Showing papers by "John Bechhoefer published in 2014"

High-Precision Test of Landauer’s Principle in a Feedback Trap

Abstract: We confirm Landauer’s 1961 hypothesis that reducing the number of possible macroscopic states in a system by a factor of 2 requires work of at least kT ln2. Our experiment uses a colloidal particle in a timedependent, virtual potential created by a feedback trap to implement Landauer’s erasure operation. In a control experiment, similar manipulations that do not reduce the number of system states can be done reversibly. Erasing information thus requires work. In individual cycles, the work to erase can be below the Landauer limit, consistent with the Jarzynski equality.

Real-Time Calibration of a Feedback Trap

Abstract: Feedback traps use closed-loop control to trap or manipulate small particles and molecules in solution. They have been applied to the measurement of physical and chemical properties of particles and to explore fundamental questions in the non-equilibrium statistical mechanics of small systems. These applications have been hampered by drifts in the electric forces used to manipulate the particles. Although the drifts are small for measurements on the order of seconds, they dominate on time scales of minutes or slower. Here, we show that a recursive maximum likelihood (RML) algorithm can allow real-time measurement and control of electric and stochastic forces over time scales of hours. Simulations show that the RML algorithm recovers known parameters accurately. Experimental estimates of diffusion coefficients are also consistent with expected physical properties.

Inferring the spatiotemporal DNA replication program from noisy data.

Abstract: We generalize a stochastic model of DNA replication to the case where replication-origin-initiation rates vary locally along the genome and with time. Using this generalized model, we address the inverse problem of inferring initiation rates from experimental data concerning replication in cell populations. Previous work based on curve fitting depended on arbitrarily chosen functional forms for the initiation rate, with free parameters that were constrained by the data. We introduce a nonparametric method of inference that is based on Gaussian process regression. The method replaces specific assumptions about the functional form of the initiation rate with more general prior expectations about the smoothness of variation of this rate, along the genome and in time. Using this inference method, we recover, with high precision, simulated replication schemes from noisy data that are typical of current experiments.

What is superresolution microscopy

Abstract: I explain what is, what is not, and what is only sort of superresolution microscopy. I discuss optical resolution, first in terms of diffraction theory, then in terms of linear systems theory, and finally in terms of techniques that use prior information, nonlinearity, and other tricks to improve performance. The discussion reveals two classes of superresolution: Pseudo superresolution techniques improve images up to the diffraction limit but not much beyond. True superresolution techniques allow substantial, useful improvements beyond the diffraction limit. The two classes are distinguished by their scaling of resolution with photon counts. Understanding the limits to imaging resolution involves concepts that pertain to almost any measurement problem, implying that the framework given here has broad application beyond optics.

Split PID control: two sensors can be better than one

Abstract: The traditional proportional-integral-derivative (PID) algorithm for regulation suffers from a tradeoff: placing the sensor near the sample being regulated ensures that its steady-state temperature matches the desired setpoint. However, the propagation delay (lag) between heater and sample can limit the control bandwidth. Moving the sensor closer to the heater reduces the lag and increases the bandwidth but introduces offsets and drifts into the temperature of the sample. Here, we explore the consequences of using two probes---one near the heater, one near the sample---and assigning the integral term to the sample probe and the other terms to the heater probe. The \textit{split-PID} algorithm can outperform PID control loops based on one sensor.

Note: Split PID control—Two sensors can be better than one

Abstract: The traditional proportional-integral-derivative (PID) algorithm for regulation suffers from a tradeoff: placing the sensor near the sample being regulated ensures that its steady-state temperature matches the desired setpoint. However, the propagation delay (lag) between heater and sample can limit the control bandwidth. Moving the sensor closer to the heater reduces the lag and increases the bandwidth but introduces offsets and drifts into the temperature of the sample. Here, we explore the consequences of using two probes—one near the heater, one near the sample—and assigning the integral term to the sample probe and the other terms to the heater probe. The split-PID algorithm can outperform PID control loops based on one sensor.

Numerical modeling of inhomogeneous DNA replication kinetics

Abstract: We present a calculation technique for modeling inhomogeneous DNA replication kinetics, where replication factors such as initiation rates or fork speeds can change with both position and time. We can use our model to simulate data sets obtained by molecular combing, a widely used experimental technique for probing replication. We can also infer information about the replication program by fitting our model to experimental data sets and also test the efficacy of planned experiments by fitting our model to simulated data sets. We consider asynchronous data sets and illustrate how a lack of synchrony affects replication profiles. In addition to combing data, our technique is well-adapted to microarray-based studies of replication.