et4N+ blocker in D2d symmetry has better binding than the other QAIs. Binding of blockers to the human cardiac hERG potassium channel is studied using a combination of homology modelling, automated docking and molecular dynamics simulations. The calculations reproduce the relative binding affinities of a set of drug derivatives very well and indicate that both polar interactions near the intracellular opening of the selectivity filter as well as hydrophobic complementarity in the region around F656 are important for blocker binding. Hence, the derived model of hERG should be useful for further interpretations of structure-activity relationships.

The main motivation of this thesis has been to validate, improve and develop new methods with respect to the ones available nowadays in the area of drug discovery, in order to be able to study more challenging targets in the near future that currently are out of our reach. As the productivity of the pharmaceutical industry is decreasing year after year over the last decades, the improvement of such methods would be a step forward. As we are mostly a computational lab, this thesis has focused on different computational approaches such as docking, molecular dynamics or chemoinformatics. On the first part of the thesis (first author on the publication in PLoS Computational Biology in 2014), I worked on Docking-based Virtual Screening (VS). Particularly, in validating rDock, a little-known but very powerful program that was published and released during this thesis as open source software. In order to validate it, we performed several benchmarking experiments with DUD and ASTEX sets to compare the performance of rDock against Glide and AutoDock Vina, two commonly used docking programs. The capabilities of rDock with respect to binding mode prediction (predict how a ligand structure will be upon binding to its receptor) and virtual screening (selecting the most likely active ligands amongst thousands or millions of drug-like molecules) were compared with Glide and Vina, and we demonstrated that rDock performed as well as them. On the second project of the thesis (first author on the publication in Nature Chemistry in 2016), we wanted to develop a novel computational tool for drug discovery not only that was complementary to the existing ones, but also that improved them by adding new ways of interpreting the data. Taking advantage of the already known technique of Steered Molecular Dynamics (SMD), we proposed an approach consisting in reducing the size of the system, focusing around a key interaction point and running SMD to discriminate between active and inactive ligands. This approach, or as we call it: "Dynamic Undocking", is intended to foster drug design efforts in the lead optimization stage by improving the efficiency of the in silico assessment of protein-ligand binding affinity. After a positive retrospective assessment of the method using different systems of the DUD set, a prospective validation was required to evaluate its feasibility in a real drug discovery project. Hsp90 was selected as the test system: A fragment library was created and a subset of fragments was selected for a first stage of docking-based VS. About 300.000 ligands were docked with rDock and the top-scoring ones were subjected to Dynamic Undocking. In a collaboration with Vernalis, a pharmaceutical company in the UK, we tested tens of compounds selected with Dynamic Undocking and we were able not only to find positive and novel hits but also to improve hit-rate with respect to standard fragment screening by almost 10 fold. Finally, we had the opportunity to participate in the D3R Grand Challenge 2015 where we could apply all the methods from this thesis (first author on the publication in Journal of Computer-Aided Molecular Design in 2016). This challenge was designed as a blind public test where different groups around the world tried to predict the binding mode and the affinity of a set of ligands for their respective protein target. Our approach consisted in a combination of docking and Dynamic Undocking and our results were placed amongst the best for the two systems of the challenge. We also discussed how the level of available data and previous knowledge on each of the systems impacted on the final results.
La motivación principal de esta tesis ha sido validar, mejorar y desarrollar nuevos métodos con relación a los disponibles hoy en día en el área del desarrollo de fármacos, para en un futuro poder estudiar dianas que actualmente están fuera de nuestro alcance. Debido a que la productividad de la industria farmacéutica está disminuyendo durante los últimos años, una mejora en los métodos disponibles sería un gran paso adelante. Esta tesis se ha centrado en diferentes métodos computacionales, como el docking o la dinámica molecular. En la primera de las partes, trabajé en el cribado virtual (Virtual Screening) basado en docking. Concretamente, participé en la validación del programa de docking rDock mediante la comparación con dos programas muy usados hoy en día de su capacidad de predecir correctamente el modo de unión de un ligando con su proteína diana y de sus resultados en el cribado virtual de posibles fármacos. En la segunda parte de la tesis, participé en el desarrollo de un método computacional novedoso en el diseño de fármacos que complementase y mejorase los métodos actualmente disponibles. Éste método, bautizado en inglés como “Dynamic Undocking”, consiste en una implementación específica de dinámica molecular mediante la cual somos capaces de detectar si un ligando puede ser activo o inactivo de manera rápida y eficiente. Se validó el método de manera retrospectiva y posteriormente se aplicó en otro proyecto con el objetivo de encontrar nuevos posibles fármacos para una proteína relacionada con cáncer. Gracias a una colaboración con una empresa del Reino Unido, encontramos nuevos ligandos de manera que aumentamos la tasa de éxito con relación a un método estándar en casi 10 veces. Por último, participé en el “D3R Grand Challenge 2015”, un experimento a escala mundial donde los participantes aplicaron diferentes métodos y compararon sus resultados respecto a dos métricas distintas: la predicción del modo de unión y la capacidad de ordenar los ligandos proporcionados por la organización por su afinidad respecto a la proteína diana. En nuestro caso, aplicamos una combinación de docking y “Dynamic Undocking” con unos resultados excelentes.

