Natural ecosystems are dynamic and complex, with many being threatened by human activity. However, humans can also be at the root of a solution to this problem by developing ecosystem engineering which can be used to design, construct, modify, upgrade, repair, remediate, and maintain ecosystems. The aim of this project was to improve virtual ecosystems that can be used to increase the knowledge base for ecological engineering by studying adaptability as a factor for the success of species. This was done by analysing adaptive species in a virtual ecosystem, a computer application with which various configurations can be designed and studied in a closed environment. The virtual ecosystems used in this project represent ecosystems in general rather than any specific ecosystem, and allow for repeatable test cases to be run so that ecosystem dynamics can be studied. Adaptability was defined as the ability of an individual to adjust to a short term environmental pressure according to two factors: the adaptation speed, which is how fast an individual can respond to a change in environment, and the adaptive capacity, which is a quantitative indicator of how much the individual is able to adapt. In this project, experiments were performed to determine the effects of adaptability when applied to one aspect of individuals in an ecosystem. From the results of the experiments it was seen that the adaptation speed value could affect the success of a producer species in an ecosystem both positively and negatively. It was also found that ecosystems with both a consumer and producer species could persist longer when adaptability was incorporated into the individuals of the consumer species.

No description available.

Thesis (S.M.)--Joint Program in Biological Oceanography (Massachusetts Institute of Technology, Dept. of Biology; and the Woods Hole Oceanographic Institution), 2008. / "February 2008." / Includes bibliographical references (p. 133). / I build on the deterministic phytoplankton growth model of Sosik et al. by introducing process error, which simulates real variation in population growth and inaccuracies in the structure of the matrix model. Adding a stochastic component allows me to use maximum likelihood methods of parameter estimation. I lay out the method used to calculate parameter estimates, confidence intervals, and estimated population growth rates, then use a simplified three-stage model to test the efficacy of this method with simulated observations. I repeat similar tests with the full model based on Sosik et al., then test this model with a set of data from a laboratory culture whose population growth rate was independently determined. In general, the parameter estimates I obtain for simulated data are better the lower the levels of stochasticity. Despite large confidence intervals around some model parameter estimates, the estimated population growth rates have relatively small confidence intervals. The parameter estimates I obtained for the laboratory data fell in a region of the parameter space that in general contains parameter sets that are difficult to estimate, although the estimated population growth rate was close to the independently determined value. / by Lorraine Thomas. / S.M.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 115-125).

Submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Chemistry, Durban University of Technology, Durban, South Africa, 2017. / In drug discovery and development of anti-tubercular therapeutics, it is necessary to study the physiology and genetics of the molecular mechanisms present in the Mycobacterium tuberculosis. The virulence of M. tuberculosis is attributed to its unique genome, which contains a high frequency of glycine-rich proteins and genes involved in the metabolism of the fatty acids. Consequently, the presence of a diversity of the pathogenic pathways such as acid tolerance and drug resistance mechanisms in M. tuberculosis makes the treatment of Tuberculosis (TB) challenging. However, the molecular basis of the virulence factors involved in the pathogenesis is not fully understood. Accordingly, the current study focuses on better understanding of the pathogenic proteins present in this bacterium using available computational techniques. In South Africa, there is an alarming increase in the drug-resistant TB in HIV co-infected patients, which is one of the biggest challenges to the current anti-tubercular therapies. An extensive literature search showed that the mutations in the virulent proteins of M. tuberculosis resulted in the development of drug tolerance in the pathogen. The molecular and genetic studies identified frequently occurring point mutations associated with the drug resistance in proteins of M. tuberculosis. Despite the efforts, TB infection is still increasing because different pathogenic pathways in the bacterial system are still undiscovered. Therefore, this study involves an in silico approach aimed at the identification of novel drug resistance implicated point mutations. The site- directed mutations leading to the development of resistance against four first-line drugs (Ethambutol, Isoniazid, Rifampicin, and Streptomycin) were studied extensively. In the primary investigation, pathogenic mutational landscapes were classified in the sequences of the studied proteins. The effects of these mutations on the stability of the proteins were studied using diverse computational techniques. The structural basis of the point mutations with the highest destabilizing effects was analyzed using the principles of the Density Functional Theory (DFT), molecular docking and molecular dynamics (MD) simulation studies. The varied conformational behavior resulted from these predicted substitutions were compared with the experimentally derived mutations reported in the literature. The outcome of this study enabled the identification of the novel drug resistance-associated point mutations which were not previously reported. Furthermore, a detailed understanding of the conformational behavior of diverse virulent proteins present in M. tuberculosis was also generated in this study. Literature study showed that inside the host’s macrophage cells, the virulent proteins such as isocitrate lyase, lipase lipF, magnesium transporter MgtC, porin protein OmpATb, a protein of two component systems PhoP, Rv2136c and Rv3671c have an established role in the development of the acid tolerance. On the other hand, information regarding their role in the acid resistance is scarce. Accordingly, the structural basis of their role in acid resistance was analyzed using constant pH based MD simulations. In the studied proteins, the lipF and PhoP showed highest structural stability in highly acidic conditions throughout the course of MD simulations. Therefore, these proteins may play a primary role in the process of resistance. In addition to these pathogenic proteins, there is a need to identify new undiscovered virulent proteins in the genome of M. tuberculosis, which increases the efficiency of the current therapy. The knowledge generated by the analyses of the proteins involved in resistance and pathogenic mechanisms of M. tuberculosis forms the basis for the identification of new virulence factors. Therefore, an in silico protocol was used for the functional annotations and analyses of the virulence characteristics. M. tuberculosis contains 1000 Hypothetical Proteins (HPs), which are functionally uncharacterized proteins and their existence was not validated at the biochemical level. In this study, the sequences of the HPs were extensively analyzed and the functions of 662 HPs were successfully predicted. Furthermore, 483 HPs were classified in the category of the enzymes, 141 HPs were predicted to be involved in the diverse cellular mechanisms and 38 HPs may function as transporters and carriers proteins. The 307 HPs among this group of proteins were less precisely predicted because of the unavailability of the reliable functional homologs. An assessment of the virulence characteristics associated with the 1000 HPs enabled the classification of 28 virulent HPs. The structure of six HPs with highest predicted virulence score was analyzed using molecular modelling techniques. Amongst the predicted virulent HPs, the clone for Rv3906c purchased from the DNASU repository because of the ease of its availability. The gene of Rv3906c was isolated and cloned into a pET-21c expression vector. The analyses of the nucleotide sequence showed that Rv3906c gene (500 bp) encodes a 169 amino acid protein of molecular weight 17.80 kDa (~18.0 kDa). The sequence analyses of Rv3906c showed that the HPs showed high similarities with pullulanase, a thermophilic enzyme. The stability profile at different temperatures for Rv3906c generated using MD simulations showed that Rv3906c maintained its structural identity at higher temperatures. It is expected that this study will result in the design of better therapeutic against the infection of M. tuberculosis, as novel undiscovered virulence factors were classified and analyzed in addition to the conformational profiles of the virulent proteins involved in the resistance mechanisms. / M

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg. in fulfilment of the requirements tor the degree of Master of Science / This thesis develops the theoretical basis of the qualitative frame based modelling technique, a paradigm recently proposed by Starfield for the modelling of ecosystems with a multiplicity of stable states. This technique is a refinement of the State-and- Transition conceptual model of Westoby et al which involves the division of the ecosystem dynamics into a catelog of stable 'states' and a suite of transitions between these states. The frame models of Starfield associate with each stable configuration of the ecosystem a qualitative rule based model for the key processes in that stable configuration. The aims of this thesis are the following, 1. A rigorous definition of frame modelling of dynamic ecosystems is proposed, and this theoretical foundation is used to demonstrate that qualitative frame models may be used to mode! dynamic ecosystems to an arbitrary accuracy. 2. The development of implementation software. A qualitative rule based frame modelling environment is presented. and a specification for an improved environment is proposed based on the theoretical work. / Andrew Chakane 2019

No description available.

The prediction of correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n⁴), so the computational requirements become prohibitive as the length increases. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs mfold and RNAfold for folding large RNA viral sequences and achieves comparable accuracy of prediction. We analyze the algorithm's concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. We provide a rigorous proof of correctness of an optimized algorithm for internal loop calculations called internal loop speedup algorithm (ILSA), which reduces the time complexity of internal loop computations from O(n⁴) to O(n³) and show that the exact algorithms such as ILSA are executed with our method in affordable amount of time. The proof gives insight into solving these kinds of combinatorial problems. We have documented detailed pseudocode of the algorithm for predicting minimum free energy secondary structures which provides a base to implement future algorithmic improvements and improved thermodynamic model in GTfold. GTfold is written in C/C++ and freely available as open source from our website.

