11/26/2023 0 Comments Process lasso activatorMoreover, the advanced technological improvements in measuring gene expression, in cellular populations or even single cells, and the increasing interest in clinical applications of genomics, confer importance and relevance to data-driven GRN inference methods. Inferring and modeling these regulatory networks, however, is a challenging reverse engineering process and requires the combination of both a thorough biological understanding of the system, as well as accurate and advanced computational inference methods. In studying GRNs, 2 main approaches for extracting information exist: (i) static network analysis, and (ii) dynamical modeling, each of which offers different amounts and types of information regarding the network organization, topology, and behavior. (B) Compressed representation of the interactions on the left as a GRN in which only genes are shown with their regulatory interactions. (A) A toy model of gene regulation of 3 genes involved in a transcriptional regulatory network, showing genes transcribed into mRNAs and translated into proteins that regulate another one of the genes. Here we will focus mostly on GRNs that describe interactions between molecular components at the transcriptomics level, thus including TFs and their targets. Strictly speaking, we define GRNs as networks including any type of regulatory interactions between regulatory and target molecular entities (miRNAs-targets, RBP-targets, kinases/phosphatases-substrates). Therefore, depending on the types of nodes and edges considered, different molecular networks exist, like protein–protein networks (PPI), gene regulatory networks (hereafter, GRNs), signal transduction networks, etc. protein binding, gene co-expression, TF–target regulation, etc.). genes, mRNAs, transcription factors (TFs), whereas the edges represent the interactions between them (e.g. In a molecular network, the nodes represent molecular objects of interest (e.g. To facilitate the representation and study of such complex systems, their interacting components can be represented as a network, commonly visualized as a graph of nodes ( vertices) connected by edges ( links) ( Fig 1). For example, characterizing the connection between genotype and phenotype and, furthermore, pathology, requires not only the identification of the molecules involved in the process and their specific characteristics but also a description of the ways in which these molecules interact with each other across spatiotemporal scales. As such, all biological systems, especially molecular systems, are inherently complex, and the global structure and behavior of the system cannot be straightforwardly inferred from the (local) properties of its components. In complex system theory, a system is defined as complex if certain properties, such as nonlinearity, feedback loops, adaptation, and nontrivial behavior, emerge from the collective interactions between the system components and the surrounding environment. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches.
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