Here we introduce LOVTRAP, an optogenetic approach for reversible, light-induced protein dissociation. LOVTRAP is based on protein A fragments that bind to the LOV domain only in the dark, with tunable kinetics and a >150-fold change in 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.

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. Here, we tracked spatial and temporal fluctuations in the molecular activities of individual moving cells to elucidate how extracellular 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 (qFSM) 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. Arp2/3 activity generates branched actin networks that can produce pushing force. 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.

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.

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 pointand-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

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.

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.

Here we generate FRET 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.

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 Ssystem equations, which results in a set of algebraic equations.