Information propagation in a noisy gene cascade.

We use information theory to study the information transmission through a simple gene cascade where the product of an unregulated gene regulates the expression activity of a cooperative genetic switch. While the input signal is provided by the upstream gene with two states, we consider that the expression of downstream gene is controlled by a cis-regulatory system with three binding sites for the regulator product, which can bind cooperatively. By computing exactly the associated probability distributions, we estimate information transmission thought the mutual information measure. We found that the mutual information associated with unimodal input signal is lower than the associated with bimodal inputs. We also observe that mutual information presents a maximum in the cooperativity intensity, and the position of this maximum depends on the kinetic rates of the promoter. Furthermore, we found that the bursting dynamics of the input signal can enhance the information transmission capacity.

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit.

It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture can play a key role for oscillatory behavior of gene expression. Finally, our results can also help to synthetic biologists on the design of the promoters architecture for new genetic oscillatory circuits.

Neuropeptide precursor gene discovery in the Chagas disease vector Rhodnius prolixus.

We show a straightforward workflow combining homology search in Rhodnius prolixus genome sequence with cloning by rapid amplification of cDNA ends and mass spectrometry. We have identified 32 genes and their transcripts that encode a number of neuropeptide precursors leading to 194 putative peptides. We validated by mass spectrometry 82 of those predicted neuropeptides in the brain of R. prolixus to achieve the first comprehensive genomic, transcriptomic and neuropeptidomic analysis of an insect disease vector. Comparisons of available insect neuropeptide sequences revealed that the R. prolixus genome contains most of the conserved neuropeptides in insects, many of them displaying specific features at the sequence level. Some gene families reported here are identified for the first time in the order Hemiptera, a highly biodiverse group of insects that includes many human, animal and plant disease agents.

Role of cooperative binding on noise expression.

The origin of stochastic fluctuations in gene expression has received considerable attention recently. Fluctuations in gene expression are particularly pronounced in cellular systems because of the small copy number of species undergoing transitions between discrete chemical states and the small size of biological compartments. In this paper, we propose a stochastic model for gene expression regulation including several binding sites, considering elementary reactions only. The model is used to investigate the role of cooperativity on the intrinsic fluctuations of gene expression by means of master-equation formalism. We found that the Hill coefficient and the level of noise increase as the interaction energy between activators increases. Additionally, we show that the model allows one to distinguish between two cooperative binding mechanisms.

Complex networks approach to gene expression driven phenotype imaging.

MOTIVATION: The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are used to measure the spatial interactions and adjacencies defined by gene expression patterns. RESULTS: Enhanced visualization of spatial interactions between elements where genes are expressed is possible, allowing the identification of structures which would go unnoticed by using conventional imaging. The quantification of the expression intensity in terms of the node degree and clustering coefficient allows the identification of different types of interactions, yielding insights about cell signaling and differentiation, and providing the basis for comparison and discrimination of the patterns along the developmental stages. AVAILABILITY: Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided. CONTACT: luciano@if.sc.usp.br; diambra@univap.br.

Modeling stochastic Ca2+ release from a cluster of IP3-sensitive receptors.

We focused our attention on Ca(2+) release from the endoplasmic reticulum through a cluster of inositol(1,4,5)-trisphosphate (IP(3)) receptor channels. The random opening and closing of these receptors introduce stochastic effects that have been observed experimentally. Here, we present a stochastic version of Othmer-Tang model (OTM) for IP(3) receptor clusters. We address the average behavior of the channels in response to IP(3) stimuli. In our stochastic simulation we found that the fraction of open channels versus [IP(3)] follows a Hill curve, whose associate Hill coefficient increases when intracellular Ca(2+) level increase. This finding suggests that feedback from cytosolic Ca(2+) plays a key role in the channel response to IP(3). We also study several aspects of the stochastic properties of Ca(2+) release and we compare with experimental observations.

Infradian rhythmicity in sleep/wake ratio in developing infants.

Although there are several reports on ultradian and circadian rhythms in newborns, we found only one report in which infradian periodicities are described for heart-rate measurements in the early stages of human development. Here, we report infradian rhythms in the monthly range in the sleep/wake cycle of four infants studied along 24 consecutive weeks. Our procedure was applied to sleep diary records from four healthy newborns. The data were arranged in binary time series representing sleep (-1) or wake (1) states. These time series were integrated in order to obtain the cumulative sleep/wake time. A measure of the sleep/wake ratio (SWR) was obtained by computing the average slope of the cumulative sleep/wake time. To extract periodicities we applied the Fourier periodogram to the temporal course of the SWR. We found a notorious difference in the SWR pattern among infants. In two infants the SWR showed a marked linear decay, spending more time asleep than awake, while in the two other infants oscillated near zero. We found robust oscillations in all children. In all cases the Fourier periodogram results present significant power in the infradian range. From these results, we suggest that sleep and wake durations are probably modulated by some internal stimuli.

Ciclograma: a tool for detection of rhythmicities in sleep/wake cycles.

The Fourier spectral analysis of binary time series (or rectangular signals) causes methodological problems, due to the fact that it is based on sinusoidal functions. We propose a new tool for the detection of periodicities in binary time series, focusing on sleep/wake cycles. This methodology is based on a weighted histogram of cycle durations. In this paper, we compare our methodology with the Fourier spectral analysis on the basis of simulated and real binary data sets of various lengths. We also provide an approach to statistical validation of the periodicities determined with our methodology. Furthermore, we analyze the discriminating power of both methods in terms of standard deviation. Our results indicate that the Ciclograma is much more powerful than Fourier analysis when applied on this type of time series.

Detecting epileptic spikes.

We present a technique for automatic detection of epileptic spikes in electroencephalogram (EEG) recordings. We used a nonlinear modeling method that enables us to detect rapidly and accurately epileptic behavior in the EEG signal. Our approach is illustrated by an application to interictal activity from a patient with focal epilepsy.

Chaos in two-loop negative feedback systems.

Multiloop delayed negative feedback systems, with each feedback loop having its own characteristic time lag (delay), are used to describe a great variety of systems: optical systems, neural networks, physiological control systems, etc. Previous investigations have shown that if the number of delayed feedback loops is greater than two, the system can exhibit complex dynamics and chaos, but in the case of two delayed loops only periodic solutions were found. Here we show that a period-doubling cascade and chaotic dynamics are also found in systems with two coupled delayed negative feedback loops.

Information theory approach to learning of the perceptron rule.

By recourse to a method based on information theory, we have studied the generalization problem in perceptrons. We considered different a priori distributions about the weights of the teacher perceptron. Our approach allows us to define the information gain from the examples used in the training procedure. The information gain can be used to choose a convenient example set for training the perceptron and to select the transfer function of the student perceptron.

Divergence measure between chaotic attractors.

We propose a measure of divergence of probability distributions for quantifying the dissimilarity of two chaotic attractors. This measure is defined in terms of a generalized entropy. We illustrate our procedure by considering the effect of additive noise in the well known Hénon attractor. Finally, we show how our approach allows one to detect nonstationary events in a time series.