Distributed biological computation with multicellular engineered networks - Study engineered yeast consortia to perform complex Boolean logic functions by compartmentalizing simple logic in individual strains and connecting via cell-cell communication using alpha-factor pheromone system. - Constructed library of 16 yeast cell modules responding to stimuli and producing GFP or alpha-factor. Successfully combined modules to produce 2 and 3-input logic functions. - Quantified single-cell outputs and demonstrated basic logic gates including NOT, AND, and NOR using the engineered yeast consortia.

1. Synthetic
Microbial communities
Gaurav Bilolikar
IIT Madras
‘ Microbial consortia engineering ’
BT5250: Synthetic Biology BE13B010

2. Introduction

3. Why synthetic Microbial communities ?
● Inability of organisms to be cultured in the laboratory, e.g. an estimated 103
–105
microbial species in
1 g of soil, less than 1% can be cultured using existing methodologies. Lett Appl Microbiol. 2008;47:
361–366.
● Limited number of exogenous elements that can be cloned and optimized in a single cell, lack of
compartmentalization. Current Opinion in Biotechnology 2012, 23:798–802
● Communities of microbes can better handle the complex process of the conversion of substrates to
products by dividing the metabolic load among multiple species. In addition, communities of
microbes exhibit increased production rates, metabolic efficiency, and robustness to changes in
environmental conditions relative to mono-cultures due to synergistic interactions between species.
Shou et al., Proc Natl Acad Sci USA, 104: 1877–1882 (2007), Team MIT iGem 2015

4. Includes,
92 named bacterial phyla, 26 archaeal phyla,
five of the Eukaryotic supergroups
and 1000+ uncultured organisms.
Nature Microbiology Article number: 16048 (2016)

5. Trends in Biotechnology Vol.26 No.9

6. Metagenomics :
● Metagenomics is based on the genomic analysis of microbial DNA that is extracted directly from
communities in environmental samples, also known as community genomics.
● Metagenomics has emerged as a powerful tool that can be used to analyze microbial communities
regardless of the ability of member organisms to be cultured in the laboratory

7. Current limitations of using microbial consortia :
● It requires simultaneous control of both the individual microbes and the ecosystem as a whole.
● Engineering individual microbes often leads to a change in their relative fitness and results in a
change in community composition that can be detrimental to the overall process

8. Applications

9. Some applications :
● Regot S, Macia J, Conde N, Furukawa K, Kjellen J, Peeters T, Hohmann S, de Nadal E, Posas F,
Sole´ R: Distributed biological computation with multicellular engineered networks. Nature
2011, 469:207-211
Yeast consortia were engineered to perform complex Boolean logic functions by compartmentalizing
simple logic functions in individual strains and connecting them via cell–cell communication.
● Hu B, Du J, Zou R-y, Yuan Y-j: An environment-sensitive synthetic microbial ecosystem. PLoS
One 2010, 5:e10619
This work describes a synthetic microbial community where environmental conditions can be tuned to
promote a range of ecosystem behaviors.

10. ● Ma Q, Zhou J, Zhang W, Meng X, Sun J, Yuan Y-j: Integrated proteomic and metabolomic
analysis of an artificial microbial community for two-step production of vitamin C. PLoS One
2011, 6:e26108.
Combined proteomic and metabolic profiles of an artificial microbial community were used to elucidate
intercellular interactions to optimize growth conditions
● Balagadde, F.K. et al. (2008) A synthetic Escherichia coli predator-prey ecosystem. Mol. Sys.
Biol. 4, e187
Increasing the circuit induction level, activates the predator– prey dynamics and induces population
oscillations, which allows the two populations to co-exist despite their competition for nutrients. In other
words, establishing predation dynamics enables greater biodiversity during long term culturing.
Trends in Biotechnology Vol.26 No.9 Current Opinion in Biotechnology 2012, 23:798–802

11. Distributed biological computation with multicellular
engineered networks
Nature 2011, 469:207-211

12. Distributed biological computation with multicellular engineered
networks
● Study used an alpha-factor ( Alpha Factor Mating Pheromone induces the expression of mating
genes, changes in nuclear architecture, and polarizes growth toward the mating partner.) -based
system in the construction of a community capable of computing complex Boolean logic functions.
● Library of 16 yeast cell modules that respond to an extracellular stimulus and/or alpha-factor and
produce GFP as a reporter or alpha-factor to propagate the signal to the next population was
constructed. The modules were successfully combined to produce 2-input and 3-input logic
functions.
Nature 2011, 469:207-211

