Narrow Results

Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper, we propose a multivariate Markov model for approximating PBNs and describing the dynamics of a genetic network for gene expression sequences. The main contribution of the new model is to preserve the strength of PBNs and reduce the complexity of the networks. The number of parameters of our proposed model is O(n²) where n is the number of genes involved. We also develop efficient estimation methods for solving the model parameters. Numerical examples on synthetic data sets and practical yeast data sequences are given to demonstrate the effectiveness of the proposed model.

Cell differentiation is a complex process governed by the timely activation of genes resulting in a specific phenotype or observable physical change. Recent reports have indicated heterogeneity in gene expression even amongst identical colonies (clones). While some genes are always expressed, others are expressed with a finite probability. In this report, a mathematical framework is provided to understand the mechanism of osteoblast (bone forming cell) differentiation. A systematic approach using a combination of entropy, pair-wise dependency and Bayesian approach is used to gain insight into the dependencies and underlying network structure. Pairwise dependencies are estimated using linear correlation and mutual information. An algorithm is proposed to identify statistically significant mutual information estimates. The robustness of the dependencies and the network structure to decreasing number of colonies (colony size) and perturbation is investigated. Perturbation is achieved by generating bootstrap samples. The methods discussed are generic in nature and can be extended to similar experimental paradigms.

In most vertebrate species, the body axis is generated by the formation of repeated transient structures called somites. This spatial periodicity in somitogenesis has been related to the genetic network oscillations in certain mRNAs and their associated gene products in the cells forming the presomitic mesoderm. The current molecular view of the mechanism underlying these oscillations involves negative-feedback regulation at transcriptional and translational levels. The spatially periodic nature of somite formation suggests that the genetic network involved must display intracellular oscillations that interact with a longitudinal positional information gradient, called determination front, down the axis of vertebrate embryos to create this spatial patterning. Here, we consider a simple model for diploid cells based on this current biological picture considering gene regulation as a noisy process relevant in a real developmental situation and study its consequences for somitogenesis. Comparison is made with the known properties of somite formation in the zebrafish embryo.

Synchronization of coupled oscillators is by now a very well studied subject. A large number of analytical and computational tools are available for the treatment of experimental results. This article focuses on a method recently proposed to construct a phase model from experimental data. The advantage of this method is that it extracts a phase model in a noninvasive manner without any prior knowledge of the experimental system. The aim of the present research is to apply this methodology to a network of electronic genetic oscillators. These electronic circuits mimic the dynamics of a synthetic genetic oscillator, called "repressilator", which is capable of synthesizing autonomous biological rhythms. The obtained phase model is shown to be capable of recovering the route leading to synchronization of genetic oscillators. The predicted onset point of synchronization agrees quite well with the experiment. This encourages further application of the present method to synthetic biological systems.

Genetic networks offer a wealth of data; this is mainly due to the genomic dimensionality rather than the samples, as the latter usually come from measurements obtained under a few experimental conditions or time points. It is therefore a challenging task to design suitable statistical models and to develop effective reverse engineering algorithms. The signature of noise is pervasive in genetic networks. For instance, in perturbation experiments only a few genes change expression value, while most genes are either noisy or constant. Consequently, a genetic regulatory network is a redundant system, due to the high-dimensionality and the dependence between genes, and also a sparse system through the gene-gene interaction matrix only partially active. In order to explore these two aspects, redundancy and sparsity, independent component analysis (ICA) is proposed as a flexible approximation model targeted to dimensionality reduction and gene feature selection.

We study the minimum number of driver nodes control of which leads a Boolean network (BN) from an initial state to a target state in a specified number of time steps. We show that the problem is NP-hard and present an integer linear programming-based method that solves the problem exactly. We mathematically analyze the average size of the minimum set of driver nodes for random Boolean networks with bounded in-degree and with a small number of time steps. The results of computational experiments using randomly generated BNs show good agreements with theoretical analyses. A further examination in realistic BNs demonstrates the efficiency and generality of our theoretical analyses.

The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to the neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE dataset. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs (Transcriptional Factors) pairs collected from literature respectively to evaluate the performance of the proposed algorithm.

In this work we simulate gene knock-out experiments in networks in which variable domains are continuous and variables can vary continuously in time. This model is more realistic than other well-known switching networks such as Boolean Networks. We show that continuous networks can reproduce the results obtained by Random Boolean Networks (RBN). Nevertheless, they do not reproduce the whole range of activation values of actual experimental data. The reasons for this behavior very close to that of RBN could be found in the specific parameter setting chosen and lines for further investigation are discussed.