The application of agent-based modeling and fuzzy-logic controllers for the study of magnesium biomaterials

Agent-based modeling (ABM) is a powerful approach for studying complex systems and their underlying properties by explicitly modeling the actions and interactions of individual agents. Over the past decade, numerous software programs have been developed to address the needs of the ABM community. However, these solutions often suffer from limitations in design, a lack of comprehensive documentation, or poor performance. As the first objective of this thesis, we introduce CppyABM-a general-purpose software for ABM that provides simulation tools in both Python and C++. CppyABM also enables ABM development using a combination of C++ and Python, taking advantage of the computational performance of C++ and the data analysis and visualization tools of Python. We demonstrate the capabilities of CppyABM through its application to various problems in ecology, virology, and computational biology. As the second objective of this thesis, we use ABM and fuzzy logic controllers (FLCs) to numerically study the effects of magnesium (Mg2+) ions on osteogenesis. Mg-based materials have emerged as the next generation of biomaterials that degrade in the body after implantation and eliminate the need for secondary surgery. We develop two computer models using ABM and FLC and calibrate them based on cell culture experiments. The models were able to capture the regulatory effects of Mg2+ ions and other important factors such as inflammatory cytokines on mesenchymal stem cells (MSC) activities. The models were also able to shed light on the fundamental differences in the cells cultured in different experiments such as proliferation capacity and sensitivity to environmental factors

Mathematical modelling of interacting mechanisms for hypoxia mediated cell cycle commitment for mesenchymal stromal cells

Background Existing experimental data have shown hypoxia to be an important factor affecting the proliferation of mesenchymal stromal cells (MSCs), but the contrasting observations made at various hypoxic levels raise the questions of whether hypoxia accelerates proliferation, and how. On the other hand, in order to meet the increasing demand of MSCs, an optimised bioreactor control strategy is needed to enhance in vitro production. Results A comprehensive, single-cell mathematical model has been constructed in this work, which combines cellular oxygen sensing with hypoxia-mediated cell cycle progression to predict cell cycle commitment as a proxy to proliferation rate. With oxygen levels defined for in vitro cell culture, the model predicts enhanced proliferation under intermediate (2–8%) and mild (8–15%) hypoxia and cell quiescence under severe (< 2%) hypoxia. Global sensitivity analysis and quasi-Monte Carlo simulation revealed that within a certain range (+/− 100%), model parameters affect (with varying significance) the minimum commitment time, but the existence of a range of optimal oxygen tension could be preserved with the hypothesized effects of Hif2α and reactive oxygen species (ROS). It appears that Hif2α counteracts Hif1α and ROS-mediated protein deactivation under intermediate hypoxia and normoxia (20%), respectively, to regulate the response of cell cycle commitment to oxygen tension. Conclusion Overall, this modelling study offered an integrative framework to capture several interacting mechanisms and allowed in silico analysis of their individual and collective roles in shaping the hypoxia-mediated commitment to cell cycle. The model offers a starting point to the establishment of a suitable mechanism that can satisfactorily explain the different existing experimental observations from different studies, and warrants future extension and dedicated experimental validation to eventually support bioreactor optimisation

Mathematical modelling of interacting mechanisms for hypoxia mediated cell cycle commitment for mesenchymal stromal cells

Background Existing experimental data have shown hypoxia to be an important factor affecting the proliferation of mesenchymal stromal cells (MSCs), but the contrasting observations made at various hypoxic levels raise the questions of whether hypoxia accelerates proliferation, and how. On the other hand, in order to meet the increasing demand of MSCs, an optimised bioreactor control strategy is needed to enhance in vitro production. Results A comprehensive, single-cell mathematical model has been constructed in this work, which combines cellular oxygen sensing with hypoxia-mediated cell cycle progression to predict cell cycle commitment as a proxy to proliferation rate. With oxygen levels defined for in vitro cell culture, the model predicts enhanced proliferation under intermediate (2–8%) and mild (8–15%) hypoxia and cell quiescence under severe (< 2%) hypoxia. Global sensitivity analysis and quasi-Monte Carlo simulation revealed that within a certain range (+/− 100%), model parameters affect (with varying significance) the minimum commitment time, but the existence of a range of optimal oxygen tension could be preserved with the hypothesized effects of Hif2α and reactive oxygen species (ROS). It appears that Hif2α counteracts Hif1α and ROS-mediated protein deactivation under intermediate hypoxia and normoxia (20%), respectively, to regulate the response of cell cycle commitment to oxygen tension. Conclusion Overall, this modelling study offered an integrative framework to capture several interacting mechanisms and allowed in silico analysis of their individual and collective roles in shaping the hypoxia-mediated commitment to cell cycle. The model offers a starting point to the establishment of a suitable mechanism that can satisfactorily explain the different existing experimental observations from different studies, and warrants future extension and dedicated experimental validation to eventually support bioreactor optimisation

Additional file 2: of Mathematical modelling of interacting mechanisms for hypoxia mediated cell cycle commitment for mesenchymal stromal cells

The effect of initial conditions on steady state E2F concentration. (DOCX 42 kb

Additional file 1: of Mathematical modelling of interacting mechanisms for hypoxia mediated cell cycle commitment for mesenchymal stromal cells

Parameter values and initial conditions. (DOCX 23 kb

Additional file 3: of Mathematical modelling of interacting mechanisms for hypoxia mediated cell cycle commitment for mesenchymal stromal cells

Compiled hypoxic MSCs growth data. (DOCX 23 kb