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. The population included polysomnography data from 350 drug-free individuals (114 narcolepsy type 1, 90 narcolepsy type 2, 105 idiopathic hypersomnia, and 41 clinical controls) collected at the National Reference Centers for Narcolepsy in Montpelier, France. Several sets of nocturnal sleep features were explored, as well as the value of time-resolving sleep architecture by analysing sleep per quarter-night. Several patterns of nighttime sleep evolution emerged that differed between narcolepsy type 1, clinical controls, narcolepsy type 2 and idiopathic hypersomnia, with increased nighttime instability observed in patients with narcolepsy type 1. Using machine learning models, we identified rapid eye movement sleep onset as the best single polysomnography feature to distinguish narcolepsy type 1 from controls, narcolepsy type 2 and idiopathic hypersomnia. By combining multiple feature sets capturing different aspects of sleep across quarter-night periods, we were able to further improve between-group discrimination and could identify the most discriminative sleep features. Our results highlight salient polysomnography features and the relevance of assessing their time-dependent changes during sleep that could aid diagnosis and measure the impact of novel therapeutics in future clinical trials.

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. Over the past decade, individuals with tetraplegia have achieved rapid on-screen typing and point-and-click control of tablet apps using intracortical brain-computer interfaces (iBCIs) that decode intended arm and hand movements from neural signals recorded by implanted microelectrode arrays. However, cables used to convey neural signals from the brain tether participants to amplifiers and decoding computers and require expert oversight, severely limiting when and where iBCIs could be available for use. Here, we demonstrate the first human use of a wireless broadband iBCI. METHODS: Based on a prototype system previously used in pre-clinical research, we replaced the external cables of a 192-electrode iBCI with wireless transmitters and achieved high-resolution recording and decoding of broadband field potentials and spiking activity from people with paralysis. Two participants in an ongoing pilot clinical trial completed on-screen item selection tasks to assess iBCI-enabled cursor control. RESULTS: Communication bitrates were equivalent between cabled and wireless configurations. Participants also used the wireless iBCI to control a standard commercial tablet computer to browse the web and use several mobile applications. Within-day comparison of cabled and wireless interfaces evaluated bit error rate, packet loss, and the recovery of spike rates and spike waveforms from the recorded neural signals. In a representative use case, the wireless system recorded intracortical signals from two arrays in one participant continuously through a 24-hour period at home. SIGNIFICANCE: Wireless multi-electrode recording of broadband neural signals over extended periods introduces a valuable tool for human neuroscience research and is an important step toward practical deployment of iBCI technology for independent use by individuals with paralysis. On-demand access to high-performance iBCI technology in the home promises to enhance independence and restore communication and mobility for individuals with severe motor impairment.

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. Through analysis of spontaneous cell protrusion events, we identify when and where the GEF Asef regulates the GTPases Cdc42 and Rac1 to control cell edge dynamics. This approach exemplifies a powerful means to elucidate the real-time connectivity of signal transduction networks.

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. Nevertheless, such functionality is fundamental if BCI-controlled robotic or prosthetic limbs are to be used for daily activities. In this chapter, we review how this challenge has been addressed by BCI researchers and how new solutions may improve the BCI user experience with robotic effectors.

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. ERK is activated by many cell surface receptors, and we found that ERK signaling specifically reinforced cellular protrusions so that they translated into rapid, sustained forward motion of the leading edge. Using quantitative fluorescent speckle microscopy and cross-correlation analysis, we showed that ERK controlled the rate and timing of actin polymerization by promoting the recruitment of the actin nucleator Arp2/3 to the leading edge. These findings support a model in which surges in ERK activity induced by extracellular cues enhance Arp2/3-mediated actin polymerization to generate protrusion power phases with enough force to counteract increasing membrane tension and to promote sustained motility.

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. Persistent migration arises from establishment of two functional modules at cell front and back. At the front, formation of a zone containing podosome-like structures (PLS) dynamically correlates with low RhoA and myosin activity and absence of a contractile lamella. At the back, myosin contractility specifically controls tail retraction with minimal crosstalk to the front module. The PLS zone is maintained in a dynamic steady state that preserves size and position relative to the cell front, allowing for long-term coordination of front and back modules. We propose that front/back uncoupling achieved by the PLS zone is crucial for persistent migration in the absence of directional cues.

