Multi-Scale Modeling of Vascular Signaling Units

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Project Summary/Abstract Hypertension is one of the largest modifiable risk factors for cardiovascular disease, which is the leading cause of mortality for men and women. As more antihypertensive therapies become available, it has become clear that males and females respond differently to these treatments. Yet the mechanisms behind these sex differences are largely unknow. The parent grant aims to develop a multi-disciplinary approach to reveal the mechanisms of male and female hypertension and by building a detailed model to predict how drugs can differentially alter vascular function between these groups. This is being achieved by comparing male and female vascular smooth muscle cells using a range of state-of-the-art techniques including electrophysiology, Ca2+ imaging, nano-scale super-resolution microscopy, and detailed in silico predictive modelling. The goals of this supplemental project is to provide reliable, reproducible, and extensible analysis software that will unify these inherent multi-modal data sets. The output of these analysis pipelines will be used as input to computational models to ensure robust ground-truth predictions of key biophysical parameters. Each software component will have a fully documented back-end engine (written in Python) with an advanced-programming-interface (API) for interoperability, extendibility, and adoption by others. Cloud based computational and graphical-user-interfaces (GUIs) will be developed. These analysis pipelines will be hardened by using engineering standards for versioning, unit testing, code documentation, and interoperability with existing software. For these tools to be accessible and used by others, we will use agreed upon file formats and biological nomenclature standards. For these pipelines to be discoverable, they will be accessibility through multiple channels including online open-source code sharing and installation from package managers. To ensure one-click access for other researchers, the pipelines will be containerized for easy and reliable installation, allowing the same code to be run on individual computers, local cluster, or in the cloud. By following best software engineering principles, we aim to create a multi-modal analysis pipeline that seamlessly facilitate collaborations between groups of researchers and allow the sharing and publishing of data and analysis with the greater scientific community and ultimately the public.