Signal processing for molecular and cellular biological physics: an emerging field

Erroneous detection of desensitization doses in the prevention of hypersensitivity reactions

BACKGROUND

Desensitization protocols have empirically established their efficacy and safety in eliminating most of the hypersensitivity reactions to drugs and other allergens. Without such procedures, the offending drugs can otherwise be lethal, for some patients, when singularly administered at therapeutic doses. These binding events and the subsequent signaling cascades have been extensively modulated by different desensitization methods, without any clear explanation as to why it is necessary to use increasing allergen doses.

PURPOSE

To use a novel theoretical approach in order to model the desensitization algorithms currently in practice, that seeks to shed light on the mechanism behind their clinical efficacy.

METHOD

An approach using signal processing concepts is applied in this work to introduce aliasing as the erroneous detection of higher drug doses responsible for the efficacy of desensitization procedures.

RESULTS

Available experimental data is modeled and correct predictions as to the efficacy of the drug treatment procedures are produced.

CONCLUSIONS

Desensitization algorithms may benefit from using concepts from signal processing theory in order to avoid hypersensitivity reactions.

PLANT: A Method for Detecting Changes of Slope in Noisy Trajectories

Time traces obtained from a variety of biophysical experiments contain valuable information on underlying processes occurring at the molecular level. Accurate quantification of these data can help explain the details of the complex dynamics of biological systems. Here, we describe PLANT (Piecewise Linear Approximation of Noisy Trajectories), a segmentation algorithm that allows the reconstruction of time-trace data with constant noise as consecutive straight lines, from which changes of slopes and their respective durations can be extracted. We present a general description of the algorithm and perform extensive simulations to characterize its strengths and limitations, providing a rationale for the performance of the algorithm in the different conditions tested. We further apply the algorithm to experimental data obtained from tracking the centroid position of lymphocytes migrating under the effect of a laminar flow and from single myosin molecules interacting with actin in a dual-trap force-clamp configuration.

Transcription leads to pervasive replisome instability in bacteria

The canonical model of DNA replication describes a highly-processive and largely continuous process by which the genome is duplicated. This continuous model is based upon in vitro reconstitution and in vivo ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells. Strikingly, the stoichiometries of the replicative helicase, DNA polymerase, and clamp loader complexes are consistent with the presence of only one active replisome in a significant fraction of cells (>40%). Furthermore, many of the observed complexes have short lifetimes (<8 min), suggesting that replisome disassembly is quite prevalent, possibly occurring several times per cell cycle. The instability of the replisome complex is conflict-induced: transcription inhibition stabilizes these complexes, restoring the second replisome in many of the cells. Our results suggest that, in contrast to the canonical model, DNA replication is a largely discontinuous process in vivo due to pervasive replication-transcription conflicts.

DOI:http://dx.doi.org/10.7554/eLife.19848.001

Transcription leads to pervasive replisome instability in bacteria

The canonical model of DNA replication describes a highly-processive and largely continuous process by which the genome is duplicated. This continuous model is based upon in vitro reconstitution and in vivo ensemble experiments. Here, we characterize the replisome-complex stoichiometry and dynamics with single-molecule resolution in bacterial cells. Strikingly, the stoichiometries of the replicative helicase, DNA polymerase, and clamp loader complexes are consistent with the presence of only one active replisome in a significant fraction of cells (>40%). Furthermore, many of the observed complexes have short lifetimes (<8 min), suggesting that replisome disassembly is quite prevalent, possibly occurring several times per cell cycle. The instability of the replisome complex is conflict-induced: transcription inhibition stabilizes these complexes, restoring the second replisome in many of the cells. Our results suggest that, in contrast to the canonical model, DNA replication is a largely discontinuous process in vivo due to pervasive replication-transcription conflicts.

A Computationally Efficient Filter for Reducing Shot Noise in Low S/N Data

Functional multineuron calcium imaging (fMCI) provides a useful experimental platform to simultaneously capture the spatiotemporal patterns of neuronal activity from a large cell population in situ. However, fMCI often suffers from low signal-to-noise ratios (S/N). The main factor that causes the low S/N is shot noise that arises from photon detectors. Here, we propose a new denoising procedure, termed the Okada filter, which is designed to reduce shot noise under low S/N conditions, such as fMCI. The core idea of the Okada filter is to replace the fluorescence intensity value of a given frame time with the average of two values at the preceding and following frames unless the focused value is the median among these three values. This process is iterated serially throughout a time-series vector. In fMCI data of hippocampal neurons, the Okada filter rapidly reduces background noise and significantly improves the S/N. The Okada filter is also applicable for reducing shot noise in electrophysiological data and photographs. Finally, the Okada filter can be described using a single continuous differentiable equation based on the logistic function and is thus mathematically tractable.

An information-based approach to change-point analysis with applications to biophysics and cell biology

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data.

Inference for the physical sciences

There is a disconnect between developments in modern data analysis and some parts of the physical sciences in which they could find ready use. This introduction, and this issue, provides resources to help experimental researchers access modern data analysis tools and exposure for analysts to extant challenges in physical science. We include a table of resources connecting statistical and physical disciplines and point to appropriate books, journals, videos and articles. We conclude by highlighting the relevance of each of the articles in the associated issue.