CH391L/S12/Synthetic Cooperation

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Synthetic Cooperation





Major challenges develop when trying to engineer multiple, interacting populations. Among these the most difficult aspect is control of populations over time. With natural, oral bacterial communities, maintaining homeostasis is relatively simple, as members in the population typically do not out compete each other, nor do they exhaust their supply of resources due the relatively constant intake of food.[3][4] For multiple populations that are synthetically designed, this task become increasingly difficult. This is due to the tendency of genetic composition in these organisms to change over time as they adapt to their environments. To overcome this difficulty, the consortia should be designed such that members of the consortium can be re-introduced or eliminated as needed, and more importantly, be monitored over time.


Finally, fine-tuning the performance of multiple populations will be an important barrier to overcome. To do this, techniques used for directed evolution that help optimize the behavior of single populations must be extended to multiple populations. Along with this, high-throughput screening and gene-chip assay procedures need to be improved to further aid in the design and evaluation of synthetic consortia.


Emergent Cooperation and Modeling Microbial Metabolism

They term this type of interaction as a synthetic mutualism in trans, or SMIT interaction. They chose their collection based on deletions that block a essential metabolite, which forces a strict dependency on an external supply of that metabolite. The strains were tagged by inserting plasmids expressing either mCherry or Venus fluorescent proteins and co-cultured together in pairs. This created a total of 1035 unique pairs. By using the growth of a given strain, they interpret the strain's relative level of cooperation. They used this data to build a model comparing the efficiencies of various pairs. What they found was that 17% of the pairs displayed synergistic growth as defined by their model.

They wanted to be able to predict these results based on an explicit value for the exchanged metabolites. To do this they borrowed a concept from linear programming models called a "shadow price." This price, when applied to SMIT interactions, is a measure of how beneficial to growth the taking up of a metabolite is to the strain, and likewise, how detrimental secreting a metabolite is to growth. These prices are called costs and benefits respectively. By dividing cost from benefit, the efficiency term is created. Using this efficiency, they are able to predict the the growth rate of their system. It should be noted that this efficiency was derived only from stoichiometric considerations, meaning it should be directly applicable to similar systems.


Mutually Obligatory Cooperation

Rational design of CoSMO. The two images compare the system in which the metabolites are produced initially versus the second attempt in which they are over-expressed. [6]
Phase diagram for CoSMO viability. Color indicates the number of viable duplicate cultures, black having zero viable cultures, purple from 1-4, cyan just 1. If the circle is open, the viability was highly variable, if closed, it's 100% viable, if broken, it's 0%. [6]

Functional Minicellulosome by Synthetic Consortia


Surface assembly of a functional minicellulosome through intracellular complementation, using a synthetic yeast consortium. The basic design consisted of four different engineered yeast strains capable of either displaying a trifunctional scaffoldin Scaf-ctf (SC) or secreting one of the three corresponding dockerin-tagged enzymes (endoglucanase [AT], exoglucanase [EC/CB], or β-glucosidase [BF]).[7]



The 2011 Peking University team attempted to develop a logic circuit based on the concept of synthetic consortia, their wiki page is


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Error fetching PMID 18675483:
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  1. Error fetching PMID 22387100: [Shong2012]
  2. Error fetching PMID 18675483: [Brenner2008]
  3. Error fetching PMID 12624191: [Marsh2006]
  4. Error fetching PMID 18063722: [Kuramitsu2007]
  5. Error fetching PMID 20823845: [Wintermute2010]
  6. Error fetching PMID 17267602: [Shou2007]
  7. Error fetching PMID 20889773: [Tsai2010]
All Medline abstracts: PubMed HubMed
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