Final Agem Mod Brochure

“Welcome to the Modeling World”

Why Modeling? Why MC?

We present a novel model of our synthetic Autoinducer-2 (AI-2) quorum sensing system in genetically engineered Escherichia coli (E.coli) bacteria using the Membrane Computing (MC) approach, which we implemented in Mathematica (www.wolfram.com). Bacteria release chemical signals that are used by the microbial population to monitor cell population density in a process known as quorum sensing. Through quorum sensing, bacteria are able to coordinate individual and population behavior by altering gene expression.

Nowadays, many biologists use mathematical and computational models as powerful tools to gain a deeper understanding of biological systems. Given that molecular biology experiments in vitro are very expensive and time consuming, building models of biological processes as a preliminary step helps to circumvent some of the drawbacks of performing hypothesis testing in the wetlab. This is why we feel that computational modelling is important.

Membrane computing is a branch of Natural Computing drawing inspiration from the compartmentalized biological cell. MC is a parallel computing approach which processes multisets of objects (agents) in a localized manner. MC has introduced a powerful computing device for modeling some of the functional and structural features of biological membranes. Membranes are arranged in a hierarchal manner, each associated with a distinct multiset of objects and a unique set of interaction rules computed using a compartmentalized version of Gillespieʼs algorithm.

Emphasizing compartmentalization as a cornerstone feature of cells, membrane computing (MC) is a powerful approach for studying reactions in biological systems. MC allows the user to focus on interactions at the level of an individual cell, but also to observe the emergent properties of entire cell populations. The MC approach seems to be ideal for the construction of a quorum sensing model since compartmentalization of the signal and the cascade proteins are critical features of this process.

Contact Information: Afshin Esmaeili:

[email protected]

Iman Yazdanbod:

[email protected]

Sonja Georgijevic:

[email protected]

Thane Kubik:

[email protected]

Christian Jacob:

[email protected]

Or visit us at:

http://2009.igem.org/Team:Calgary

2500 University Drive Artistic Illustration

NW, Calgary, AB, Canada T2N 1N4

University of Calgary iGEM 2009

Modelling Bacterial Chatter with Mathematica

Objects

Our Model:

Periplasmic Space Cytoplasmic Space Environment

Simulation Visualization

AI-2 Signaling System:

Our MC Framework In our model each bacterium is associated with two regions

biological systems  in stochastic enables us to manipulate the system and observe its changed computations.

compatible file formats to save

dynamics both on a

cytoplasmic space) and the

cellular level as well

One of the strengths of

as population level.

this model is that the

We are also planing to

results could be

post this model online.

objects involved are localized within these two regions based on

Results: Quorum sensing is a complicated

their actual location in the

to the signaling molecules in bacteria is referred to as quorum

bacteria. 16 rules determine the

however its fundamental principles

sensing. In this quorum sensing system, AI-2 (autoinducer 2) is

interactions between the objects.

are simple. This model provides a

used as the inducer. This novel autoinducer was originally

Our incorporation of the Gillespie's

discovered

in the quorum-sensing bacterium Vibrio harveyi.

algorithm to the simulation brings

LuxS synthase is responsible for the production of AI-2. AI-2 is

us a step closer to real life as this

system interact, and how cell

algorithm is a proven approach for

communication is affected by each

attached  to  another  membrane-bound  kinase protein, LuxQ,

We will implement SBML-

(periplasmic space and

The process of producing, releasing, detecting, and responding

bound to a periplasmic protein, called LuxP. LuxP  is  always 

Future directions

implementing randomness of

mechanism in its biological details,

deeper understanding of how the components of this signaling

of the interactions. Our model

Emergent properties of

bacteria

populations can

represented in various

be identified in our

ways such as matrixes,

model resulting from

charts, and graphs.

and exchange our experimental results.

In addition, we intend to substitute Gillespie's algorithm with swarm based approach. With

features added such

doing so, spatial

as cell division and cell to cell

dimensions will be introduced to

communication. The following

the

figures present some results:

simulation.

objects involved in the

and the  LuxPQ  complex  mediates the signal transduction of AI-2. At low cell density, in the absence of sufficient amounts of AI-2 in the environment, this sensor acts as a kinase and phosphorylates cytoplasmic protein LuxU. Phosphorylated LuxU then acts as a kinase itself and adds a phosphate to DNA-binding response regulator protein, LuxO. Lastly, the phosphorylated LuxO would bind to a complex of transcription factor namely sigma 54 and Pqrr4 promoter, which signals the expression of GFP protein. GFP protein emits light and as a result, the bacteria glow. At high cell density, AI-2 is accumulated in the environment and eventually it enters the periplasmic space of bacteria and will be detected by LuxPQ. Hence LuxPQ switches from being kinase to being phosphatase. As a result, LuxPQ removes the phosphate group from LuxU. LuxU acts as kinase, and thus it cannot de-phosphorylate the LuxO. However, the housekeeping phosphotases slowly take away the phosphate off of LuxO, which does not bind to Pqrr4; thus turning off the signal. Without the signal, the bacteria are no longer able to produce GFP and they will turn dark by degradation of existing GFP.

Left: AI-2 Binding to the LuxP-LuxQ Protein Complex. Each column represents one of twenty E.coli bacteria. The state of the modelled bacteria over a period of 50 simulated time steps is depicted along the vertical axis. The color of each cell indicates the binding degree between AI-2 and the LuxP-Q complex. The color spectrum spans from red (no binding) over white to blue (complete binding). As time progresses an increasing amount of AI-2 is produced by LuxS and gets bound to LuxP-Q.

Middle: AI-2 Concentrations. On X-axis, time steps are aligned, and on Y-axis number of AI-2 molecules are placed. This graph shows that the simulation is ran for 3000 time steps and started with one cell (blue line) and other cells are generated over time by divisions (red, green, and yellow lines). This type of results provides information about the general trend of change of concentrations in the bacteria. demonstrates that   AI-2 concentration changes logarithmically between divisions, and suddenly drops at each division.

Right: Distributions of Applied Rules. For each rule r0 to r16 the number of its application is charted. Charts like this help to understand which rules, i.e. which interactions, are more or less important within the simulated system or how changes in the rates of reactions affect the rule distributions.