Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understanding of nocturnal polysomnography narcolepsy type 1 features, there remains a need for improving methods to differentiate narcolepsy type 1 nighttime sleep features from those of individuals without narcolepsy type 1. We aimed to develop a machine learning framework for identifying sleep features to discriminate narcolepsy type 1 from clinical controls, narcolepsy type 2 and idiopathic hypersomnia.

Home Use of a Percutaneous Wireless Intracortical Brain-Computer Interface by Individuals With Tetraplegia.

Objective: Individuals with neurological disease or injury such as amyotrophic lateral sclerosis, spinal cord injury or stroke may become tetraplegic, unable to speak or even locked-in. For people with these conditions, current assistive technologies are often ineffective. Brain-computer interfaces are being developed to enhance independence and restore communication in the absence of physical movement.

Multiplexed GTPase and GEF biosensor imaging enables network connectivity analysis.

Here we generate fluorescence resonance energy transfer biosensors for guanine exchange factors (GEFs) by inserting a fluorescent protein pair in a structural 'hinge' common to many GEFs. Fluorescent biosensors can map the activation of signaling molecules in space and time, but it has not been possible to quantify how different activation events affect one another or contribute to a specific cell behavior. By imaging the GEF biosensors in the same cells as red-shifted biosensors of Rho GTPases, we can apply partial correlation analysis to parse out the extent to which each GEF contributes to the activation of a specific GTPase in regulating cell movement.

Applications of brain-computer interfaces to the control of robotic and prosthetic arms.

Brain-computer interfaces (BCIs) have the potential to improve the quality of life of individuals with severe motor disabilities. BCIs capture the user's brain activity and translate it into commands for the control of an effector, such as a computer cursor, robotic limb, or functional electrical stimulation device. Full dexterous manipulation of robotic and prosthetic arms via a BCI system has been a challenge because of the inherent need to decode high dimensional and preferably real-time control commands from the user's neural activity.

LOVTRAP: an optogenetic system for photoinduced protein dissociation.

LOVTRAP is an optogenetic approach for reversible light-induced protein dissociation using protein A fragments that bind to the LOV domain only in the dark, with tunable kinetics and a >150-fold change in the dissociation constant (Kd). By reversibly sequestering proteins at mitochondria, we precisely modulated the proteins' access to the cell edge, demonstrating a naturally occurring 3-mHz cell-edge oscillation driven by interactions of Vav2, Rac1, and PI3K proteins.

ERK reinforces actin polymerization to power persistent edge protrusion during motility.

Cells move through perpetual protrusion and retraction cycles at the leading edge. These cycles are coordinated with substrate adhesion and retraction of the cell rear. We tracked spatial and temporal fluctuations in the molecular activities of individual moving cells to elucidate how extracellular signal-regulated kinase (ERK) signaling controlled the dynamics of protrusion and retraction cycles.

A growth factor-induced, spatially organizing cytoskeletal module enables rapid and persistent fibroblast migration.

Directional migration requires robust front/back polarity. We find that fibroblasts treated with platelet-derived growth factor (PDGF) and prepolarized by plating on a fibronectin line substrate exhibit persistent migration for hours. This does not occur in the absence of PDGF or on uniformly coated fibronectin substrates.

Manipulation of endogenous kinase activity in living cells using photoswitchable inhibitory peptides.

Optogenetic control of endogenous signaling can be an important tool for probing cell behavior. Using the photoresponse of the LOV2 domain of Avena sativa phototropin 1, we developed analogues of kinase inhibitors whose activity is light dependent. Inhibitory peptides were appended to the Jα helix, where they potently inhibited kinases in the light but were sterically blocked from kinase interaction in the dark.

Fluctuation analysis of activity biosensor images for the study of information flow in signaling pathways.

Comprehensive understanding of cellular signal transduction requires accurate measurement of the information flow in molecular pathways. In the past, information flow has been inferred primarily from genetic or protein-protein interactions. Although useful for overall signaling, these approaches are limited in that they typically average over populations of cells.

What's wrong with correlative experiments?

Here, we make a case for multivariate measurements in cell biology with minimal perturbation. We discuss how correlative data can identify cause-effect relationships in cellular pathways with potentially greater accuracy than conventional perturbation studies.

Mathematical model of a cell size checkpoint.

How cells regulate their size from one generation to the next has remained an enigma for decades. Recently, a molecular mechanism that links cell size and cell cycle was proposed in fission yeast. This mechanism involves changes in the spatial cellular distribution of two proteins, Pom1 and Cdr2, as the cell grows.

Identification of neutral biochemical network models from time series data.

Background: The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.

Exploratory analysis of the copy number alterations in glioblastoma multiforme.

Background: The Cancer Genome Atlas project (TCGA) has initiated the analysis of multiple samples of a variety of tumor types, starting with glioblastoma multiforme. The analytical methods encompass genomic and transcriptomic information, as well as demographic and clinical data about the sample donors. The data create the opportunity for a systematic screening of the components of the molecular machinery for features that may be associated with tumor formation.

Parameter optimization in S-system models.

Background: The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations.

Automated smoother for the numerical decoupling of dynamics models.

Background: Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a particularly convenient solution because its parameters are kinetic-order coefficients which directly identify the topology of the underlying network of processes. We have previously proposed a numerical decoupling procedure that allows the identification of multivariate dynamic models of complex biological processes.