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. Photoactivatable inhibitors for cyclic-AMP dependent kinase (PKA) and myosin light chain kinase (MLCK) are described, together with studies that shed light on proper positioning of the peptides in the LOV domain. These inhibitors altered endogenous signaling in living cells and produced light-dependent changes in cell morphodynamics.

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. Single-cell data of signaling states are emerging, but these data are usually snapshots of a particular time point or limited to averaging over a whole cell. However, many signaling pathways are activated only transiently in specific subcellular regions. Protein activity biosensors allow measurement of the spatiotemporal activation of signaling molecules in living cells. These data contain highly complex, dynamic information that can be parsed out in time and space and compared with other signaling events as well as changes in cell structure and morphology. We describe in this chapter the use of computational tools to correct, extract, and process information from time-lapse images of biosensors. These computational tools allow one to explore the biosensor signals in a multiplexed approach in order to reconstruct the sequence of signaling events and consequently the topology of the underlying pathway. The extraction of this information, dynamics and topology, provides insight into how the inputs of a signaling network are translated into its biochemical or mechanical outputs.

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. Pom1 inhibits Cdr2 while Cdr2 promotes the G2 → M transition. Cdr2 is localized in the middle cell region (midcell) whereas the concentration of Pom1 is highest at the cell tips and declines towards the midcell. In short cells, Pom1 efficiently inhibits Cdr2. However, as cells grow, the Pom1 concentration at midcell decreases such that Cdr2 becomes activated at some critical size. In this study, the chemistry of Pom1 and Cdr2 was modeled using a deterministic reaction-diffusion-convection system interacting with a deterministic model describing microtubule dynamics. Simulations mimicked experimental data from wild-type (WT) fission yeast growing at normal and reduced rates; they also mimicked the behavior of a Pom1 overexpression mutant and WT yeast exposed to a microtubule depolymerizing drug. A mechanism linking cell size and cell cycle, involving the downstream action of Cdr2 on Wee1 phosphorylation, is proposed.

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.e., if it is constructed according to strict guidelines. RESULTS: In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. CONCLUSION: The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

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. The wealth of existing mechanistic information about cancer cell biology provides a natural reference for the exploratory exercise. METHODOLOGY/PRINCIPAL FINDINGS: Glioblastoma multiforme DNA copy number data was generated by The Cancer Genome Atlas project for 167 patients using 227 aCGH experiments, and was analyzed to build a catalog of aberrant regions. Genome screening was performed using an information theory approach in order to quantify aberration as a deviation from a centrality without the bias of untested assumptions about its parametric nature. A novel Cancer Genome Browser software application was developed and is made public to provide a user-friendly graphical interface in which the reported results can be reproduced. The application source code and stand alone executable are available at (http://code.google.com/p/cancergenome) and (http://bioinformaticstation.org), respectively. CONCLUSIONS/SIGNIFICANCE: The most important known copy number alterations for glioblastoma were correctly recovered using entropy as a measure of aberration. Additional alterations were identified in different pathways, such as cell proliferation, cell junctions and neural development. Moreover, novel candidates for oncogenes and tumor suppressors were also detected. A detailed map of aberrant regions is provided.

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. RESULTS: A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. CONCLUSION: A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.

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. While described here within the context of BST, this procedure has a general applicability to signal extraction. Our original implementation relied on artificial neural networks (ANN), which caused slight, undesirable bias during the smoothing of the time courses. As an alternative, we propose here an adaptation of the Whittaker's smoother and demonstrate its role within a robust, fully automated structure identification procedure. RESULTS: In this report we propose a robust, fully automated solution for signal extraction from time series, which is the prerequisite for the efficient reverse engineering of biological systems models. The Whittaker's smoother is reformulated within the context of information theory and extended by the development of adaptive signal segmentation to account for heterogeneous noise structures. The resulting procedure can be used on arbitrary time series with a nonstationary noise process; it is illustrated here with metabolic profiles obtained from in-vivo NMR experiments. The smoothed solution that is free of parametric bias permits differentiation, which is crucial for the numerical decoupling of systems of differential equations. CONCLUSION: The method is applicable in signal extraction from time series with nonstationary noise structure and can be applied in the numerical decoupling of system of differential equations into algebraic equations, and thus constitutes a rather general tool for the reverse engineering of mechanistic model descriptions from multivariate experimental time series.