CaMKII-NMDAR interactions in learning and memory: a case study in ethical and reproducible modelling approaches

Abstract

This PhD project integrates biological inquiry with ethical considerations. On the one hand, this PhD investigates the molecular interactions between N-methyl-D-aspartate receptors (NMDARs) and calcium/calmodulin-dependent protein kinase II (CaMKII) in the postsynaptic dendrite during Long-Term Potentiation (LTP), a key process believed to underlie memory formation in animals.

Understanding these molecular interactions can provide valuable insights into cognitive function and neurological disorders. Beyond the biological scope, this research also examines how computational models can be employed to study these mechanisms in a way that is both reproducible and ethically responsible.

A key contribution of this research is the development of a novel computational model that simulates CaMKII as a dodecamer interacting dynamically with NMDARs in both time and space within a neuronal dendrite environment. By doing so, it offers new predictions regarding the role of these molecules in synaptic strengthening and memory-related processes.

Additionally, this project addresses the ethical considerations of such models. Emphasising principles of research integrity, reproducibility, and transparency, this work aligns with FAIR (Findable, Accessible, Interoperable, Reusable) data principles.

The model developed in this thesis serves as a case study of how to create a PhD project designed to be open and accessible, ensuring that it can be replicated and extended by future researchers, thereby reducing waste and increasing scientific reliability. In keeping with this approach, this project uses a Data Hazards framework to critically assess potential risks associated with computational modelling, such as environmental impact of the project, how it will be used in the future and what biases it might be perpetuating, implementing strategies to mitigate these risks. By demonstrating how to use this tool, this PhD thesis offers a framework for reducing waste and enhancing scientific reliability across future associated research projects.

Ultimately, this PhD goes beyond the creation of a biological model; it serves as a case study for responsible scientific practice. By integrating molecular neuroscience with ethical research methodologies, this work highlights the importance of both advancing knowledge and conducting science with integrity, ensuring that computational approaches contribute meaningfully to the broader scientific community.