SUURPh projects

SUURPh funds cohorts of 8 doctoral fellows, who work with advisors in Oslo and at UCSD on projects in key fields of computational biology and medicine. The project topics range from computational electrophysiology and neuroscience to mechanics and fluid dynamics, and involve leading experts in these fields. These projects are described below.

Current SUURPh projects:

Computational approaches for monitoring and prognosis of
single ventricle disorder

Single ventricle disorder (SVD) comprises a number of distinct congenital heart defects (CHD), all characterized by a single functional ventricle in the patient. Patients with SVD undergo complex surgical procedures in infancy. While a large portion of patients survive into adulthood, serial monitoring of the heart is fundamental to managing long-term complications. Cardiac magnetic resonance (CMR) imaging data enables opportunities for  quantitative analysis of cardiac functional state and prediction of adverse remodeling. The overarching goal of this project is to discover improved, mechanistic predictors of differential SVD remodeling and outcomes, powered by statistical shape analysis and deep learning, to uncover relevant material properties, and finite element analysis to compute mechanics. Our hypothesis is that features of regional ventricular wall biomechanics will be better early predictors of future clinical outcomes than existing clinical metrics. This project aims to complete an automated end-to-end, deep-learning powered shape modeling pipeline for SVD. We hope to lend insight into long-term treatment response in SVD and allow further segmentation of the population towards improved prognostics and treatment planning.

PhD candidate: Ahmad Shaikh completed his MSc in Bioengineerig at the University of California, San Diego. During his MSc work, he developed computational models of the heart and vasculature in a small animal model of pulmonary arterial hypertension.

Supervisors: Molly Maleckar (Simula, Tulane University), Andrew McCulloch (UCSD)

Improving machine learning techniques for predicting adaptive immune receptor:epitope recognition

This project seeks to develop and apply machine learning approaches to map how the genetic sequence of immune receptors determines their ability to target specific antigens on diseased cells or pathogens. This work will involve utilising large scale sequence databases with validated immune receptor-epitope interactions to define structure-blind methods for assessing how immune receptor sequence determines the range of epitopes that offer likely targets.

PhD candidate: Charlotte Würtzen completed her Masters work in Bioinformatics and Systems biology at the Technical University of Denmark. In a similar vein to the project she is pursuing with SUURPh, there she applied graph neural networks for predicting structural relationships between T-cell immune receptors and peptide major histocompatibility complex binding. 

Supervisors: Geir Kjetil Sandve (UiO, IFI), Bjoern Peters (UCSD, La Jolla Institute for Immunology) 

Decoding Human Memory and Perception through Machine Intelligence 

As part of a collaboration between the Departments of Informatics and Cognitive Neurophysiology at UiO and the Voytek lab at UCSD, this project will apply machine learning to decompose recordings of brain activity into those signals most relevant for learning and memory. This will yield models that we hope will be capable of predicting the semantic category, success of memory storage and retrieval, and certain pathological characteristics from similar recordings.

PhD candidate: Amir Arfan is a data scientist with a background in developing machine learning and AI methods. He received his Masters from the Norwegian University of Life Sciences where he sought to develop alternative methods with performance comparable to convolutional neural networks.

Supervisors: Adín Ramírez Rivera (UiO, IFI), Bradley Voytek (UCSD, Cognitive Science)

Neurocognitive Mapping: Exploring Hippocampal Navigation and Memory with Neural Sampling 

This PhD project will use Neural Sampling (NS) to explore the hippocampus's role in navigation and memory. By simulating hippocampal neural dynamics, the NS supports generative predictive coding in specialized neurons like place and grid cells during navigational tasks. Leveraging statistical and machine learning techniques, estimates of synaptic interactions and signal transmission from rat-brain measurements will be used to validate the model. This work aims to enrich our understanding of navigation and memory and offer applications in artificial intelligence systems.

PhD Candidate: Nicolai Haug is a computational neuroscientist, with a Masters specialty in imaging and biomedical computing from the Norwegian University of Life Sciences. His research to date has focused on building simulation-based inference for identifying parameters of mechanistic models of neural dynamics.

