From 2014.igem.org

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Background

Design

Achievements

Models

Circuit Development

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Conceptual Framework

All multicellular systems, and many examples of collectives of unicellular organisms, communicate in order to respond and adapt to their environment. However, biological systems are pervaded by noise, and surprisingly in the face of that, many biological responses are robust and withstand cell-to-cell variation [1].

Using endogenous signaling pathways found in yeast, communicating “sense-and-secrete” circuits can be designed to interrogate cell-to-cell signaling, and collective response, in these organisms. Depending on the communication parameters — signal strength, receptor abundance, and signal degradation — researchers were able to tune the strength and output of these communicating cells [2].

We expand on this previous work by investigating further the relationship between cell-to-cell variation and the collective community response of cells. We have engineered yeast strains to report two responses by fluorescence: 1) a cell autonomous response triggered by an experimental induction and 2) a collective community response driven by cell-cell signaling. We are thus able to read simultaneously the relationship between the individual cell response and the post-cellular signal processing that occurs through extracellular signaling.

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Conceptual Framework: Stimulus

In our generalized circuit design, we would like to be able to stimulate the cell response reproducibly, and will therefore use a cell-permeable small molecule inducer to drive gene expression. Cells should have a range of responses given different amounts of stimuli.

An analoguous native stimulus includes antigen that stimulate immune response.

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Conceptual Framework: Autonomous Response

To read an output that allows us to measure the natural variation in genetically identical cells, we need to engineer an output signal or response. This external stimulus will directly induce a fluorescence output, allowing us to read the natural variation in a population.

Often, there is no analogy to this in natural systems as this variation is suppressed for a coordinated community response.

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Conceptual Framework: Communication Response

The stimulus will also trigger a communication circuit, driven by the same induction system as the cell autonomous fluorescence response.

In T regulatory and T killer cells, this communication response pathway corresponds to the IL-2 downstream pathway that includes upregulation of IL-2R alpha and IL-2 secretion.

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Conceptual Framework: Secondary Signal

The communication circuit will incorporate a secreted signal produced by the induced cell.

This communication signal is IL-2 in T cells and autoinducer molecules in V. fischeri.

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Conceptual Framework: Community Integration

The secreted signal will be produced and is transmitted between all cells in the population.

While in our experiment, we will stick with simple culture tubes, the location and structure of how cells are arranged and allowed to move can impact how the secondary signal is integrated. T cells are usually arranged in the highly structured lymph node when first exposed to pathological antigen.

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Conceptual Framework: Community Response

Each cell in the population will also have receptors to recognize the secondary signal. Receptor binding will trigger a signaling cascade to induce expression of a downstream reporter gene to indicate cellular communication levels — in this case, fluorescence.

In V. fischeri, one can find an analogous community response once the population has reached quorum. This response, however, is processed to binary form while our circuit will strive to output a signal that reports the continuous distribution of community responses.

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Implementation

Here we diagram the realized implementation of our circuit design.

There are two components to the overall circuit. The first (left), is a cell autonomous response driven by induction by a small-molecule (doxycycline). This induction is read out by the reporter GFP.

The second component integrates communication by stimulating the production of a secreted signaling factor after doxycycline induction. This factor can communicate to the originating cell as well as neighboring cells, and receptor binding of this signal stimulates RFP reporter expression.

This design thus allows us to view both individual cell responses (GFP) and community responses (RFP) simultaneously in a population of cells.

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Implementation: Doxycycline

For our stimulus, we have opted to use the well-characterized small molecule antibiotic, doxycycline, as an inducer.

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Implementation: rtTA

Doxycycline is able to induce gene expression by binding to the engineered transcription factor rtTA. This protein is a fusion between the E. coli Tet repressor (which binds doxycycline) and the S. cerevisiae transcription factor Msn2 [3].

We have further elaborated on this component of the circuit by engineering yeast strains that respond differently to doxycycline induction. We have achieved this by altering the constitutive promoter driving rtTA expression. We have implemented the endogenous pTEF1 promoter as well as several mutants (see promoter characterization below) which have altered expression levels [4]. The altered doxycycline dose response can be viewed in the Achievements section.

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Implementation: GFP

The doxycycline-rtTA complex is able to initiate transcription at the TET promoter. We have placed GFP behind pTET in order to read out the direct doxycycline response.

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Implementation: Mating Factor Alpha

Our communication circuit is also driven by the TET promoter — doxycycline induction results in expression of a secreted signaling molecule. We have repurposed an endogenous yeast signaling system for this circuit, the mating pathway. Our yeast strains contain the receptors and downstream signaling cascade to be able to respond to the presence of mating factor alpha; however, the only alpha production gene is controlled by our communication circuit [5,6].

