Abstract
Recent advances in our ability to watch the molecular and cellular processes of life in action—such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer—raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.
Highlights
It exposes the problems with classical linear digital signal processing (DSP) algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists
It is argued that these new methods applied in this context typify the nascent field of biophysical DSP
Molecular and cellular biological physics is interested in the physical mechanisms that make up life at the smallest spatial scales [1]
Summary
Molecular and cellular biological physics is interested in the physical mechanisms that make up life at the smallest spatial scales [1]. Biological physicists have developed numerous experimental tools that provide unprecedented insight into the real-time, molecular basis of the chemical processes of life These measurement techniques record the dynamic changes in configurations of molecules or sets of interacting molecules, such as protein assemblies. Abrupt transitions are pervasive because the dynamics of molecular motion often occurs in a sequence of small steps, as this makes the optimum use of the available free energy stored in molecular bonds [1] This requires the use of non-classical nonlinear and/or non-Gaussian signal processing algorithms. A largely complementary signal processing approach is the use of hidden Markov modelling (HMM) [9], among many other experimental applications, to studies of ion-channel currents [10], the conformational changes of Holliday junctions and monomer DNA binding and unbinding measured using single-molecule fluorescence energy transfer [11], and the dynamics of molecular motors [12].
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