Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the micro-environment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions

Optimização dos parâmetros de um robô hexápode através de algoritmos genéticos

Mestrado em Engenharia Electrotécnica e de ComputadoresOs robôs com pernas apresentam vantagens significativas quando comparados com os veículos tradicionais que apresentam rodas e lagartas. A sua maior vantagem é o facto de permitirem a locomoção em terrenos inacessíveis a outro tipo de veículos uma vez que não necessitam de uma superfície de suporte contínua. No entanto, no estado de desenvolvimento em que se encontram, existem vários aspectos que têm que ser necessariamente melhorados e optimizados. Tendo esta ideia em mente, têm sido propostas e adoptadas diferentes estratégias de optimização a estes sistemas, quer durante a fase de projecto e construção, quer durante a sua operação. Entre os critérios de optimização seguidos por diferentes autores podem- -se incluir aspectos relacionados com a eficiência energética, estabilidade, velocidade, conforto, mobilidade e impacto ambiental. As estratégias evolutivas são uma forma de “imitar a natureza” replicando o processo que a natureza concebeu para a geração e evolução das espécies. O objectivo deste trabalho passa por desenvolver um algoritmo genético, sobre uma aplicação de simulação de robôs com pernas já existente e desenvolvida em linguagem C, que permita optimizar diferentes parâmetros do modelo do robô e do seu padrão de locomoção para diferentes velocidades de locomoção.Legged robots have significant advantages when compared with traditional vehicles using wheels and tracks. Their biggest advantage is that they allow the locomotion on terrains inaccessible to other type of vehicles because they don’t need a continuous support surface. However, in their actual stage of development, there are several aspects that must necessarily be improved and optimized. With these ideas in mind, different strategies have been proposed and adopted for the optimization of these systems, either during their design phase and construction, or during their operation. Among the different optimization criteria followed by different authors, it is possible to find issues related to energy efficiency, stability, speed, comfort, mobility and environmental impact. Evolutionary strategies are a way to "imitate nature" replicating the process that nature designed for the generation and evolution of species. The objective of this project is the development of a genetic algorithm, running over a simulation application of legged robots, already developed in C, which allows the optimization of various parameters of the robot model and of its gaits for different locomotion speeds

Evolutionary Approaches to Neural Control in Mobile Robots

This article is centered on the application of evolutionary techniques to the automatic design of neural controllers for mobile robots. About 30 papers are reviewed and classified in a framework that takes into account the specific robots involved, the behaviors that are evolved, the characteristics of the corresponding neural controllers, how these controllers are genetically encoded, and whether or not an individual learning process complements evolution. Related research efforts in evolutionary robotics are occasionally cited. If it is yet unclear whether such approaches will scale up with increasing complexity, foreseeable bottlenecks and prospects of improvement are discussed in the text. Keywords--- Evolutionary Robotics, Neural Networks, Control Architectures, Behavior. I. Introduction T HE design of the control architecture of a robot able to fulfil its mission in changing and possibly unpredictable environments is a highly challenging task for a human. This is due to the v..