Revision as of 15:19, 3 September 2014

iGEM ETH Zurich 2014

Introduction to Mosaicoli

Mosaicoli : from simplicity to complexity with biologic gates

Abstract

Emergence of complex patterns in nature is a fascinating and widely spread phenomenon, which is not fully understood yet. Mosaicoli aims to investigate emergence of complex patterns from a simple rule by engineering a cellular automaton into E. coli bacteria. This automaton comprises a grid of colonies on a 3D-printed millifluidic chip. Each colony is either in an ON or OFF state and updates its state by integrating signals from its neighbors according to a genetically pre-programmed logic rule. Complex patterns such as Sierpinski triangles are visualized by fluorescence after several steps of row-wise propagation. Sequential logic computation based on quorum sensing is challenged by leakiness and crosstalk present in biological systems. Mosaicoli overcomes these issues by exploiting multichannel orthogonal communication, riboregulators and integrase-based XOR logic gates. Engineering such a reliable system not only enables a better understanding of emergent patterns, but also provides novel building blocks for biological computers.

Mosaicoli : from simplicity to complexity with biologic gates

Figure 1 A Textile Cone Snail ([http://en.wikipedia.org/wiki/Conus_textile Conus textile])
Location: Cod Hole, Great Barrier Reef, Australia
[http://commons.wikimedia.org/wiki/File:Textile_cone.JPG Photographer: Richard Ling (CC-BY-SA 3.0)]

What if these surprising patterns on sea snail shells would come from a simple rule, followed by all subsection on the shell?

For example in the picture below, you can see that a similar pattern can be produced by already a simple rule, in which every cell decides on its state (white or green) depending on the state of the 2 cells above it. This rule is called an XOR gate: If one of the two cells above is ON, the cell below them is ON, if both cells above are ON or if both cells above are OFF, the cell below is OFF.

As a matter of fact, many of the complex patterns you can see in nature come from simple rules. It is the case for hurricanes, flocks of birds, neural networks etc. We call this phenomenon emergence. Emergent phenomena are not predictable from the initial conditions and this is why they surprise us. If you are interested in emergence of complexity in general, you can read more about the background of our project.

As a team, we are driven by our fascination for the emergence of complexity and try to make these snail shells patterns, called Sierpinski triangles, emerge on our own grid of bacteria. To achieve this goal we encode the rule that leads to emergence of these patterns in the DNA of these bacteria. This approach, defined Synthetic Biology, is the application of engineering principles to the fundamental components of biology. Here we are using information processing principles in order to control the path of the information through all cells.

This way, Mosaicoli not only endeavors to learn more about how a complex pattern can emerge from simple rules, but also develops new tools for controlling communication between cells and implementing reliable biological circuits. For a more detailed description of our goals, go to the Goals section. For an outlook on the possible applications of our project, jump directly to Applications and outlook.

More details

E. coli bacteria are able to communicate by producing molecules that can cross their cell membrane by simple diffusion. These molecules are called quorum sensing molecules. In our project Mosaicoli, the cells in every colony on the grid are able to sense these molecules coming from the two colonies above it, and to produce a molecule for the next colonies below it.

In order to make a pattern appear on our grid, we need to tell every cell on this grid:

to sense the signals coming from the two cells above.
if it senses only one signal, to produce a fluorescent protein and generate the signal for the cells below
to produce nothing if it senses both signals or if it does not sense any signal.

In synthetic biology, you can tell the cell to compute this algorithm by inserting a genetic circuit. Here is how we did it:

For sensing the signals coming from above, we added in every cell two genes (luxR and lasR) that produce two proteins (LuxR and LasR) that will bind respectively the blue and the red quorum sensing molecules of the figure above. The blue and red complexes created this way trigger the production of other proteins called integrases (Bxb1 and ΦC31). Integrases flip a DNA sequence between two flanking sequences called att-sites (the triangles in the animation above).
This sequence between att-sites (see the large, black T in the animation above) is placed in such a way that it blocks production of a fluorescent protein. Once an integrase is present, the DNA sequence is flipped and production becomes possible. You can see in the animation that if an integrase is present, it can remove this blocking sequence (turn the black T upside down). However if both integrases are present, this sequence is flipped twice and it blocks production of fluorescence again.
When the fluorescent protein is produced, a second protein is produced as well which triggers the production of a quorum sensing molecule sent to the cells below.

If you want to know more about quorum sensing and integrases, you can read the article Biological tools in our Background page.

More details

Mosaicoli involves three main plasmid constructs per cell, one for quorum sensing, one for production of integrases, and the last with the integrase-based XOR logic gate to perform computation. Each cell can receive two orthogonal N-acyl homoserine latones (AHLs) - the LuxI product N-(3-oxohexanoyl)-L-homoserine (3OC₆-HSL) and the LasI product N-(3-oxododecanoyl)-L-homoserine (3OC₁₂-HSL). The 3OC₆-HSL or the 3OC₁₂-HSL received by the cell bind to their corresponding receptor proteins LuxR and LasR, thus activating them. The LuxR-3OC₆-HSL or LasR-3OC₁₂-HSL complexes bind to the corresponding promoters Plux and Plas on the second plasmid and induce the expression of two integrases Bxb1 or ΦC31, respectively. Additionally, we use riboregulators to reduce leakiness in the expression of the integrase genes from the promoters P_lux or P_las^[10].

The integrases act on an asymmetric transcription terminator present present on the third plasmid, that contains the XOR gate. The terminator is located between a constitutive promoter and the start of two consecutive downstream genes in one operon, luxI and gfp. If the terminator is in its "functional” orientation, expression from these genes is not possible, because it is prematurely terminated. Further, the terminator is flanked by two pairs of opposing recombination sites recognised by ΦC31 and Bxb1, respectively. In the absence of both integrases, the terminator is in its functional orientation and thus blocks transcription. Expression of either integrase alone inverts the DNA encoding the terminator. It is no longer in its functional orientation and transcription of luxI (or lasI) and gfp is enabled. Presence of both integrases inverts the terminator twice bringing it back to its original functional orientation. Thus, transcription is blocked again^[9]. After the integrases acted on their corresponding recombination sites, the sites recombine in a way leaving the integrases unable to invert the region a second time.

Colonies of such cells are placed in a grid in a 3D-printed millifluidic chip. In each colony, all cells can exist in one of two states - ON or OFF. The cells are OFF if they do not produce any GFP and LuxI (or LasI) and ON when they produce GFP and LuxI (or LasI). The produced LuxI or LasI enzymes catalyse the production of the corresponding AHL molecules which diffuse out and are propagated to the colonies in the next row. Each colony updates its state by integrating signals from its neighbours (colonies in the previous rows). We expect to see complex fluorescent patterns, such as the Sierpinski triangles, after several rows of colonies on the grid have updated their states.

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Team:ETH Zurich/project/overview

From 2014.igem.org

Revision as of 15:19, 3 September 2014

Introduction to Mosaicoli

Mosaicoli : from simplicity to complexity with biologic gates

Abstract

Mosaicoli : from simplicity to complexity with biologic gates

More details

More details

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