Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.

Myopia is predicted to affect 50% of all people worldwide by 2050, and is a risk factor for significant, potentially blinding ocular pathologies, such as retinal detachment and glaucoma. Thus, there is significant motivation to better understand the process of myopigenesis and to develop effective anti-myopigenic treatments. In nearly all cases of human myopia, scleral remodeling is an obligate step in the axial elongation that characterizes the condition. Here I will describe the development of a biomechanical assay based on transient unconfined compression of scleral samples. By treating the scleral as a poroelastic material, one can determine scleral biomechanical properties from extremely small samples, such as obtained from the mouse eye. These properties provide proxy measures of scleral remodeling, and have allowed us to identify all-trans retinoic acid (atRA) as a myopigenic stimulus in mice. I will also describe nascent collaborative work on modeling the transport of atRA in the eye.

My research uses electrophysiological techniques to evaluate normal retinal function, dysfunction caused by blinding retinal diseases and the restoration of function using a variety of therapeutic strategies. We can use our understanding or normal retinal function and disease-related changes to construct optimal therapeutic strategies and evaluate how they ameliorate the effects of disease. Retinitis pigmentosa (RP) is a family of blinding eye diseases caused by photoreceptor degeneration. The absence of the cells that for this primary signal leads to blindness. My interest in RP involves the evaluation of therapies to restore vision: replacing degenerated photoreceptors either with: (1) new stem or other embryonic cells, manipulated to become photoreceptors or (2) prosthetics devices that replace the photoreceptor signal with an electronic signal to light. Glaucoma is caused by increased intraocular pressure and leads to ganglion cell death, which eliminates the link between the retinal output and central visual processing. We are parsing out of the effects of increased intraocular pressure and aging on ganglion cells. Congenital Stationary Night Blindness (CSNB) is a family of diseases in which signaling is eliminated between rod photoreceptors and their postsynaptic targets, rod bipolar cells. This deafferents the retinal circuit that is responsible for vision under dim lighting. My interest in CSNB involves understanding the basic interplay between excitation and inhibition in the retinal circuit and its normal development. Because of the targeted nature of this disease, we are hopeful that a gene therapy approach can be developed to restore night vision. My work utilizes rodent disease models whose mutations mimic those found in human patients. While molecular manipulation of rodents is a fairly common approach, we have recently developed a mutant NIH miniature swine model of a common form of autosomal dominant RP (Pro23His rhodopsin mutation) in collaboration with the National Swine Resource Research Center at University of Missouri. More genetically modified mini-swine models are in the pipeline to examine other retinal diseases.

Axons degenerate before the neuronal soma in many neurodegenerative diseases. Programmed axon death (Wallerian degeneration) is a widely-occurring mechanism of axon loss that is well understood and preventable in animals. Its aberrant activation by mutation of the pro-survival gene Nmnat2 directly causes axonopathy in mice with severity ranging from mild polyneuropathy to perinatal lethality. Rare biallelic mutations in the homologous human gene cause related phenotypes in patients. NMNAT2 is a negative regulator of the prodegenerative NADase SARM1. Constitutive activation of SARM1 is cytotoxic and the human SARM1 locus is significantly associated with sporadic ALS. Another negative regulator, STMN2, has also been implicated in ALS, where it is commonly depleted downstream of TDP-43. In mice, programmed axon death can be robustly blocked by deletion of Sarm1, or by overexpression, axonal targeting and/or stabilization of various NMNAT isoforms. This alleviates models of many human disorders including some forms of peripheral neuropathy, motor neuron diseases, glaucoma, Parkinson’s disease and traumatic brain injury, and it confers lifelong rescue on the lethal Nmnat2 null phenotype and other conditions. Drug discovery programs now aim to achieve similar outcomes in human disease. In order to optimize the use of such drugs, we have characterized a range of human NMNAT2 and SARM1 functional variants that underlie a spectrum of axon vulnerability in the human population. Individuals at the vulnerable end of this spectrum are those most likely to benefit from drugs blocking programmed axon death, and disorders associated with these genotypes are promising indications in which to apply them.