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Implementation: Yeast Population Integration

Alpha factor is secreted by all of the cells in the population, and can be similarly sensed by all cells.

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Implementation: RFP

Binding of alpha factor to its receptor, Ste2, triggers a signaling cascade that stimulates induction at alpha factor responsive promoters, of which we characterized and submitted 11 to the Parts Registry (see below). We chose the highly expressing pAGA1 as the promoter to drive the reporter RFP in order to read the community-stimulated response.

Conceptual Framework
Stimulus
Autonomous Response
Communication Response
Secondary Signal
Community Integration
Community Response
Implementation
Doxycycline
rtTA
GFP
Mating Factor Alpha
Yeast Population Integration
RFP

Experiment Design

To confirm that our circuits behave as we have designed them to behave, we set up two different experiments:

1. Simple doxycycline dose response to test cell autonomous behavior. The results of this experiment can be found here.

2. Mixing experiment (on the right), where we test the ability of our circuit to produce convergent behavior with artificially induced divergence. In our experiment, we mix our lowest responding cell autonomous circuit with rtTA expressed by pTEF1(m7) with our higest responding circuit with pTEF1. We expect that through intercellular signaling and community circuit, the two populations will look in distinguishable by the community response reporter. Our results for this experiment can be found here.

Promoter Characterization>

We have characterized two types of promoters endogenous to S. Cerevisiae; eleven alpha-factor responsive promoters (AFRP) and five constitutive promoters. To characterize these promoters, we cloned them into a shuttle vector plasmid to drive expression of GFP, which was subsequently integrated into yeast to create stable strains. Each construct’s fluorescence was measured using flow cytometry.

Alpha Factor Responsive Promoters (AFRP)

AFRPs were selected from a microarray study where Roberts et. al. searched for genes responsive to yeast MAPK pathways including the alpha factor response pathway [5]. We picked the promoters of the genes that were shown to be up-regulated in the presence of alpha factor. For each AFRP, we measured its construct’s fluorescence over 7 concentrations of alpha factor [0nM, 0.5nM, 1nM, 10nM, 100nM, 1000nM, 3000nM] after 90 minutes of induction. Through multiple rounds of testing, we found pAGA1 to have the highest expression level and with very low basal expression and pPCL2 to have the highest basal expression and the second highest induced expression.

Constitutive Promoters

Constitutive promoters were selected from a study where Nevoigt et. al. used error prone PCR to create 14,000 pTEF1 mutants [4]. The study then selected 11 mutants with a wide range of promoter activity for further characterization. Out of these 11, we chose 5 of these mutants to use in our circuit. We confirmed that constitutive promoter pTEF1 m6 had the highest expression of GFP whereas constitutive promoter m7 had the lowest expression of GFP. Our data closely correlates with the results of the Nevoigt study.

Characterization data for all of our promoters can be found in Biobricks Submitted of our Achievements Section.

• 1. Cotari, J. W., Voisinne, G., & Altan-Bonnet, G. (2013). Diversity training for signal transduction: leveraging cell-to-cell variability to dissect cellular signaling, differentiation and death. Current Opinion in Biotechnology, 24(4), 760–6. doi:10.1016/j.copbio.2013.05.002
• 2. Youk, H., & Lim, W. A. (2014). Secreting and Sensing the Same Molecule Allows Cells to Achieve Versatile Social Behaviors. Science (New York, N.Y.), 343(6171), 1242782. doi:10.1126/science.1242782
• 3. Gossen, M., Pak, D.T.S., Hansen, S.K., Acharya, J.K., Botchan, M.R. (1995). A Drosophila homolog of the yeast origin recognition complex. Science 270(5242): 1674--1677
• 4. Nevoigt, E., Kohnke, J., Fischer, C. R., Alper, H., Stahl, U., & Stephanopoulos, G. (2006). Engineering of promoter replacement cassettes for fine-tuning of gene expression in Saccharomyces cerevisiae. Applied and Environmental Microbiology, 72(8), 5266–73. doi:10.1128/AEM.00530-06
• 5. Roberts, C. J. (2000). Signaling and Circuitry of Multiple MAPK Pathways Revealed by a Matrix of Global Gene Expression Profiles. Science, 287(5454), 873–880. doi:10.1126/science.287.5454.873
• 6. Gross, a, Rödel, G., & Ostermann, K. (2011). Application of the yeast pheromone system for controlled cell-cell communication and signal amplification. Letters in Applied Microbiology, 52(5), 521–6. doi:10.1111/j.1472-765X.2011.03035

Background

Design

Achievements

Models

Computational Models of Bar1 Negative Feedback

Part 1: Negative Feedback and Noise Reduction

Background: Noise can be defined as extracellular or intracellular perturbation which affects signal transduction as well as gene expression[1]. A signaling pathway can include mechanisms that reduce noise. Such reduction in biological noise may be necessary for pathway functionality. For example, the variety of T cell receptors (TCR) in a population of T cells generates a wide range of responses to a particular antigen, yet somehow, a very specific, low noise, response is achieved through its biological circuitry [2]. The regulatory mechanisms downstream of TCR include negative feedback which allows stimulation signals to be controlled, guaranteeing appropriate responses to external perturbations. Also, noise is regarded as a quantitative index of variability within a population, which can be shown by the distribution of population response when stimulated. We wonder if adding a negative feedback to the production of extracellular communication factor will reduce population noise.

