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Revision as of 15:53, 19 October 2010

Modeling of the bacterial movement

While the biologists are busy teaching the E. coli how to respond to light, the modelers thought of simulating the light driven E. coli electronically. This will close the loop of our virtual E.lemming and will help us check the imaging pipeline, microscope analysis & joystick control even without actually having the real E. lemming under the microscope.

Schematical overview of the relation between chemotaxis and the movement model.

Analogous to the highly complex signal transduction in the Chemotaxis receptor model, there is an equally challenging molecular system on side of the flagella, responsible for the movement of the bacterium. In analyzing this system, we implemented a probabilistic model, meant to realistically simulate the behavior of the cell, by reproducing the main statistical features of the existing empirical data [1]. The chemotactic movement might employ two states, either running or tumbling. The outcome of the flagellar system is under the main influence of CheYp concentration, which is received as an input from the Chemotaxis pathway at every time point of the simulation.

Statistical Objectives

The main target of our algorithm was obtaining very similar statistical results of our simulated E.Coli's movement, when compared to the real E.coli's movement. These statistical results include:

Tumbling Angle

The tumbling angle is the change of direction from run to run. We approximated the empirically observed distribution [1] (left panel) with a Weibull distribution (right panel), with same mean and variance as the empirically observed ones.

Mean Tumbling Length

The mean tumbling length is the average time the cell spends in between two runs.

Mean Run length

The mean run length is the average time the cell spends in the running state.

Assumptions

The assumptions of our model were taken in accordance to the existing empirical data on the chemotaxis movement [1]. The mean tumbling length was assumed to be constant, unlike the mean running length, which is a function of the bias. Besides the experimental evidence, this assumption also has an intuitive meaning: the running probability doesn't influence the amount of time the cell spends in the tumbling state, whereas it matters when deciding the next move, if the cell already is in the running state. Also, the mean velocity during runs was considered constant.

Features of the Movement Model

The Bias

The sigmoid dependency between CheYp concentration and probability of being in the running state

The link between CheYp concentration and the type of movement chosen by the bacterium is called bias and it is formally defined as the fraction of time spent in the running state with respect to the total observation time. Our choice for bias formulation is a nonlinear Hill - type function of the CheYp concentration, with one of the parameters being the CheYp concentration for which a steady - state bias was obtained, well documented from the literature [2]. [NEED FURTHER EXPLANATION!!!]At every time point of the simulation, the cell probabilistically chooses whether to run or to tumble, depending on the bias value. The choice of the running state is equivalent to a change in spatial coordinates, together with a slight change in direction (movement angle), while the choice of the tumbling state corresponds to new movement angle and unchanged spatial coordinates.

Practical Challenges

One of the biggest practical challenges regarding the movement model was obtaining a time - step - invariant behavior of the cell, whose reliability of statistical estimates should be the same, regardless of how often the cell had to choose its future state. In order to achieve this, we opted for a two - state model, in which the probability of running was controlled separately depending on whether the cell was running or tumbling.

We derived the two central parameters of our model - the running probabilities - by exploiting the mathematical dependency between the mean run/tumbling length and the running/tumbling probabilities:

[derivation of the probability formulas]

Parameter estimation In order to obtain time - invariant estimates

Simulated Random Walk

[1] Chemotaxis in Escherichia coli analysed by three - dimensional Tracking. H.C. Berg, D.A.Brown: Nature 239, 500 - 504. 1972