How can a flatworm regenerate a complete head after being cut in half?

In this episode, we speak with Michael Levin, developmental biologist and director of the Allen Discovery Center at Tufts University, about the emerging field of developmental bioelectricity. Levin explains how voltage gradients, ion channels, and gap junctions form a layer of biological control that operates alongside genetics and biochemistry to regulate embryonic development, regeneration, and anatomical patterning.

We explore the experimental foundations of bioelectricity research, including the use of voltage-sensitive dyes, ion channel manipulation, and computational models to read and write electrical information in living tissues. Levin discusses how bioelectric signals help establish left-right asymmetry in embryos, coordinate communication across developing tissues, and encode large-scale anatomical information that individual cells cannot possess on their own.

The conversation examines classic and surprising experiments from the field, including the creation of two-headed planarian worms, the induction of ectopic eyes in frog embryos, and the restoration of normal development after severe genetic and environmental disruptions. Levin explains how bioelectric circuits can act as a control architecture for morphogenesis, allowing tissues to make collective decisions about growth, form, and regeneration.

We also discuss voltage gradients, membrane potentials, gap junction networks, developmental pattern formation, regenerative medicine, collective cellular intelligence, and the relationship between electrophysiology and gene regulation. Throughout the episode, Levin argues that understanding development requires looking beyond genes alone to the dynamic electrical communication networks that coordinate living systems across scales.

Whether you're interested in developmental biology, embryology, regeneration, electrophysiology, bioelectricity, morphogenesis, systems biology, ion channels, pattern formation, or the future of regenerative medicine, this episode provides a deep technical exploration of how electrical signals help shape living organisms.

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Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Michael Dubrovsky: https://www.linkedin.com/in/michael-dubrovsky/
Xinghui Yin: https://www.linkedin.com/in/xinghui-yin-168b94130/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:40 - Early Interest in Bioelectricity
05:22 - External Electric Stimulation
19:54 - Two-Headed Planarians
31:40 - Designing Bioelectric Experimental Methods
56:37 - Different Model Organisms
1:07:34 - TAME Theory
1:24:16 - Xenobots and Advice for Young Scientists

#planaria #morphology #neuroscience #biology #bioelectricity

Are quantum computers changing the way we discover cancer treatments?

In this episode, Misha and Yudong spoke with Fred Chong, Seymour Goodman Professor at the University of Chicago, about the future of quantum computer architecture and how quantum algorithms could eventually help solve real-world problems in medicine, optimization, and scientific computing.

Chong explains the transition from the NISQ era toward fault-tolerant quantum computing, why hardware-aware software design remains essential, and how compiler architectures, error correction, and quantum system design all interact across the full computing stack. The conversation explores the challenges of building scalable quantum machines, the tradeoffs between superconducting qubits, trapped ions, and neutral atoms, and why many quantum systems may ultimately function as specialized accelerators alongside classical computers.

We also discuss quantum optimization algorithms like QAOA and how Chong’s group is applying them to cancer biomarker discovery and treatment prediction. By analyzing complex multimodal biological data, including DNA, mRNA, and pathology imaging, these methods aim to uncover patterns that are difficult for conventional machine learning systems to identify without overfitting.

Along the way, Fred shares stories from the early days of supercomputing at Thinking Machines, the origins of his quantum research career, the founding of Super.tech, and his perspective on where quantum computing is genuinely making progress versus where hype still dominates the conversation.

Topics include quantum computing, QAOA, fault-tolerant quantum computing, quantum error correction, quantum compilers, NISQ systems, neutral atoms, superconducting qubits, quantum architecture, cancer biomarkers, biomedical optimization, hybrid quantum-classical systems, and the future of quantum software and hardware co-design.

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Follow our hosts!
Mikhail Shalaginov: https://www.linkedin.com/in/mikhail-shalaginov/
Yudong Cao: https://www.linkedin.com/in/yudong-cao-25b6a929/

Subscribe:
Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269
Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR
Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:34 - From Jurassic Park to Quantum Computing
10:13 - Modernizing NISQ Research
13:45 - Designing Around Quantum Hardware
20:30 - Variational Quantum Algorithms
23:07 - Quantum Computers for Cancer Research
30:35 - How Q4Bio Began
37:20 - Will We Need QEC in the Future?
40:25 - What Quantum Computers Can Learn from Classical Architecture
43:08 - Would Fred Return to Classical Computing?
46:11 - Quantum Software and Quantum Compilers
55:19 - Starting Super.tech
1:01:43 - Classical Analogs to Quantum Hardware
1:12:21 - Advice for Young Scientists
1:17:43 - Is AI Impacting Quantum Research?
1:22:38 - Importance of Formal Verification
1:30:40 - QLDPC Codes
1:35:48 - Fred’s Beginnings in Computer Science
1:42:48 - Chicago vs Silicon Valley
1:46:27 - Do We Need More Quantum Software Companies?
1:53:17 - Future of Quantum Computing and Cryptography

#quantumcomputing #quantumalgorithms #cancerresearch #computerscience

How do you measure something as small as a single electron or map quantum behavior at the nanoscale?

In this episode, Misha spoke with Amir Yacoby, professor at Harvard University, about the cutting edge of quantum sensing and the experimental tools redefining how we probe the quantum world.

Yacoby explains how physicists build ultra-sensitive detectors, from single-electron transistors to quantum dots and NV centers in diamond, that can measure charge, spin, and magnetic fields with extraordinary precision. These tools make it possible to study both strongly correlated systems, like those exhibiting the fractional quantum Hall effect, and isolated quantum systems used as qubits.

We explore how accidental discoveries in the lab can evolve into entirely new sensing techniques, including momentum-resolved tunneling and nanoscale imaging methods. The conversation also highlights how quantum sensors are enabling researchers to bridge two regimes: complex many-body systems and controllable quantum devices, opening the door to new insights in topological physics and quantum information processing.

Whether you're interested in quantum measurement, nanoscale imaging, or the future of quantum technologies, this episode offers a detailed look at how new instruments are driving discovery at the frontiers of physics.

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LinkedIn: https://www.linkedin.com/company/632nm/about/

Substack: https://632nmpodcast.substack.com/

Follow our hosts!

Mikhail Shalaginov: https://x.com/MYShalaginov

Michael Dubrovsky: https://x.com/MikeDubrovsky

Xinghui Yin: https://x.com/XinghuiYin

Subscribe:

Apple Podcasts: https://podcasts.apple.com/us/podcast/632nm/id1751170269

Spotify: https://open.spotify.com/show/4aVH9vT5qp5UUUvQ6Uf6OR

Website: https://www.632nm.com

Timestamps:
00:00 - Intro
01:23 - The Process of Creating Quantum Tools
11:28 - Graduate School at Weizmann
14:51 - From Aerospace to Condensed Matter
26:53 - Starting at Harvard
39:44 - Working at Bell Labs
47:42 - Diamond NV Centers
1:00:52 - Spin Waves
1:16:10 - SQUIDs
1:29:57 - State of the Art Sensors
1:33:08 - Motivations for Building Better Sensors
1:36:52 - Fabrication Challenges
1:40:14 - New Sensors
1:45:49 - Majoranas
1:53:25 - Finding New Applications for Sensors
1:57:16 - The Use of AI in Physics
1:58:55 - Advice for Young Scientists