Integrins are an important class of transmembrane receptors that relay signals bidirectionally across the plasma membrane, regulating several cell functions and playing a key role in diverse pathological processes. Specifically, integrin subtype αIIbβ3 is involved in thrombosis and stroke, while subtypes αvβ3 and α5β1 play an important role in angiogenesis and tumor progression. They therefore emerged as attractive pharmacological targets. In the past decades several peptides and peptidomimetics targeting these proteins and based on the integrin recognition motif RGD (Arg-Gly-Asp) have been developed, whereby their affinity and selectivity for a specific integrin subtype have been fine-tuned by modulation of RGD flanking residues, by cyclization or by introduction of chemical modifications. Thus far, the design and development of RGD-based cyclopeptides have been mainly based on empirical approaches, requiring expensive and time-consuming synthesis campaigns. In this field, the employment of computational tools, that could be valuable to accelerate the drug design and optimization process, has been limited by the inherent difficulties to predict in silico the three-dimensional structure and the inhibitory activity of cyclopeptides. However, recent improvements in both computational resources and in docking and modeling techniques are expected to open new perspectives in the development of cyclopeptides as modulators of protein-protein interactions and, particularly, as integrin inhibitors. Within this PhD project, I have investigated the applicability of computational techniques in predicting and rationalizing how the environment of the recognition-motif in cyclopeptides (i.e. flanking residues and introduction of chemical modification) could influence their integrin affinity and selectivity. These features can regulate integrin affinity both by favoring direct interactions with the receptor and/or by modulating the three-dimensional conformation properties of the recognition motif. To take into account both these aspects, I have proposed and optimized a multi-stage computational protocol in which an exhaustive conformational sampling of the investigated cyclopeptides is followed by docking calculations and re-scoring techniques. Specifically: i) the exhaustive sampling could be achieved by using Metadynamics in its Bias Exchange variant (BE-META), an enhanced sampling technique which represents a valuable methodology for the acceleration of rare events, allowing to cross the high free energy barriers characteristic of cyclopeptides and providing reliable estimations of the populations of the accessible conformers. ii) The docking calculations, complemented with the re-scoring technique MM-GB/SA (Molecular Mechanics Generalized Born Surface Area) and the cluster analysis of the decoy poses, allow to evaluate the ability of each peptide to engage interactions with the receptors and to rank the docking poses according to their binding ability; iii) a joint analysis of the previous outcomes results in a reliable ranking of cyclopeptides based on their binding affinity and in the rationalization of their structure-activity relationship. This computational protocol has been exploited in two different applications, illustrated within the thesis. In the first application the protocol has been applied to rationalize how the introduction of chemical modifications, specifically backbone N-methylation, impacts on the equilibrium conformation and consequently on the integrin affinity of five RGD containing cyclic hexapeptides, which were previously generated by the group of professor Kessler to modulate their selectivity for αIIbβ3 integrin. The study revealed that backbone N-methylation affects the preferences of the φ dihedral angle of the methylated residue, specifically favoring the adoption of additional conformations, characterized by a 180° twist of the peptide bond plane preceding the methylated residue. These twists of dihedral angles were found to have relevant consequences on the cyclopeptides conformation, influencing the formation of intra-molecular hydrogen bonds as well as some structural features which are known to be fundamental in integrin binding. Both structural analysis and docking calculations allowed to identify the “bioactive” conformation (i.e. an extended RGD conformation able to recapitulate the canonical electrostatic and the additional stabilizing hydrophobic interactions). Of note, the cyclopeptides that are pre-organized, already in their free state, in this bioactive conformation are the ones displaying the best αIIbβ3 binding affinity in terms of IC50 values, confirming that pre-organization of cyclopeptides in solution can strongly affect their binding strength to the receptor and demonstrating that the knowledge of their conformational equilibrium is fundamental to provide reliable affinity predictions. In the second application, I have focused my attention on cyclopeptides harboring a recently discovered integrin recognition motif: isoDGR (isoAsp-Gly-Arg), deriving from the spontaneous deamidation of NGR (Asp-Gly-Arg) sequence present in integrin natural ligands. As a preliminary step, I have systematically tested the accuracy of eight Molecular Mechanics force fields in reproducing the equilibrium properties of isoDGR-based cyclopeptides, for which NMR experiments have been acquired. The comparison between simulated and NMR-derived data (i.e. chemical shifts and J scalar couplings) revealed that, while most of the investigated force fields can properly reproduce the equilibrium conformational properties of cyclic peptides, only two of them (i.e. the AMBER force fields ff99sb-ildn and ff99sb*-ildn) are able to recover the NMR observables characteristics of the non-standard residue isoAspartate with an accuracy close to the systematic uncertainty. Overall, these results suggest that the transferability of force field parameters to non standard amino acids is not straightforward. However, two force fields allowed to obtain a satisfactory accuracy and have been therefore employed for the subsequent investigation. I thus applied the computational protocol to rationalize the diverse selectivity and affinity profiles for integrins αvβ3 and α5β1, both related to cancer, displayed by three isoDGR-based cyclic hexapeptides. These molecules differ in the residues flanking the isoDGR motif and show appealing tumor-homing properties; specifically it has been shown that one of these, c(CGisoDGRG), can be coupled with human serum albumin through a chemical linker to be used as a drug delivery agent for functionalized gold nanoparticles. Herein, I investigated the role of the chemical linker in improving affinity and selectivity of c(CGisoDGRG) for αvβ3. The application of the multi-stage protocol allowed to propose an explanation for the different selectivity profiles displayed by these molecules, where the direct interactions engaged by the flanking residues and/or their steric hindrance seem to be largely responsible for the observed different affinities. As a last result, through the combination of MD and NMR techniques, I demonstrated that the chemical linker improved the αvβ3 affinity of c(CGisoDGRG) by engaging direct interactions with the receptor and I proposed two possible complex models, which well-reproduce data from Saturation Transfer Difference experiments. Overall, in this PhD work I have shown that the combination of different computational techniques, BE-META, docking and MM-GB/SA re-scoring, could be a reliable approach to perform structure-activity relationship studies in cyclopeptides. Specifically, the proposed protocol is able to predict the influence of the recognition motif environment (i.e. chemical modification and flanking residues) on integrin affinities. These two features regulate integrin affinity differently: the first one by conformational modulation of the recognition motif, the second by engaging direct interactions with the receptor. Of note, the approach can deal with both these mechanisms of affinity modulation. We expect that the protocol herein described could be used in future to screen novel peptides library or to complement biochemical experiments during the drug optimization stages, assisting organic chemists in the design of more effective integrin-targeting peptides.

