June 26, 2006

Synthetic Biology

Follow-up to Group meeting: Boston and Jerusalem from Schreibfaul in Warwick

…and it comes back to haunt you. Need to make an overview of the Heidelber conference. Let's look at the Collins talk first :

Title: "Integrating synthetic biology and systems biology"
Collins, J. J., 20 Oct 2005

  1. Building synthetic gene networks: Gene toggle and RNA swithces
  2. Applications in microbiology: (anti)toxins and cell death pathways
  3. Inferring genetic networks: using the network model to infer the mode of action of drugs

To one. Synthetic gene networks:

  • Forward Engineering. Design switch, simple modeling,produce plasmids and see if it works. Gardner in Nature 2000. (Toggle switch in E.coli).
  • moving from e. coli up to yeast and mammalian systems. coupling to natural systems.
  • RNA based synthetic biology: Enginieered ribo–regulators. Issacs Nature Biotechnology 2004

To two. Applications in microbiology:

  • biosensors and programmable cells
  • wirkung von toxins. Afif 2001 CcdB
  • gradielles anschalten von toxins, antibiotics. following along the cell death pathway. iron comes in at later stage. unpublished at the time.

To Three. Infering genetic networks:

  • Reverse engineering gene networks
  • overexpress genes of interest, obtain expression profiles, feed into NIR algorithm, reverse engineer regulatory network
  • Yeung PNAS 2002 and Tegner PNAS 2003
  • Example with E Coli SOS pathway: Gardner Science 2003
  • 9 gene subnetwork. perturb gene expression. output by real time pcr. algorithm gives graphs, sign and strength of interaction.
  • next step: use result to find key players with most influence over the other genes. recA, lexA which confirms previous biological results.

  • Most exciting application: Infer the mode of action of compounds.
  • drugs: know drug hits some target. but: what else does that drug hit? difficult
  • now if you know the network and you know the output, you can infer what the input was.
  • test approach with the sos pathway by upregulating recA, lexA, measure output, run NIR and infer that recA, lexA was upregulated. horay.

  • up to higher organism. very costly to turn genes up or down. So you are only left with the output. hope is that by a lot of perturbations like drugs, mutations etc. might still infer network. Bernardo Nature Biotechnology 2005

*Future applications:"

  • Mammalian Systems
  • Network based drug development
  • Multi–scale approaches

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Ulrich Janus

June 2006

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