Coding of periodic pulse stimulation in chemoreceptors

Transient frequency preference responses in cell signaling systems

Ligand-receptor systems, covalent modification cycles, and transcriptional networks are the fundamental components of cell signaling and gene expression systems. While their behavior in reaching a steady-state regime under step-like stimulation is well understood, their response under repetitive stimulation, particularly at early time stages is poorly characterized. Yet, early-stage responses to external inputs are arguably as informative as late-stage ones. In simple systems, a periodic stimulation elicits an initial transient response, followed by periodic behavior. Transient responses are relevant when the stimulation has a limited time span, or when the stimulated component's timescale is slow as compared to the timescales of the downstream processes, in which case the latter processes may be capturing only those transients. In this study, we analyze the frequency response of simple motifs at different time stages. We use dose-conserved pulsatile input signals and consider different metrics versus frequency curves. We show that in ligand-receptor systems, there is a frequency preference response in some specific metrics during the transient stages, which is not present in the periodic regime. We suggest this is a general system-level mechanism that cells may use to filter input signals that have consequences for higher order circuits. In addition, we evaluate how the described behavior in isolated motifs is reflected in similar types of responses in cascades and pathways of which they are a part. Our studies suggest that transient frequency preferences are important dynamic features of cell signaling and gene expression systems, which have been overlooked.

Abnormality Detection in Correlated Gaussian Molecular Nano-Networks: Design and Analysis

A nano-abnormality detection scheme (NADS) in molecular nano-networks is studied. This is motivated by the fact that early detection of diseases such as cancer plays a crucial role in their successful treatment. The proposed NADS is in fact a two-tier network of sensor nano-machines (SNMs) in the first tier and a data-gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells (abnormality) by variations in input and/or parameters of a nano-communications channel. The noise of SNMs as their nature suggest is considered correlated in time and space and herein assumed additive Gaussian. In the second step, the SNMs transmit micro-scale messages over a noisy micro-communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. We find an optimum design of detectors for each of the NADS tiers based on the end-to-end NADS performance. The detection performance of each SNM is analyzed by setting up a generalized likelihood ratio test. Next, taking into account the effect of the MCC, the overall performance of the NADS is analyzed in terms of probabilities of misdetection and false alarm. In addition, computationally efficient expressions to quantify the NADS performance are derived by providing, respectively, an approximation and an upper bound for the probabilities of misdetection and false alarm. This in turn enables formulating a design problem, where the optimized concentration of SNMs in a sample is obtained for a high probability of detection and a limited probability of false alarm. The results indicate that otherwise ignoring the spatial and temporal correlation of SNM noise in the analysis, leads to an NADS that noticeably underperforms in operations.The results indicate how effective fusion of the noisy observations collected from a number of SNMs with limited capabilities could provide an acceptable detection performance.

Stochastic pulse stimulation in chemoreceptors and its properties

Orientation towards food and mate, especially in insects, is an olfactory-controlled behavior which relies on the detection of small amounts of odorant molecules delivered in turbulent atmospheric conditions, so that randomness in magnitude and time is a major feature of the natural stimulus. The effect of random delivery on the initial step of olfactory transduction, the formation of the receptor-ligand complex, is analyzed in the case of the moth pheromonal system. Two types of randomness are compared, Gaussian (regular) and exponential (irregular). The influence of noise is quantified either with the maxima of the receptor-ligand complex, or with the times at which the concentration of complex crosses a given threshold level. It is shown that the stochastic features of the stimulus helps its detection and that the exponential distribution appears not only as a better description of the natural stimulus, but also as the most efficient from a biological point of view.