We’re re-running an important episode on Alzheimer’s disease — a topic that touches many people. We still don’t have a complete understanding of the disease and that makes it hard to design effective therapies. In 2022, Russ Altman sat down with mechanical engineer Ellen Kuhl who offered a glimpse into the way she’s using computational modeling to help improve our understanding of Alzheimer’s disease.Connect With Us:Episode Transcripts The Future of Everything WebsiteConnect with Russ Threads / Bluesky / MastodonConnect with School of Engineering Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) Introduction Russ introduces the episode on Alzheimer's disease, highlighting its global impact, challenges treating it, and Dr. Ellen Kuhl’s research on it.(00:02:08) The Approach and Research MethodsEllen Kuhl discusses her lab's interdisciplinary approach, and method of using existing data to create dynamic models to study the brain's lifecycle and degeneration in Alzheimer's disease.(00:03:46) Key Features of Alzheimer's Disease and DetectionBiomarkers of Alzheimer's, their role in brain cell death and cognitive decline, and the possibilities for early detection methods of these protein issues.(00:07:20) How Computational Models FunctionHow the models integrate various data points and physics principles to comprehensively understand Alzheimer's progression.(00:08:43) Spread of the DiseaseExploring the mechanisms of how Alzheimer's spreads from cell to cell in the brain, and the progression through the lobes of the brain, regardless of the cause genetic or trauma induced.(00:12:33) Interdisciplinary Collaboration The challenges and benefits of working as a mechanical engineer in Alzheimer's research and the opportunities of a multidisciplinary approach.(00:14:33) Alzheimer's Drug Development Modeling a controversial Alzheimer's drug, its potential impact, and the importance of early diagnosis for effective treatment.(00:16:04) Transition to COVID Research and ModelingHow the Alzheimer's model was rapidly adapated to study the spread of COVID-19, drawing parallels between brain regions and city networks.(00:18:38) Covid Modeling Learnings and ApplicationsHow their COVID models highlighted the importance of asymptomatic transmission and helped governments with reopening strategies.(00:20:24) Responsible Model Application The rampant and at times irresponsible use of models during the pandemic, and metrics for measuring credibility of models(00:23:59) COVID Data SharingThe positive legacy of COVID-19, focusing on the accelerated progress facilitated by open and transparent data sharing.(00:24:53) Model Interpretability and ClosingInsights into the importance of model interpretability and the value of reducing complexity to enhance understanding. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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