Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network

Background  

Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs.     

Comparison of evolutionary algorithms in gene regulatory network model inference

Background  

The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.     

A Kalman-Filter Based Approach to Identification of Time-Varying Gene Regulatory Networks

Motivation

Conventional identification methods for gene regulatory networks (GRNs) have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs.

Results

It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.     

Cross-platform microarray data normalisation for regulatory network inference

Background

Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

Methods

We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

Conclusions

Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.     

Taste input from tarsal sensilla is related to egg‐laying behavior in Papilio hospiton

In herbivorous insects, host selection involves various sensory modalities (sight, smell, taste), but the contact chemoreceptors capable of detecting stimuli both from host and non‐host plants play an important role in the final steps of oviposition behavior. Female butterflies scratch and drum the leaf surface and taste the compounds present in plant saps with their tarsal chemosensilla. We assumed that tarsal taste sensitivity may be related to the breadth of host selection in ovipositing females of Papilio hospitonGéné (Lepidoptera: Papilionidae). The spike activity of tarsal taste basiconic sensilla was recorded in response to stimulation with NaCl, bitter compounds, and carbohydrates, with the aim of characterizing the gustatory receptor neurons (GRNs) and of comparing the response patterns in the light of differences in acceptability of host plants. Then we studied the sensitivity of GRNs to saps of the host plants Ferula communis L., Peucedanum paniculatumLoisel, Pastinaca latifolia (Duby) DC. (all Apiaceae), and Ruta lamarmorae Bacch., Brullo et Giusso (Rutaceae), and evaluated the relationship between taste sensitivity and oviposition preference. The results indicate that (1) each sensillum houses sugar‐, bitter‐, and salt‐sensitive cells; (2) the spike activity of the gustatory neurons in response to plant saps produces a different response pattern across all active GRNs; and (3) the number of eggs laid on each plant is highest on F. communis and lowest on R. lamarmorae. These results suggest that the varying activity of the tarsal GRNs may affect host plant acceptability and that ovipositing females of P. hospiton seem to be able to discriminate between host plants.     

An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection

Background

The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics. However, Bayesian network methods are time consuming and cannot handle large-scale networks due to their high computational complexity, while the mutual information-based methods are highly effective but directionless and have a high false-positive rate.

Results

To solve these problems, we propose a Candidate Auto Selection algorithm (CAS) based on mutual information and breakpoint detection to restrict the search space in order to accelerate the learning process of Bayesian network. First, the proposed CAS algorithm automatically selects the neighbor candidates of each node before searching the best structure of GRN. Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses on faster learning the structure with little loss of quality.

Conclusion

Results show that the proposed CAS algorithm can effectively reduce the search space of Bayesian networks through identifying the neighbor candidates of each node. In our experiments, the CAS + G method outperforms the state-of-the-art method on simulation data for inferring GRNs, and the CAS + L method is significantly faster than the state-of-the-art method with little loss of accuracy. Hence, the CAS based methods effectively decrease the computational complexity of Bayesian network and are more suitable for GRN inference.

     

Telomere length determined by the fluorescence in situ hybridisation distinguishes malignant and benign cells in cytological specimens

Background

Telomeres are tandem repeats of TTAGGG at the end of eukaryotic chromosomes that play a key role in preventing chromosomal instability. The aim of the present study is to determine telomere length using fluorescence in situ hybridisation (FISH) on cytological specimens.

Methods

Aspiration samples (n = 41) were smeared on glass slides and used for FISH.

Results

Telomere signal intensity was significantly lower in positive cases (cases with malignancy, n = 25) as compared to negative cases (cases without malignancy, n = 16), and the same was observed for centromere intensity. The difference in DAPI intensity was not statistically significant. The ratio of telomere to centromere intensity did not show a significant difference between positive and negative cases. There was no statistical difference in the signal intensities of aspiration samples from ascites or pleural effusion (n = 23) and endoscopic ultrasound‐guided FNA samples from the pancreas (n = 18).

Conclusions

The present study revealed that telomere length can be used as an indicator to distinguish malignant and benign cells in cytological specimens. This novel approach may help improve diagnosis for cancer patients.     

Tooth loss and its relationship with protein intake by elderly Brazilians—A structural equation modelling approach

Objective

This study aimed at assessing the relationship between self‐perceived tooth loss and wearing dentures, on the one hand, and the consumption of protein, on the other hand, among the elderly population of Botucatu, SP. Food consumption tends to decrease with ageing, especially protein intake, and one of the causes could be the precariousness of oral health. Several risk factors associated with deficient dietary protein intake have been identified, namely greater physical dependence, reduced caloric intake and food insecurity, but no studies have analysed whether tooth loss and prostheses interfere with protein intake.

Methods

An interview was conducted among 365 elderly individuals, in which we examined oral health‐related quality of life (OHRQoL) as the only latent variable, in a 24‐hour nutritional assessment dietary recall repeated 3 times, conducted in person by a trained nutritionist and also performed an analysis of nutritional needs using the Nutrition Data System Research (NDSR) Program.

Results

The structural equation model, performed using Stata v.14, showed that lack of teeth (standardised coefficient [SC] = 0.21, P < .001), and prosthesis use (SC = ?0.21, P < .001) was associated with OHRQoL. Lack of teeth had a direct effect on the consumption of animal protein (SC = 0.08, P = .02), a strong total effect on animal protein intake (SC = 0.51, P = .04) and a medium effect on total protein intake (SC = 0.20, P = .03), adjusted for confounders (depression and medical problems).

Conclusion

Tooth loss had a strong and significant total effect on animal protein intake and a medium effect on total protein intake among elderly Brazilians.     

Biochanin A improves fibre fermentation by cellulolytic bacteria

Aims

The objective was to determine the effect of the isoflavone biochanin A (BCA) on rumen cellulolytic bacteria and consequent fermentative activity.

Methods and Results

When bovine microbial rumen cell suspensions (n = 3) were incubated (24 h, 39°C) with ground hay, cellulolytic bacteria proliferated, short‐chain fatty acids were produced and pH declined. BCA (30 μg ml^?1) had no effect on the number of cellulolytic bacteria or pH, but increased acetate, propionate and total SCFA production. Addition of BCA improved total digestibility when cell suspensions (n = 3) were incubated (48 h, 39°C) with ground hay, Avicel, or filter paper. Fibrobacter succinogenes S85, Ruminococcus flavefaciens 8 and Ruminococcus albus 8 were directly inhibited by BCA. Synergistic antimicrobial activity was observed with BCA and heat killed cultures of cellulolytic bacteria, but the effects were species dependent.

Conclusions

These results indicate that BCA improves fibre degradation by influencing cellulolytic bacteria competition and guild composition.

Significance and Impact of the Study

BCA could serve as a feed additive to improve cellulosis when cattle are consuming high‐fibre diets. Future research is needed to evaluate the effect of BCA on fibre degradation and utilization in vivo.