Properties of sparse penalties on inferring gene regulatory networks from time‐course gene expression data

Genes regulate each other and form a gene regulatory network (GRN) to realise biological functions. Elucidating GRN from experimental data remains a challenging problem in systems biology. Numerous techniques have been developed and sparse linear regression methods become a promising approach to infer accurate GRNs. However, most linear methods are either based on steady‐state gene expression data or their statistical properties are not analysed. Here, two sparse penalties, adaptive least absolute shrinkage and selection operator and smoothly clipped absolute deviation, are proposed to infer GRNs from time‐course gene expression data based on an auto‐regressive model and their Oracle properties are proved under mild conditions. The effectiveness of those methods is demonstrated by applications to in silico and real biological data.Inspec keywords: genetics, autoregressive processesOther keywords: sparse penalties, gene regulatory networks, time‐course gene expression data, GRN, biological functions, systems biology, sparse linear regression methods, steady‐state gene expression data, adaptive least absolute shrinkage, selection operator, smoothly clipped absolute deviation, autoregressive model, Oracle properties     

Retrieving relevant time‐course experiments: a study on Arabidopsis microarrays

Understanding time‐course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta‐analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.Inspec keywords: botany, lab‐on‐a‐chip, genetics, bioinformatics, information retrieval, data mining, data analysis, associative processingOther keywords: relevant time‐course experiment retrieval, time‐course Arabidopsis microarray, time‐course gene regulation, stimulus response, systems biology, computational method, gene behaviour model, gene networked interaction, latent parameter, model parameter estimation, meta‐analysis, data analysis, time‐course gene expression experiment, information retrieval, computational framework, time‐course experiment query, relevant experiment list, repository, environmental factor, query experiment, experimental content similarity     

Mining gene link information for survival pathway hunting

This study proposes a gene link‐based method for survival time‐related pathway hunting. In this method, the authors incorporate gene link information to estimate how a pathway is associated with cancer patient''s survival time. Specifically, a gene link‐based Cox proportional hazard model (Link‐Cox) is established, in which two linked genes are considered together to represent a link variable and the association of the link with survival time is assessed using Cox proportional hazard model. On the basis of the Link‐Cox model, the authors formulate a new statistic for measuring the association of a pathway with survival time of cancer patients, referred to as pathway survival score (PSS), by summarising survival significance over all the gene links in the pathway, and devise a permutation test to test the significance of an observed PSS. To evaluate the proposed method, the authors applied it to simulation data and two publicly available real‐world gene expression data sets. Extensive comparisons with previous methods show the effectiveness and efficiency of the proposed method for survival pathway hunting.Inspec keywords: cancer, physiological models, bioinformatics, genomicsOther keywords: permutation test, pathway survival score, gene link‐based Cox proportional hazard model, cancer patient survival time, survival time‐related pathway hunting, gene link‐based method     

Investigating receptor enzyme activity using time‐scale analysis

At early drug discovery, purified protein‐based assays are often used to characterise compound potency. In the context of dose response, it is often perceived that a time‐independent inhibitor is reversible and a time‐dependent inhibitor is irreversible. The legitimacy of this argument is investigated using a simple kinetics model, where it is revealed by model‐based analytical analysis and numerical studies that dose response of an irreversible inhibitor may appear time‐independent under certain parametric conditions. Hence, the observation of time‐independence cannot be used as sole evidence for identification of inhibitor reversibility. It has also been discussed how the synthesis and degradation of a target receptor affect drug inhibition in an in vitro cell‐based assay setting. These processes may also influence dose response of an irreversible inhibitor in such a way that it appears time‐independent under certain conditions. Furthermore, model‐based steady‐state analysis reveals the complexity nature of the drug–receptor process.Inspec keywords: enzymes, molecular biophysics, drugs, biochemistry, reaction kinetics, cellular biophysicsOther keywords: receptor enzyme activity, time‐scale analysis, drug discovery, purified protein‐based assays, compound potency, dose response, reversible time‐independent inhibitor, irreversible time‐dependent inhibitor, kinetics model, target receptor degradation, drug inhibition, in vitro cell‐based assay setting, model‐based steady‐state analysis, drug‐receptor process     

Genetic programming‐based approach to elucidate biochemical interaction networks from data

