Tibor Ganti proposed the chemoton as the minimal system capable of open-ended evolution, consisting of three stoichiometrically couplec autocatalytic systems, a metabolism, a membrane and a template replicating system. Our models show that an autocatalytic metabolic system couJd evolve only if chemical and physical niches could be discovered that limited the extent of tapping side-reactions.

The goal of this project is to develop a method for using an agent-based approach for modelling bacterial behaviour without the need for accurate parameters. The parameters, when available, are optimised using a genetic algorithm and are tested using data from experimental biological results. The parameter optimisation step, through genetic algorithms coupled to an agent-based model is new and adds an extra level of testing to biological hypotheses. To begin with a differential equation model was constructed to simulate basic interaction of the bacterial cells with a host. This was compared with a simple agent-based model. It was established that even though the two systems could produce a similar outcome, they are fundamentally different. The discrete aspect of agent-based modelling was further explored in testing a basic biological experiment with a goal to measure the frequency of the cells to move from not developing fimbriae (OFF) to developing fimbriae (ON). In experimental data an anomaly was found at roughly one in seven of the experiments. Computer modelling established that the cause was related to how many cells were present at the beginning of the experiment (assumption was a maximum of one per flask) and how many of those were fimbriate at the start (assumption was none). A combination of statistics and modelling showed that one in seven flasks contained two cells or more and that a higher percentage of the cells was fimbriate at the start of the experiment. The computer model was further enhanced to establish the best hypothesis on how regulatory protein and their antagonist are able to interact and bind to the regulatory sites on the DNA. In this case the focus was on fimB expression, responsible for turning on the switch to enable the production of fimbriae. From the modelling it was established that H·NS binds to two sites on the DNA, but its effect is curilUlative rather than cooperative. SlyA can bind to two other sites to partially remove the repression of H·NS on fimB expression. Other sites and further hypotheses were tested, among them the assumption that H-NS and SlyA are involved in temperature regulation of limB expression. The assumptions made on how the temperature regulation functions were proven to be invalid by both biological experiments and by computer modelling. A working hypothesis was not established. Finally to test if the agent-based model with parameter optimisation could be applied for different systems it was attempted to model chemotaxis. Reaction rates for the processes within chemotaxis are known and the computer model was adapted so the parameter optimisation would lead the bacteria to a location with an optimum amount of nutrients, providing US with panmeters we can compare with literature. Not all parameters matched up with those provided, but the bacteria were able to find an optimum and a potential for using the parameters absolute values was established. All in all an agent-based computer model that is capable of functioning with- out (accurate) parameters has been established and the model can be used in a wide variety of applications to test hypotheses and potentially predict biological parameters.

Biological systems typically generate complex data that encapsulate the dynamics of interactions among measurables over time. To support the formation of insights into time series data from a biological system, there is a requirement to develop new methods that can analyse and translate such complex data into a form that allows trends, patterns, and predictions to be easily viewed, verified and tested. Here, a suite of novel analytical and matrix-based techniques for dynamical systems modelling are developed that are time-efficient and data-driven. These techniques facilitate a range of scientific analyses through novel matrix-based system identification and parameter estimation methods. The inference techniques are fast, optimised, and do not require a priori information to successfully infer network of interactions or automatically construct data-consistent models from data. Two distinct principal (Jacobian and power-law) models (solutions) that are data-consistent may be constructed from a single time series data set. A recast technique has also been developed to reconstruct either one of the principal models from the other, providing support for model interoperability and multiple model integration. The thesis demonstrates the effectiveness of a new theoretical framework developed to incorporate a modelling and visualization pipeline able to deal with a wide range of time-series data sets relating to complex biological systems. The integrated framework is able to infer and depict interaction networks implicit in time series data in just a matter of seconds and then display the evolution of that network dynamics in response to network perturbation such as drug treatments. Beyond this, there is a broader contribution to the field of biochemical system theory (BST), evidenced by establishing methods for transforming a constructed jacobian model to equivalent power-law models, and vice versa. The effectiveness of these new techniques is demonstrated using artificial time series data samples, simulated pseudo-data of biologically plausible models of real biological systems, and real experimental data derived from biological experiments.

Artificial Chemistry seeks to explore how life-like systems can emerge from a pre-biotic environment. This thesis begins with the background of this research area and a re-implementation of an existing Artificial Chemistry as a case study. From this basis, ingredients and properties of Artificial Chemistries are identified. This leads to a novel form of molecular representation -- sub-symbolic. A group of novel Artificial Chemistries called RBN-World is developed using Random Boolean Networks as a sub-symbolic molecular representation. It is shown that RBN-World has several properties of interest, and variants of RBN-World and elemental subsets with those properties are identified from many alternatives. This thesis concludes by comparing RBNWorld to the case study and properties discussed earlier, and identifies avenues for future work.

Molecular docking algorithms are computational methods that predict the binding site and docking pose of specified ligands with a protein target. They have proliferated in recent years, due to the explosion of structural data in biology. Oxdock is an algorithm that uses various techniques to simplify this complex task, the most significant being the use of a multiscale approach to analyse the problem using a simple representation in the early stages. Oxdock is shown to be a very useful tool in computational biology, as exemplified by two cases. The first case is the analysis of the NMDA subclass of neuronal glutamate receptors and the subsequent elucidation of their function. The second is the investigation of the newly discovered plant glutamate receptors and the clarification of their natural ligands. The results in both instances open new areas of research into exciting areas of biology. Despite its effectiveness in solving many problems, Oxdock does fail in a number of circumstances. It is thus important to devise a new and improved method for molecular docking. This is achieved by combining the speed of the multiscale approach with the optimising ability of Evolutionary Programming. This yields an algorithm that is shown to be precise, accurate and specific. The new algorithm, Eve, is then modified to illustrate its potential in both lead optimisation and de novo drug design. These capacities, combined with its ability to predict the location of binding sites and the docking pose of a ligand, highlight the promise of computational methods in solving problems in many areas of biological chemistry.