In fast-transcribing prokaryotic genes, such as an rrn gene in Escherichia coli, many RNA polymerases (RNAPs) transcribe the DNA simultaneously. Active elongation of RNAPs is often interrupted by pauses, which has been observed to cause RNAP traffic jams; yet some studies indicate that elongation seems to be faster in the presence of multiple RNAPs than elongation by a single RNAP. We propose that an interaction between RNAPs via the torque produced by RNAP motion on helically twisted DNA can explain this apparent paradox. We have incorporated the torque mechanism into a stochastic model and simulated transcription both with and without torque. Simulation results illustrate that the torque causes shorter pause durations and fewer collisions between polymerases. Our results suggest that the torsional interaction of RNAPs is an important mechanism in maintaining fast transcription times, and that transcription should be viewed as a cooperative group effort by multiple polymerases.

This study presents two computational schemes for the numerical approximation of solutions to eddy viscosity models as well as transient Navier-Stokes equations. The eddy viscosity model is one example of a class of Large Eddy Simulation models, which are used to simulate turbulent flow. The first approximation scheme is a first order single step method that treats the nonlinear term using a semi-implicit discretization. The second scheme employs a two step approach that applies a Crank-Nicolson method for the nonlinear term while also retaining the semi-implicit treatment used in the first scheme. A finite element approximation is used in the spatial discretization of the partial differential equations. The convergence analysis for both schemes is discussed in detail, and numerical results are given for two test problems one of which is the two dimensional flow around a cylinder.

Bio-polymerization processes like transcription and translation are central to proper function of a cell. The speed at which the bio-polymer grows is affected both by the number of pauses of elongation machinery, as well the number of bio-polymers due to crowding effects. In order to quantify these effects in fast transcribing ribosome genes, we rigorously show that a classical traffic flow model is the limit of a mean occupancy ODE model. We compare the simulation of this model to a stochastic model and evaluate the combined effect of the polymerase density and the existence of pauses on the instantaneous transcription rate of ribosomal genes.

An eddy viscosity model can be used as a computationally tractable alternative to that of the Navier-Stokes equations. Model errors immediately become a concern when considering such an approach, and quantifying this error is essential to understanding and using model predictions within an engineering design process. In this paper, sensitivity analysis is presented for a subgrid eddy viscosity model with respect to variations of the eddy viscosity parameter. We demonstrate the analysis utilizing the sensitivity equation method. Approximating the sensitivity requires the solution of the eddy viscosity model. Therefore, the eddy viscosity model and sensitivity equation are coupled in our analysis and computations. An implicit-explicit time-stepping method is developed and analyzed for this set of equations. Our numerical assessments present the role of the sensitivity in quantifying the modeling error arising from the choice of various values of the eddy viscosity parameter. The sensitivity computation allows one to identify an interval of reliability for the eddy viscosity parameter. This gives the user a range of parameter values for which the eddy viscosity model can be considered to be a reliable approximation to the Navier-Stokes equations. A two-dimensional cavity problem is used to illustrate the ideas. In addition, for the standard model problem of two-dimensional flow around a cylinder, the sensitivity computations are shown to be very useful in improving the flow functional approximations that may be used within an optimal design algorithm.

Flapping insect wings deform during flight. This deformation benefits the insect's aerodynamic force production as well as energetic efficiency. However, it is challenging to measure wing displacement field in flying insects. Many points must be tracked over the wing's surface to resolve its instantaneous shape. To reduce the number of points one is required to track, we propose a physics-based reconstruction method called System Equivalent Reduction Expansion Processes (SEREP) to estimate wing deformation and strain from sparse measurements. Measurement locations are determined using a Weighted Normalized Modal Displacement (NMD) method. We experimentally validate the reconstruction technique by flapping a paper wing from 5-9 Hz with 45 • and measuring strain at three locations. Two measurements are used for the reconstruction and the third for validation. Strain reconstructions had a maximal error of 30% in amplitude. We extend this methodology to a more realistic insect wing through numerical simulation. We show that wing displacement can be estimated from sparse displacement or strain measurements, and that additional sensors spatially average measurement noise to improve reconstruction accuracy. This research helps overcome some of the challenges of measuring full-field dynamics in flying insects and provides a framework for strain-based sensing in insect-inspired flapping robots.