Chris H. Wiggins

Opposing Effects of PKCθ and WASp on Symmetry Breaking and Relocation of the Immunological Synapse

The immunological synapse (IS) is a junction between the T cell and antigen-presenting cell and is composed of supramolecular activation clusters (SMACs). No studies have been published on naive T cell IS dynamics. Here, we find that IS formation during antigen recognition comprises cycles of stable IS formation and autonomous naive T cell migration. The migration phase is driven by PKCtheta, which is localized to the F-actin-dependent peripheral (p)SMAC. PKCtheta(-/-) T cells formed hyperstable IS in vitro and in vivo and, like WT cells, displayed fast oscillations in the distal SMAC, but they showed reduced slow oscillations in pSMAC integrity. IS reformation is driven by the Wiscott Aldrich Syndrome protein (WASp). WASp(-/-) T cells displayed normal IS formation but were unable to reform IS after migration unless PKCtheta was inhibited. Thus, opposing effects of PKCtheta and WASp control IS stability through pSMAC symmetry breaking and reformation.

Bayesian Approach to Network Modularity

We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models.

Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

Time series data provided by single-molecule Förster resonance energy transfer (smFRET) experiments offer the opportunity to infer not only model parameters describing molecular complexes, e.g., rate constants, but also information about the model itself, e.g., the number of conformational states. Resolving whether such states exist or how many of them exist requires a careful approach to the problem of model selection, here meaning discrimination among models with differing numbers of states. The most straightforward approach to model selection generalizes the common idea of maximum likelihood--selecting the most likely parameter values--to maximum evidence: selecting the most likely model. In either case, such an inference presents a tremendous computational challenge, which we here address by exploiting an approximation technique termed variational Bayesian expectation maximization. We demonstrate how this technique can be applied to temporal data such as smFRET time series; show superior statistical consistency relative to the maximum likelihood approach; compare its performance on smFRET data generated from experiments on the ribosome; and illustrate how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics. Source code used in this analysis, including a graphical user interface, is available open source via http://vbFRET.sourceforge.net.

Inferring network mechanisms: The Drosophila melanogaster protein interaction network

Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication-mutation-complementation network over preferential attachment, small-world, and a duplication-mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.

Flexive and Propulsive Dynamics of Elastica at Low Reynolds Number

A stiff one-armed swimmer in glycerine goes nowhere, but if its arm is elastic, exerting a restorative torque proportional to local curvature, the swimmer can go on its way. Considering this happy consequence and the principles of elasticity, we study a hyperdiffusion equation for the shape of the elastica in viscous flow, find solutions for impulsive or oscillatory forcing, and elucidate relevant aspects of propulsion. These results have application in a variety of physical and biological contexts, from dynamic biopolymer bending experiments to instabilities of elastic filaments.

Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation

Determining the mechanism by which tRNAs rapidly and precisely transit through the ribosomal A, P, and E sites during translation remains a major goal in the study of protein synthesis. Here, we report the real-time dynamics of the L1 stalk, a structural element of the large ribosomal subunit that is implicated in directing tRNA movements during translation. Within pretranslocation ribosomal complexes, the L1 stalk exists in a dynamic equilibrium between open and closed conformations. Binding of elongation factor G (EF-G) shifts this equilibrium toward the closed conformation through one of at least two distinct kinetic mechanisms, where the identity of the P-site tRNA dictates the kinetic route that is taken. Within posttranslocation complexes, L1 stalk dynamics are dependent on the presence and identity of the E-site tRNA. Collectively, our data demonstrate that EF-G and the L1 stalk allosterically collaborate to direct tRNA translocation from the P to the E sites, and suggest a model for the release of E-site tRNA.

Quantification of Cell Edge Velocities and Traction Forces Reveals Distinct Motility Modules during Cell Spreading

Actin-based cell motility and force generation are central to immune response, tissue development, and cancer metastasis, and understanding actin cytoskeleton regulation is a major goal of cell biologists. Cell spreading is a commonly used model system for motility experiments – spreading fibroblasts exhibit stereotypic, spatially-isotropic edge dynamics during a reproducible sequence of functional phases: 1) During early spreading, cells form initial contacts with the surface. 2) The middle spreading phase exhibits rapidly increasing attachment area. 3) Late spreading is characterized by periodic contractions and stable adhesions formation. While differences in cytoskeletal regulation between phases are known, a global analysis of the spatial and temporal coordination of motility and force generation is missing. Implementing improved algorithms for analyzing edge dynamics over the entire cell periphery, we observed that a single domain of homogeneous cytoskeletal dynamics dominated each of the three phases of spreading. These domains exhibited a unique combination of biophysical and biochemical parameters – a motility module. Biophysical characterization of the motility modules revealed that the early phase was dominated by periodic, rapid membrane blebbing; the middle phase exhibited continuous protrusion with very low traction force generation; and the late phase was characterized by global periodic contractions and high force generation. Biochemically, each motility module exhibited a different distribution of the actin-related protein VASP, while inhibition of actin polymerization revealed different dependencies on barbed-end polymerization. In addition, our whole-cell analysis revealed that many cells exhibited heterogeneous combinations of motility modules in neighboring regions of the cell edge. Together, these observations support a model of motility in which regions of the cell edge exhibit one of a limited number of motility modules that, together, determine the overall motility function. Our data and algorithms are publicly available to encourage further exploration.

Viscous Nonlinear Dynamics of Twist and Writhe

Exploiting the “natural” frame of space curves, we formulate an intrinsic dynamics of a twisted elastic filament in a viscous fluid. Coupled nonlinear equations describing the temporal evolution of the filament’s complex curvature and twist density capture the dynamic interplay of twist and writhe. These equations are used to illustrate a remarkable nonlinear phenomenon: geometric untwistingof open filaments, whereby twisting strains relax through a transient writhing instability without axial rotation. Experimentally observed writhing motions of fibers of the bacterium B. subtilis [N. H. Mendelson et al., J. Bacteriol. 177, 7060 (1995)] may be examples of this untwisting process. [S0031-9007(98)06244-9]

Fast dynamics of supercoiled DNA revealed by single-molecule experiments

The dynamics of supercoiled DNA play an important role in various cellular processes such as transcription and replication that involve DNA supercoiling. We present experiments that enhance our understanding of these dynamics by measuring the intrinsic response of single DNA molecules to sudden changes in tension or torsion. The observed dynamics can be accurately described by quasistatic models, independent of the degree of supercoiling initially present in the molecules. In particular, the dynamics are not affected by the continuous removal of the plectonemes. These results set an upper bound on the hydrodynamic drag opposing plectoneme removal, and thus provide a quantitative baseline for the dynamics of bare DNA.

Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments

Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.