Biochemical systems are characterised by cyclic/reversible reciprocal actions, non‐linear interactions and a mixed relationship structures (linear and non‐linear; static and dynamic). Deciphering the architecture of such systems using measured data to provide quantitative information regarding the nature of relationships that exist between the measured variables is a challenging proposition. Causality detection is one of the methodologies that are applied to elucidate biochemical networks from such data. Autoregressive‐based modelling approach such as granger causality, partial directed coherence, directed transfer function and canonical variate analysis have been applied on different systems for deciphering such interactions, but with limited success. In this study, the authors propose a genetic programming‐based causality detection (GPCD) methodology which blends evolutionary computation‐based procedures along with parameter estimation methods to derive a mathematical model of the system. Application of the GPCD methodology on five data sets that contained the different challenges mentioned above indicated that GPCD performs better than the other methods in uncovering the exact structure with less false positives. On a glycolysis data set, GPCD was able to fill the ‘interaction gaps’ which were missed by other methods.Inspec keywords: autoregressive processes, biochemistry, biology computing, genetic algorithms, parameter estimation, transfer functionsOther keywords: elucidate biochemical interaction networks, biochemical systems, cyclic‐reversible reciprocal actions, nonlinear interactions, autoregressive‐based modelling, granger causality, transfer function, canonical variate analysis, genetic programming‐based causality detection methodology, GPCD methodology, mathematical model, parameter estimation methods     

Modelling epigenetic regulation of gene expression in 12 human cell types reveals combinatorial patterns of cell‐type‐specific genes

The maintenance of the diverse cell types in a multicellular organism is one of the fundamental mysteries of biology. Modelling the dynamic regulatory relationships between the histone modifications and the gene expression across the diverse cell types is essential for the authors to understand the mechanisms of the epigenetic regulation. Here, the authors thoroughly assessed the histone modification enrichment profiles at the promoters and constructed quantitative models between the histone modification abundances and the gene expression in 12 human cell types. The author''s results showed that the histone modifications at the promoters exhibited remarkably cell‐type‐dependent variability in the cell‐type‐specific (CTS) genes. They demonstrated that the variable profiles of the modifications are highly predictive for the dynamic changes of the gene expression across all the cell types. Their findings revealed the close relationship between the combinatorial patterns of the histone modifications and the CTS gene expression. They anticipate that the findings and the methods they used in this study could provide useful information for the future studies of the regulatory roles of the histone modifications in the CTS genes.Inspec keywords: cellular biophysics, genetics, genomics, physiological models, proteinsOther keywords: CTS gene expression, variable profiles, cell‐type‐dependent variability, histone modification abundances, constructed quantitative models, promoters, histone modification enrichment profiles, dynamic regulatory relationship modelling, biology, multicellular organism, cell‐type‐specific genes, combinatorial patterns, human cell types, epigenetic regulation modelling     

Construction and investigation of breast‐cancer‐specific ceRNA network based on the mRNA and miRNA expression data

It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast‐cancer‐specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale‐free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients’ risks of distant metastasis in all the three independent data sets.Inspec keywords: cancer, genetics, medical computing, molecular biophysics, RNAOther keywords: breast‐cancer specific ceRNA network construction, miRNA expression data, mRNA expression data, gene pairs, computational method, dense clusters, cancer hallmarks, biological mechanism, discriminative hub genes     

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.Inspec keywords: Bayes methods, biology computing, circadian rhythms, Gaussian processes, genetics, genomics, graphs, molecular biophysics, proteinsOther keywords: Gaussian graphical model, responsive regulatory networks, time course high‐throughput data, biological molecules, dynamic gene expression proflling, circadian rhythm, consistency measurement, matching network structure, simulated time course microarray data, true time course microarray data, dynamic Bayesian network model, time course gene expression proflles, network architectures, documented regulatory networks, speciflc gene expression proflling data, phenotypes, proteins, functional linkages, databases, knowledge‐based networks     

Multi‐scale approach for simulating time‐delay biochemical reaction systems

This study presents a multi‐scale approach for simulating time‐delay biochemical reaction systems when there are wide ranges of molecular numbers. The authors construct a new efficient approach based on partitioning into slow and fast subsets in conjunction with predictor–corrector methods. This multi‐scale approach is shown to be much more efficient than existing methods such as the delay stochastic simulation algorithm and the modified next reaction method. Numerical testing on several important problems in systems biology confirms the accuracy and computational efficiency of this approach.Inspec keywords: biochemistry, delays, biological techniques, predictor‐corrector methodsOther keywords: multiscale approach, time‐delay biochemical reaction systems, predictor–corrector methods, delay stochastic simulation algorithm, modified next reaction method, numerical testing, systems biology, method accuracy, computational efficiency     

