Study Of Gene Regulatory Networks Research Articles

P_UNSAT approach of attractor calculation for Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which combined the predecessor approach and the logic unsatisfiability approach to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks even the networks with a relatively large average degree.

Single-cell transcriptomics reveals a new dynamical function of transcription factors during embryonic hematopoiesis.

Recent advances in single-cell transcriptomics techniques have opened the door to the study of gene regulatory networks (GRNs) at the single-cell level. Here, we studied the GRNs controlling the emergence of hematopoietic stem and progenitor cells from mouse embryonic endothelium using a combination of single-cell transcriptome assays. We found that a heptad of transcription factors (Runx1, Gata2, Tal1, Fli1, Lyl1, Erg and Lmo2) is specifically co-expressed in an intermediate population expressing both endothelial and hematopoietic markers. Within the heptad, we identified two sets of factors of opposing functions: one (Erg/Fli1) promoting the endothelial cell fate, the other (Runx1/Gata2) promoting the hematopoietic fate. Surprisingly, our data suggest that even though Fli1 initially supports the endothelial cell fate, it acquires a pro-hematopoietic role when co-expressed with Runx1. This work demonstrates the power of single-cell RNA-sequencing for characterizing complex transcription factor dynamics.

Gene expression analysis of human adipose tissue-derived stem cells during the initial steps of in vitro osteogenesis

Mesenchymal stem cells (MSCs) have been widely studied with regard to their potential use in cell therapy protocols and regenerative medicine. However, a better comprehension about the factors and molecular mechanisms driving cell differentiation is now mandatory to improve our chance to manipulate MSC behavior and to benefit future applications. In this work, we aimed to study gene regulatory networks at an early step of osteogenic differentiation. Therefore, we analyzed both the total mRNA and the mRNA fraction associated with polysomes on human adipose tissue-derived stem cells (hASCs) at 24 h of osteogenesis induction. The RNA-seq results evidenced that hASC fate is not compromised with osteogenesis at this time and that 21 days of continuous cell culture stimuli are necessary for full osteogenic differentiation of hASCs. Furthermore, early stages of osteogenesis induction involved gene regulation that was linked to the management of cell behavior in culture, such as the control of cell adhesion and proliferation. In conclusion, although discrete initial gene regulation related to osteogenesis occur, the first 24 h of induction is not sufficient to trigger and drive in vitro osteogenic differentiation of hASCs.

Modelling gene regulation: (De)compositional and template-based strategies

Although the interdisciplinary nature of contemporary biological sciences has been addressed by philosophers, historians, and sociologists of science, the different ways in which engineering concepts and methods have been applied in biology have been somewhat neglected. We examine – using the mechanistic philosophy of science as an analytic springboard – the transfer of network methods from engineering to biology through the cases of two biology laboratories operating at the California Institute of Technology. The two laboratories study gene regulatory networks, but in remarkably different ways. The research strategy of the Davidson lab fits squarely into the traditional mechanist philosophy in its aim to decompose and reconstruct, in detail, gene regulatory networks of a chosen model organism. In contrast, the Elowitz lab constructs minimal models that do not attempt to represent any particular naturally evolved genetic circuits. Instead, it studies the principles of gene regulation through a template-based approach that is applicable to any kinds of networks, whether biological or not. We call for the mechanists to consider whether the latter approach can be accommodated by the mechanistic approach, and what kinds of modifications it would imply for the mechanistic paradigm of explanation, if it were to address modelling more generally.

Causal Concepts Guiding Model Specification in Systems Biology

Abstract In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

Omics approaches to study gene regulatory networks for development in echinoderms.

