What to expect in this project:
- The run-and-tumble motion has been implemented using support vector machine (SVM).
- The optimal weights for the model are non-zero only for the most recent history of the signal, suggesting an effective measurement time.
- Necessary data to reproduce figure 3 of the manuscript/article -- "Learning run-&-tumble chemotaxis with support vector machines" Rahul O. Ramakrishnan and Benjamin M. Friedrich
- Source code in Python to reproduce the data
In run-&-tumble chemotaxis, the bacteria (agent) move in a straight line (the "run" phase) and record the binding events of chemoattractant molecules. If they move up the gradient, they prefer to continue in their current direction, but if they move down the gradient, they will randomly change direction (the "tumble" phase) and move in a new direction. This movement allows the bacteria to efficiently explore their environment and locate sources of food.
run-&-tumble chemotaxis
There are two physical constraints that make chemotaxis more challenging:
- motility noise, which results from rotational diffusion causing the agents (bacteria) to not move in a straight line, and
- sensing noise, which occurs when the binding events that make up the signal are stochastic.
The run-and-tumble mechanism is interesting because of its simplicity, and it is worth exploring whether machine-learning techniques can be used to model it and how well they approximate the benchmark measure.
Because of rotational diffusion, the agent changes direction too quickly and relies only on recent history (past events become irrelevant). The effective measurement time, or how long the agent needs to retain past events, becomes an important factor. We found an empirical relationship for the effective kernel time as a function of rotational diffusion.
To reproduce the figures, run the Python script as
python document.py
which will generate figure3.eps in the working directory and some additional figures inside the directory fig
Necessary data has already been provided in the directory data
To reproduce the entire data (this will take several hours), one may start afresh by cleaning the directory data and run the main Python script as:
python main.py
python document.py
If necessary, modify the current settings/parameters specified in
inputs.py
before running the main.py.
If run correctly, the expected figure would be:
Figure 3. (a) Weight profiles. (b) Effective measurement time