Data-Driven Morphological Growth and Material Transport Regulation for Biological Neural Circuits Design and Prediction

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Biological neural circuits (BNCs) are complex neurite networks with interwoven neurons that process information and exchange communications through synapse (the small gap at the end of a neuron that allows a signal to pass from one neuron to the next) connections. Within BNCs, neuron arrangement and connectivity vary based on specific tasks. BNCs can coordinate physiological behaviors throughout the body by transmitting electric impulses and chemical signals. Together with patterned neurons, BNCs show great potential in many applications in computational neuroscience, biohybrid robotics and as a testbed for validating computational and machine learning paradigms. BNCs design requires computational tools to fully understand morphological growth and the regulation of material transport in neural circuits. The morphological growth of neurons is a very complex process involving both genetic and environmental components. How a neurite initiates from the soma (the body of the cell) and creates the axon (that carries signals from the soma to other targets) from dendrites (the receiving portion of the neuron) during growth remains challenging to predict. In addition, intracellular material transport is especially crucial to ensure necessary materials are delivered to the right locations for the development, function, and survival of neural circuits. The transport disruption can lead to abnormal accumulations of certain cellular material and extreme axonal swelling. This project will advance knowledge of the fundamental mechanism of neural growth, material transport regulation, and circuit dynamics. The resulting computational tools will support BNCs design and future development of biohybrid robotics and new therapies. The developed simulation software, research and educational materials will be disseminated broadly, including National Biomechanics Days in Pittsburgh and the network of female researchers. The goal of this project is to develop a new computational framework to predict neuron growth and transport regulation based on isogeometric analysis (IGA), phase field, partial differential equation (PDE)-constrained optimization, and machine learning techniques. This goal will be achieved through pursuit of three specific aims: (1) Feature-driven multi-stage neuron growth using isogeometric collocation-based phase field method and convolutional neural networks; (2) Data-driven material transport regulation and traffic jam simulations in neurons using physics-informed graph neural networks; and (3) Validation of BNC dynamics and prediction tools in micropatterned network cultures for healthy and degenerating cell types. This project will yield new computational tools to enable realistic 3D modeling and data-driven simulation of neuron growth and transport regulation for BNCs design and prediction. It will advance knowledge of neurobiology at the subcellular and cellular levels as well as knowledge of neural engineering on growing and controlling material transport for applications such as repair and renewal of damaged or degenerative neurons. It will develop new computer simulation software for growing and analyzing traffic control within the complex neuronal circuits. The proposed computational tools and experimental validation are critical, leading to transformative advances in BNC dynamics design in biohybrid robotics applications (e.g., biohybrid controllers). The IGA-based data-driven techniques can also be used to solve a wide variety of PDEs that describe cellular processes other than growth and transport regulation This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.