Thesis (PhD)--Stellenbosch University, 2005.
ENGLISH ABSTRACT: Since their inception, biology and biochemistry have been spectacularly successful in characterising the living cell and its components. As the volume of information about cellular components continues to increase, we need to ask how we should use this information to understand the functioning of the living cell? Computational systems biology uses an integrative approach that combines theoretical exploration, computer modelling and experimental research to answer this question. Central to this approach is the development of computational models, new modelling strategies and computational tools. Against this background, this study aims to: (i) develop a new modelling package: PySCeS, (ii) use PySCeS to study discontinuous behaviour in a metabolic pathway in a way that was very difficult, if not impossible, with existing software, (iii) develop an interactive, web-based repository (JWS Online) of cellular system models. Three principles that, in our opinion, should form the basis of any new modelling software were laid down: accessibility (there should be as few barriers as possible to PySCeS use and distribution), flexibility (pySCeS should be extendable by the user, not only the developers) and usability (PySCeS should provide the tools we needed for our research). After evaluating various alternatives we decided to base PySCeS on the freely available programming language, Python, which, in combination with the large collection of science and engineering algorithms in the SciPy libraries, would give us a powerful modern, interactive development environment.
AFRIKAANSE OPSOMMING: Sedert hul totstandkoming was biologie en, meer spesifiek, biochemie uiters suksesvol in die karakterisering van die lewende sel se komponente. Steeds groei die hoeveelheid informasie oor die molekulêre bestanddele van die sel daagliks; ons moet onself dus afvra hoe ons hierdie informasie kan integreer tot 'n verstaanbare beskrywing van die lewende sel se werking. Om dié vraag te beantwoord gebruik rekenaarmatige sisteembiologie 'n geïntegreerde benadering wat teorie, rekenaarmatige modellering en eksperimenteeIe navorsing kombineer. Sentraal tot die benadering is die ontwikkeling van nuwe modelle, strategieë vir modellering, en sagteware. Teen hierdie agtergrond is die hoofdoelstelling van hierdie projek: (i) die ontwikkeling van 'n nuwe modelleringspakket, PySCeS (ii) die benutting van PySCeS om diskontinue gedrag in n metaboliese sisteem te bestudeer (iets wat met die huidiglik beskikbare sagteware redelik moeilik is), (en iii) die ontwikkeling vann interaktiewe, internet-gebaseerde databasis van sellulêre sisteem modelle, JWS Online. Ons is van mening dat nuwe sagteware op drie belangrike beginsels gebaseer behoort te wees: toeganklikheid (die sagteware moet maklik bekombaar en bruikbaar wees), buigsaamheid (die gebruiker moet self PySCeS kan verander en ontwikkel) en bruikbaarheid (al die funksionalitiet wat ons vir ons navorsing nodig moet in PySCeS ingebou wees). Ons het verskeie opsies oorweeg en besluit om die vrylik verkrygbare programmeringstaal, Python, in samehang die groot kolleksie wetenskaplike algoritmes, SciPy, te gebruik. Hierdie kombinasie verskaf n kragtige, interaktiewe ontwikkelings- en gebruikersomgewing. PySCeS is ontwikkel om onder beide die Windows en Linux bedryfstelsels te werk en, meer spesifiek, om gebruik te maak van 'n 'command line interface'. Dit beteken dat PySCeS op enige interaktiewe rekenaar-terminaal Python ondersteun sal werk. Hierdie eienskap maak ook moontlik die gebruik van PySCeS as 'n modelleringskomponent in 'n groter sagteware pakket onder enige bedryfstelsel wat Python ondersteun. PySCeS is op 'n modulere ontwerp gebaseer, wat dit moontlik vir die eindgebruiker maak om die sagteware se bronkode verder te ontwikkel. As 'n toepassing is PySCeS gebruik om die oorsaak van histeretiese gedrag van 'n lineêre, eindproduk-geïnhibeerde metaboliese pad te ondersoek. Ons het hierdie interessante gedrag in 'n vorige studie ontdek, maar kon nie, met die sagteware wat op daardie tydstip tot ons beskikking was, hierdie studie voortsit nie. Met PySCeS se ingeboude vermoë om parameter kontinuering te doen, kon ons die oorsake van hierdie diskontinuë gedrag volledig karakteriseer. Verder het ons 'n nuwe metode ontwikkel om hierdie gedrag te visualiseer as 'n interaksie tussen die volledige sisteem se subkomponente. Tydens PySCeS se ontwikkeling het ons opgemerk dat dit baie moeilik was om metaboliese modelle wat in die literature gepubliseer is te herbou en te bestudeer. Hierdie situasie is grotendeels die gevolg van die feit dat nêrens 'n sentrale databasis vir metaboliese modelle bestaan nie (soos dit wel bestaan vir genomiese data of proteïen strukture). Die JWS Online databasis is spesifiek ontwikkel om hierdie leemte te vul. JWS Online maak dit vir die gebruiker moontlik om, via die internet en sonder die installasie van enige gespesialiseerde modellerings sagteware, gepubliseerde modelle te bestudeer en ook af te laai vir gebruik met ander modelleringspakkette soos bv. PySCeS. JWS Online het alreeds 'n onmisbare hulpbron vir sisteembiologiese navorsing en onderwys geword.