Circuit Diagram: We have added the Bar1 gene, which is activated by alpha factor in the primary circuit [3]. Bar1 is a protease that cleaves alpha factor in the extracellular zone. So, alpha factor inhibits the accumulation of itself by producing Bar1, which forms a negative feedback for extracellular alpha factor. We can tune the strength of negative feedback with different alpha factor responsive promoters.

Modeling Result: Here is the data plot using a stochastic model based on Chemical Langevin Equation [4]. The data along the whole time period is integrated into histograms on the right. From the results, we find that strains with negative feedback loop have lower noise (Upper panel), while strains without it have higher noise (Lower panel).

Part 2: Bimodal Response

Background: We explored another behavior pattern, bimodal response, which means when stimulated, the whole cell group differentiates into two subgroups - one of them activated while the other one remains in OFF state.

Circuit Design: This is the circuit we designed to achieve bimodal response. Based on the primary secrete-sense circuit, we add a positive feedback, and a degradation factor Bar1. The strength of positive feedback can be tuned by doxycycline concentration, and the strength of degradation factor can be tuned by changing pTEF promoter in front of Bar1, which we characterized experimentally.

Modeling Formula: In order to describe the dynamic of extracellular alpha factor concentration, we build a deterministic model based on this formula.

The first two terms represents generation of alpha factor: the first is the basal synthesis, and the second describes the positive feedback for generating alpha factor. We use sigmoidal form based on our pre-experiment results. The last two terms represents degradation of alpha factor. In this circuit, Bar1 is constitutively expressed, and the degradation of alpha factor by Bar1 is of first-order. The last term is natural degradation.

If we define this formula as the driving force, then the negative value of its integration can be defined as potential.

Modeling Results: Assuming the basal synthesis of alpha factor is zero, we plot the secretion term and degradation term against alpha concentration respectively.

Tuning the parameters to change the strength of positive feedback and degradation term, when extracellular alpha factor concentration stops changing at last, there are three fixed points – among which 2 are stable and 1 is unstable judged from Jacobian matrix. By calculating the potential (defined above), we correspond 2 of fixpoints to the 2 potential barriers where cells may gather. Thus a bimodal response appears.

Parameter Tuning: In this model, the strength of positive feedback changes by tuning V, while the strength of Bar1 degradation changes by tuning deltaBar1. So, we set a range for these two parameters to see under which condition bimodal response appears. This figure shows tuning deltaBar1, the slope of the degradation line. For different values, the pattern is distinctive. Along with its decrease, the potential difference between the barrier and of the well decreases, which means cells may have bigger probability to jump out of the well by stochastic events, thus bimodal pattern collapses.

This figure shows the results of tuning V, the highest value of secretion curve. Also, along with its decrease, the trend is similar with changing deltaBar1, resulting in difficulty maintaining bimodal response within cell population.

Kilobots

What are Kilobots?

Kilobots are small robots designed by the Self-Organizing Research Group at Harvard University to operate in large groups[5]. When programmed, the kilobots can cooperate through local interactions to perform collective behaviors such as swarming and self-assembly. They can flash LED lights, transmit and receive electronic signals to and from each other, and move using two vibration modules. For our purposes, we only need to use the LED light to report robot response.

Why model with Kilobots?

With their simple design, we hope to produce complex collective behavioral models that mimic our yeast circuits. By programming Kilobots to behave as our yeast cells, we hope to produce predicted community responses such as converging or diverging behaviors.

In our models, we utilized their ability to sense and secrete signals to produce community responses. A Kilobot can be in one of five states: NONE*, LOW, MED, HIGH, or MAX, which describe how frequently the cell transmits signals. A Kilobot’s state may vary depending on its program and the states of nearby Kilobots.

A Kilobot’s internal state is reported using its LEDs, which correspond to the following key:

• None - Blue
• Low - Cyan
• Medium - White
• High - Orange
• Maximum - Red

*Kilobots in the NONE state send signals at a very low frequency; this is so that if isolated, the Kilobot will still be able to self-signal.

