This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigor and transparency in development and application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a model's development, underpinning biological knowledge, and analyses of model behaviors are constructed to determine the confidence that a model is fit for the purpose for which it will be applied.

Automata chemistries are good vehicles for experimentation in open-ended evolution, but they are by necessity complex systems whose low-level properties require careful design. To aid the process of designing automata chemistries, we develop an abstract model that classifies the features of a chemistry from a physical (bottom up) perspective and from a biological (top down) perspective. There are two levels: things that can evolve, and things that cannot. We equate the evolving level with biology and the non-evolving level with physics. We design our initial organisms in the biology, so they can evolve. We design the physics to facilitate evolvable biologies. This architecture leads to a set of design principles that should be observed when creating an instantiation of the architecture. These principles are Everything Evolves, Everything's Soft, and Everything Dies. To evaluate these ideas, we present experiments in the recently developed Stringmol automata chemistry. We examine the properties of Stringmol with respect to the principles, and so demonstrate the usefulness of the principles in designing automata chemistries.

The malfunction of safety-critical systems may cause damage to people and the environment. Software within those systems is rigorously designed and verified according to domain specific guidance, such as ISO26262 for automotive safety. This paper describes academic and industrial co-operation in tool development to support one of the most stringent of the requirements --- achieving full code coverage in requirements-driven testing. We present a verification workflow supported by a tool that integrates the coverage measurement tool RapiCover with the test-vector generator FShell. The tool assists closing the coverage gap by providing the engineer with test vectors that help in debugging coverage-related code quality issues and creating new test cases, as well as justifying the presence of unreachable parts of the code in order to finally achieve full effective coverage according to the required criteria. To illustrate the practical utility of the tool, we report about an application of the tool to a case study from automotive industry.

We want to implement a variety of computer programs capable of generating unbounded novelty via emergent evolution. We also want a principled, structured way of analysing our novelty-generation programs and improving them over time.We have developed a definition of embodiment in terms of a conceptual framework of hierarchical phenomena, mechanisms, and worlds, for use in analysing and building novelty-generation programs. We present these concepts and demonstrate them on two existing artificial chemistry systems: Stringmol and GraphMol. Our two demonstration systems behave in very different ways. We describe these differences, explaining why they arise in terms of our framework. The systems better able to generate novelty have more embodied implementations.

We want to implement a variety of computer programs capable of generating unbounded novelty via emergent evolution.We also want a principled, structured way of analysing our novelty-generation programs and improving them over time.We have developed a definition of embodiment in terms of a conceptual framework of hierarchical phenomena, mechanisms, and worlds, for use in analysing and building noveltygeneration programs.We present these concepts and demonstrate them on two existing artificial chemistry systems: Stringmol and GraphMol.Our two demonstration systems behave in very different ways.We describe these differences, explaining why they arise in terms of our framework.The systems better able to generate novelty have more embodied implementations.

Meta-evolution, the ability to generate novel ways of generating novelty, is one of the major goals of Artificial Life research. Biological systems can be seen to perform open-ended meta-evolution, but unfortunately this has proved very difficult to replicate within computer programs. This thesis defines a theoretical framework for thinking about the issues in this area and charting out possible solutions. We use this framework to take some tentative steps towards meta-evolutionary algorithms. We begin with a review of how meta-evolution has developed historically, through different novelty-generation algorithms. We describe previous algorithms in terms of their biological model and their computational model, to distinguish their biological inspiration from their computational implementation details. We conclude that the route to meta-evolution lies in enriching the biological models of current algorithms, rather than their computational ones. We use the theoretical idea of embodiment to analyse both biological and computational systems, and decompose the problem of achieving meta-evolution. We present a new definition of embodiment, allowing the concept to be applied to biological systems. We conclude that biological systems achieve meta-evolution by their embodiment within the physical world. We notice that current computational systems have poor embodiment within their virtual worlds. This leads to the hypothesis that improving the embodiment of computational systems, within virtual worlds, is a route to improving their meta-evolution. In discussing how embodiment can be realised in virtual worlds, we use Artificial Chemistries as a language in which to program embodiment. We present a new definition of Artificial Chemistries, mapping their components onto our definition of embodiment. We end by presenting a new Artificial Chemistry, GraphMol, designed with embodiment in mind. We use GraphMol to embody the process of copying a string, and analyse its meta-evolution via a range of different experiments.