Orientador: Zanoni Dias
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-17T08:10:32Z (GMT). No. of bitstreams: 1 Baudet_Christian_D.pdf: 3490604 bytes, checksum: 7f0a8868574d06e11524e5a5de9d1fd0 (MD5) Previous issue date: 2010
Resumo: O estudo de rearranjo de genomas tem o objetivo de auxiliar o entendimento da evolução. Através da análise dos eventos de mutação como inversões, transposições, fissões, fusões, entre outros, buscamos compreender as suas influências sobre o fenômeno da diferenciação das espécies. Dentro deste contexto, esta tese ataca dois temas distintos: a Enumeração de Traces e a Identificação de Breakpoints. Os algoritmos de ordenação de permutações por reversões orientadas produzem uma única solução ótima enquanto o conjunto de soluções é imenso. A enumeração de traces de soluções para este problema oferece um modo mais compacto de representar o conjunto completo de soluções ótimas. Dessa maneira, esta técnica fornece aos biólogos a possibilidade de análise de diversos cenários evolutivos. Neste trabalho, realizamos um estudo para melhora da eficiência do algoritmo de enumeração através da adoção de uma estrutura de dados mais simples. Devido ao caráter exponencial do problema, grandes permutações não podem ser processadas em um tempo satisfatório. Assim, com o objetivo de produzir cenários evolucionários alternativos para grandes permutações, propomos e avaliamos estratégias para a enumeração parcial de traces. Os pontos de quebra (ou breakpoints) são regiões que delimitam os segmentos conservados existentes nos cromossomos e denotam a ocorrência de rearranjos evolutivos. As técnicas de identificação de breakpoints têm a função de identificar tais pontos nas sequências dos cromossomos. Nesta tese, implementamos um método de detecção e refinamento de pontos de quebra proposto na literatura e o disponibilizamos como um pacote que pode ser utilizado por outros pesquisadores. Além disso, introduzimos uma nova metodologia de identificação de breakpoints baseada na análise da cobertura de hits observada nos alinhamentos de sequências intergênicas, provenientes dos genomas das espécies comparadas
Abstract: The study of genome rearrangements helps biologists understand the evolution of species. The species differentiation phenomenon are derived by analyzing mutational events (inversions, transpositions, fissions, fusions, etc) and their effects. In this context, this work aims the study of two different subjects: Traces Enumeration and Breakpoint Identification. Algorithms that solve the problem of sorting oriented permutations through reversals output only one optimal solution, although the set of solutions can be huge. The enumeration of traces of solutions for this problem allows a compact representation of the set of all optimal solutions which sort a permutation. By using this technique, biologists can study many evolutionary scenarios. We carried out a study to improve the efficiency of the enumeration algorithm by adopting a simple data structure. Due to the exponential nature of the problem, large permutations cannot be processed at a satisfactory time. Thus, in order to produce alternative evolutionary scenarios for large permutations, we proposed and evaluated strategies for partial enumeration of traces. Breakpoints are regions that border conserved segments in the chromosomes and reflect the occurrence of evolutionary rearrangements. The techniques for breakpoint identification are meant to identify such points in the chromosome sequences. In this work, we implemented a method proposed in the literature, that performs detection and refinement of breakpoints. The implementation is available as a package to other researchers. Additionally, we introduced a new methodology for breakpoint identification based on the analysis of the hit coverage observed in the alignments of intergenic sequences
Doutorado
Ciência da Computação
Doutor em Ciência da Computação

I used molecular dynamics (MD) simulations as a primary tool to study folding and dynamics of signaling and regulatory proteins. Specifically, I have studied two classes of proteins: the first part of my thesis reports studies on peptides and receptors of the insulin family, and the second part reports on studies of regulatory proteins from the G-protein coupled receptor family. The first problem that I investigated was understanding the folding mechanism of the insulin B-chain and its mimetic peptide (S371) which were studied using enhanced sampling simulation methods. I validated our simulation approaches by predicting the known solution structure of the insulin B-chain helix and then applied them to study the folding of the mimetic peptide S371. Potentials of mean force (PMFs) along the reaction coordinate for each peptide are further resolved using the metadynamics method. I further proposed receptor-bound models of S371 that provide mechanistic explanations for competing binding properties of S371 and a tandem hormone-binding element of the receptor known as the C-terminal (CT) peptide. Next, I studied the all-atom structural models of peptides containing 51 residues from the transmembrane regions of IR and the type-1 insulin-like growth factor receptor (IGF1R) in a lipid membrane. In these models, the transmembrane regions of both receptors adopt helical conformations with kinks at Pro961 (IR) and Pro941 (IGF1R), but the C-terminal residues corresponding to the juxta-membrane region of each receptor adopt unfolded and flexible conformations in IR as opposed to a helix in IGF1R. I also observe that the N-terminal residues in IR form a kinked-helix sitting at the membrane-solvent interface, while homologous residues in IGF1R are unfolded and flexible. These conformational differences result in a larger tilt-angle of the membrane-embedded helix in IGF1R in comparison to IR to compensate for interactions with water molecules at the membrane-solvent interfaces. The metastable/stable states for the transmembrane domain of IR, observed in a lipid bilayer, are consistent with a known NMR structure of this domain determined in detergent micelles, and similar states in IGF1R are consistent with a previously reported model of the dimerized transmembrane domains of IGF1R. I further studied dimerization propensities of IR transmembrane domains using three different constructs in a lipid bilayer (isolated helices, ectodomain-anchored helices, and kinase-anchored helices). These studies revealed that the transmembrane domains can dimerize in isolation and in kinase-anchored forms, but not significantly in the ectodomain construct. The final studies in my thesis are focused on interplay of protein dynamics and small-molecule inhibition in a set of regulatory proteins known as the Regulators of G-protein Signaling (RGS) proteins. Thiadiazolidinone (TDZD) compounds have been shown to inhibit the protein-protein interaction between RGS and the alpha subunit of G-proteins by covalent modification of cysteine residues in RGS proteins. However, some of these cysteines in RGS proteins are not surface-exposed. I hypothesized that transient binding pockets expose cysteine residues differentially between different RGS isoforms. To explore this hypothesis, long time-scale classical MD simulations were used to probe the dynamics of three RGS proteins (RGS4, RGS8, and RGS19), and characterize flexibility in various helical motifs. The results from simulation studies were validated by hydrogen-deuterium exchange (HDX) studies, and revealed motions indicating solvent exposure of buried cysteine residues, thereby providing insights into inhibitor binding mechanisms. In addition, I used different published HDX models which have resulted in a comprehensive comparison of existing models. Furthermore, I developed the new HDX models with optimized parameters which had comparable accuracy and more computational efficiency compared to other models. Overall, my thesis has resulted in the development and applications of several state-of-the-art computational methods that have provided a detailed mechanistic understanding of peptide and small-molecule based inhibitors and their interactions with large proteins that are potentially useful in designing novel approaches to target protein-protein interactions.

