A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints (original) (raw)
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Data- and knowledge-based modeling of gene regulatory networks: an update
EXCLI journal, 2015
Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.
Gene regulatory network inference: Data integration in dynamic models—A review
Biosystems, 2009
Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.
BMC Systems Biology, 2011
Background Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. Results This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies. Conclusions The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.
A survey of models for inference of gene regulatory networks
Nonlinear Analysis: Modelling and Control, 2013
In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.
Current Bioinformatics, 2014
In the post-genome era, designing and conducting novel experiments have become increasingly common for modern researchers. However, the major challenge faced by researchers is surprisingly not the complexity in designing new experiments or obtaining the data generated from the experiments, but instead it is the huge amount of data to be processed and analyzed in the quest to produce meaningful information and knowledge. Gene regulatory network (GRN) inference from gene expression data is one of the common examples of such challenge. Over the years, GRN inference has witnessed a number of transitions, and an increasing amount of new computational and statistical-based methods have been applied to automate the procedure. One of the widely used approaches for GRN inference is the dynamic Bayesian network (DBN). In this review paper, we first discuss the evolution of molecular biology research from reductionism to holism. This is followed by a brief insight on various computational and statistical methods used in GRN inference before focusing on reviewing the current development and applications of DBN-based methods. Chai et al. Category Inference Model Logical models Boolean networks Probabilistic Boolean networks [30, 31] Bayesian networks Continuous models Continuous linear models [32] Dynamic Bayesian networks Ordinary differential equations Regulated flux balance analysis [33] Single-molecule level Stochastic simulation algorithm [34] Inferring Gene Regulatory Networks
Frontiers in genetics, 2012
In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.
Supervised inference of gene-regulatory networks
BMC Bioinformatics, 2008
Background: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. Results: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. Conclusion: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.
Gene regulatory network inference from sparsely sampled noisy data
Nature Communications, 2020
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping...
Identifying gene regulatory networks from experimental data
2001
Handbook of Computational Molecular Biology chapter, are described in Section 27.2. Some properties, like low average connectivity, or the nature of cis-trans interactions during transcription have been used repeatedly in modeling and inference of gene networks. This chapter is de Some background on the nature of large-scale gene expression experiments together with very short description of methods used for the analysis of the observed data is given in Section 27.3. More detailed descriptions of both the technology and the data analysis methods can be found elsewhere in this book. Before the actual methods are described in detail, general properties of modeling formalisms which both define and limit them are described in Section 27.4. Those properties (synchronicity, stochasticity, etc.) are necessary considerations when modeling gene networks and define the resulting models to a large extent. The four large classes of modeling formalisms covered in this chapter are graph theoretical models in Section 27.5, Bayesian networks in Sec. 27.6, Boolean networks in Sec. 27.7, and Linearized differential equation models 27.8. Together with the models and their properties inference methods and algorithms are presented from the most influential research articles in the area, together with pertinent results on both steady-state and time-course gene expression data. The relationship between model complexity and amount/type of data required versus the quality of the results is underlined. At the end of the chapter the models and methods for inference are summarized and future directions toward better gene network inference are outlined. This chapter is not comprehensive with respect to the different frameworks available for modeling gene networks, and more thorough reviews exist in that respect [17]. The emphasis here is on an integrated presentation of the models and the methods for network inference for them. Gene network modeling using feedback control theory is presented in another chapter of this book. 27.2 Gene Networks 27.2.1 Definition Gene regulation is a general name for a number of sequential processes, the most well known and understood being transcription and translation, which control the level of a gene's expression, and ultimately result with specific quantity of a target protein. A gene regulation system consists of genes, cis-elements, and regulators. The regulators are most often proteins, called transcription factors, but small molecules, like RNAs and metabolites, sometimes also participate in the overall regulation. The interactions and binding of regulators to cis-elements in the cis-region of genes controls the level of gene expression during transcription. The cis-regions serve to aggregate the input signals, mediated by the regulators, and thereby effect a very specific gene expression signal. The genes, regulators, and the regulatory connections between them, together with an interpretation scheme form gene networks. Depending on the degree of abstraction and availability of empirical data, there are different levels of modeling of gene networks. Figure 27.1 shows a hypothetical gene network together with different levels at which it can be modeled. The particular modeling level depends on the biological knowledge and the available data, as well as the experiment goal, which can be as simple as hypothesis testing, or as complex as quantitative network modeling. There are a number of gene regulatory networks known in great detail: the lysis/lysogeny cycle regulation of bacteriophage-λ [51], the endomesoderm development network in Sea Urchin [16], and the segment polarity network in Drosophila development [68, 5]. The first Identifying Gene Regulatory Networks from Gene Expression Data