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A simple [[/ODE_Model | ODE model]] was assumed in order to model the concentrations of the interacting proteins. | A simple [[/ODE_Model | ODE model]] was assumed in order to model the concentrations of the interacting proteins. | ||
==References== | ==References== | ||
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Revision as of 06:50, 11 August 2008
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<html><a href=http://openwetware.org/wiki/IGEM:IMPERIAL/2008/Prototype><img width=50px src=http://openwetware.org/images/f/f2/Imperial_2008_Logo.png></img</a></html> | Home | The Project | B.subtilis Chassis | Wet Lab | Dry Lab | Notebook |
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Data collection and analysis
We plan to capture video footage of our B. subtilis and analyse the motility. We should obtain a sequence of images. Using a tracking software we hope to determine the x and y position of the cell at each frame. From this data, we can quite easily derive the velocity and angle at each frame. However, it will a bit more complicated to derive the run time and tumbling time. Assuming that for some frames the velocity will be equal to zero, we can suppose that this corresponds to the tumbling phase. Therefore, we can create a new array containing the tumbling (or run) duration for each frame. (eg:If the velocity at frame n is 0 then the tumbling duration at frame n is the duration of the frame. Similarly, if the velocity at frame n is different to 0, then the tumbling duration at frame n is 0 ). From the frame by frame run/tumbling durations, we should determine the run/tumbling times. For that to be done, we would have to find sequences of frames that would correspond to the run phase or tumbling phase, and then sum them to determine the duration of the run/tumbling phases. We will obtain an array with the run/tumbling times. This data can then be analysed.
Video Methods
A video camera attached to the microscope can be used to capture images of moving cells. Video images are captured into memory by the system at a video frame rate of 25 or 30 Hz, after which algorithms which detect moving objects over a series of digitized images are applied.
In the initial frame, objects which satisfy the criteria for cell bodies are distinguished from the background and their positions stored as an array of x,y-coordinates with time. The boundaries of the cell are defined and the centroid of the cell is determined.
In the next frame, we can assume that the cell has moved a short arbitrary maximum distance and lies within a region with respect to their original position. A search for the cell may be carried out within this region, or all cells in the current frame are detected and those which satisfy certain criteria to match cells in the previous frame are assumed to be the same. This allows the system to track the movement of cells from frame to frame, captured from a video.
Software
- Volocity
Captures 3D images in real time, allowing the user to find, track and measure objects. Algorithms are available to eliminate noise and blur, in data. Used in Imperial College by various researchers. Also available for use with wide field microscopes located in SAF building. We will most likely be using this software as it can be acccessed in College.
- Zeiss LSM
Allows for 3D image analysis, used with a laser scanning microscope. Macros are available for download.
- ImageJ
Free to download, written in Java by NIH. ETH (in Switzerland) has an algorithm to track multiple cells. We will be doing preliminary work with this software to generate data and analysis on motile E.Coli or B.Subtilis.
Modelling Bacillus subtilis motility
Our supervisor, Mattieu Butelle, devised a series of three tutorials to help the Dry Lab crew develop our understanding of modelling and its limitations.
The problems involved in the design and analysis of experiments (such as motility assays) are complex. We have little information on the statistical process involved. All we know is that there is a physiological process that underpins bacterium motility. It is unlikely that we will be able to test comprehensive motility models against the data we will gather. On the positive side, we are not interested in building the best, most accurate model ever. What we really want is a model that is simple enough to be testable and that matches the data effectively.
In order to develop an acceptable model, the modelling process is broken into three steps, corresponding to the successive layers of a Bayesian data analysis routine.
- Step 1 Generation of the hypotheses
Thanks to a bibliographical research, we can identify a set of hypotheses that we wish to compare to the data we have collected. In our case, a hypothesis consists in a model for the unhindered movement of bacteria. Each candidate model should depend on a few parameters only – the present exercise is complex enough as it is.
- Step 2 Match Each Hypothesis to the Data
Before knowing whether a model is supported by the data or not, we seek to match it to the data we have. In practice this step amounts to finding out what its best-fitting parameters are – see Tutorial 2.
- Step 3 Hypothesis Testing
Once we have obtained for each model the best available match with the data, it is time to compare the various hypotheses. Again several approaches exist – see Tutorial 2.
- Improving the Design of the Experiment
The Bayesian approach provides intuitive methods for quantifying the accuracy of our predictions. The quantity and quality of the data available are crucial factors in determining the quality of the whole process. Being able to quantify the accuracy of our predictions has appealing consequences. Of particular interest to us is the possibility to design our experiment such that the reliability and accuracy of the predictions improve. Of course the design will be better if we have real data - and in particular if we have data representative of the phenomenon we wish to study. However, interesting results can be obtained using synthetic data generated by the various candidate models.
The first tutorial focuses on the construction of a relevant statistical model for the movement of a bacterium like B. subtilis and on the generation of the synthetic data required to train our analysis routines and design of the wet lab experiments. During the second tutorial, we assume that we have an unrealistic level of control on the data acquisition process. Under such ideal assumptions, we will introduce the basics of Bayesian data analysis and how the results can be used to design experiments. Finally in the third tutorial, we will study the far more complex – and more realistic - case where the data acquisition process only gives us imperfect access to the data. As you will see the quality of the predictions that we can make is degraded and from an experimental point of view we need to gather more data.
- Lessons to be learnt from these tutorials
- When we know a little about a phenomenon, we can still effectively design experiments and hope to model the phenomenon
- Basis of Experiment Design 1: Increasing the confidence of the predictions
- Basis of Experiment Design 2: Make sure we only collect relevant data
- Recommendations
- Use Matlab for the calculations - Matlab is an iGEM partner.
- Try to solve the problem on paper first and then do simulations. You will learn a lot about the limitations of theory and the pitfalls of simulations.
Dry Lab Tutorial 1: Creating Synthetic Data
Dry Lab Tutorial 2: Statistical Data Analysis
Dry Lab Tutorial 3
Modelling the genetic circuit
A simple ODE model was assumed in order to model the concentrations of the interacting proteins.
References
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