Enantioselective reactions are one of the ways to produce pure chiral compounds. Understanding the basis of this selectivity makes it possible to guide enzyme design towards more efficient catalysts. One approach to study enzymes involved in chiral chemistry is through the use of computational models that are able to simulate the chemical reaction taking place. The potato epoxide hydrolase is one enzyme that is known to be both highly enantioselective, while still being robust upon mutation of residues to change substrate scope. The enzyme was used to investigate the epoxide hydrolysis mechanism for a number of different substrates, using the EVB approach to the reaction both in solution and in several enzyme variants. In addition to this, work has been performed on new ways of performing simulations of divalent transition metals, as well as development of new simulation software.

The ever-growing size and complexity of molecular network models makes them difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine together smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining different types of ports. Not all modules connect together the same way, therefore multiple connection options need to exist.
Master of Science

Although nonflexible, scaled molecular models like Pauling-Corey's and its descendants have made significant contributions in structural biology research and pedagogy, recent technical advances in 3D printing and electronics make it possible to go one step further in designing physical models of biomacromolecules: to make them conformationally dynamic. We report the design, construction, and validation of a flexible, scaled, physical model of the polypeptide chain, which accurately reproduces the bond rotational degrees-of-freedom in the peptide backbone. The coarse-grained backbone model consists of repeating amide and alpha-carbon units, connected by mechanical bonds (corresponding to phi and psi angles) that include realistic barriers to rotation that closely approximate those found at the molecular scale. Longer-range hydrogen-bonding interactions are also incorporated, allowing the chain to easily fold into stable secondary structures. This physical model can serve as the basis for linking tangible bio-macromolecular models directly to the vast array of existing computational tools to provide an enhanced and interactive human-computer interface. We have explored the boundaries of this direction at the interface of computational tools and physical models of biological macromolecules at the nano-scale. Using a CAD-biocomputational framework, we have provided a methodology to design and build physical protein models focusing on shape and dynamics. We have also developed a workflow and an interface implemented for such bio-modeling tools. This physical-digital interface paradigm, at the intersection of native state proteins (P), computational models (C) and physical models (P), provides new opportunities for building an interactive computational modeling tool for protein folding and drug design. Furthermore, this model is easily constructed with readily obtainable parts and promises to be a tremendous educational aid to the intuitive understanding of chain folding as the basis for macromolecular structure.
Ph. D.

This project compared the performance of the correlation coefficient to show similarities in annotations between a predictive automated bacterial annotation database and the curated EcoCyc database. EcoCyc is a conservative multidimensional annotation system that is exclusively based on experimentally validated findings by over 15,000 publications. The automated annotation system, used in the comparison was BASys. It is often used as a first pass annotation tool that tries to add as many annotations as possible by drawing upon over 30 information sources. Gene ontology served as one basis of comparison between these databases because of the limited common terms in the ontology annotations. Translation libraries were used to extend the number of BASys terms that could be compared to the gene ontology terms in EcoCyc. Additional, non-ontology terms and metadata in BASys were compared to EcoCyc terms after parsing them into root words. The different term sources were quantitatively compared by using the correlation coefficient as the evaluation metric. The direct gene ontology comparison gave the lowest correlation coefficient. The addition of gene ontology terms to BASys by using translation tables of metadata greatly increased the correlation coefficient, which was comparable to the parsed word comparison. The combination of enhanced gene ontology and parsed word methods gave the highest correlation coefficient of 0.16. The controlled vocabulary system of gene ontology was not sufficient to compare two annotated databases. The addition of gene ontology terms from translation libraries greatly increased the performance of these comparisons. In general, as the number of comparison terms increased the correlation coefficient increased. Future comparisons should include the enhanced gene ontology dataset in order to monitor the organization pertaining to formal nomenclature and the datasets generated from Word parsing can be used to monitor the degree of additional terms might be incorporated with translation libraries.

Lipases are enzymes that play fundamental roles in fat digestion and metabolism, and function at the interface formed between hydrophobic molecules and the surrounding aqueous environment. These interfacial interactions are thought to induce conformational changes in a "lid" region of the lipase, leading to a dramatic increase in activity. This thesis aims to provide insight into the interactions that govern lipase association with interfaces of di erent structural characteristics, and the possible conformational changes that arise as a function of these interactions. A multi-scale molecular simulation approach (combining atomistic and coarse-grained methods) was applied to study two different lipases with a range of interfaces, including "soft" biological surfaces and "hard" non-biological surfaces. Three major insights were gained from these studies. First, interactions of a small bacterial lipase (M37) with lipid interfaces resulted in substantial structural changes in a lid region, uncovering of the underlying active site. A mechanism of interfacial ac- tivation is proposed for this lipase. Second, the interaction of M37 with non-biological interfaces di er from lipid interfaces, leading to altered interfacial orientations with possible functional consequences. Third, the amino acid composition of the lid region of a yeast lipase (TLL) is shown to play crucial roles in lipase activation and structural stability.

