Morphospace / Biomedical Synthetic Biology

Biomedical Synthetic Biology

  • Date published: 2026-10-29
  • Speakers: Lior Nissim (guest talk to the Levin lab), with discussion
  • Format: Guest talk
  • Watch: https://www.youtube.com/watch?v=zTE72BksqpE
  • Topics: AI and biology

In one paragraph

Lior Nissim, who runs a synthetic biology lab at the Hadassah medical center in Israel ("Dasa" in the captions), explains how his group engineers cells the way engineers build autonomous electronic systems: sensors, a processor and outputs. Most of the talk is about synthetic promoters, short DNA switches built to switch on only in certain cell states, and gene circuits that do logic inside cells.

Main ideas (plain words)

  • He separates bioengineering (copying existing genes and parts) from synthetic biology (building new genetic parts and circuits that did not exist before).
  • Inputs are mostly synthetic promoters: short stretches of DNA (around 100 to 250 base pairs) made of repeats of one transcription-factor binding site, so they turn on in very specific cell states.
  • Processors are gene circuits that compute logic in cells, for example an XOR-style circuit built from two promoters that suppress each other's output.
  • His lab applies these platforms to cancer, immune cell therapy, chemotherapy resistance, vaccines and ALS. He says the screening data analysis is hard and machine learning is helping.
  • In the discussion he says electrical engineers, computer scientists and synthetic biologists should work together more.

Key quotes (word for word from YouTube's auto-generated captions)

"this is kind of rewriting biology with a full keyboard rather than the only copy paste."
https://www.youtube.com/watch?v=zTE72BksqpE&t=150s

"what I will describe now is basically how we we engineer biology the same way and cells the same way that you engineer autonomous electronic systems for example autonomous cars."
https://www.youtube.com/watch?v=zTE72BksqpE&t=180s

"If the cells can respond to something by changing his gene expression patterns, we can sense it. And the way we sense it is mostly using synthetic promoters."
https://www.youtube.com/watch?v=zTE72BksqpE&t=270s

"by the way I tell you analysis it's a pain and we are doing machine learning and all kind of models and it's helping us."
https://www.youtube.com/watch?v=zTE72BksqpE&t=3420s

Source: YouTube auto-generated captions, speakers not labeled; wording is provisional. Timestamps point to the start of the 30-second stretch that holds the quote.