Explicit Green's Function of a Boundary Value Problem for a Sphere and Trapped Flux Analysis in Gravity Probe B Experiment

Magnetic flux trapped on the surface of superconducting rotors of the Gravity Probe B (GP-B) experiment produces some signal in the SQUID readout. For the needs of GP-B error analysis and simulation of data reduction, this signal is calculated and analyzed in the paper. We first solve a magnetostatic problem for a point source (fluxon) on the surface of a sphere, finding the closed form elementary expression for the corresponding Green's function. Second, we calculate the flux through the pick-up loop as a function of the fluxon position. Next, the time dependence of a fluxon position, caused by rotor motion according to a symmetric top model, and thus the time signature of the flux are determined, and the spectrum of the trapped flux signal is analyzed. Finally, a multi-purpose program of trapped flux signal generation based on the above results is described, various examples of the signal obtained by means of this program are given, and their features are discussed.Comment: 14 pages, including 7 figures. Submitted to: "Journal of Applied Physics

Gain control in molecular information processing: Lessons from neuroscience

Statistical properties of environments experienced by biological signaling systems in the real world change, which necessitate adaptive responses to achieve high fidelity information transmission. One form of such adaptive response is gain control. Here we argue that a certain simple mechanism of gain control, understood well in the context of systems neuroscience, also works for molecular signaling. The mechanism allows to transmit more than one bit (on or off) of information about the signal independently of the signal variance. It does not require additional molecular circuitry beyond that already present in many molecular systems, and, in particular, it does not depend on existence of feedback loops. The mechanism provides a potential explanation for abundance of ultrasensitive response curves in biological regulatory networks.Comment: 10 pages, 5 figure

Minimal subtraction and the Callan-Symanzik equation

The usual proof of renormalizability using the Callan-Symanzik equation makes explicit use of normalization conditions. It is shown that demanding that the renormalization group functions take the form required for minimal subtraction allows one to prove renormalizability using the Callan-Symanzik equation, without imposing normalization conditions. Scalar field theory and quantum electrodynamics are treated.Comment: 6 pages, plain Te

Statistical properties of multistep enzyme-mediated reactions

Enzyme-mediated reactions may proceed through multiple intermediate conformational states before creating a final product molecule, and one often wishes to identify such intermediate structures from observations of the product creation. In this paper, we address this problem by solving the chemical master equations for various enzymatic reactions. We devise a perturbation theory analogous to that used in quantum mechanics that allows us to determine the first () and the second (variance) cumulants of the distribution of created product molecules as a function of the substrate concentration and the kinetic rates of the intermediate processes. The mean product flux V=d/dt (or "dose-response" curve) and the Fano factor F=variance/ are both realistically measurable quantities, and while the mean flux can often appear the same for different reaction types, the Fano factor can be quite different. This suggests both qualitative and quantitative ways to discriminate between different reaction schemes, and we explore this possibility in the context of four sample multistep enzymatic reactions. We argue that measuring both the mean flux and the Fano factor can not only discriminate between reaction types, but can also provide some detailed information about the internal, unobserved kinetic rates, and this can be done without measuring single-molecule transition events.Comment: 8 pages, 3 figure