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MULTI-AGENT MODELING OF TISSUE GROWTH AND ANGIOGENESIS WITH HIGH PERFORMANCE COMPUTING
Title
MULTI-AGENT MODELING OF TISSUE GROWTH AND ANGIOGENESIS WITH HIGH PERFORMANCE COMPUTING
Creator
Bayrak, Elif Seyma
Date
2015, 2015-07
Description
Tissue engineering emerged as a result of the high demand of transplant organ and tissues in spite of low number of donors. Rapid and stable...
Show moreTissue engineering emerged as a result of the high demand of transplant organ and tissues in spite of low number of donors. Rapid and stable vascularization still presents the major challenge for three-dimensional functional tissues. Bone is a highly vascularized tissue. Regeneration of vascularized bone tissue from osteogenic cells in biodegradable scaffolds is strongly affected by the interplay between scaffold properties, chemical cues and precursor cells. The number of variables that contribute to the formation of engineered tissues present a challenging optimization problem that cannot be addressed with the experimentation alone. Complex system such as vascularized tissue growth can benefit from properly developed computational models. Computational models can help us understand interactions between the various parts of the complex systems, imagine all possible outcomes of a specific event, explain reasons and causes and forecast future trajectories. Agent-based modeling (ABM) is a powerful modeling and simulation technique that builds a structure from bottom-up to model and understand systems comprised of autonomous, interacting entities. ABM is a natural choice to model biological system that is comprised of many interacting cells. ABM possesses great advantages including simulating of each individuals behavior, holding their history, allowing them to adapt to dynamic conditions and learn through simple to complex algorithms. One main concern of the modelers is the computational heaviness of ABMs that limits the use of this technique in real time optimization, monitoring and control applications. Discovering the full potential of ABM in biological system with huge population size depend on the computational power available.A multi layer agent based model to simulate vascular bone regeneration in degradable porous hydrogels is developed both for personal computer (PC) environment and high performance computing (HPC) platforms. The personal computer (PC) version of this model is built upon the angiogenesis model that was previously developed by Arsun Artel and Hamidreza Mehdizadeh. This work is focused on development of bone tissue growth layer while considering the interactions and improving the existing layers and uses the parallel processing paradigm for running tissue growth more efficiently and more quickly. This model aims to help investigating and understanding the interactions between soluble factors, scaffolds and cells, and finding the optimal biomaterial structure and soluble cues to maximize vascularization and differentiation to bone tissue.
Ph.D. in Chemical Engineering, July 2015
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MODELING OF MAMMALIAN CELL CULTURE
Title
MODELING OF MAMMALIAN CELL CULTURE
Creator
Jackson, Robert David
Date
2019
Description
This work uses two different techniques for modeling mammalian cell culture: Differential Equation (DE) based Modeling and Agent-Based...
Show moreThis work uses two different techniques for modeling mammalian cell culture: Differential Equation (DE) based Modeling and Agent-Based Modeling (ABM). The development of both models was done in free open-source software instead of the traditional software that requires the purchase of licenses. The DE model was developed in Python and can predict total, viable, and dead cell densities, glucose, lactate, glutamine, ammonia, and product titer. To expand on the detail level capabilities of previous DE models it has added temperature, pH, and dissolved oxygen dependence. The ABM can predict viable cell density, glucose, lactate, and the distribution of the three experimentally detectable cell cycle phases G1G0, S, and G2M. The ABM was developed for high-performance computing to improve on a previous ABM and allow for running at a hundred-fold smaller run-time with a much higher capacity for the amount of agents that can be simulated.
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