Gene regulatory networks (GRNs) describe the interactions for a developmental process at a given time and space. Historically, perturbation experiments represent one of the key methods for analyzing and reconstructing a GRN, and the GRN governing early development in the sea urchin embryo stands as one of the more deeply dissected so far. As technology progresses, so do the methods used to address different biological questions. Next-generation sequencing (NGS) has become a standard experimental technique for genome and transcriptome sequencing and studies of protein-DNA interactions and DNA accessibility. While several efforts have been made toward the integration of different omics approaches for the study of the regulatory genome in many animals, in a few cases, these are applied with the purpose of reconstructing and experimentally testing developmental GRNs. Here, we review emerging approaches integrating multiple NGS technologies for the prediction and validation of gene interactions within echinoderm GRNs. These approaches can be applied to both 'model' and 'non-model' organisms. Although a number of issues still need to be addressed, advances in NGS applications, such as assay for transposase-accessible chromatin sequencing, combined with the availability of embryos belonging to different species, all separated by various evolutionary distances and accessible to experimental regulatory biology, place echinoderms in an unprecedented position for the reconstruction and evolutionary comparison of developmental GRNs. We conclude that sequencing technologies and integrated omics approaches allow the examination of GRNs on a genome-wide scale only if biological perturbation and cis-regulatory analyses are experimentally accessible, as in the case of echinoderm embryos.

A Model-Based Tool for the Analysis and Design of Gene Regulatory Networks.

Computational and mathematical models have significantly contributed to the rapid progress in the study of gene regulatory networks (GRN), but researchers still lack a reliable model-based framework for computer-aided analysis and design. Such tool should both reveal the relation between network structure and dynamics and find parameter values and/or constraints that enable the simulated dynamics to reproduce specific behaviors. This paper addresses these issues and proposes a computational framework that facilitates network analysis or design. It follows a modeling cycle that alternates phases of hypothesis testing with parameter space refinement to ultimately propose a network that exhibits specified behaviors with the highest probability. Hypothesis testing is performed via qualitative simulation of GRNs modeled by a class of nonlinear and temporal multiscale ODEs, where regulation functions are expressed by steep sigmoid functions and incompletely known parameter values by order relations only. Parameter space refinement, grounded on a method that considers the intrinsic stochasticity of regulation by expressing network uncertainty with fluctuations in parameter values only, optimizes parameter stochastic values initialized by probability distributions with large variances. The power and ease of our framework is demonstrated by working out a benchmark synthetic network to get a synthetic oscillator.

Sliding windows and persistence

The use of geometric and topological ideas as a means to tackle problems in signal analysis has seen a sharp increase in the last few years. The goal of this talk is to show how ideas from dynamical systems (e.g., time delay embeddings) with tools from topological data analysis (e.g., persistent homology) allows one to extract highly non-trivial features from vector-valued time series data. As an example, we describe a paradigm for quantifying (quasi)periodicity in video data and provide applications including the study of gene regulatory networks in biology, biphonation in mammals, and speech pathologies in humans.

Imputation for transcription factor binding predictions based on deep learning.

Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.

Computational Approaches to Study Gene Regulatory Networks.

The goal of the gene regulatory network (GRN) inference is to determine the interactions between genes given heterogeneous data capturing spatiotemporal gene expression. Since transcription underlines all cellular processes, the inference of GRN is the first step in deciphering the determinants of the dynamics of biological systems. Here, we first describe the generic steps of the inference approaches that rely on similarity measures and group the similarity measures based on the computational methodology used. For each group of similarity measures, we not only review the existing approaches but also describe specifically the detailed steps of the existing state-of-the-art algorithms.

Computational inference of gene regulatory networks: Approaches, limitations and opportunities

Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches.Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions.Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks

Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

A Novel Boolean Network for Analyzing the p53 Gene Regulatory Network

Boolean network is a powerful tool for the study of gene regulatory networks, and dynamics of a Boolean network is mainly determined by its attractors. In this study, a new approach to construct Boolean network is proposed based on biochemical reaction differential equations. We attempt to investigate gene regulatory networks by means of comparing the experimental results from relevant literature with the attractors obtained by the Boolean network. The Boolean regulatory network proposed is simple and robust to alteration of the parameters of the network. The model is applied to investigate p53 gene regulatory network by analyzing the interplay of the key ingredients including p53, Mdm2 (murine double minute 2) and the external signal of DNA damage. The attractors obtained by p53 Boolean network are consistent with the experimental results of relevant literature. Furthermore, we speculate that there is an unknown protein XXXp in the p53 gene regulatory network, where p53p promotes the DNA of XXXp to form the mRNA of mXXXp. mXXXp generates protein XXXp to promote p53 phosphorylation to form p53p, creating a positive feedback loop of XXXp-p53p. Keywords: Gene regulatory network, boolean network, attractor.

