The Integration Of Synthetic Biology With Metabolically And Phenotypically Emergent Multicellular Simulations For Medical And Other Applications

“The Integration Of Synthetic Biology With Metabolically And Phenotypically Emergent Multicellular Simulations For Medical And Other Applications” aimed to extend Synthetic Biology computer assisted design spatiotemporal capabilities via multicellular simulation, with translational contextualization, targeting the biophysical 3D spatial extension of a Synthetic Biology associated solver of stochastic Gillespie algorithms, NGSS, achieved best through ChemicalMatrixFusion. Following extensive reviews, the methodological strategies explored agent-based, vertex-based and domain-based modelling. Unreal Engine 4 development (UnrealMulticell3D) would be contrasted with multithreaded mesh generation (SynthMeshBuilder) and a novel biophysical high performance diffusion solver (ChemicalMatrixRM_CUDA). Concluding insights included bias-reducing Monte Carlo strategies, accelerated development yet performance hurdles of specialized gaming engines, careful memory management and resolution of biophysics using CUDA GPU acceleration, and a practical demonstration of the benefits of integrating performant modular solutions for biophysical, bioregulatory and mechanistic capabilities for visualized, high-dimensional time-course multicellular simulations, implicated primarily for hypothesis modelling. Such simulations had tomographic and topological characteristics, implicating “recursive” multimodal, hybrid strategies at multiple scales. Multiplanar visualization, as illustrated, and digital reconstruction could be deemed underutilized. The novel cytohistological genetics encyclopaedia and biological network explorer, BioNexusSentinel, developed here evidenced artificial intelligence mediated development and R preprocessing as beneficial. Omics data was implicated for future parameterization of translational medical simulations, along with genome scale network models, and the morphological representation of complex substructure. Meanwhile, the translational review would emphasize the importance of assays and micrographs, high-throughput combinatorial strategies and machine learned inferences within automated Design-Build-Test-Learn engineering, as well as documenting resources for Synthetic Biology computational modelling.

Grantor dc:publisher.institution

University of Bradford

Author dc:creator

• Matzko, Richard O.V.

Advisor dc:contributor.advisor

• Konur, Savas

• Synthetic
• Biology
• Computer
• CUDA
• CAD
• Multicellular
• Simulation
• Bioregulation
• Parallel
• Modelling

Statement dc:rights

• <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.

Language dc:language.iso

en

Repository record dc:identifier.uri

https://bradscholars.brad.ac.uk/handle/10454/20397

OAI identifier oai:identifier

oai:bradscholars.brad.ac.uk:10454/20397