Investigation of graphene‐on‐metal substrates for SPR‐based sensor using finite‐difference time domain

In this study, the authors investigated the effects of a single layer graphene as a coating layer on top of metal thin films such as silver, gold, aluminum and copper using finite‐difference time domain method. To enhance the resolution of surface plasmon resonance (SPR) sensor, it is necessary to increase the SPR reflectivity and decrease the full‐width‐half maximum (FWHM) of the SPR curve so that there is minimum uncertainty in the determination of the resonance dip. Numerical data was verified with analytical and experimental data where all the data were in good agreement with resonance angle differing in <10% due to noise present in components such as humidity and temperature. In further analysis, reflectivity and FWHM were compared among four types of metal with various thin film thicknesses where graphene was applied on top of the metal layers, and data was compared against pure conventional metal thin films. A 60 nm‐thick Au thin film results in higher performance with reflectivity of 92.4% and FWHM of 0.88° whereas single layer graphene‐on‐60 nm‐thick Au gave reflectivity of 91.7% and FWHM of 1.32°. However, a graphene‐on‐40 nm‐thick Ag also gave good performance with narrower FWHM of 0.88° and reflection spectra of 89.2%.Inspec keywords: graphene, surface plasmon resonance, finite difference time‐domain analysis, reflectivity, metallic thin films, silver, gold, aluminium, copper, chemical sensors, biological techniquesOther keywords: graphene‐on‐metal substrates, SPR‐based sensor, finite‐difference time domain, metal thin films, surface plasmon resonance sensor, SPR curve, resonance angles, reflectivity, C, Ag, Au, Al, Cu     

Fold change based approach for identification of significant network markers in breast,lung and prostate cancer

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA‐seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA‐seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut‐off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log‐FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.Inspec keywords: cancer, proteins, RNA, bioinformatics, statistical analysis, genetics, molecular biophysics, ontologies (artificial intelligence), lungOther keywords: cancer network, PPI data, gene sets, multiple cancers, Gene Ontology functional enrichment, prostate cancer, gene expression data, RNA‐seq platforms, protein–protein interaction network, DEG, microarray data, RNA‐seq data, cancer types, lung cancer, diseases, breast cancer, network markers, differentially expressed genes, fold change based approach, CFinder, statistical methods     

Inferring catalysis in biological systems

In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data. This may be due to unknown but present catalytic reactions. From a modelling perspective, the question of whether a certain reaction is catalysed leads to a large increase of model candidates. For large networks the calibration of all possible models becomes computationally infeasible. We propose a method which determines a substantially reduced set of appropriate model candidates and identifies the catalyst of each reaction at the same time. This is incorporated in a multiple‐step procedure which first extends the network by additional latent variables and subsequently identifies catalyst candidates using similarity analysis methods. Results from synthetic data examples suggest a good performance even for non‐informative data with few observations. Applied on CD95 apoptotic pathway our method provides new insights into apoptosis regulation.Inspec keywords: catalysis, catalysts, biochemistry, genetics, enzymes, biology computing, calibration, molecular clustersOther keywords: inferring catalysis, biological systems, systems biology, communication patterns, genes, enzymes, proteins, time‐resolved experiments, time‐resolved communication, reaction networks, gene regulatory networks, biochemical networks, signalling pathways, mathematical data modelling, catalytic reactions, calibration, catalyst, multiple‐step procedure, latent variables, similarity analysis methods, noninformative data, differentiation apoptotic pathway, cluster     

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer‐related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)‐based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP‐based prediction models. Prediction performance evaluations and comparisons between the authors’ GEP models and three representative machine learning methods, support vector machine, multi‐layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross‐data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.Inspec keywords: lung, cancer, medical diagnostic computing, patient diagnosis, genetic algorithms, feature selection, learning (artificial intelligence), support vector machines, multilayer perceptrons, radial basis function networks, reliability, sensitivity analysisOther keywords: lung cancer prediction, cancer‐related death, cancer diagnosis, gene profiles, gene expression programming‐based model, gene selection, GEP‐based prediction models, prediction performance evaluations, representative machine learning methods, support vector machine, multilayer perceptron, radial basis function neural network, real microarray lung cancer datasets, cross‐data set validation, reliability, receiver operating characteristic curve     