Análisis de la teoría genética a la luz de la estructura de las revoluciones científicas

En estos momentos nos encontramos en el ámbito de la biología molecular en una nueva etapa, denominada Post-Genómica, que se caracteriza desde un punto de vista epistémico y metodológico por la incorporación de nuevos métodos de secuenciación tanto de ADN como de ARN, y por el desarrollo de genomas que permiten referenciar de una forma precisa los resultados moleculares obtenidos. Además, se está produciendo un cambio de perspectiva sobre la expresión de caracteres donde el foco se centra en el estudio del ARN. Esta nueva perspectiva biológico-molecular basada en las nuevas metodologías y enfoques está afectando a la Teoría Genética Molecular existente así como a la definición de gen y del Dogma Central de la Biología Molecular. El principal reto filosófico que plantea esta nueva situación consiste en analizar si estamos ante una sofisticación aún mayor de la Teoría Genética Molecular o ante un cambio completo de paradigma que producirá una Nueva Teoría Genética de Procesos Moleculares además de una inconmensurabilidad con las teorías científicas precedentes derivada de los cambios que originan estas nuevas metodologías. En este trabajo evaluamos estos cambios a la luz de algunas ideas de Kuhn con el fin de plantear una alternativa entre una interpretación moderada que entendería que la Post-genómica no entraña propiamente un cambio de paradigma o, si lo hace, se trata de una revolución menor, y una interpretación fuerte para la que las anomalías que la Post-genómica descubre en la Genética Molecular y los cambios conceptuales y metodológicos por ellas entrañadas suponen un cambio de paradigma científico.

MicroRNA transcriptome analysis identifies miR-365 as a novel negative regulator of cell proliferation in Zmpste24-deficient mouse embryonic fibroblasts

Zmpste24 is a metalloproteinase responsible for the posttranslational processing and cleavage of prelamin A into mature laminA. Zmpste24−/− mice display a range of progeroid phenotypes overlapping with mice expressing progerin, an altered version of lamin A associated with Hutchinson-Gilford progeria syndrome (HGPS). Increasing evidence has demonstrated that miRNAs contribute to the regulation of normal aging process, but their roles in progeroid disorders remain poorly understood. Here we report the miRNA transcriptomes of mouse embryonic fibroblasts (MEFs) established from wild type (WT) and Zmpste24−/− progeroid mice using a massively parallel sequencing technology. With data from 19.5×106 reads from WT MEFs and 16.5×106 reads from Zmpste24−/− MEFs, we discovered a total of 306 known miRNAs expressed in MEFs with a wide dynamic range of read counts ranging from 10 to over 1 million. A total of 8 miRNAs were found to be significantly down-regulated, with only 2 miRNAs upregulated, in Zmpste24−/− MEFs as compared to WT MEFs. Functional studies revealed that miR-365, a significantly down-regulated miRNA in Zmpste24−/− MEFs, modulates cellular growth phenotypes in MEFs. Overexpression of miR-365 in Zmpste24−/− MEFs increased cellular proliferation and decreased the percentage of SA-β-gal-positive cells, while inhibition of miR-365 function led to an increase of SA-β-gal-positive cells in WT MEFs. Furthermore, we identified Rasd1, a member of the Ras superfamily of small GTPases, as a functional target of miR-365. While expression of miR-365 suppressed Rasd1 3′ UTR luciferase-reporter activity, this effect was lost with mutations in the putative 3′ UTR target-site. Consistently, expression levels of miR-365 were found to inversely correlate with endogenous Rasd1 levels. These findings suggest that miR-365 is down-regulated in Zmpste24−/− MEFs and acts as a novel negative regulator of Rasd1. Our comprehensive miRNA data provide a resource to study gene regulatory networks in MEFs.

