A probabilistic model for the evaluation of module extraction algorithms in complex biological networks

This thesis presents CiGRAM, a model of complex networks ith known modular structure that is capable of generating realistic graph topology. Much of the recent focus on module detection has been geared towards developing new algorithms capable of detecting biologically significant clusters. However, evaluating clusterings detected by different methods shows that there is little topological agreement or consensus in terms of meta-data despite most methods discovering modules with significant ontology. In this thesis an approach to modelling complex networks with ground-truth modular structure is presented. This approach is capable of generating graphs with heterogeneous degree distributions, high clustering coefficients and assortative degree correlations observed in real data but often ignored in existing benchmarks. Moreover, the model for modular structure concludes that non-modular random graphs are indistinguishable from modules. This model can be tuned to fit many empirical biological and non-biological datasets through fitting target graph summary statistics. The ground-truth structure allows the evaluation of module extraction algorithms in a domain specific context. Furthermore, it was found that degree assortativity appears to negatively impact several module extraction methods such as the popular infomap and modularity maximisation methods. Results presented disagree with other benchmark models highlighting the potential for future research into improving existing methods in ways that challenge assumptions about the detectability of modules

A probabilistic model for the evaluation of module extraction algorithms in complex biological networks

This thesis presents CiGRAM, a model of complex networks ith known modular structure that is capable of generating realistic graph topology. Much of the recent focus on module detection has been geared towards developing new algorithms capable of detecting biologically significant clusters. However, evaluating clusterings detected by different methods shows that there is little topological agreement or consensus in terms of meta-data despite most methods discovering modules with significant ontology. In this thesis an approach to modelling complex networks with ground-truth modular structure is presented. This approach is capable of generating graphs with heterogeneous degree distributions, high clustering coefficients and assortative degree correlations observed in real data but often ignored in existing benchmarks. Moreover, the model for modular structure concludes that non-modular random graphs are indistinguishable from modules. This model can be tuned to fit many empirical biological and non-biological datasets through fitting target graph summary statistics. The ground-truth structure allows the evaluation of module extraction algorithms in a domain specific context. Furthermore, it was found that degree assortativity appears to negatively impact several module extraction methods such as the popular infomap and modularity maximisation methods. Results presented disagree with other benchmark models highlighting the potential for future research into improving existing methods in ways that challenge assumptions about the detectability of modules

Effect of photoperiod and host distribution on the horizontal transmission of Isaria fumosorosea (Hypocreales: Cordycipitaceae) in greenhouse whitefly assessed using a novel model bioassay

A model bioassay was used to evaluate the epizootic potential and determine the horizontal transmission efficiency of Isaria fumosorosea Trinidadian strains against Trialeurodes vaporariorum pharate adults under optimum conditions (25±0.5°C, ~100% RH) at two different photoperiods. Untreated pharate adults were arranged on laminated graph paper at different distributions to simulate varying infestation levels on a leaf surface. Four potential hosts were located 7, 14 and 21 mm away from a central sporulating cadaver simulating high, medium and low infestation levels, respectively. Percent hosts colonized were recorded 7, 12, 14 and 21 days post-treatment during a 16- and 24-h photophase. After 21 days, mean percent hosts colonized at the highest, middle and lowest infestation levels were 93 and 100%, 22 and 58%, 25 and 39% under a 16- and 24-h photophase, respectively. From the results, it was concluded that the longer the photophase, the greater the percentage of hosts colonized, and as host distance increased from the central sporulating cadaver, colonization decreased. The use of this novel model bioassay technique is the first attempt to evaluate the epizootic potential and determine the horizontal transmission efficiency of I. fumosorosea Trinidadian strains under optimal environmental conditions at different photoperiods. This bioassay can be used to assess horizontal transmission efficiency for the selection of fungi being considered for commercial biopesticide development

Density-functional embedding using a plane-wave basis

The constrained electron density method of embedding a Kohn-Sham system in a substrate system (first described by P. Cortona, Phys. Rev. B {\bf 44}, 8454 (1991) and T.A. Wesolowski and A. Warshel, J. Phys. Chem {\bf 97}, 8050 (1993)) is applied with a plane-wave basis and both local and non-local pseudopotentials. This method divides the electron density of the system into substrate and embedded electron densities, the sum of which is the electron density of the system of interest. Coupling between the substrate and embedded systems is achieved via approximate kinetic energy functionals. Bulk aluminium is examined as a test case for which there is a strong interaction between the substrate and embedded systems. A number of approximations to the kinetic-energy functional, both semi-local and non-local, are investigated. It is found that Kohn-Sham results can be well reproduced using a non-local kinetic energy functional, with the total energy accurate to better than 0.1 eV per atom and good agreement between the electron densities.Comment: 11 pages, 4 figure

Isospin Effects in Nuclear Multifragmentation

We develop an improved Statistical Multifragmentation Model that provides the capability to calculate calorimetric and isotopic observables with precision. With this new model we examine the influence of nuclear isospin on the fragment elemental and isotopic distributions. We show that the proposed improvements on the model are essential for studying isospin effects in nuclear multifragmentation. In particular, these calculations show that accurate comparisons to experimental data require that the nuclear masses, free energies and secondary decay must be handled with higher precision than many current models accord.Comment: 46 pages, 16 figure