In modern drug discovery, lead discovery is a term used to describe the overall process from hit discovery to lead optimisation, with the goal being to identify drug candidates. This can be greatly facilitated by the use of computer-aided (or in silico) techniques, which can reduce experimentation costs along the drug discovery pipeline. The range of relevant techniques include: molecular modelling to obtain structural information, molecular dynamics (which will be covered in Chapter 2), activity or property prediction by means of quantitative structure activity/property models (QSAR/QSPR), where machine learning techniques are introduced (to be covered in Chapter 1) and quantum chemistry, used to explain chemical structure, properties and reactivity. This thesis is divided into five parts. Chapter 1 starts with an outline of the early stages of drug discovery; introducing the use of virtual screening for hit and lead identification. Such approaches may roughly be divided into structure-based (docking, by far the most often referred to) and ligand-based, leading to a set of promising compounds for further evaluation. Then, the use of machine learning techniques, the issue of which will be frequently encountered, followed by a brief review of the "no free lunch" theorem, that describes how no learning algorithm can perform optimally on all problems. This implies that validation of predictive accuracy in multiple models is required for optimal model selection. As the dimensionality of the feature space increases, the issue referred to as "the curse of dimensionality" becomes a challenge. In closing, the last sections focus on supervised classification Random Forests. Computer-based analyses are an integral part of drug discovery. Chapter 2 begins with discussions of molecular docking; including strategies incorporating protein flexibility at global and local levels, then a specific focus on an automated docking program – AutoDock, which uses a Lamarckian genetic algorithm and empirical binding free energy function. In the second part of the chapter, a brief introduction of molecular dynamics will be given. Chapter 3 describes how we constructed a dataset of known binding sites with co-crystallised ligands, used to extract features characterising the structural and chemical properties of the binding pocket. A machine learning algorithm was adopted to create a three-way predictive model, capable of assigning each case to one of the classes (regular, orthosteric and allosteric) for in silico selection of allosteric sites, and by a feature selection algorithm (Gini) to rationalize the selection of important descriptors, most influential in classifying the binding pockets. In Chapter 4, we made use of structure-based virtual screening, and we focused on docking a fluorescent sensor to a non-canonical DNA quadruplex structure. The preferred binding poses, binding site, and the interactions are scored, followed by application of an ONIOM model to re-score the binding poses of some DNA-ligand complexes, focusing on only the best pose (with the lowest binding energy) from AutoDock. The use of a pre-generated conformational ensemble using MD to account for the receptors' flexibility followed by docking methods are termed “relaxed complex” schemes. Chapter 5 concerns the BLUF domain photocycle. We will be focused on conformational preference of some critical residues in the flavin binding site after a charge redistribution has been introduced. This work provides another activation model to address controversial features of the BLUF domain.

Accurate calculations of free energies for molecular association and solvation are important for the understanding of biochemical processes, and are useful in many pharmaceutical applications. In this thesis, molecular dynamics (MD) simulations are used to calculate thermodynamic properties for solvation and ligand binding.

The thermodynamic integration technique is used to calculate pK_a values for three aspartic acid residues in two different proteins. MD simulations are carried out in explicit and Generalized-Born continuum solvent. The calculated pK_a values are in qualitative agreement with experiment in both cases. A combination of MD simulations and a continuum electrostatics method is applied to examine pK_a shifts in wild-type and mutant epoxide hydrolase. The calculated pK_a values support a model that can explain some of the pH dependent properties of this enzyme.

Development of the linear interaction energy (LIE) method for calculating solvation and binding free energies is presented. A new model for estimating the electrostatic term in the LIE method is derived and is shown to reproduce experimental free energies of hydration. An LIE method based on a continuum solvent representation is also developed and it is shown to reproduce binding free energies for inhibitors of a malaria enzyme. The possibility of using a combination of docking, MD and the LIE method to predict binding affinities for large datasets of ligands is also investigated. Good agreement with experiment is found for a set of non-nucleoside inhibitors of HIV-1 reverse transcriptase.

Approaches for decomposing solvation and binding free energies into enthalpic and entropic components are also examined. Methods for calculating the translational and rotational binding entropies for a ligand are presented. The possibility to calculate ion hydration free energies and entropies for alkali metal ions by using rigorous free energy techniques is also investigated and the results agree well with experimental data.

