Zoepin
commented
3 years ago Question: A lot of the examples were implemented in yeast, why is that?
Open Zoepin opened 3 years ago
Zoepin
commented
3 years ago Question: A lot of the examples were implemented in yeast, why is that?
Zoepin
commented
3 years ago Theory of finite-state machine: https://stackabuse.com/theory-of-computation-finite-state-machines/ A short and easy explanation of the theory Imran talked about.
Zoepin
commented
3 years ago A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis https://www.nature.com/articles/nmicrobiol201783
I found this article on cell lysis through quorum sensing but I believe Imran is already working on a similar topic. This one can be complementary if needed.
Zoepin
commented
3 years ago This arcticle would definitely be useful if we were to develop a sensor-relay-effector circuit using various populations of bacteria.
https://www.nature.com/articles/s41467-020-17475-z
As synthetic biocircuits become more complex, distributing computations within multi-strain microbial consortia becomes increasingly beneficial. However, designing distributed circuits that respond predictably to variation in consortium composition remains a challenge. Here we develop a two-strain gene circuit that senses and responds to which strain is in the majority. This involves a co-repressive system in which each strain produces a signaling molecule that signals the other strain to down-regulate production of its own, orthogonal signaling molecule. This co-repressive consortium links gene expression to ratio of the strains rather than population size. Further, we control the cross-over point for majority via external induction. We elucidate the mechanisms driving these dynamics by developing a mathematical model that captures consortia response as strain fractions and external induction are varied. These results show that simple gene circuits can be used within multicellular synthetic systems to sense and respond to the state of the population.
They also engineered a 'minority wins' consortium in which strains turn on gene expression when they are in the minority.
JulietteB-cri
commented
3 years ago Question: A lot of the examples were implemented in yeast, why is that?
I think it could be because for biosensors and cell-communication, they are maybe more interesting than other organisms (apparently form what I read they can sense many kind of stimuli (and in most case the organism they are using seem be some really well-known one like S.cerevisiae) .
What do you think?
research555
commented
3 years ago I like your last paper!
also something I discussed with a friend of mine last night was that creating these three things in isolation is super simple. All you need to do is just clone and transform. The biggest issues are creating the relays and also making sure that the system can communicate with each other effectively. We cant rely on QS because natural systems might be affected. We need to find a workaround for this, perhaps with synthetis QS systems like in your first review
Zoepin
commented
3 years ago Question: A lot of the examples were implemented in yeast, why is that?
I think it could be because for biosensors and cell-communication, they are maybe more interesting than other organisms (apparently form what I read they can sense many kind of stimuli (and in most case the organism they are using seem be some really well-known one like S.cerevisiae) .
What do you think?
Makes a lot of sense! It might be difficult to reproduce in bacteria then. It could be worth looking more into it.
Zoepin
commented
3 years ago I like your last paper!
also something I discussed with a friend of mine last night was that creating these three things in isolation is super simple. All you need to do is just clone and transform. The biggest issues are creating the relays and also making sure that the system can communicate with each other effectively. We cant rely on QS because natural systems might be affected. We need to find a workaround for this, perhaps with synthetis QS systems like in your first review
Good to know the separate components can be achieved rather easily! Synthetic communication could definitely be a way to insure effective communication between the cell populations. A lot has been published already. Let's look more into it.
JulietteB-cri
commented
3 years ago After rereading your nice summary & the comments below, I went through articles to study different ways to create synthetic cell-to-cell communication. - I love this idea of creating a synthetic QS system (maybe our own). One article, in particular, kept my attention (Marchand et al. 2017). Basically, they constructed a synthetic c-to-c communication pathway between a Gram-negative bacteria (E.coli) and a Gram-positive bacteria (B. megaterium) based on the machinery of from the agr QS system of S.aureus
Synthetic Quorum Sensing and Cell–Cell Communication in Gram-Positive Bacillus megaterium Link: https://pubs.acs.org/doi/abs/10.1021/acssynbio.5b00099?casa_token=58qIv64_aJsAAAAA:HAjKfipUsvHtQ85HrLUWsGDvyMaGtkvn4z5IXQs2560TuPD5dkCAEMnRh4mMAJ7l2JNm9CC_3fGZbtrNrw
Artificial cell-cell communication as an emerging tool in synthetic biology applications
https://jbioleng.biomedcentral.com/articles/10.1186/s13036-015-0011-2
This article is a review on the different types of existing synthetic cell communication and gives many examples.
Synthetic quorum sensing systems
Mimic quorum sensing with positive feedback loop Ex: acetate => triggers production of isopentenyladenine (IP) => fixation on IP receptor => signalization => GFP production Requires 2 cell populations. When cells were engineered to secrete IP and respond to it by increased IP synthesis (thus engineering a positive feedback loop), the output gene was expressed in a density-dependent manner.
Ex: Amino acids => triggers α-factor secretion => increased pheromone secretion + GFP production
Biological computation
Biological computation, i.e., the ability of living matter to execute logic functions. => This part was already explained and examples were given by Imran in issue #30
!P: Performing logic operations in a single population often requires complex and multiple genetic elements to be engineered, which have to be transformed and tested extensively in the desired host. Using various populations => less laborious, less metabolic burden, enhanced stability, reliability and long-term functionality.
Ex: able to signal transmission from one layer of cells to the next down- stream layer by horizontal DNA transfer using conjugation or bacteriophages. => DNA stretches for artificial communication represents a promising strategy as more complex messages can be passed on to the receivers.
Ex: able to implement a multiplexer and a 1-bit adder with carry in the yest system.
!P: Complexity comes with increased noise and the need that the wiring molecules to not interfere with each other Microfluidics platforms or spatial separation => use a single wiring molecule for more than one communication channel and thus to limit the required number of different communication systems. Alternatively, distributed output production (i.e., multiple subpopulations are designed to synthesize a similar output in response to individual trigger signals) harbors great potential to efficiently reduce the number of wiring molecules and cellular populations required.
Synthetic ecosystems - Biomedicine and tissue engineering - Synthetic pattern formation
Very interesting but a bit off topic.
Biosensors
!P: Devices based on a single sen-sor cell population might suffer from low signals and/or high background signals due to cellular noise. => cellular communication may overcome this limitation.
Ex: Oscillations within a biopixel were synchronized by an engineered quorum sensing system, whereas long-range synchronization of all biopixels was achieved by volatile hydrogen peroxide. By introducing arsenite-dependent regulatory elements in their design, the authors were able to generate a sen-sor device that tuned its oscillatory amplitude in re-sponse to trace amounts of arsenic, thus yielding a biosensor for arsenic contaminations.
Ex: Trigger => α-factor secretion => population growth + GFP production => more GFP production => signal amplification
Such systems might be especially interesting for environmental monitoring by coupling the detection of a pollutant with its degradation via the formation of respective enzymes.
Furthermore, by employing different communication molecules, this approach can pave the way to control multiple actor populations by a single sensor cell type, thus generating sensor-actor systems for numerous analytes.