Pluripotent embryonic stem cells (ESCs) can differentiate into all somatic cell types, making them a useful platform for studying a variety of cellular phenomenon. Furthermore, ESCs can be induced to form aggregates called embryoid bodies (EBs) which recapitulate the dynamics of development and morphogenesis. However, many different factors such as gradients of soluble morphogens, direct cell-to-cell signaling, and cell-matrix interactions have all been implicated in directing ESC differentiation. Though the effects of individual factors have often been investigated independently, the inherent difficulty in assaying combinatorial effects has made it difficult to ascertain the concerted effects of different environmental parameters, particularly due to the spatial and temporal dynamics associated with such cues. Dynamic computational models of ESC differentiation can provide powerful insight into how different cues function in combination both spatially and temporally. By combining particle based diffusion models, cellular agent based approaches, and physical models of morphogenesis, a multi-scale, rules-based modeling framework can provide insight into how each component contributes to differentiation. I propose to investigate the complex regulatory cues which govern complex morphogenic behavior in 3D ESC systems via a computational rules based modeling approach. The objective of this study is to examine how spatial patterns of differentiation by ESCs arise as a function of the microenvironment. The central hypothesis is that spatial control of soluble morphogens and cell-cell signaling will allow enhanced control over the patterns and efficiency of stem cell differentiation in embryoid bodies.

Made available in DSpace on 2015-03-05T13:56:59Z (GMT). No. of bitstreams: 0 Previous issue date: 10 / Hewlett-Packard Brasil Ltda / A simulação de sistemas celulares tem recebido grande interesse nos últimos anos. Em particular, o modelo de Potts celular é o mais utilizado na área dada a sua precisão em representar estes sistemas. Este modelo, na sua forma convencional, possui uma série de operações e cálculos que são executados de maneira pouco eficiente, o que impossibilita sua utilização em simulações grandes e que exigem considerável tempo e memória para sua conclusão. Com base nisso propomos um novo algoritmo de maior desempenho que permite obter resultados aproximados dos obtidos com o algoritmo Monte Carlo em tempo bem menor. Técnicas de execução concorrente e comunicação foram introduzidas no algoritmo através do uso de processos leves para execução em computadores com memória compartilhada e usando aglomerados de computadores, respectivamente, buscando reduzir o tempo de processamento e viabilizando a execução de simulações de grande porte. Os resultados obtidos de simulações de segregação celular e evolução de espumas mostra / The simulation of cellular systems has received great interest in the last years. In particular, the cellular Potts model is widely used in the area given its precision in representing these systems. This model, in its standard form, takes a series of operations and calculations that are executed in an ine±cient way, what disables its use in large scale simulations that demand considerable time and memory for conclusion. Based on that, we propose a new algorithm of higher performance that allows to obtain results close from those obtained with the Monte Carlo algorithm in much shorter time. Techniques of concurrent execution and communication have been introduced in the algorithm, through the use of light processes for execution in computers with shared memory and using clusters of computers, respectively, aiming to reduce the processing time and making possible the execution of large scale simulations. The results presented obtained from simulation of cellular segregation and foam evolution show a minimum s