Regional specification, or pattern formation, is the process by which developing cells in different regions are switched into different developmental pathways. We investigate this process through an ALife model of multicellular development using fractal proteins, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns, that in turn can be used to solve problems. Here fractal gene regulatory networks are incorporated into a multicellular model of development, and tested on the morphological problem of regional specification, using Map-Elites to explore the space of solutions. The results indicate the ability of this system to learn regularities in solutions and automatically create and use developmental modules, illustrating how an artificial system can replicate some of the fundamental processes of development.

We can talk about learning optimisation in terms of three biological processes: evolution, development and learning. It has been argued that all three are necessary for intelligence to emerge. Together, they shape the brain through regressive and progressive plasticity. In this paper, we explored the effects of structural plasticity on learning in spiking neural networks with spike-timing-dependent plasticity: first, we systematically analysed three synapse pruning approaches (random, weight-dependent and activity-dependent) and their effects on networks’ weights, spiking activity and performance on a clustering task. Then, we examined the use of a minimalistic evolutionary approach to develop growth rules for spiking neural networks with or without pruning. We found that pruning combined with a simple weight homeostasis mechanism can be used to reduce spiking neural networks’ size without a performance loss; pruning of weak connections increases the learning rate. Evolution of developmental rules led to a rapid fitness increase of the rudimentary embryo networks; addition of pruning significantly improved the learning rate of the model, and synaptic homeostasis preserved stable spiking activity in the networks even during drastic growth.

A regular feature of cells in most tumours is an abnormal number of chromosomes – a feature known as aneuploidy. A key mechanism towards this state is whole chromosome mis-segregation (CMS), whose role in cancer is still debated. For a long time, CMS was considered a side effect of oncogenesis, however recent research suggests instead a role as a key initiating driver of malignant transformation. Specifically, whether the mechanism of CMS can lead to the kind of mutational signature observed in early stage tumours is unknown. Furthermore, the signalling pathways themselves are still being elucidated, and the impact that these different mechanisms have on the network are yet not defined. Because of the high biological complexity, experimental limitations and overall uncertainty, ALife methods are well suited to untangle the role of CMS and shed light on its role in oncogenesis. Here we investigate the effects that CMS and point mutation have on a biologically inspired genome, implemented in silico though a gene-regulatory network (GRN) within an agent-based model (ABM). Importantly, the implementation aims to mimic real biology to facilitate possible emergent features. Each cell is equipped with chromosomes containing abstractions of key interconnected genes that are known to play a role in many cancers. We compare the effects of random mutations, where a gene is functionally altered, against CMS, where many genes are lost or gained simultaneously. Our results show that CMS is a viable mechanism for oncogenesis. Comparing CMS with the more traditional view of mutation accumulation, we show that both share similar emergent phenotypes, but that they are genotypically different. We highlight that loss of tumour suppression by either means might be the first step towards oncogenesis, and conclude that cancers probably utilize these two mechanisms in tandem. Finally, we propose that measurements of these aberrations could help to better characterize the evolution of tumours.

Colorectal cancer (CRC) is the second most common tumour in the world (Bray, 2018). It has been proposed that morbidity and mortality could be mitigated by screening methods that identify key genetic mutations in the DNA of a patient’s biosample (Traverso, 2002). However, for this to work, a theoretical understanding of the most likely mutations that initiate malignant transformation, and how they affect subsequent microevolution, is needed. Specifically, we hypothesise that there is a CRC-proliferative mutation that is more likely to be initially fixated in the crypt . To investigate this, we developed an agent-based model of cells in the colon crypt that shows emergent biological homeostasis at the tissue level from the cellular and molecular interactions. We equipped each of the cells with a molecular gene network which, in their wildtype state, regulates homeostasis in the crypt and recapitulates known behaviour. We identified and modelled key genes implicated in CRC which, when mutated, alter the rate of death and division of cells. We used this model to study the biological first principles of the fixation of mutations, offering key spatial and temporal understanding of this process. We discuss the impact and clinical relevance of proliferative genetic mutations in isolation, pointing to the KRAS gene as a likely mutation to be initially fixed in the crypt.

Change is inevitable in this fast-moving world. As the environment and people’s needs continuously change, so must the project. In our previous work, we developed an agent-based model of human collaboration that incorporates individual personalities. In this work, we applied a genetic algorithm to select the optimal personality combinations of a team in order to cope with different types of project change. We studied change in the context of three types of tasks: disjunctive (team performance is the performance achieved by the best performing individual), conjunctive (team performance is the performance achieved by the worst performing individual), and additive (team performance is the total performance of the group). Results reveal that different compositions of team personalities are suitable for different dynamic problems and task types. In particular, optimal personalities found for static problems differ from optimal personalities found for dynamic problems.

In this work we present IHDNs: an original model of computation for the simulation of interacting, dynamic, multi-scale systems. We show that a novel message passing mechanism that operates across layers of abstraction in hierarchical dynamic networks is effective in expressing the complex dependencies of living systems. Using a conventional computational model of cell evolution in cancerous tumour growth for comparison, we demonstrate the validity of IHDNs in emulating the behaviour of life-like systems, as well as the additional capabilities in enabling Neo4j Cypher patternmatching queries, demonstrated here in the analysis of evolutionary cell heritage.

Intestinal glands in the small intestine and colon, or intestine crypts, are an important example of tissue homeostasis regulated by the extracellular environment. The crypts are invaginated structures made of a layer of cells that help absorb nutrients from passing food. However, they are continuously worn away by this process and are being continually renovated by stem cells at the bottom of the crypt. These stem cells divide to replace worn cells and may even displace other stem cells so that at a given time the whole crypt becomes monoclonal- a descendant of one single stem cell. From a theoretical standpoint, the time it takes to reach monoclonality is crucial to the understanding of colorectal cancer (CRC) as it offers a key metric for the establishment of cancer initiating mutations; however, the biggest biological contributor to this feature is highly debated. Three key hypotheses have been put forwards, which we investigated with ALife methods. We have abstracted key biological features and modelled them in a bottom-up Agent-Based Model that allowed us to study the biological first principles that rule the fixation of mutations, offering key spatial and temporal understanding of this process. Our results show that the number of basal stem cells have a direct influence on the fixations of mutations and suggesting a lesser role for extracellular influences, while proposing the existence of a threshold to the contribution of cell side displacement

Collaboration is an essential aspect of human interaction. Despite being mutually beneficial to everyone involved, it often fails due to behaviour differences as individuals process information, form opinions, and interact with each other, especially when their task contains uncertainty. Thus, to understand collaboration on noisy problems effectively, it is necessary to consider the psychology of the individuals involved. We propose an agent-based model of collaboration that incorporates human psychology. We abstract the shared goal as a shared optimisation task, and model personality differences as strategies for moving within, interpreting and sharing information about the solution space. Although used to explore a specific hypothesis here, the model is psychology theoryagnostic and problem-independent and can also be used to investigate other tasks and different psychology theories.

Abstract concepts are rules about relationships such as identity or sameness. Instead of learning that specific objects belong to specific categories, the abstract concept of same/different applies to any objects that an organism might encounter, even if those objects have never been seen before. In this paper we investigate learning of abstract concepts by computer, in order to recognize same/different in novel data never seen before. To do so, we integrate recursive self-organizing maps with the data they are processing into a single graph to enable a brain-like self- adaptive learning system. We perform experiments on simple same/different datasets designed to resemble those used in animal experiments and then show an example of a practical application of same/different learning using the approach.