General features of echinoderm skeleton formation

Available data on the formation of echinoderm skeleton are reviewed based on the literature. The development and structural features of the skeleton and morphological, histological, and molecular data on the skeleton formation in different echinoderm groups are described. Recent data on the study of gene regulatory networks and their effect on biomineraization and skeleton formation are also included. The skeletogenic mechanisms are in general highly conservative within echinoderms at the levels of genes, gene regulatory networks, and involved cell populations. At the same time, the formation mechanism of the echinoderm skeleton is unique and has not been recorded in other taxonomic groups.

A tutorial to identify nonlinear associations in gene expression time series data.

The study of gene regulatory networks is the basis to understand the biological complexity of several diseases and/or cell states. It has become the core of research in the field of systems biology. Several mathematical methods have been developed in the last decade, especially in the analysis of time series gene expression data derived from microarrays and sequencing-based methods. Most of the models available in the literature assumes linear associations among genes and do not infer directionality in these connections or uses a priori biological knowledge to set the directionality. However, in several cases, a priori biological information is not available. In this context, we describe a statistical method, namely nonlinear vector autoregressive model to estimate nonlinear relationships and also to infer directionality at the edges of the network by using the temporal information of the time series gene expression data without a priori biological information.

Chromosomal integration of transcriptional fusions.

The characterization and parameterization of promoters is crucial for the study of gene regulatory networks. While a number of techniques are available for this purpose, the use of reporter fusions integrated in the chromosome of a bacterial host affords precise quantification of transcriptional responses with high reproducibility. Here, we describe the integration of green fluorescent protein (GFP) and lacZ reporter cassettes using either mini-Tn7-based vectors or homologous chromosomal recombination to analyze gene regulation at transcriptional and post-transcriptional levels.

Single site-specific integration targeting coupled with embryonic stem cell differentiation provides a high-throughput alternative to in vivo enhancer analyses

SummaryComprehensive analysis of cis-regulatory elements is key to understanding the dynamic gene regulatory networks that control embryonic development. While transgenic animals represent the gold standard assay, their generation is costly, entails significant animal usage, and in utero development complicates time-course studies. As an alternative, embryonic stem (ES) cells can readily be differentiated in a process that correlates well with developing embryos. Here, we describe a highly effective platform for enhancer assays using an Hsp68/Venus reporter cassette that targets to the Hprt locus in mouse ES cells. This platform combines the flexibility of Gateway® cloning, live cell trackability of a fluorescent reporter, low background and the advantages of single copy insertion into a defined genomic locus. We demonstrate the successful recapitulation of tissue-specific enhancer activity for two cardiac and two haematopoietic enhancers. In addition, we used this assay to dissect the functionality of the highly conserved Ets/Ets/Gata motif in the Scl+19 enhancer, which revealed that the Gata motif is not required for initiation of enhancer activity. We further confirmed that Gata2 is not required for endothelial activity of the Scl+19 enhancer using Gata2−/− Scl+19 transgenic embryos. We have therefore established a valuable toolbox to study gene regulatory networks with broad applicability.

Gene regulatory networks reconstruction from time series datasets using genetic programming: a comparison between tree-based and graph-based approaches

Genetic programming researchers have shown a growing interest in the study of gene regulatory networks in the last few years. Our team has also contributed to the field, by defining two systems for the automatic reverse engineering of gene regulatory networks called GRNGen and GeNet. In this paper, we revise this work by describing in detail the two approaches and empirically comparing them. The results we report, and in particular the fact that GeNet can be used on large networks while GRNGen cannot, encourage us to pursue the study of GeNet in the future. We conclude the paper by discussing the main research directions that we are planning to investigate to improve GeNet.