Neurophysiological signals as potential translatable biomarkers for modulation of metabotropic glutamate 5 receptors.

The Group I metabotropic glutamate receptor subtype 5 (mGluR5) is widely distributed in the brain with dense expression in the cerebral cortex, hippocampus, and basal ganglia. These receptors have been implicated in psychiatric and neurological disorders such as schizophrenia, Fragile X syndrome, addiction, anxiety/depression, Parkinson's disease and neuropathic pain. The present study evaluated the effects of the mGluR5 negative allosteric modulators (NAMs) 4-difluoromethoxy-3-(pyridine-2-ylethynyl)phenyl)5H-pyrrolo[3,4-b]pyridine-6(7H)-yl methanone (GRN-529) and methyl (3aR,4S,7aR)-4-hydroxy-4-[(3-methylphenyl)ethynyl]octahydro-1H-indole-1-carboxylate (AFQ056) on polysomnographic (PSG) and quantitative electroencephalographic (qEEG) measures in freely moving rats. Furthermore, the anxiolytic profile of GRN-529 was characterized in anesthetized rats by measuring stimulation-induced hippocampal theta oscillation. The present findings demonstrate that inhibition of mGluR5 via its allosteric site profoundly modulates high-level neuronal network activities as indicated by changes in sleep-wake activity and power distribution of qEEG. Both GRN-529 and AFQ056 reduced the total time spent in rapid-eye movement with AFQ056 producing a significant increase in wakefulness at the highest dose tested. Additionally, qEEG revealed significant compound-induced increases in delta power concomitant with more subtle decreases in theta and alpha band power. Receptor occupancy (RO) studies revealed that GRN-529 and AFQ056 at all doses resulted in over 45% mGluR5 occupancy. Furthermore, GRN-529 dose-dependently decreased elicited hippocampal theta frequency, consistent with previous findings using clinically active anxiolytic compounds. The described changes in neurophysiological signals identified in freely moving rats may be considered suitable translational biomarkers for the clinical evaluation of mGluR5 NAMs.

Stochastically driven genetic circuits.

Transcriptional regulation in small genetic circuits exhibits large stochastic fluctuations. Recent experiments have shown that a significant fraction of these fluctuations is caused by extrinsic factors. In this paper we review several theoretical and computational approaches to modeling of small genetic circuits driven by extrinsic stochastic processes. We propose a simplified approach to this problem, which can be used in the case when extrinsic fluctuations dominate the stochastic dynamics of the circuit (as appears to be the case in eukaryots). This approach is applied to a model of a single nonregulated gene that is driven by a certain gating process that affects the rate of transcription, and to a simplified version of the galactose utilization circuit in yeast.

Dynamics of a bouncing dimer.

We investigate the dynamics of a dimer bouncing on a vertically oscillated plate. The dimer, composed of two spheres rigidly connected by a light rod, exhibits several modes depending on initial and driving conditions. The first excited mode has a novel horizontal drift in which one end of the dimer stays on the plate during most of the cycle, while the other end bounces in phase with the plate. The speed and direction of the drift depend on the aspect ratio of the dimer. We employ event-driven simulations based on a detailed treatment of frictional interactions between the dimer and the plate in order to elucidate the nature of the transport mechanism in the drift mode.

Cellular growth and division in the Gillespie algorithm.

Recent experimental studies elucidating the importance of noise in gene regulation have ignited widespread interest in Gillespie's stochastic simulation technique for biochemical networks. We formulate modifications to the Gillespie algorithm which are necessary to correctly simulate chemical reactions with time-dependent reaction rates. We concentrate on time dependence of kinetic rates arising from the periodic process of growth and division of the cellular volume, and demonstrate that a careful re-derivation of the Gillespie algorithm is important when all stochastically simulated reactions have rates slower or comparable to the cellular growth rate. For an unregulated single-gene system, we illustrate our findings using recently proposed hybrid simulation techniques, and systematically compare our algorithm with analytic results obtained from the chemical master equation.