Supervisors: Mikkel Lepperød (Simula Research Laboratory), Gert Cauwenberghs (UCSD, Bioengineering)

Mechanochemical interplay underlying human performance: computational modelling of skeletal muscle signaling and mechanics in response to exercise 

This project seeks to define a new multiscale model of mechano-chemical coupling in skeletal muscle, specifically for the purpose of capturing and predicting the response of muscle to different forms of exercise-induced stress. This involves developing detailed ordinary differential equation models of electro-chamical signaling in muscle cells, and coupling these with models of larger scale deformation and mechanics of the intact muscle fiber.

PhD candidate: Ingvild Devold holds a Masters in Physics and Applied Mathematics from the Norwegian University of Science and Technology. She has worked in developing hybrid graph-based applications for partial differential equations in computational geosciences and is now applying those skills to biology in this interdisciplinary SUURPh project. 

Supervisors: Marie E. Rognes (Simula Research Laboratory), Padmini Rangamani (Mechanical and Aerospace Engineering, UCSD), Andrew McCulloch (UCSD, Bioengineering)

Computational models of growth and remodelling in Pulmonary Hypertension

This PhD project attempts to develop a framework for modelling the right ventricular hypertrophy and functional remodelling that contribute to compensated systolic and diastolic function, during pulmonary hypertension. His work mainly involves multi-scale computational modelling using data from murine experiments to validate the model-predicted myocardial growth and remodelling.

PhD Candidate: Oscar Odeigah has a background in mechanical engineering and engineering design from Ahmadu Bello University, Nigeria, and University of Tromsø, Norway. For his master thesis, he used patient-specific bi-ventricular models to investigate the differences (and similarities) between the active stress and active strain methods of mathematically modelling the active contraction of cardiac muscle. His interest in cardiovascular biomechanics and patient-specific modelling, led him to join the SUURPh program, where he will have the opportunity to continue pursuing these interests, in a highly stimulating research environment.

Supervisors: Joakim Sundnes (Simula Research Laboratory), Samuel Wall (Simula Research Laboratory), Daniela Valdez-Jasso (UCSD, Bioengineering), Andrew McCulloch (UCSD, Bioengineering)

Using personalized cardiac models to predict patient risk to arrhythmias and optimal therapy in patients with congenital heart disease

The goal of this project is to extend the use of population-based personalized forward heart models to stratify risk treatment in congenital heart disease (CHD). The models take into account the unique structural and electrophysiological characteristics of the population group by utilizing imaging and clinical data from CHD patients in the Cardiac Atlas Project and the FORCE registry. This will involve GPU acceleration and other high performance computing techniques (including optimized finite element solvers) to create atlases of population-specific arrhythmia risk and predict outcomes for future patients. 

PhD candidate: Alessandro Gatti studied Mathematics and Statistics for Life and Social Sciences at the University of Trento, Italy. There he modeled biophysical properties of myelination in neurons. 

Supervisors: Tor Skeie (UiO, IFI), Xing Cai (UiO, IFI), Hermenegild Arevalo (Simula Research Laboratory), Andrew McCulloch (UCSD, Bioengineering)

Behavior Dependent Neural Stimulation in Freely Moving Animals

The objective of the project is to combine computational modelling with animal experiments to reveal new insight about memory processing in the hippocampal-entorhinal circuit. Maria will use closed-loop stimulation of neurons in the hippocampal-entorhinal circuit involved in various memory tasks, such as spatial navigation in freely moving animals. While experiments with this system can examine the changes in the stimulus responses of target neurons, many questions about the underlying mechanism of those changes can only be answered in computational models. In particular,  artificial intelligence (AI) approaches will be used to study the hippocampus-entorhinal circuit function, which has recently opened new perspectives to understand cognitive behaviors.

PhD Candidate: Maria Perona Fjeldstad holds a B.Sc. in Biomedical Sciences with Pharmacology and a M.Sc. in Integrative Neuroscience from the University of Edinburgh. She is interested in the nervous system, and how different types of neuronal and glial cells communicate in an immensely complicated way to give rise to complex behaviour in living animals. Her Masters degree research involved experimentally analysing sciatic nerve conduction properties.

Supervisors: Torkel Hafting (UiO, IMB), Phillip Häfliger (UiO, IFI), Gert Cauwenberghs (UCSD, Bioengineering)