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Circular dichroism (CD) is the differential absorption of left and right circularly polarized light. It arises from molecular electron oscillations that are driven by both the light’s electric and magnetic fields, where the effects are in phase for one circular polarization and out of phase for the other. We describe these interactions, which have not been presented at an intuitive level before, with classical and quantum treatments. A rotational average of the latter solution leads to the Rosenfeld equation, which underlies most CD research. We illustrate its use with a chiral version of a harmonic oscillator, which we then apply to the far-ultraviolet CD of protein α-helices. This presentation is aimed at the level of upper-division undergraduates and could be usefully incorporated into a physical chemistry course.

Several bacterial proteins have been shown to polymerize into coils or rings on cell membranes. These include the cytoskeletal proteins MreB, FtsZ, and MinD, which together with other cell components make up what is being called the bacterial cytoskeleton. We believe that these shapes arise, at least in part, from the interaction of the inherent mechanical properties of the protein polymers and the constraints imposed by the curved cell membrane. This hypothesis, presented as a simple mechanical model, was tested with numerical energy-minimization methods from which we found that there are five low-energy polymer morphologies on a rod-shaped membrane: rings, lines, helices, loops, and polar-targeted circles. Analytic theory was used to understand the possible structures and to create phase diagrams that show which parameter combinations lead to which structures. Inverting the results, it is possible to infer the effective mechanical bending parameters of protein polymers from fluorescence images of their shapes. This theory also provides a plausible explanation for the morphological changes exhibited by the Z ring in a sporulating Bacillus subtilis; is used to calculate the mechanical force exerted on a cell membrane by a polymer; and allows predictions of polymer shapes in mutant cells.

Quantitative vibrational difference spectroscopy of small signals requires techniques that minimize spectral noise. As simple modulation of the sample followed by signal demodulation is often problematic due to interference with the FT-IR Fourier frequencies, alternate methods are necessary. Using vibrational Stark spectroscopy as an example, the DC method involves alternating complete interferogram scans in which a DC electric field is turned on with scans in which the field is turned off. A new synchronized AC method is similar, but the field is switched at every interferogram sampling point. In the step-scan method, a sinusoidal electric field is used and the detector signal is demodulated with a lock-in amplifier at each mirror position. Noise levels for these methods are derived analytically in this paper and compared with experimental values, yielding results that are broadly applicable to FT-IR difference spectroscopy. It was found that the DC method performed the best, although specific experimental factors may favor other methods.

Many cell signaling systems, including the yeast pheromone response system, exhibit “dose-response alignment” (DoRA), in which output of one or more downstream steps closely matches the fraction of occupied receptors. DoRA can improve the fidelity of transmitted dose information. Here, we searched systematically for biochemical network topologies that produced DoRA. Most networks, including many containing feedback and feedforward loops, could not produce DoRA. However, networks including “push-pull” mechanisms, in which the active form of a signaling species stimulates downstream activity and the nominally inactive form reduces downstream activity, enabled perfect DoRA. Networks containing feedbacks enabled DoRA, but only if they also compared feedback to input and adjusted output to match. Our results establish push-pull as a non-feedback mechanism to align output with variable input and maximize information transfer in signaling systems. They also suggest genetic approaches to determine whether particular signaling systems use feedback or push-pull control.