
The Beyond Lab Walls podcast features chronobiologist Emily Manoogian discussing how circadian rhythms underpin everyday health. Working in Satchin Panda’s lab at the SulkQ Institute, she explains that virtually every physiological process—from glucose handling to hormone release—follows a roughly 24‑hour cycle, and that aligning daily behaviors with these internal clocks can extend lifespan and improve wellbeing. Manoogian highlights three core insights: light exposure synchronizes the central brain clock that governs sleep‑wake cycles; meal timing directly entrains peripheral clocks throughout the body; and time‑restricted feeding, a low‑effort dietary strategy, can restore misaligned rhythms and lower disease risk. She notes that while animal models like hamsters reveal precise rhythm patterns, translating findings to humans is feasible because everyone eats and sleeps, making the interventions broadly applicable. Concrete examples include the My Circadian Clock app, which timestamps meals to capture eating patterns without burdensome calorie counting, and the “healthy hero” firefighter study that linked shift‑work–induced rhythm disruption to early cardiometabolic disease and cancer. Manoogian also describes how blind individuals rely on melatonin supplements to compensate for absent light cues, underscoring the flexibility of external zeitgebers. The implications are clear: businesses, clinicians, and policymakers can leverage timing‑based interventions—light management, scheduled meals, and wearable monitoring—to mitigate the health costs of modern, irregular schedules. As shift work expands globally, integrating circadian science into workplace design and public health guidelines could yield substantial economic and societal benefits.

In this Beyond Lab Walls episode, computational neuroscientist Terrence Sejnowski discusses the convergence of artificial intelligence and brain science. He recounts his journey from a childhood volcano experiment to pioneering the Boltzmann machine—a learning algorithm that made multi‑layer neural networks...