14. Full description of each cell used in the biological circuits :
Cell#1 is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression and
STE3 deletion to prevent mating with MATa cells within the circuit. It also contain MFα1 gene under the control of the STL1
osmo-responsive promoter in the episomal plasmid pRS424STL1-MFα1 to express α-factor in the presence of NaCl.
YCplac195-fps1Δ1 plasmid encodes a constitutively open version of the Fps1 glycerol channel. fps1Δ1 mutation is used to
increase sensitivity to high osmo-stress, and thus induce higher α-factor expression. This cell implements an IDENTITY
function.
Cell#2 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. GFP was introduced in the FUS1 gene locus under its promoter.
GALS::fus3as construct was integrated to regulate fus3as expression in galactose/glucose growing conditions. GALS version
of GAL promoter was used to prevent leakiness in glucose. ADGEV construct encoding the hybrid transcription factor “GEV”
(Gal4DBD-hER-VP16 fusion protein) under the control of the ADH1 promoter was also integrated to regulate GAL genes with
17β-estradiol. This cell implements an AND function with 17β-estradiol and an N-IMPLIES function with glucose as input in
galactose based circuits.
Cell3# is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression and
STE3 deletion to prevent mating with MATa cells within the circuit. It contains MFα1 gene under the control of two
TetOperators in the centromeric plasmid pCM183-MFα1 that also express the Tet Transactivator. This allows cells to repress
α-factor expression in the presence of doxycycline. This cell implements a NOT function.

15. Cell#4 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. GFP was introduced in FUS1 gene locus under its promoter.
fus3as construct with its own promoter was integrated to regulate fus3as activity with 6a inhibitor. This cell implements an
N-IMPLIES function.
Cell#5 is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression and
STE3 deletion to prevent mating with MATa cells within the circuit. MFα1 gene is under the control of the GAL1 promoter in
the episomal plasmid pBEVY-GU-MFα1 to express α-factor in galactose. This cell implements an IDENTITY function upon
galactose addition or a NOT function in glucose in galactose based circuits.
Cell#6 (reporter cell) is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity. GFP was introduced in
FUS1 gene locus under its promoter. This cell implements an IDENTITY function.
Cell#7 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. GFP was introduced in FUS1 gene locus under its promoter.
fus3as gene under the control of 7 TetOperators in the episomal plasmid pRS413TetO7-fus3as that also express the
reverse Tet Transactivator was introduced to regulate fus3as expression in doxycycline. STE2 deletion is to prevent S.
cerevisiae α- factor signaling. CaSTE2 was expressed from the pAJ1CaSTE2 plasmid to make cells competent for C.
albicans α-factor signaling. This cell implements an AND function with doxycycline but with C. albicans α-factor as a wire.
Cell8# is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression.
STE3 deletion to prevent mating with MATa cells within the circuit. MFα1 gene is under the control of the glucose
responsive promoter HXT1 in the episomal plasmid YEpHXT1-MFα1. This cell implements an IDENTITY function

16. Cell#9 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
constitutive signaling ability. GFP was introduced in FUS1 gene locus under its promoter. fus3as gene under the control of
seven TetOperators in the integrative plasmid YIpTetO7-fus3as that also express the reverse Tet Transactivator was
introduced to regulate fus3as expression in doxycycline. This cell implements an AND function.
Cell10# is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression.
STE3 deletion is to prevent mating with MATa cells within the circuit. MFα1 gene under the control of 2 TetOperators in the
centromeric plasmid YCpTetO2-MFα1 that also express the reverse Tet Transactivator was introduced to regulate α-factor
expression in doxycycline. This cell implements an IDENTITY function.
Cell#11 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. GFP was introduced in FUS1 gene locus under its promoter.
fus3as gene under the control of 7 TetOperators in the integrative plasmid YIpTetOff7-fus3as that also express the Tet
Transactivator was introduced to repress fus3as expression in doxycycline. This cell implements an N-IMPLIES function.
Cell#12 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. GFP was introduced in FUS1 gene locus under its promoter.
GALS::fus3as construct was integrated and ADGEV construct encoding the hybrid transcription factor “GEV” (Gal4DBD-
hER-VP16 fusion protein) under the control of the ADH1 promoter was also integrated to regulate GAL genes with 17β-
estradiol. GAL4 was deleted to prevent activation of GAL genes in galactose. This cell implements an AND function.

