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A formalized design process for bacterial consortia that perform logic computing.

Ji W, Shi H, Zhang H, Sun R, Xi J, Wen D, Feng J, Chen Y, Qin X, Ma Y, Luo W, Deng L, Lin H, Yu R, Ouyang Q - PLoS ONE (2013)

Bottom Line: Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules.The construction and characterization of logic operators is independent of "wiring" and provides predictive information for fine-tuning.This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.

View Article: PubMed Central - PubMed

Affiliation: Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University, Beijing, China.

ABSTRACT
The concept of microbial consortia is of great attractiveness in synthetic biology. Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules. To manage such problem, here we propose a formalized design process: (i) determine the basic logic units (AND, OR and NOT gates) based on mathematical and biological considerations; (ii) establish rules to search and distribute simplest logic design; (iii) assemble assigned basic logic units in each logic operating cell; and (iv) fine-tune the circuiting interface between logic operators. We in silico analyzed gene circuits with inputs ranging from two to four, comparing our method with the pre-existing ones. Results showed that this formalized design process is more feasible concerning numbers of cells required. Furthermore, as a proof of principle, an Escherichia coli consortium that performs XOR function, a typical complex computing operation, was designed. The construction and characterization of logic operators is independent of "wiring" and provides predictive information for fine-tuning. This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.

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XOR computation operates robustly.(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, P<0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using χ2 test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.
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pone-0057482-g004: XOR computation operates robustly.(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, P<0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using χ2 test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.

Mentions: USC and DSC were subsequently co-cultured as a microbial consortium to operate XOR computation. Notably, adjusting population proportions of inoculation between USC and DSC could affect, even disrupt XOR function because of unbalanced growth rates of logic operating cells. Our experimental results showed that the growth rate of USC was slightly faster compared with that of DSC [Fig. 4(A)], so population proportion would indeed vary during co-culture. Fortunately, as presented in Fig. 4(B), final population proportion was only determined by initial population proportion (regulated by inoculation), but almost unrelated to inducement conditions (blank, arabinose treatment, salicylate treatment, and both). To examine how population proportion correlates with XOR function, diverse population proportions between USC and DSC were applied in inoculation. Fig. 4(C) provides experimental data for three different inoculation ratios of USC:DSC, i.e., 1∶10, 1∶5 and 1∶2, whose final population proportions are close to 1∶4, 1∶3 and 1∶1 [Fig. 4(B)]. All three sets of inoculation ratios allow the microbial consortium to perform XOR function: when input is either only arabinose or only salicylate, output is high; but with both inputs or no input existing, output is low. Among all three sets, high contrast of XOR output is allowed (mostly higher than 10-fold difference). This result indicates that XOR computation is operated robustly despite varied population proportion in microbial consortium. Our simulation results also proved that population proportion is not a quite sensitive parameter in the system (See Figure S10).


A formalized design process for bacterial consortia that perform logic computing.

Ji W, Shi H, Zhang H, Sun R, Xi J, Wen D, Feng J, Chen Y, Qin X, Ma Y, Luo W, Deng L, Lin H, Yu R, Ouyang Q - PLoS ONE (2013)

XOR computation operates robustly.(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, P<0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using χ2 test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585339&req=5

pone-0057482-g004: XOR computation operates robustly.(A). Growth curve of USC and DSC, showing OD600 as a function of time. Error bars are calculated as mean ± s. d. The lines are for guiding eyes. (B). Population proportions of USC and DSC under various conditions. Upper panel: initial population proportions at inoculation. Lower panel: corresponding population proportions after growth. Inducers were supplemented when inoculation. Cells were diluted and plated after growth, and colonies were counted to calculate population proportions. For all cases, P<0.001 (n = 3) for the differences in variations of USC population proportion under different treatments (Blank, Ara, Sal or Ara+Sal), using χ2 test. (C). Microbial consortia with diverse initial proportions (1∶10, 1∶5 and 1∶2, respectively) all exhibited properties of XOR function. The results were measured by flow cytometry. Error bars are calculated as mean ± s. d.
Mentions: USC and DSC were subsequently co-cultured as a microbial consortium to operate XOR computation. Notably, adjusting population proportions of inoculation between USC and DSC could affect, even disrupt XOR function because of unbalanced growth rates of logic operating cells. Our experimental results showed that the growth rate of USC was slightly faster compared with that of DSC [Fig. 4(A)], so population proportion would indeed vary during co-culture. Fortunately, as presented in Fig. 4(B), final population proportion was only determined by initial population proportion (regulated by inoculation), but almost unrelated to inducement conditions (blank, arabinose treatment, salicylate treatment, and both). To examine how population proportion correlates with XOR function, diverse population proportions between USC and DSC were applied in inoculation. Fig. 4(C) provides experimental data for three different inoculation ratios of USC:DSC, i.e., 1∶10, 1∶5 and 1∶2, whose final population proportions are close to 1∶4, 1∶3 and 1∶1 [Fig. 4(B)]. All three sets of inoculation ratios allow the microbial consortium to perform XOR function: when input is either only arabinose or only salicylate, output is high; but with both inputs or no input existing, output is low. Among all three sets, high contrast of XOR output is allowed (mostly higher than 10-fold difference). This result indicates that XOR computation is operated robustly despite varied population proportion in microbial consortium. Our simulation results also proved that population proportion is not a quite sensitive parameter in the system (See Figure S10).

Bottom Line: Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules.The construction and characterization of logic operators is independent of "wiring" and provides predictive information for fine-tuning.This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.

View Article: PubMed Central - PubMed

Affiliation: Peking University Team for the International Genetically Engineered Machine Competition (iGEM), Peking University, Beijing, China.

ABSTRACT
The concept of microbial consortia is of great attractiveness in synthetic biology. Despite of all its benefits, however, there are still problems remaining for large-scaled multicellular gene circuits, for example, how to reliably design and distribute the circuits in microbial consortia with limited number of well-behaved genetic modules and wiring quorum-sensing molecules. To manage such problem, here we propose a formalized design process: (i) determine the basic logic units (AND, OR and NOT gates) based on mathematical and biological considerations; (ii) establish rules to search and distribute simplest logic design; (iii) assemble assigned basic logic units in each logic operating cell; and (iv) fine-tune the circuiting interface between logic operators. We in silico analyzed gene circuits with inputs ranging from two to four, comparing our method with the pre-existing ones. Results showed that this formalized design process is more feasible concerning numbers of cells required. Furthermore, as a proof of principle, an Escherichia coli consortium that performs XOR function, a typical complex computing operation, was designed. The construction and characterization of logic operators is independent of "wiring" and provides predictive information for fine-tuning. This formalized design process provides guidance for the design of microbial consortia that perform distributed biological computation.

Show MeSH
Related in: MedlinePlus