Circuit Components

1. i_sense_rand_bar

   This code allows Kilobots (yeast cells) to sense and transmit electronic signals (mating factor alpha) that will influence other Kilobots’ states and may influence its own state (self-sensing). The Kilobots are randomized into different initial states, representing different initial constitutive expressions. There are five possible states, which describe the Kilobot’s output: NONE* [blue], LOW [cyan], MED [white], HIGH [orange], and MAX [red]. At each state, the cell lights up in the assigned color and transmits signals [value = 0] at the assigned frequency, NONE being the lowest level of frequency and MAX being highest. If a Kilobot senses enough signals transmitted by other Kilobots, it will go up in stage to the next highest level (so from NONE to LOW to MED to HIGH to MAX), until it reaches the MAX stage, at which point it will stay red. If the Kilobots sense enough signals transmitted by a i_bar1 Kilobot [value =1], it will go down a stage.

2. i_bar1

   This code is meant to mimic yeast cells that produce Bar1, which degrades mating alpha factor and can cause negative feedback to reduce activity in nearby cells. The Kilobot will flash magenta every time it transmits a signal [value = 1], which may inhibit nearby bar1-sensitive Kilobots to reduce signal if exposed for long enough.

3. i_sense_average_bar_rand

   This code allows Kilobots (yeast cells) to sense and transmit electronic signals (mating factor alpha) that will influence other Kilobots’ states and may influence its own state (self-sensing) in order to converge to a medium level of expression. The Kilobots are randomized into different initial states, representing different initial constitutive expressions. There are five possible states, which describe the Kilobot’s output: NONE [blue], LOW [cyan], MED [white], HIGH [orange], and MAX [red]. At each state, the cell lights up in the assigned color and transmits signals [value = 0] at the assigned frequency, NONE being the lowest level of frequency and MAX being highest. If a Kilobot senses enough signals transmitted by other Kilobots, it will go up in stage to the next highest level (so from NONE to LOW to MED). If the Kilobot initially is at a HIGH or MAX state, it will gradually reduce its expression until it matches that of other cells at the MED.

Results

ConvergeHigh

When all the Kilobots were programmed with code 1, they each started at different initial states, but they all reached maximum level of expression very quickly. Note that robots in the center seem to turn red faster while the slowest robot to turn red is in the bottom corner, where it gets relatively fewer signals due to having fewer neighbors.

This progression from random to maximum is similar to how a noisy but highly expressing yeast strain would respond to mating factor alpha.

Diverging Bar1

In this clip, seven Kilobots were given program 1 and three were given program 2 (flashing magenta) to mimic Bar1 cells. This produced a diverging response, in which robots further away from the Bar1 Kilobots still reached maximum expression level, but those closer to the Bar1 Kilobots stayed at low levels of expression, eventually settling at a medium level of expression. This diverging response may be similar to what happens when different strains of yeast with and without bar1 are co-cultured.

RandomtoBimodal

In this clip, half of the Kilobots were programmed with code 1 and the other half were programmed with code 2. The initial state is similar to what happens in the first scenario (ConvergeHigh), in that initial response is random and noisy; however, the robots have clearly differentiated into medium and high levels of expression (white and red, respectively) by the end. This diverging yet coordinated response may be similar to what happens when two noisy strains of yeast are intermixed.

LowtoBimodal

In this clip, half of the Kilobots were programmed with code 1 and the other half were programmed with code 2, this time with a few modifications such that they all started at the lowest level of expression (blue for NONE). Again, the robots ultimately produced a bimodal response, with half of the robots emitting white light for medium expression and the other half emitting red light for maximum expression.

This diverging response may model what happens when two strains of yeast with the same initial response but different secondary responses are mixed together and induced with mating factor alpha.

• 1. Blake, W. J., KAErn, M., Cantor, C. R., & Collins, J. J. (2003). Noise in eukaryotic gene expression. Nature, 422(6932), 633–7. doi:10.1038/nature01546
• 2. Höfer, T., Krichevsky, O., & Altan-Bonnet, G. (2012). Competition for IL-2 between Regulatory and Effector T Cells to Chisel Immune Responses. Frontiers in Immunology, 3(September), 268. doi:10.3389/fimmu.2012.00268
• 3. Gross, a, Rödel, G., & Ostermann, K. (2011). Application of the yeast pheromone system for controlled cell-cell communication and signal amplification. Letters in Applied Microbiology, 52(5), 521–6. doi:10.1111/j.1472-765X.2011.03035.x
• 4. Gillespie, D. T., & Introduction, I. (2001). Approximate accelerated stochastic simulation of chemically reacting systems, 115(4), 1716–1733.
• 5. Rubenstein, M., Ahler, C., & Nagpal, R. (2012). Kilobot: A low cost scalable robot system for collective behaviors. 2012 IEEE International Conference on Robotics and Automation, 3293–3298. doi:10.1109/ICRA.2012.6224638