Method For Gene Regulatory Networks Research Articles

NSRGRN: a network structure refinement method for gene regulatory network inference.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a critical task. In this paper, we propose EnGRNT (Ensemble methods for Gene Regulatory Networks using Topological features) approach to infer GRNs with high accuracy that uses ensemble-based methods. We compare the performance of state-of-the-art inference methods on simulated networks under different scaling conditions. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. Consequently, the proposed method can provide satisfactory performance to infer GRNs for networks with (<150) nodes in experimental conditions (knockout, knockdown, and multifactorial). For large networks, it is vital to consider biological conditions for selecting an appropriate algorithm. The aim of this study is to explore the effective method to infer GRN, which helps biologists and medical specialists in drug design.

Inference and analysis of gene regulatory networks using data mining techniques

Causality draws the relationship between the cause and the effect among a pair of items. Causality inference in Gene Regulatory Networks (GRNs) is a challenging task due to the limits of appropriate data and large dataset size of real networks. Such an inference is very important because it might give the blueprint of how a complex organism operates which might further reveal the probable cause of some of the terminal diseases. A number of techniques have been proposed and applied to infer causal relationships in various domains. However, they are not specific to regulatory network inference. In our work, we made an empirical study on a few selective methods in inferring causal GRNs. To assess the candidate methods in terms of their quality of inference we used time-series expression data from the DREAM challenges. Our findings revealed that among the selected causality finding techniques, Causation Entropy gives the best result, however, it is computationally expensive. We used the second-best approach, Transfer Entropy, to infer casual GRN of breast cancer datasets. The topological analysis of the network revealed that top out-degree genes such as SLC39A5 which are considered central genes, plays an important role in cancer progression. The main intention behind this research work was to develop an effective inference method for causal Gene Regulatory Network which is computationally less expensive and also considers the effects of time-delay during regulation of one gene by the other. We proposed a novel time delayed inferring method for GRNs using association rule mining. We tried to infer the regulatory sign of the edges i.e. whether influencing positively or negatively. Comparative results highlight that our proposed technique produced better inference results in comparison to several of the existing techniques. We applied the proposed method to perform post inference analysis on malignant cancer network and benign network. Various networks parameters like the degree distribution, average path length, clustering coefficient, network re-wiring structure analysis, network topological variation analysis, and connectivity variation analysis are performed to identify the differentially expressed genes between the two networks. To avoid inference biasness and to obtain a consensus based conclusion we made a comparative analysis between our proposed method and transfer entropy based method and also computed the average score. Such an analysis of the network has revealed four genes, namely, STON 2 , UMODL 1 , STK 11 , and COBL which are already under investigation for its role in various types of cancer as seen from various researches.

Assessing the Effectiveness of Causality Inference Methods for Gene Regulatory Networks.

Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.

Next-generation Bioinformatics: connecting bases to genes, networks and disease

In this issue, we present a collection of papers inspired by ISCB-Asia/SCCG 2012, an international conference on bioinformatics co-organized by the International Society of Computational Biology and BGI, known for their Sequencing Center near the conference site in Shenzhen, China. The issue begins at the whiteboard fastidiously counting bits, gradually moves into the more abstract world of inferring various kinds of gene networks and ends near the clinic reviewing methods to detect diabetes biomarkers and cancer driver mutations. In the first paper, Shrestha et al. give the reader a look ‘under-the-hood’ at how to efficiently construct suffix arrays, a data structure forming the basis for most methods used in the initial processing of high-throughput sequencing data. In the second paper, Gromiha & Ou review databases, online resources and sequence analysis techniques to characterize membrane proteins primarily from their amino acid sequence. In the third paper, Vreven et al. compare methods to predict protein–protein complexes from sequence. The next three papers focus on the inference of various kinds of networks. B. Chen et al. review methods for inferring protein–protein interaction networks from a collection of (possibly error-prone) protein–protein interaction pairs. Maetschke et al. show a little teaching can go a long way in their comparison of supervised versus unsupervised inference methods for gene regulatory networks. Kim et al. provide a comprehensive review of methods for inferring dynamic networks from time course data. Both B. Chen et al. and Kim et al. discuss the decomposition of interactions in spatial (subcellular localization) and temporal dimensions. The last two papers focus on medical applications. Li et al. review the dynamical network biomarkers method to identify genes marking pre-disease to disease transition from time course expression data, applying the method on animal models of type 2 diabetes as a test case. Finally, Zhang et al. bring us back to the genome by reviewing methods to distinguish driver mutations from passenger mutations in cancer. Despite the broad range of topics covered, several issues are encountered in multiple papers. Among the common issues are low concordance between different prediction methods, high variance in performance across samples and the difficulties in constructing realistic and informative performance benchmarks. Another common thread is the need to integrate highly diverse sources of information to produce biologically relevant results. One clear conclusion is that we have a long road ahead of us before our discipline fully catches up to the potential insights, which high-throughput sequencing promises. Even simply constructing an efficient data structure to hold sequence data is nontrivial, predicting a single interaction pair challenging, and automatically inferring genome-scale gene networks is currently infeasible. However, this issue also gives us a glimpse of what is waiting for us at the end of that road—improved health for mankind.

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

MotivationConventional 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.ResultsIt 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.

A flood-based information flow analysis and network minimization method for gene regulatory networks

BackgroundBiological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context.ResultsThis paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data.ConclusionsThe method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various “omics” levels.

Optimal Control of Boolean Biological Networks Modeled by Petri Nets

A Boolean network model is one of the models of gene regulatory networks, and is widely used in analysis and control. Although a Boolean network is a class of discrete-time nonlinear systems and expresses the synchronous behavior, it is important to consider the asynchronous behavior. In this paper, using a Petri net, a new modeling method of asynchronous Boolean networks with control inputs is proposed. Furthermore, the optimal control problem of Petri nets expressing asynchronous Boolean networks is formulated, and is reduced to an integer programming problem. The proposed approach will provide us one of the mathematical bases of control methods for gene regulatory networks.

DEEP—differential evolution entirely parallel method for gene regulatory networks

The Differential Evolution Entirely Parallel (DEEP) method is applied to the biological data fitting problem. We introduce a new migration scheme, in which the best member of the branch substitutes the oldest member of the next branch that provides a high speed of the algorithm convergence. We analyze the performance and efficiency of the developed algorithm on a test problem of finding the regulatory interactions within the network of gap genes that control the development of early Drosophila embryo. The parameters of a set of nonlinear differential equations are determined by minimizing the total error between the model behavior and experimental observations. The age of the individuum is defined by the number of iterations this individuum survived without changes. We used a ring topology for the network of computational nodes. The computer codes are available upon request.

Nested canalyzing, unate cascade, and polynomial functions

This paper focuses on the study of certain classes of Boolean functions that have appeared in several different contexts. Nested canalyzing functions have been studied recently in the context of Boolean network models of gene regulatory networks. In the same context, polynomial functions over finite fields have been used to develop network inference methods for gene regulatory networks. Finally, unate cascade functions have been studied in the design of logic circuits and binary decision diagrams. This paper shows that the class of nested canalyzing functions is equal to that of unate cascade functions. Furthermore, it provides a description of nested canalyzing functions as a certain type of Boolean polynomial function. Using the polynomial framework one can show that the class of nested canalyzing functions, or, equivalently, the class of unate cascade functions, forms an algebraic variety which makes their analysis amenable to the use of techniques from algebraic geometry and computational algebra. As a corollary of the functional equivalence derived here, a formula in the literature for the number of unate cascade functions provides such a formula for the number of nested canalyzing functions.