We address the process of copying in Artificial Life organisms. Copying is a source of mutations, a crucial component in evolution. We propose that rich copying mechanisms, and thereby rich evolutionary systems, can be obtained by embodying the copying process in a lower-level simulation. We demonstrate an embodied copying process that has the potential to alter its own mutation rate, without having the concept of a mutation rate parameter explicit in the system.

We hypothesise that degeneracy in the components of an artificial chemistry (AChem) facilitates the complexity of the system as a whole. We introduce definitions of degeneracy and redundancy, and show how these quantities can be calculated for the binding system of an AChem. We present a case study using the AChem Stringmol, in order to support our hypothesis. We demonstrate that the binding system in Stringmol has degeneracy and we create a deliberately poor variant: ‘sticky-Stringmol’, that has a binding system with no degeneracy. Comparing sticky-Stringmol to Stringmol, we note the loss of many simulation artifacts that have been used as evidence of the complexity of Stringmol, including: emergent macro-mutations, hypercycles, sweeps and parasite evasion. These results are evidence that degeneracy in the components of an AChem facilitates the complexity of the system as a whole.

We model some of the crucial properties of biological novelty generation, and abstract these out into minimal requirements for an ALife system that exhibits constant novelty generation (open ended evolution) combined with robustness. The requirements are an embodied genome that supports runtime metaprogramming (‘self modifying code’), generation of multiple behaviours expressible as interfaces, and specialisation via (implicit or explicit) removal of interfaces. The main application of self modifying code to date has been top down, in the branch of Artificial Intelligence concerned with learning to learn. However, here we take the bottom up Artificial Life philosophy seriously, and apply the concept to low level behaviours, in order to develop emergent novelty.

We introduce multi-level Artificial Chemistries as a way of tackling difficult problems in the evolution of complexity. We present two algorithms for moving between levels of abstraction in a multi-level Artificial Chemistry. (1) Moving upwards from a low-level description to a high-level description involves making approximations. We discuss these, and provide an algorithm to perform the approximations. (2) Moving downwards is more problematic. We discuss the issues involved in moving down, including conservation of mass. We present an algorithm to generate constraints that any lowlevel implementation of the system must satisfy. These constraints can be used to: obtain information about the system; automatically generate a low-level implementation of the system; guide a search for suitable low-level implementations of the system.

We use Artificial Chemistries (ACs) as a way of addressing problems in Artificial Life (ALife) and evolution, by considering Eigen’s paradox — small replicators with poor fidelity can not encode sufficient information to build a replicator with improved fidelity. We describe three AC case studies for different periods in the early evolution of the earth. From these, we discuss more general properties that are useful for ACs to possess for evolution, and compare our properties to those described by other authors. We do not present a resolution of Eigen’s paradox; rather we demonstrate a way of thinking about AC in the context of early evolution. Eigen’s paradox is one key issue in this period. We use ACs as a model paradigm and from these we extract relevant properties that can be considered separately from the specific ACs that informed them; these properties can be used to inform design and analysis of future ACs.

We have developed an artificial chemistry that allows selfmaintaining molecular systems to mutate and exhibit innovative behaviour. The molecular species in the chemistry are defined by strings of symbols that specify both the binding affinity and the reaction. We define a replicase molecule that can copy any other molecule that binds at a particular region on the replicase. Molecules are copied on a symbolby-symbol basis. Occasional mis-copying of an individual symbol forms our mutation scheme. This paper describes the characteristics of the resulting evolutionary system. We ran 1,000 open-ended trials and observed an unexpectedly wide range of emergent phenomena, with many parallels to biological systems. We report these phenomena in qualitative terms, and give details of one of the most interesting among them: the emergence of co-dependent replicase hypercycles.

Bacteria offer an evolutionary model in which rich interactions between phenotype and genotype lead to compact genomes with efficient metabolic pathways.We seek an analogous computational process that supports a rich artificial heredity. These systems can be simulated by stochastic chemistry models, but there is currently no scope for open-ended evolution of the molecular species that make up the models. Instruction-set based Artifical Life has appropriate evolutionary properties, but the individual is represented as a single executing sequence with little additional physiology. We describe a novel combination of stochastic chemistries and evolvable molecule microprograms that gives a rich evolutionary framework. A single organism is represented by a set of exectuing sequences. Key to this approach is the use of inexact sequence matching for binding between individual molecules and for branching of molecular microprograms. We illustrate the approach by implementation of two steady-state replicase RNA analogues that demonstrate "invasion when rare".