The impact of cellular characteristics on the evolution of shape homeostasis

The importance of individual cells in a developing multicellular organism is well known but precisely how the individual cellular characteristics of those cells collectively drive the emergence of robust, homeostatic structures is less well understood. For example cell communication via a diffusible factor allows for information to travel across large distances within the population, and cell polarisation makes it possible to form structures with a particular orientation, but how do these processes interact to produce a more robust and regulated structure? In this study we investigate the ability of cells with different cellular characteristics to grow and maintain homeostatic structures. We do this in the context of an individual-based model where cell behaviour is driven by an intra-cellular network that determines the cell phenotype. More precisely, we investigated evolution with 96 different permutations of our model, where cell motility, cell death, long-range growth factor (LGF), short-range growth factor (SGF) and cell polarisation were either present or absent. The results show that LGF has the largest positive impact on the fitness of the evolved solutions. SGF and polarisation also contribute, but all other capabilities essentially increase the search space, effectively making it more difficult to achieve a solution. By perturbing the evolved solutions, we found that they are highly robust to both mutations and wounding. In addition, we observed that by evolving solutions in more unstable environments they produce structures that were more robust and adaptive. In conclusion, our results suggest that robust collective behaviour is most likely to evolve when cells are endowed with long range communication, cell polarisation, and selection pressure from an unstable environment

Evolving hierarchical visually guided neural network agents to investigate complex interactions.

A complex system is a system with a large number of interacting components without any mechanism for central control that displays self organisation. Understanding how these interactions affect the overall behaviour of a system is of great interest to science. Indeed, researchers use a wide variety of models to investigate complex systems. The problem with most models is that they disregard the hierarchical nature of complex systems: they ignore the fact that components of real world systems tend to be complex systems as well. This prevents researchers from investigating the interactions taking place between the lower and the higher levels of the model which may be crucial in order to gain a full understanding of the examined phenomena and of complex systems in general. Therefore, this thesis introduces Mosaic World, a multi-agent model for the purpose of investigating interactions (focusing on 'complex' multilevel interactions) within a hierarchical complex system, in addition to other computational and biological hypotheses. Mosaic World comprises a population of evolving neural network agents that inhabit a changing visual environment. By analysing the interactions that occur within Mosaic World, this thesis demonstrates the importance of incorporating hierarchical complexity into a model, and contributes to our understanding of hierarchical complex systems by showing how selective pressures cause differentiation across levels. Additionally, the study of multilevel interactions is used to probe several hypotheses and provides the following contributions among others: Analysis of agent evolvability as affected by the usage of different types of structural mutations in the evolutionary process. Demonstration that agents controlled by modular neural networks are fitter than agents that are controlled by non-modular neural networks the improvement in fitness occurs through specialisation of modules. Empirical support for a biological theory suggesting that colour vision evolved as a method of dealing with ambiguous stimuli

Artificial life meets computational creativity?

I review the history of work in Artificial Life on the problem of the open-ended evolutionary growth of complexity in computational worlds. This is then put into the context of evolutionary epistemology and human creativity

A hybrid physics-based and data-driven framework for cellular biological systems: Application to the morphogenesis of organoids

How cells orchestrate their cellular functions remains a crucial question to unravel how they organize in different patterns. We present a framework based on artificial intelligence to advance the understanding of how cell functions are coordinated spatially and temporally in biological systems. It consists of a hybrid physics-based model that integrates both mechanical interactions and cell functions with a data-driven model that regulates the cellular decision-making process through a deep learning algorithm trained on image data metrics. To illustrate our approach, we used data from 3D cultures of murine pancreatic ductal adenocarcinoma cells (PDAC) grown in Matrigel as tumor organoids. Our approach allowed us to find the underlying principles through which cells activate different cell processes to self-organize in different patterns according to the specific microenvironmental conditions. The framework proposed here expands the tools for simulating biological systems at the cellular level, providing a novel perspective to unravel morphogenetic patterns

Yeast prion physiology

2016 Summer.Includes bibliographical references.Prions, or proteinaceous infections, are caused by proteins that have the unique ability to adopt an alternative, self-replicating structure. These self-replicating structures are the causative agent of a number of mammalian diseases including Bovine spongiform encephalopathy, Creutzfeldt-Jakob disease, and Kuru. More recently, yeast were discovered to carry at least a dozen proteins capable of making this structural conversion. Yeast prions are unique in that their prion-forming domains are intrinsically disordered domains, with unusual compositional biases. This thesis addresses two broad questions about yeast prion physiology. First, a recent mutagenic screen suggested that both aromatic and non-aromatic hydrophobic residues strongly promote prion formation. However, while aromatic residues are common in yeast prion domains, non-aromatic hydrophobics are strongly under-represented. The second chapter of this dissertation explores the effects of hydrophobic and aromatic residues on prion formation. Insertion of even a small number of hydrophobic residues is found to strongly increase prion formation. These data, combined with bioinformatics analysis of glutamine/asparagine-rich domains, suggest a limit on the number of strongly prion-promoting residues tolerated in glutamine/asparagine-rich domains. Recent studies have demonstrated that aromatic residues play a key role in the maintenance of yeast prions during cell division. Taken together, these results imply that non-aromatic hydrophobic residues are excluded from prion domains not because they inhibit prion formation, but instead because they too strongly promote aggregation, without promoting prion propagation. Despite more than 20 years of research, we still don’t know why yeast carry so many prion and prion-like domains. It has been proposed that prions may serve some biological function. Chapter Three presents progress on two lines of investigation designed to resolve this issue First, a novel bioinformatics algorithm (GARRF) is used to screen a wide range of proteomes to find examples of Q/N rich domains outside of Saccharomyces cerevisiae. Identifying other species that carry these unusual regions provides insight into their role in cellular biology. We find a wide range species carry prion-like domains at levels comparable to Saccharomyces cerevisiae, and a small number carry up to an order of magnitude more. Second, currently researchers rely primarily on yeast genetic methods to discover and monitor prions. These methods have a number of drawbacks, including a glacially slow readout time. Chapter Three reports on progress towards the development of a novel fluorescence based prion assay. This assay takes advantage of bi-molecular fluorescence complementation, a technique that uses complementary fragments of a fluorescent protein to indicate when two interacting domains are in proximity to one another. When completed, this assay will provide a means to monitor protein aggregations that is both faster and more sensitive than any existing assay

Amorphous Computing

The goal of amorphous computing is to identify organizationalprinciples and create programming technologies for obtainingintentional, pre-specified behavior from the cooperation of myriadunreliable parts that are arranged in unknown, irregular, andtime-varying ways. The heightened relevance of amorphous computingtoday stems from the emergence of new technologies that could serve assubstrates for information processing systems of immense power atunprecedentedly low cost, if only we could master the challenge ofprogramming them. This document is a review of amorphous computing