The work presented in this thesis centres on the development of a work-flow in which coarse-grained molecular dynamics (MD) simulations of a planar phospholipid bilayer, containing membrane proteins, is used to parameterize a larger-scale simplified bilayer model. Using this work-flow, repeat simulations and simulations of larger systems are possible, better enabling the calculation of bulk statistics for the system. The larger-scale simulations can be run on commercial hardware, once the initial parameterization has been performed. In the simplified representation, each protein was initially only represented by the position of its centre of mass and later with the inclusion of its orientation. The membrane protein used throughout most of this work was the bacterial outer membrane protein NanC, a member of the KdgM family of proteins. To parameterize the motion and interaction of proteins using MD, the potential of mean force (PMF) for the pairwise association of two proteins in a bilayer was calculated for a variety of orientational combinations, using a modified umbrella sampling procedure. The relative orientations chosen represented extreme examples of the contact regimes between the two proteins: they approximately corresponded to maxima and minima of the solvent inaccessible surface area, calculated when the proteins were in contact. These PMFs showed that there was a correlation between the buried surface area and the depth of the potential well in the PMF; this is something that, to date, has only been observed in these relatively-'featureless' membrane proteins (but is seen in globular proteins), where the effect of the interactions with lipids in the bilayer plays a larger role. Features in the PMF were observed that resulted from the preferential organization of lipids in the region between the two proteins. These features were small wells in the PMF, which occurred at protein separations that corresponded to the intervening lipids being optimally packed between the proteins. This result further highlighted the role that the lipids in the bilayer played in the interaction between the NanC proteins. The simplified bilayer model was parameterized using the PMFs and the relationship between buried surface area and potential well depth. The initial model included only the proteins' positions. A series of Monte Carlo simulations were performed in order to compare the system behaviour to that of an equivalent MD simulation. Initially, the MD simulation and our parameterized model did not show a good agreement, so a Monte Carlo scheme that incorporated cluster-based movements was implemented. The agreement between the MD simulation and the simulations of our model using the cluster-based scheme, when comparing diffusive and clustering behaviour, was good. Including the orientation-dependent features of the parameterization resulted in the emergence of behaviour that was not clearly detectable in the MD simulation. Finally, attempts were made to parameterize the model using PMFs for the association of rhodopsin from the literature. Rhodopsin was a much more complicated protein to represent: there was not a clear correlation between surface area and the features of the PMF, and the geometry of the interaction between two rhodopsins was more complicated. Simulations of the 'rows-of-dimers' system of rhodopsin, observed in disc membranes, was not entirely well represented by the model; for such a closely packed system, where the number of lipids is much closer to the number of proteins, the use of an implicit-lipid model meant that the effect of the reduced lipid mobility was not adequately captured. However, the model accurately captures the orientational composition of the system. Future work should be focussed on incorporating explicit representations of the lipid in the system so that the behaviour of close-packed systems are better represented.

This thesis covers a wide variety of projects within the domain of computational structural biology. Structural biology is concerned with the molecular structure of proteins and nucleic acids, and the relationship between structure and biological function. We used molecular modeling and simulation, a purely computational approach, to study DNA-linked molecular nanowires. We developed a computational tool that allows potential designs to be screened for viability, and then we used molecular dynamics (MD) simulations to test their stability. As an example of using molecular modeling to create experimentally testable hypotheses, we were able to suggest a new design based on pyrrylene vinylene monomers. In another project, we combined experiments and molecular modeling to gain insight into factors that influence the kinetic binding dynamics of fibrin "knob" peptides and complementary "holes." Molecular dynamics simulations provided helpful information about potential peptide structural conformations and intrachain interactions that may influence binding properties. The remaining projects discussed in this thesis all deal with RNA structure. The underlying approach for these studies is a recently developed chemical probing technology called 2'-hydroxyl acylation analyzed by primer extension (SHAPE). One study focuses on ribosomal RNA, specifically the 23S rRNA from T. thermophilus. We used SHAPE experiments to show that Domain III of the T. thermophilus 23S rRNA is an independently folding domain. This first required the development of our own data processing program for generating quantitative and interpretable data from our SHAPE experiments, due to limitations of existing programs and modifications to the experimental protocol. In another study, we used SHAPE chemistry to study the in vitro transcript of the RNA genome of satellite tobacco mosaic virus (STMV). This involved incorporating the SHAPE data into a secondary structure prediction program. The SHAPE-directed secondary structure of the STMV RNA was highly extended and considerably different from that proposed for the RNA in the intact virion. Finally, analyzing SHAPE data requires navigating a complex data processing pipeline. We review some of the various ways of running a SHAPE experiment, and how this affects the approach to data analysis.

L’aneuploïdie a été historiquement associé à des phénotypes nuisibles, notamment le cancer et le syndrome de Down. Cependant, des résultats expérimentaux récents suggèrent que l’aneuploïdie permettrait l'adaptation à des stresseurs variés, notamment résistance aux médicaments, en rendant la compréhension critique au domaine biomédical. Cependant, les mécanismes moléculaires permettant cette adaptation restaient à élucider. Une telle élucidation selon plusieurs axes a été justement l'objet de ce travail. Premièrement, nous avons développé un modèle mathématique représentant l'adaptation aux environnements adverses comme un compromis dans la position dans un espace des traits. L’aneuploïdie y permet une exploration plus rapide. Ce modèle a été validé sur des données expérimentaux et a été utilisé pour prédire une combinaison médicamenteuse ciblant les populations cellulaires hétérogènes dans le cancer du sein. Deuxièmement, nous avons utilisé les concepts du domaine de la biologie en réseaux et des résultats de théorie de graphes pour prédire la distribution des gènes essentiels, des interactions létales et des gènes essentiels évolutifs - des gènes essentiels qui peuvent être supprimés dans des organismes devenus aneuploïdes. Nous avons également construit un algorithme pour prédire les mécanismes moléculaires qui expliquerait les phénotypes associés à des perturbations génétiques à grande échelle. Finalement, nous avons exploré plusieurs mécanismes par lesquels l’aneuploïdie pourrait impacter la régulation génétique, conduisant au développement des outil informatiques publiés
Aneuploidy has historically been associated with detrimental phenotypes and diseases, notably cancer and Down Syndrome. However, recent experimental evidence suggests aneuploidy provides adaptation to numerous stressors, including drug resistance, making aneuploidy study critical to biomedical research. However, the molecular mechanisms underlying this process remained elusive until now. This work focused on exploring several approaches to understanding those mechanisms. Frist, we have developed a general mathematical model of organism adaptation to adverse environments. In our model, the adaptation to environments takes place as a trade-off in the space of traits, of which aneuploidy allows a more efficient and rapid sampling. This model was validated on experimental data and used to predict optimal drug combinations targeting heterogeneous populations breast tumor cells. Second, we used the framework of network biology to model biomolecular networks and apply to them results from the graph theory and existing results on weighted graphs from other domains. We were able to predict the distribution of essential genes, lethal genetic interactions and essential evolvable genes - essential genes that can be deleted in the aneuploid background. We were as well able to build a predictive model for inferring most likely pathways underlying the phenotype of large-scale genetic perturbations. Finally, we attempted to explore several possible modes besides dosage effects by which aneuploidy could impact the gene expression regulation. This required a development of an image analysis toolkit that was validated and released for as open-source software

