Team:BCCS-Bristol/Modeling-Progress

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Modelling Progress Report

16th - 23rd July
- Implemented run & tumble motion
- With/without chemotaxis
- Implemented interactions between objects (bacteria and particles)
24th - 30th July
- Adapted gamma distribution for tumble angle
- Added trace to particles, and to the mean position of the bacteria
- Changed run length calculation heuristic
31st July - 5th August
- Implemented wrapping boundaries
- Written a class that allows parameters to be altered whilst the program is running
7th August - 13th August
- Implemented bacterium sensing memory
4th August - 20th August
- Added solid boundaries
- Parameter file defined and new class created to handle batch jobs
21st August - 27th August
- Set up blue crystal account for each member of modelling team
- Implemented multi-thread optimisations
- Ran first simulation batches of on blue crystal
- Formulated GRN
28th August - 3rd September
4th September - 10th September
11th September - 17th September
- Continued running simulations on blue crystal
- Researched GRN parameters
- Implemented GRN in MATLAB
- Analysis of simulation results.
18th September - 24th September
- Ran last few simulations
- Investigated GRN sensitivity to variation of C_pMax and AHL input
- Further analysis of simulation results.
- Wrote modelling section of IGEM presentation

Modelling Progress Details

16th - 23rd July

Run and Tumble Motion

A fairly realistic model of bacterial motion; bacterial motion consists of two phases:

The run; movement in a straight line. Speed is constant and equal for all bacteria
The tumble; stationary rotation

All parameters are determined at the start of the respective phase. The run time is sampled by feeding a random number in the range [0, 1] to an inverse exponential function, with parameters found from the literature. Tumble time and tumble angle are determined independently, as tumbles can occur at different speeds due to the independent nature of the flagella motors. Tumble time is gamma-distributed, and determined in the same way as run length; tumble angle is sampled from a fourth-order polynomial approximation of an inverse gamma distribution. The tumble angle is bi-directional, with an equal probability of clockwise or anticlockwise tumbles; however, there is a slight forward bias – i.e. the mean angle is less than 90 degrees.

Chemotaxis

The chemotactic gradient has an effect on the mean run length of a bacterium; runs up the gradient have a longer mean length. A chemotactic gradient can therefore be simulated by sampling run length from one of two exponential distributions with different means, depending on the direction of movement. Using a single distribution for run length regardless of direction corresponds to the isotropic case, where no chemotactic gradient is in place.

Interactions

Implementation of interactions required a change in the movement heuristic. The movement was previously determined by speed and direction. This was substituted in favour of a force-based physics engine. A bacterium experiences forces due to its own intended movement, if it is in a run phase, and the reaction forces resulting from interactions between other objects (bacteria and particles). Particles only experience reaction forces. For each object, all forces are resolved to determine a single resultant force with x and y components; this resultant force is used to calculate a velocity using Stokes’ Law for spheres moving through fluids with low Reynolds numbers. Because the Reynolds numbers involved are so small (around 10-6), inertia is negligible so acceleration is ignored and velocity is calculated at each time step independently of the previous velocity.

24th - 30th July

Gamma Distribution

Mario commented that the polynomial approximation seemed to result in greater tumbling angles than those expected from the literature. This problem was resolved using a database of 1000 pre-computed values derived from the matlab inverse gamma distribution.

Object Tracing

Whilst watching the simulations we noticed that is hard to follow a particles path; we therefore added tracers, with a colour gradient representing time, to the particle. The positions of the particles over time are stored in a database. This could be used for statistical analysis in future.

We also trace the mean position of all bacteria over time to observe any emergent behaviour.

Run Length Calculation

Previously the run length was determined at the start of each run. To allow chemotactic behaviour over more complex chemical fields, a Bernoulli Process was implemented instead; during a run, at each time step there is a fixed probability that the run will be terminated. The probability takes one of three values, depending on the bacterium’s perception of the chemoattractant. The three cases are:

If the bacterium is unable to detect a change in chemoattractant concentration it perceives itself to be in an isotropic environment. This occurs when the concentration change of the chemoattractant is below a sensitivity threshold.
A perceived increase in chemoattractant concentration will result in a lower run termination probability.
A perceived decrease in chemoattractant will result in a higher run termination probability.

31st July - 6th August

Wrapping Boundaries

New class was implemented to limit the diffusion of bacteria in the simulation space. A wrapping boundary consists of a line and an offset vector. When an object touches the line its position is changed according to the offset vector. These can be used to simulate a continuous stream of bacteria past an area of interest (the neighbourhood of the particles), whilst only computing the movement of a small number of bacteria.

Run-time Parameter Control

Previously all parameters were ‘hardwired’ into the program, meaning that they had to be changed by hand before the simulation was run. A new parameters class was constructed that allows us to change parameters whilst the program is running, enabling us to run batches of simulations for statistical analysis. A GUI was also added so that the user can change parameters without needed to alter the code.

7th August - 13th August

Bacterium Memory

Bacteria require a memory to enable temporal comparison of chemoattractant concentrations, giving each bacterium an indication of how its actions affect the percieved quality of its environment. Previously this memory was simply a comparision of the concentration bacterium's current and previous position, i.e. the bacteria had a memory of one time step ~ 0.01 seconds. The memory of a bacteria was implemented using a model by Segall et all who proposed that bacteria in fact have a 4 second memory, comparing the average concentration of the last second with the average concentration of the previous 3 seconds.

14th August - 20th August

Solid Boundaries

Some simulations require solid boundaries rather than the wrapping boundaries. These solid boundaries are included using potential functions to apply resistive forces in a similar manner to the implementation of object interactions.

Parameter File Definition and Batch Jobs

Due to the stochastic nature of the model simulations will have to be run several times before any significant conclusions can be drawn. A new class BSimBatch was written which allows a batch of simulations to be defined in a single file. The file shall include parameters defining each of the parameters of the simulation as well as parameters defining properties of the batch simulation such as simulation length and number of simulations to be run. The batch simulation outputs files containing data related to the positions of bacteria and particles as well as videos of all simulations.