17. Cell#13 is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression.
STE3 deletion is to prevent mating with MATa cells within the circuit. CaMFα1 gene is under the control of 7 TetOperators
in the episomal plasmid YEpTetOff7-CaMFα1 that also express the Tet Transactivator. CaMFα1 gene contains the S.
cerevisiae MFα1 signal peptide for secretion and proteolysis followed by just one copy of C. albicans MFα1 peptide
sequence. This allows cells to repress C albicans α-factor expression in presence of doxycycline. This cell implements a
NOT function.
Cell#14 is a MATα cell that contains MFα1 and MFα2 deletions to avoid expression of endogenous α-factor expression and
STE3 deletion to prevent mating with MATa cells within the circuit. CaMFα1 gene under the control of 7 TetOperators in the
centromeric plasmid YCpTetO7-CaMFα1 that also express the reverse Tet Transactivator was introduced to regulate α-
factor expression in doxycycline. CaMFα1 gene contains the S. cerevisiae MFα1 signal peptide for secretion and
proteolysis followed by just one copy of C. albicans MFα1 peptide sequence. This cell implements an IDENTITY function.
Cell#15 is a MATa cell that contains BAR1 and SST2 deletions to increase α-factor sensitivity and FUS3 and KSS1
deletions to prevent activation of the mating pathway unless fus3 is expressed. GFP was introduced in FUS1 gene locus
under its promoter. GALS::fus3 construct was integrated to regulate fus3 expression in galactose/glucose growing
conditions. GALS version of GAL promoter was used to prevent leakiness in glucose. STE2 deletion is to prevent S
Cerevisiae α-factor signaling. CaSTE2 is expressed in the YIpCaSTE2 plasmid to make cell competent for C albicans α-
factor signaling. This cell implements an AND function.
Cell#16 is a MATa cell that contains BAR1 deletion to increase α-factor sensitivity and FUS3 and KSS1 deletions to prevent
activation of the mating pathway unless fus3as is expressed. FUS1::mCherry was integrated for different output production.
fus3as gene under the control of 7 TetOperators in the integrative plasmid YIpTetO7-fus3as that also express the reverse
Tet Transactivator was introduced to regulate fus3as expression in doxycycline. This cell implements an AND function.

18. Quantification of single cell computational output. Truth table and schematic representation of a cell with a NOT logic . The
NOT function is implemented in Cell 3, and the reporter cell (Cell 6) is used to quantify alpha factor production in vivo.
Doxycycline (DOX) was added as indicated and cells were analysed by FACS. Data are expressed as the percentage of
GFP-positive cells versus cells treated with pheromone. Results represent the mean 6 s.d. of three independent experiments.

19. Transfer functions of basic logic cells. Schematic representation of cells implementing N-IMPLIES, AND, IDENTITY and
NOT functions. Indicated cells were treated with indicated input concentrations (2 inputs, left; 1 input, right). 17bE2,
oestradiol; GLU, glucose.

20. Truth table and schematic representation of cells in the AND circuit. Cells were mixed proportionally and inputs
(NaCl and oestradiol) were added at the same time

21. Panel ordered as in (a) following NOR logic. Indicated cells were treated using as inputs doxycycline and 6a

22. OR gate. Indicated cells were treated using as inputs 0.4M NaCl and 2% galactose (GAL).

23. NAND gate. Indicated strains were treated using as inputs doxycycline and 2% glucose. Data represent the mean
and standard deviation of three independent experiments.

24. Design and in vivo implementation of a multiplexer (MUX2to1) : Truth table and schematic representation of the cells
used in the MUX2to1. Indicated cells were treated using doxycycline (selector) and the inputs oestradiol and/or 2%
galactose. Data are expressed as the percentage of GFP-positive cells using a sample treated with either S. cerevisiae or
C. albicans alpha factor as a reference for Cell 12 or Cell 15, respectively.

25. Design and in vivo implementation of a 1-bit adder with carry :Truth table and schematic representation of cells used
for 1-bit adder with carry. Four cells with two wiring systems that respond to glucose and doxycycline with an XOR logic
were combined with an extra cell that respond to same stimuli but with an AND logic in which instead of GFP, mCherry was
expressed as output. The final outcome was measured as in Fig. 3a. Green bars indicate the adder output (GFP) whereas
red bars represent the carry bit (mCherry). GFP and mCherry images of cells are shown (right panels). Data represent the
mean and standard deviation of three independent experiments.

26. Thank You.
Brainbow :
Brainbow is the process by which individual neurons in the brain can be distinguished from neighboring neurons
using fluorescent proteins. By randomly expressing different ratios of red, green, and blue derivatives of green
fluorescent protein in individual neurons, it is possible to flag each neuron with a distinctive color. This process has
been a major contribution to the field of connectomics, or the study of neural connections in the brain.