Orientadores: Anamaria Gomide, Jorge Stolfi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-19T08:33:31Z (GMT). No. of bitstreams: 1 Rodrigues_ElisadeCassiaSilva_M.pdf: 2606848 bytes, checksum: 5290f0c06ecad44b5b23aa69a8920245 (MD5) Previous issue date: 2011 / Resumo: Métodos de deformação são importantes em áreas como modelagem geométrica e animação computacional. Na biologia, a modelagem de forma, crescimento, movimento e patologias de organismos microscópicos vivos ou células requerem deformações suaves, as quais são essencialmente 2D com poucas mudanças de profundidade. Nesta dissertação, apresentamos um método de deformação do espaço 2.5D suave. O modelo 3D do organismo é modificado deformando uma grade de controle formada por prismas que o envolve. A técnica de interpolação spline é usada para satisfazer o requisito de suavidade ('C POT. 1'). Implementamos esse método em um editor que torna possível definir e modificar a deformação de uma forma amigável usando o mouse. Os resultados experimentais mostram que o método é simples e efetivo / Abstract: Shape deformation methods are important in such fields as geometric modeling and computer animation. In biology, the modeling of shape, growth, movement and pathologies of living microscopic organisms or cells requires smooth deformations, which are essentially 2D with little change in depth. In this master thesis, we present a smooth 2.5D space deformation method. The 3D model of the organism is modified by deforming an enclosing control grid of prisms. Spline interpolation is used to satisfy the smoothness ('C POT. 1') requirement. We implemented this method in an editor which makes it possible to define and modify the deformation with the mouse in a user-friendly way. The experimental results show that the method is simple and effective / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Submitted in the fulfillment of the requirement for the Degree of Master's in Chemistry, Durban University of Technology, 2017. / P53 is a tumour suppressor protein that is dysfunctional in most human cancer cells. Mutations in the p53 genes result in the expression of mutant proteins which accumulate to high levels in tumour cells. Several studies have shown that majority of the mutations are concentrated in the DNA-binding domain where they destabilize its conformation and eliminate the sequence- specific DNA-binding to abolish p53 transcription activities. Accordingly, this study involved an investigation of the effects of mutations associated with cancer, based on the framework of sequences and structures of p53 DNA-binding domains, analysed by SIFT, Pmut, I-mutant, MuStab, CUPSAT, EASY-MM and SDM servers. These analyses suggest that 156 mutations may be associated with cancer, and may result in protein malfunction, including the experimentally validated mutations. Thereafter, 54 mutations were further classified as disease- causing mutations and probably have a significant impact on the stability of the structure. The detailed stability analyses revealed that Val143Asp, Ala159Pro, Val197Pro, Tyr234Pro, Cys238Pro, Gly262Pro and Cys275Pro mutations caused the highest destabilization of the structure thus leading to malfunctioning of the protein. Additionally, the structural and functional consequences of the resulting highly destabilizing mutations were explored further using molecular docking and molecular dynamics simulations. Molecular docking results revealed that the p53 DNA-binding domain loses its stability and abrogates the specific DNA-binding as shown by a decrease in binding affinity characterized by the ZRANK scores. This result was confirmed by the residues Val143Asp, Ala159Pro, Val197Pro, Tyr234Pro and Cys238Pro p53-DNA mutant complexes inducing the loss of important hydrogen bonds, and introduced non-native hydrogen bonds between the two biomolecules. Furthermore, Molecular dynamics (MD) simulations of the experimental mutant forms showed that the structures of the p53 DNA-binding domains were more rigid comparing to the wild-type structure. The MD trajectories of Val134Ala, Arg213Gly and Gly245Ser DNA-binding domain mutants clearly revealed a loss of the flexibility and stability by the structures. This might affect the structural conformation and interfere with the interaction to DNA. Understanding the effects of mutations associated with cancer at a molecular level will be helpful in designing new therapeutics for cancer diseases. / M