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Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression.

Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells.

A wide range of growth factors encode information into specific temporal patterns of MAP kinase (MAPK) and CREB phosphorylation, which are further decoded by expression of immediate early gene products (IEGs) to exert biological functions. However, the IEG decoding system remain unknown. We built a data-driven based on time courses of MAPK and CREB phosphorylation and IEG expression in response to various growth factors to identify how signal is processed. We found that IEG expression uses common decoding systems regardless of growth factors and expression of each IEG differs in upstream dependency, switch-like response, and linear temporal filters. Pulsatile ERK phosphorylation was selectively decoded by expression of EGR1 rather than c-FOS. Conjunctive NGF and PACAP stimulation was selectively decoded by synergistic JUNB expression through switch-like response to c-FOS. Thus, specific temporal patterns and combinations of MAPKs and CREB phosphorylation can be decoded by selective IEG expression via distinct temporal filters and switch-like responses. The data-driven modeling is versatile for analysis of signal processing and does not require detailed prior knowledge of pathways.

Gonadotropin-releasing hormone (GnRH) neurons in the terminal nerve (TN) show endogenous pacemaker activity, which is suggested to be dependent on the physiological conditions of the animal. The TN-GnRH neurons have been suggested to function as a neuromodulatory neuron that regulates long-lasting changes in the animal behavior. It has been reported that the TN-GnRH neurons are immunoreactive to FMRFamide. Here, we find that the pacemaker activity of TN-GnRH neuron is inhibited by FMRFamide: bath application of FMRFamide decreased the frequency of pacemaker activity of TN-GnRH neurons in a dose-dependent manner. This decrease was suppressed by a blockage of G protein-coupled receptor pathway by GDP-β-S. In addition, FMRFamide induced an increase in the membrane conductance, and the reversal potential for the FMRFamide-induced current changed according to the changes in [K(+)](out) as predicted from the Nernst equation for K(+). We performed cloning and sequence analysis of the PQRFamide (NPFF/NPAF) gene in the dwarf gourami and found evidence to suggest that FMRFamide-like peptide in TN-GnRH neurons of the dwarf gourami is NPFF. NPFF actually inhibited the pacemaker activity of TN-GnRH neurons, and this inhibition was blocked by RF9, a potent and selective antagonist for mammalian NPFF receptors. These results suggest that the activation of K(+) conductance by FMRFamide-like peptide (≈NPFF) released from TN-GnRH neurons themselves causes the hyperpolarization and then inhibition of pacemaker activity in TN-GnRH neurons. Because TN-GnRH neurons make tight cell clusters in the brain, it is possible that FMRFamide-like peptides released from TN-GnRH neurons negatively regulates the activities of their own (autocrine) and/or neighboring neurons (paracrine).

Background: Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling., Methodology/principal Findings: We developed a fully automatable assay technique, termed quantitative image cytometry (QIC), which integrates a quantitative immunostaining technique and a high precision image-processing algorithm for cell identification. With the aid of an automated sample preparation system, this device can quantify protein expression, phosphorylation and localization with subcellular resolution at one-minute intervals. The signaling activities quantified by the assay system showed good correlation with, as well as comparable reproducibility to, western blot analysis. Taking advantage of the high spatio-temporal resolution, we investigated the signaling dynamics of the ERK pathway in PC12 cells., Conclusions/significance: The QIC technique appears as a highly quantitative and versatile technique, which can be a convenient replacement for the most conventional techniques including western blot, flow cytometry and live cell imaging. Thus, the QIC technique can be a powerful tool for investigating the systems biology of cellular signaling.