MULTI-OBJECTIVE DESIGN AUTOMATION FOR RECONFIGURABLE MULTI-PROCESSOR SYSTEMS

Ph.DDOCTOR OF PHILOSOPH

A review of modelling and verification approaches for computational biology

This paper reviews most frequently used computational modelling approaches and formal verification techniques in computational biology. The paper also compares a number of model checking tools and software suits used in analysing biological systems and biochemical networks and verifiying a wide range of biological properties

Creation and Application of Various Tools for the Reconstruction, Curation, and Analysis of Genome-Scale Models of Metabolism

Systems biology uses mathematics tools, modeling, and analysis for holistic understanding and design of biological systems, allowing the investigation of metabolism and the generation of actionable hypotheses based on model analyses. Detailed here are several systems biology tools for model reconstruction, curation, analysis, and application through synthetic biology. The first, OptFill, is a holistic (whole model) and conservative (minimizing change) tool to aid in genome-scale model (GSM) reconstructions by filling metabolic gaps caused by lack of system knowledge. This is accomplished through Mixed Integer Linear Programming (MILP), one step of which may also be independently used as an additional curation tool. OptFill is applied to a GSM reconstruction of the melanized fungus Exophiala dermatitidis, which underwent various analyses investigating pigmentogenesis and similarity to human melanogenesis. Analysis suggest that carotenoids serve a currently unknown function in E. dermatitidis and that E. dermatitidis could serve as a model of human melanocytes for biomedical applications. Next, a new approach to dynamic Flux Balance Analysis (dFBA) is detailed, the Optimization- and Runge-Kutta- based Approach (ORKA). The ORKA is applied to the model plant Arabidopsis thaliana to show its ability to recreate in vivo observations. The analyzed model is more detailed than previous models, encompassing a larger time scale, modeling more tissues, and with higher accuracy. Finally, a pair of tools, the Eukaryotic Genetic Circuit Design (EuGeneCiD) and Modeling (EuGeneCiM) tools, is introduced which can aid in the design and modeling of synthetic biology applications hypothesized using systems biology. These tools bring a computational approach to synthetic biology, and are applied to Arabidopsis thaliana to design thousands of potential two-input genetic circuits which satisfy 27 different input and logic gate combinations. EuGeneCiM is further used to model a repressilator circuit. Efforts are ongoing to disseminate these tools to maximize their impact on the field of systems biology. Future research will include further investigation of E. dermatitidis through modeling and expanding my expertise to kinetic models of metabolism. Advisor: Rajib Sah

UQ and AI: data fusion, inverse identification, and multiscale uncertainty propagation in aerospace components

A key requirement for engineering designs is that they offer good performance across a range of uncertain conditions while exhibiting an admissibly low probability of failure. In order to design components that offer good performance across a range of uncertain conditions, it is necessary to take account of the effect of the uncertainties associated with a candidate design. Uncertainty Quantification (UQ) methods are statistical methods that may be used to quantify the effect of the uncertainties inherent in a system on its performance. This thesis expands the envelope of UQ methods for the design of aerospace components, supporting the integration of UQ methods in product development by addressing four industrial challenges. Firstly, a method for propagating uncertainty through computational models in a hierachy of scales is described that is based on probabilistic equivalence and Non-Intrusive Polynomial Chaos (NIPC). This problem is relevant to the design of aerospace components as the computational models used to evaluate candidate designs are typically multiscale. This method was then extended to develop a formulation for inverse identification, where the probability distributions for the material properties of a coupon are deduced from measurements of its response. We demonstrate how probabilistic equivalence and the Maximum Entropy Principle (MEP) may be used to leverage data from simulations with scarce experimental data- with the intention of making this stage of product design less expensive and time consuming. The third contribution of this thesis is to develop two novel meta-modelling strategies to promote the wider exploration of the design space during the conceptual design phase. Design Space Exploration (DSE) in this phase is crucial as decisions made at the early, conceptual stages of an aircraft design can restrict the range of alternative designs available at later stages in the design process, despite limited quantitative knowledge of the interaction between requirements being available at this stage. A histogram interpolation algorithm is presented that allows the designer to interactively explore the design space with a model-free formulation, while a meta-model based on Knowledge Based Neural Networks (KBaNNs) is proposed in which the outputs of a high-level, inexpensive computer code are informed by the outputs of a neural network, in this way addressing the criticism of neural networks that they are purely data-driven and operate as black boxes. The final challenge addressed by this thesis is how to iteratively improve a meta-model by expanding the dataset used to train it. Given the reliance of UQ methods on meta-models this is an important challenge. This thesis proposes an adaptive learning algorithm for Support Vector Machine (SVM) metamodels, which are used to approximate an unknown function. In particular, we apply the adaptive learning algorithm to test cases in reliability analysis.Open Acces

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u