Despite numerous advances in experimental methodologies capable of addressing the various phenomenon occurring on metal surfaces, atomic scale resolution of the microscopic dynamics remains elusive for most systems. Computational models of the processes may serve as an alternative tool to fill this void. To this end, parallel molecular dynamics simulations of self-diffusion on metal surfaces have been developed and employed to address microscopic details of the system. However these simulations are not without their limitations and prove to be computationally impractical for a variety of chemically relevant systems, particularly for diffusive events occurring in the low temperature regime. To circumvent this difficulty, a corresponding coarse-grained representation of the surface is also developed resulting in a reduction of the required computational effort by several orders of magnitude, and this description becomes all the more advantageous with increasing system size and complexity. This representation provides a convenient framework to address fundamental aspects of diffusion in nonequilibrium environments and an interesting mechanism for directing diffusive motion along the surface is explored. In the ensuing discussion, additional topics including transition state theory in noisy systems and the construction of a checking function for protein structure validation are outlined. For decades the former has served as a cornerstone for estimates of chemical reaction rates. However, in complex environments transition state theory most always provides only an upper bound for the true rate. An alternative approach is described that may alleviate some of the difficulties associated with this problem. Finally, one of the grand challenges facing the computational sciences is to develop methods capable of reconstructing protein structure based solely on readily-available sequence information. Herein a checking function is developed that may prove useful for addressing whether a particular proposed structure is a viable possibility.

Over the last few decades, computer simulation techniques have been established as an essential tool for understanding biochemical processes. This thesis deals mainly with the application of free energy calculations to ribosomal complexes and a cardiac ion channel. The linear interaction energy (LIE) method is used to explore the energetic properties of the essential process of codon–anticodon recognition on the ribosome. The calculations show the structural and energetic consequences and effects of first, second, and third position mismatches in the ribosomal decoding center. Recognition of stop codons by ribosomal termination complexes is fundamentally different from sense codon recognition. Free energy perturbation simulations are used to study the detailed energetics of stop codon recognition by the bacterial ribosomal release factors RF1 and RF2. The calculations explain the vastly different responses to third codon position A to G substitutions by RF1 and RF2. Also, previously unknown highly specific water interactions are identified. The GGQ loop of ribosomal RFs is essential for its hydrolytic activity and contains a universally methylated glutamine residue. The structural effect of this methylation is investigated. The results strongly suggest that the methylation has no effect on the intrinsic conformation of the GGQ loop, and, thus, that its sole purpose is to enhance interactions in the ribosomal termination complex. A first microscopic, atomic level, analysis of blocker binding to the pharmaceutically interesting potassium ion channel Kv1.5 is presented. A previously unknown uniform binding mode is identified, and experimental binding data is accurately reproduced. Furthermore, problems associated with pharmacophore models based on minimized gas phase ligand conformations are highlighted. Generalized Born and Poisson–Boltzmann continuum models are incorporated into the LIE method to enable implicit treatment of solvent, in an effort to improve speed and convergence. The methods are evaluated and validated using a set of plasmepsin II inhibitors.

Numerical simulation of stochastic biochemical reaction networks has received much attention in the growing field of computational systems biology. Systems are frequently modeled as a continuous-time discrete space Markov chain, and the governing equation for the probability density of the system is the (chemical) master equation. The direct numerical solution of this equation suffers from an exponential growth in computational time and memory with the number of reacting species in the model. As a consequence, Monte Carlo simulation methods play an important role in the study of stochastic chemical networks. The stochastic simulation algorithm (SSA) due to Gillespie has been available for more than three decades, but due to the multi-scale property of the chemical systems and the slow convergence of Monte Carlo methods, much work is currently being done in order to devise more efficient approximate schemes. In this thesis we review recent work for the solution of the chemical master equation by direct methods, by exact Monte Carlo methods and by approximate and hybrid methods. We also describe two conceptually different numerical methods to reduce the computational time when studying models using the SSA. A hybrid method is proposed, which is based on the separation of species into two subsets based on the variance of the copy numbers. This method yields a significant speed-up when the system permits such a splitting of the state space. A different approach is taken in an algorithm that makes use of low-discrepancy sequences and the method of uniformization to reduce variance in the computed density function.

The chemical master equation (CME) describes the probability for the discrete molecular copy numbers that define the state of a chemical system. Each molecular species in the chemical model adds a dimension to the state space. The CME is a difference-differential equation which can be solved numerically if the state space is truncated at an upper limit of the copy number in each dimension. The size of the truncated CME suffers from an exponential growth for an increasing number of chemical species. In this thesis the chemical master equation is approximated by a continuous Fokker-Planck equation (FPE) which makes it possible to use sparser computational grids than for CME. FPE on conservative form is used to compute steady state solutions by computation of an extremal eigenvalue and the corresponding eigenvector as well as time-dependent solutions by an implicit time-stepping scheme. The performance of the numerical solution is compared to a standard Monte Carlo algorithm. The computational work for a solutions with the same estimated error is compared for the two methods. Depending on the problem, FPE or the Monte Carlo algorithm will be more efficient. FPE is well suited for problems in low dimensions, especially if high accuracy is desirable.