Measurement of bacterial random motility and chemotaxis coefficients: I. Stopped‐flow diffusion chamber assay

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Abstract

Abstract Bacterial chemotaxis, the directed movement of a cell population in response to a chemical gradient, plays a critical role in the distribution and dynamic interaction of bacterial populations in nonmixed systems. Therefore, in order to make reliable predictions about the migratory behavior of bacteria within the environment, a quantitative characterization of the chemotactic response in terms of intrinsic cell properties is needed. The design of the stopped‐flow diffusion chamber (SFDC) provides a well‐characterized chemical gradient and reliable method for measuring bacterial migration behavior. During flow through the chamber, a step change in chemical concentration is imposed on a uniform suspension of bacteria. Once flow is stopped, diffusion causes a transient chemical gradient to develop, and bacteria respond by forming a band of high cell density which travels toward higher concentrations of the attractant. Changes in bacterial spatial distributions observed through light scattering are recorded on photomicrographs during a 10‐min period. Computer‐aided image analysis converts absorbance of the photographic negatives to a digital representation of bacterial density profiles. A mathematical model (part II) is used to quantitatively characterize these observations in terms of intrinsic cell parameters: a chemotactic sensitivity coefficient, μ 0 , from the aggregate cell density accumulated in the band and a random motility coefficient, μ, from population dispersion in the absence of a chemical gradient. Using the SFDC assay and an individual‐cell‐based mathematical model, we successfully determined values for both of these population parameters for Escherichia coli K12 responding to fucose. The values obtained were μ = 1.1 ± 0. 4 × 10 −5 cm 2 /s and χ o = 8 ± 3 ± 10 −5 cm 2 /s. We have demonstrated a method capable of determining these parameter values from the now validated mathematical model which will be useful for predicting bacterial migration in application systems.

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