Data-driven Multiscale Modeling of Signal Processing in Bacterial Chemotaxis

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Change point analysis that does not require the knowledge of noise model has been developed to detect the time instants at which the observable value and the linear trend change in a time series. The model free change point detection has been applied to various single molecule time series, such as from single protein motor rotary measurements, gating current fluctuation in single ion channel patch clamp measurements, etc., to capture event switching that cannot be resolved using conventional thresholding and binning methods. The outcome from the change point analysis is the dwell time statistics of the system residing in various states, such as the pause and rotation states in rotary protein motor, sub-conductance states in ion channel gating, clockwise and counter-clockwise rotation modes in flagellar motor, different diffusive modes in bacterial chemotaxis, etc. As a next step of the project, the resulting dwell time statistics will then serve as the input to infer the underlying kinetic scheme of the system to reveal hidden kinetic states and transitions among the states.On the other hand, the mathematical formalism of "transfer entropy" to quantify and detect information flow between multivariate time series has been generalized to detect "time dependent" flows. Before applying the new mathematical formalism to real bacterial chemotaxis time series, the theory is currently tested using time series generated from simple simulation models to benchmark its performance and validity.