Overlapping functional modules detection in PPI network with pair‐wise constrained non‐negative matrix tri‐factorisation

A large amount of available protein–protein interaction (PPI) data has been generated by high‐throughput experimental techniques. Uncovering functional modules from PPI networks will help us better understand the underlying mechanisms of cellular functions. Numerous computational algorithms have been designed to identify functional modules automatically in the past decades. However, most community detection methods (non‐overlapping or overlapping types) are unsupervised models, which cannot incorporate the well‐known protein complexes as a priori. The authors propose a novel semi‐supervised model named pairwise constrains nonnegative matrix tri‐factorisation (PCNMTF), which takes full advantage of the well‐known protein complexes to find overlapping functional modules based on protein module indicator matrix and module correlation matrix simultaneously from PPI networks. PCNMTF determinately models and learns the mixed module memberships of each protein by considering the correlation among modules simultaneously based on the non‐negative matrix tri‐factorisation. The experiment results on both synthetic and real‐world biological networks demonstrate that PCNMTF gains more precise functional modules than that of state‐of‐the‐art methods.Inspec keywords: proteins, molecular biophysics, cellular biophysics, matrix algebraOther keywords: overlapping functional module detection, PPI network, pair‐wise constrained nonnegative matrix trifactorisation, protein–protein interaction data, cellular functions, protein complexes, real‐world biological networks, synthetic biological networks     

Two faces of competition: target‐mediated reverse signalling in microRNA and mitogen‐activated protein kinase regulatory networks

Biomolecular regulatory networks are organised around hubs, which can interact with a large number of targets. These targets compete with each other for access to their common hubs, but whether the effect of this competition would be significant in magnitude and in function is not clear. In this review, the authors discuss recent in vivo studies that analysed the system level retroactive effects induced by target competition in microRNA and mitogen‐activated protein kinase regulatory networks. The results of these studies suggest that downstream targets can regulate the overall state of their upstream regulators, and thus cannot be ignored in analysing biomolecular networks.Inspec keywords: reviews, RNA, molecular biophysics, enzymesOther keywords: target‐mediated reverse signalling, mitogen‐activated protein kinase regulatory networks, biomolecular regulatory networks, microRNA regulatory networks, review, in vivo study     

Time‐invariant biological networks with feedback loops: structural equation models and structural identifiability

Quantitative analyses of biological networks such as key biological parameter estimation necessarily call for the use of graphical models. While biological networks with feedback loops are common in reality, the development of graphical model methods and tools that are capable of dealing with feedback loops is still in its infancy. Particularly, inadequate attention has been paid to the parameter identifiability problem for biological networks with feedback loops such that unreliable or even misleading parameter estimates may be obtained. In this study, the structural identifiability analysis problem of time‐invariant linear structural equation models (SEMs) with feedback loops is addressed, resulting in a general and efficient solution. The key idea is to combine Mason''s gain with Wright''s path coefficient method to generate identifiability equations, from which identifiability matrices are then derived to examine the structural identifiability of every single unknown parameter. The proposed method does not involve symbolic or expensive numerical computations, and is applicable to a broad range of time‐invariant linear SEMs with or without explicit latent variables, presenting a remarkable breakthrough in terms of generality. Finally, a subnetwork structure of the C. elegans neural network is used to illustrate the application of the authors’ method in practice.Inspec keywords: matrix algebra, least squares approximations, statistical analysis, parameter estimation, biologyOther keywords: structural identifiability analysis problem, time‐invariant linear structural equation models, feedback loops, identifiability equations, time‐invariant linear SEMs, time‐invariant biological networks, graphical model methods, parameter identifiability problem, biological parameter estimation, subnetwork structure, C. elegans neural network     

Adaptive modelling of gene regulatory network using Bayesian information criterion‐guided sparse regression approach

Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)‐guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l ₁ ‐norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual. Especially, the proposed method provides a clear interpretation of combinatorial regulations of gene expression by optimally extracting regulation coordination for a given target gene. Experimental results on both simulation data and real‐world microarray data demonstrate the competent performance of discovering regulatory relationships in GRN reconstruction.Inspec keywords: genetics, Bayes methods, genomics, regression analysis, inference mechanisms, bioinformaticsOther keywords: adaptive modelling, gene regulatory network, Bayesian information criterion‐guided sparse regression approach, GRN, microarray expression data, systems biology, GRN reconstruction, optimisation, l₁ ‐norm regularisation