Category Archives: Systems Biology

Over and out: Synthetic biology enables two-way communication between mammalian cells

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HEK-293 cells

HEK-293 cells were used to synthetically design a two-communication system in mammalian cells. (Image: ATCC)

Published online in Nature Biotechnology last week, a multi-national team from Switzerland, Germany and France demonstrated the first example of rationally designed, two-way communication between mammalian cells (Source). While the microbiologist might scoff at this and point to any of the glut of literature describing synthetically design genetic circuits and phenotypic responses achieved in bacterial and yeast systems, the mammalian cell poses a significantly greater challenge. In the Letters publication, Bacchus et al. describe the engineering of “sender,” “processer” and “receiver” cells (all HEK-293 cells, a favorite for mammalian genetic engineers due to the ease of introducing foreign DNA) to exchange a variety of signaling molecules (including L-tryptophan, VEGF and acetaldehyde) in order to trigger specific, and pre-determined phenotypic responses.

This is huge.

This is the first time that mammalian cells have been rationally designed to mimic natural behavior in order to produce a specifically desired response. Even more impressively, they  didn’t leave their demonstration at just a simple signaling exchange – they showed a realistic, biologically relevant phenotypic response! The authors designed an experiment wherein a co-culture of cells designed to express and respond to different stimuli simulated the production and maturation of blood vessels. Using the vascular endothelial growth factor (VEGF) and angiopoietin-1 (Ang1), the authors observed that their system reproduced the characteristic transient permeability and tightening of endothelial layers which are critically important to the growth of blood vessels.

The authors emphasize the important point that, in contrast to bacteria and yeast, multicellular organisms are composed of specialized cell populations that tightly coordinate their physiology by intercellular communication. This article represents the first reverse-engineering of this intercellular communication, and in particular the first to reproduce a realistic biological phenomena. This development can provide two important opportunities for the research community: first, it can provide the tools to dissect important communication pathways and finely understand the nuances of these communications, much in the same way that recombinant DNA technology allowed us to do for gene networks; and second it provides the opportunity to design more biocompatible implants capable of communicating and interfacing with our natural physiology.

Catch the article in the link below!

References

  • Bacchus, W. et al. Synthetic two-way communication between mammalian cells. Nature Biotechnology (2012).doi:10.1038/nbt.2351 (Source)

Ready, Set, Model!

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Whole-Cell Computational Model Model

Wish you could do complex cellular and molecular biology from the comfort of your couch? The lab of your dreams (aka, sitting on your computer in front of your TV) may be just over the horizon.

Just as Peter L. Freddolino and Saeed Tavazoie title their July 20th commentary in Cell, this month has witnessed the “dawn of virtual cell biology” (Source). Published in the journal Cell, a group lead by researchers at Stanford University released the first ever whole-cell computational model capable of predicting phenotype from an associated genotype (Source).

The authors of the model, which describes a complete life cycle for the human pathogen Mycoplasma genitalium, claim their work accounts for all included molecules and their interactions. Using a combination of data aggregated from over 900 publications in addition to over 1900 individual experimental observations, the effort is certainly titanic. This is the first time that such a complete model has been published, and the confirming evidence is convincing; the group characterized their model with observations of, among other things, cell growth rate  (by OD550), gene expression levels (by mRNA) and metabolism (using the intracellular concentrations of ATP, GTP, FAD(H2), NAD(H), and NADP(H)).

Personally, I’m excited to see how the systems biology community responds to this paper. I’ve found that a recurring (and often times frustrating) statement in cellular and molecular biology is the “well, that’s not the whole story” line. While this model is certainly complex, information-dense and stands up to testing, I find it hard to look at it and say “yep, that’s all.” I can’t help but wonder how our lack of completely understanding biological regulation (a la methylation, post-transcriptional and translational modifications, cell signaling, etc) will lead to refinements of the model – but then again, that is exactly where models like this come in handy. When the model doesn’t agree with biology, that’s where insights are hiding. Both the paper and commentary are highly recommended reading.

References

  • Freddolino, P.L. & Tavazoie, S. The Dawn of Virtual Cell Biology. Cell 150, 248-250 (2012). (Source)
  • Karr, J.R. et al. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150, 389-401 (2012). (Source)