Genetics

Open the episode, then seek to 0:00.

Kimmel considers selective pressures and constraints that might explain limited longevity, using optimisation as an analogy.

Evolution and ageing

Research note: Selection experiments illuminate trade-offs, not a human expiry date →

Open the episode, then seek to 5:00.

Patel and Kimmel discuss speculative explanations of intellectual achievement and Kimmel introduces a qualified kin-selection argument about longevity.

Evolution and ageing

Open the episode, then seek to 9:20.

Kimmel discusses mutation constraints and host–pathogen competition, then considers why humans do not make antibiotics like microorganisms.

Evolution and ageing

Open the episode, then seek to 14:18.

Kimmel uses CRISPR and TRIM5alpha as examples of historical host–pathogen interactions while acknowledging limits to his antibiotic speculation.

Host–pathogen evolution

Research note: Antiviral protection has a specific target →

Open the episode, then seek to 18:00.

Kimmel explains how gene duplication can permit new functions while preserving an existing copy.

Gene duplication

Open the episode, then seek to 22:28.

Kimmel rejects a single simple cause of ageing and describes NewLimit’s aim of changing gene regulation through transcription factors.

Cell programming

Open the episode, then seek to 27:13.

Kimmel distinguishes youthful expression profiles from useful cell function and stresses retaining identity and checking potential harms.

Cell programming

Open the episode, then seek to 30:52.

Kimmel explains why detecting and amplifying rare pluripotent cells made Yamanaka’s problem different from therapeutic rejuvenation.

Cell programming

Research note: Changing cell identity is not the same as preserving function →

Open the episode, then seek to 35:13.

Kimmel describes single-cell measurements and computational prediction as ways to search a large space of transcription-factor combinations.

Genome interpretation

Open the episode, then seek to 38:11.

Kimmel argues that developmental transcription factors provide useful experimental controls, while presenting evolutionary optimisation as an analogy.

Cell programming

Open the episode, then seek to 41:42.

Patel and Kimmel compare gene regulation and neuroscience with machine-learning mechanisms before asking how pathogens and medicines affect transcription factors.

AI and biology

Open the episode, then seek to 45:23.

Kimmel discusses viral interactions with gene regulation and explains physical challenges in targeting transcription factors with different drug types.

Gene delivery

Open the episode, then seek to 50:07.

Kimmel describes lipid nanoparticles and viral vectors, including targeting limits and the distinction between delivery and downstream expression control.

Gene delivery

Research note: Successful delivery still has a target and a measured endpoint →

Open the episode, then seek to 53:04.

Kimmel proposes engineered immune cells as future delivery systems, discusses CAR-T comparisons and acknowledges compartments they cannot readily reach.

Gene delivery

Open the episode, then seek to 58:20.

Kimmel argues that improving one tissue might benefit others, but states that ageing reprogramming medicines do not yet exist.

Ageing

Open the episode, then seek to 1:03:04.

Kimmel discusses payload size and hopes for durable effects while explicitly limiting the available reprogramming evidence to weeks.

Cell programming

Open the episode, then seek to 1:06:22.

Kimmel considers engineered transcription factors and suggests that repairing extracellular structures may require additional cell states.

Cell programming

Open the episode, then seek to 1:09:35.

Kimmel explains Eroom’s Law and discusses why drug-development economics differ from his expectations for AI investment.

Drug development

Research note: Eroom’s Law is a historical productivity measure →

Open the episode, then seek to 1:14:59.

Kimmel distinguishes identifying useful targets from making molecules, then describes predictive cell models as a proposed route to better discovery.

Drug development

Open the episode, then seek to 1:19:21.

Kimmel separates predicting expression changes from choosing desirable cell states and clarifies that his team is not currently using reinforcement learning.

Genome interpretation

Open the episode, then seek to 1:24:15.

Kimmel explains Perturb-seq, describes technical improvements and says combinatorial experiments require reliable identification of multiple interventions.

Genome interpretation

Research note: A richer assay is an input to discovery →

Open the episode, then seek to 1:28:02.

Kimmel describes NewLimit’s narrower modelling problem, selected cell types and proprietary data strategy as steps toward a broader ambition.

Drug development

Open the episode, then seek to 1:31:32.

Kimmel offers hypotheses about reimbursement and direct purchasing for durable treatments while setting aside broader intellectual-property enforcement questions.

Healthcare economics

Open the episode, then seek to 1:36:01.

Patel and Kimmel discuss spending categories and speculate that effective preventive medicines could reduce intensive healthcare use.

Healthcare economics

Research note: Healthcare spending and drug spending have different boundaries →

Open the episode, then seek to 1:40:21.

Kimmel qualifies his dataset claim and discusses pharmaceutical partnerships; Patel closes by disclosing a small investment in NewLimit.

Drug development