Alexander P. Turner

Transplantation of Autologous Differentiated Urothelium in an Experimental Model of Composite Cystoplasty

Background Enterocystoplasty is associated with serious complications resulting from the chronic interaction between intestinal epithelium and urine. Composite cystoplasty is proposed as a means of overcoming these complications by substituting intestinal epithelium with tissue-engineered autologous urothelium. Objective To develop a robust surgical procedure for composite cystoplasty and to determine if outcome is improved by transplantation of a differentiated urothelium. Design, setting, and participants Bladder augmentation with in vitro–generated autologous tissues was performed in 11 female Large-White hybrid pigs in a well-equipped biomedical centre with operating facilities. Participants were a team comprising scientists, urologists, a veterinary surgeon, and a histopathologist. Measurements Urothelium harvested by open biopsy was expanded in culture and used to develop sheets of nondifferentiated or differentiated urothelium. The sheets were transplanted onto a vascularised, de-epithelialised, seromuscular colonic segment at the time of bladder augmentation. After removal of catheters and balloon at two weeks, voiding behaviour was monitored and animals were sacrificed at 3 months for immunohistology. Results and limitations Eleven pigs underwent augmentation, but four were lost to complications. Voiding behaviour was normal in the remainder. At autopsy, reconstructed bladders were healthy, lined by confluent urothelium, and showed no fibrosis, mucus, calculi, or colonic regrowth. Urothelial morphology was transitional with variable columnar attributes consistent between native and augmented segments. Bladders reconstructed with differentiated cell sheets had fewer lymphocytes infiltrating the lamina propria, indicating more effective urinary barrier function. Conclusions The study endorses the potential for composite cystoplasty by (1) successfully developing reliable techniques for transplanting urothelium onto a prepared, vascularised, smooth muscle segment and (2) creating a functional urothelium-lined augmentation to overcome the complications of conventional enterocystoplasty.

Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Biological organisms exist within environments in which complex nonlinear dynamics are ubiquitous. They are coupled to these environments via their own complex dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behavior of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organization of biochemical networks. We refer to these as artificial biochemical networks and show how they can evolve to control trajectories within three behaviorally diverse complex dynamical systems: 1) the Lorenz system; 2) Chirikovs standard map; and 3) legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks (ABNs). We find both approaches to be advantageous in certain situations, though we note that the relative tradeoffs between different models of ABN strongly depend on the type of dynamical systems being controlled.

The incorporation of epigenetics in artificial gene regulatory networks.

Artificial gene regulatory networks are computational models that draw inspiration from biological networks of gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world, such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper describes a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. Our results demonstrate that AERNs are more adept at controlling multiple opposing trajectories when applied to a chaos control task within a conservative dynamical system, suggesting that AERNs are an interesting area for further investigation.

Computational models of signalling networks for non-linear control

Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

Biochemical connectionism

In this paper, we discuss computational architectures that are motivated by connectionist patterns that occur in biochemical networks, and speculate about how this biochemical approach to connectionism might complement conventional neural approaches. In particular, we focus on three features of biochemical networks that make them distinct from neural networks: their diverse, complex nodal processes, their emergent organisation, and the dynamical behaviours that result from higher-order, self-modifying processes. We also consider the growing use of evolutionary algorithms in the design of connectionist systems, noting how this enables us to explore a wider range of connectionist architectures, and how the close relationship between biochemical networks and biological evolution can guide us in this endeavour.

The artificial epigenetic network

In this paper we describe an Artificial Gene Regulatory Network (AGRN), whose form and function are inspired by biological epigenetics. This new architecture, termed an Artificial Epigenetic Network (AEN), is applied to the coupled inverted pendulum task, a control task that has complex non-linear dynamics. The AENs show significant benefits over previous AGRNs. Firstly, when applied to the coupled inverted pendulum task, they show a significant performance increase. In addition, the AENs self-partition, applying different genes to control different dynamics within the task, which is more analogous to gene regulation in nature. These networks also make it possible to gain user control over the dynamics of the network via the modification of the epigenetic layer.

Using artificial epigenetic regulatory networks to control complex tasks within chaotic systems

Artificial gene regulatory networks are computational models which draw inspiration from real world networks of biological gene regulation. Since their inception they have been used to infer knowledge about gene regulation and as methods of computation. These computational models have been shown to possess properties typically found in the biological world such as robustness and self organisation. Recently, it has become apparent that epigenetic mechanisms play an important role in gene regulation. This paper introduces a new model, the Artificial Epigenetic Regulatory Network (AERN) which builds upon existing models by adding an epigenetic control layer. The results demonstrate that the AERNs are more adept at controlling multiple opposing trajectories within Chirikovs standard map, suggesting that AERNs are an interesting area for further investigation.

Adaptive robotic gait control using coupled artificial signalling networks, hopf oscillators and inverse kinematics

A novel bio-inspired architecture comprising three layers is introduced for a six-legged robot in order to generate adaptive rhythmic locomotion patterns using environmental information. Taking inspiration from the intracellular signalling processes that decode environmental information, and considering the emergent behaviours that arise from the interaction of multiple signalling pathways, we develop a decentralised robot controller composed of a collection of artificial signalling networks. Crosstalk, a biological signalling mechanism, is used to couple such networks favouring their interaction. We also apply nonlinear oscillators to model gait generators, which induce symmetric and rhythmical locomotion movements. The trajectories are modulated by a coupled artificial signalling network, which yields adaptive and stable robotic locomotive patterns. Gait trajectories are converted into joint angles by means of inverse kinematics. The architecture is implemented in a simulated version of the real robot T-Hex. Our results demonstrate the ability of the architecture to generate adaptive and periodic gaits.

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network

The artificial epigenetic network (AEN) is a computational model which is able to topologically modify its structure according to environmental stimulus. This approach is inspired by the functionality of epigenetics in nature, specifically, processes such as chromatin modifications which are able to dynamically modify the topology of gene regulatory networks. The AEN has previously been shown to perform well when applied to tasks which require a range of dynamical behaviors to be solved optimally. In addition, it has been shown that pruning of the AEN to remove non-functional elements can result in highly compact solutions to complex dynamical tasks. In this work, a method has been developed which provides the AEN with the ability to self prune throughout the optimisation process, whilst maintaining functionality. To test this hypothesis, the AEN is applied to a range of dynamical tasks and the most optimal solutions are analysed in terms of function and structure.