Models Of Regulatory Networks Research Articles

Transcriptional reprogramming of nucleotide metabolism in response to altered pyrimidine availability in Arabidopsis seedlings.

In Arabidopsis seedlings, inhibition of aspartate transcarbamoylase (ATC) and de novo pyrimidine synthesis resulted in pyrimidine starvation and developmental arrest a few days after germination. Synthesis of pyrimidine nucleotides by salvaging of exogenous uridine (Urd) restored normal seedling growth and development. We used this experimental system and transcriptional profiling to investigate genome-wide responses to changes in pyrimidine availability. Gene expression changes at different times after Urd supplementation of pyrimidine-starved seedlings were mapped to major pathways of nucleotide metabolism, in order to better understand potential coordination of pathway activities, at the level of transcription. Repression of de novo synthesis genes and induction of intracellular and extracellular salvaging genes were early and sustained responses to pyrimidine limitation. Since de novo synthesis is energetically more costly than salvaging, this may reflect a reduced energy status of the seedlings, as has been shown in recent studies for seedlings growing under pyrimidine limitation. The unexpected induction of pyrimidine catabolism genes under pyrimidine starvation may result from induction of nucleoside hydrolase NSH1 and repression of genes in the plastid salvaging pathway, diverting uracil (Ura) to catabolism. Identification of pyrimidine-responsive transcription factors with enriched binding sites in highly coexpressed genes of nucleotide metabolism and modeling of potential transcription regulatory networks provided new insights into possible transcriptional control of key enzymes and transporters that regulate nucleotide homeostasis in plants.

Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks.

Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner's Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.

Pan-Cancer Analysis and Experimental Validation of SOX4 as a Potential Diagnosis, Prognosis, and Immunotherapy Biomarker.

SOX4 plays an important role in tumorigenesis and cancer progression. The role of SOX4 in pan-cancer and its underlying molecular mechanism in liver hepatocellular carcinoma (LIHC) are not fully understood. In this study, a comprehensive analysis and experimental validation were performed to explore the function of SOX4 across tumor types. Raw data in regard to SOX4 expression in malignant tumors were downloaded from the TCGA and GTEx databases. The expression levels, prognostic values, genetic mutation, and DNA promoter methylation of SOX4 across tumor types were explored via systematic bioinformatics analysis. The ceRNA regulatory network, immune characteristics, and prognostic models were analyzed in LIHC. Finally, we conducted in vitro experiments including Western blotting, cell proliferative assay, trypan blue staining, and fluorescence microscopy to further explore the function of SOX4 in LIHC. SOX4 expression was significantly upregulated in 24 tumor types. SOX4 expression level was strongly associated with unfavorable prognoses, genetic mutations, and DNA methylation levels across different tumor types. Especially in LIHC, LINC00152/hsa-miR-139-3p/SOX4 was identified as a crucial ceRNA network. Moreover, this study also provides insight into the roles of SOX4 expression in immune cell infiltration, macrophage polarization, immune subtype, molecular subtype, and immunomodulators, as well as the tumor immune microenvironment (TIME)-related prognosis, in LIHC. The study established six favorable prognostic models to predict LIHC prognosis based on the SOX4-associated genes. Finally, lenvatinib treatment can increase the expression of SOX4 in hepatocellular carcinoma cells and lead to drug resistance. Silencing SOX4 can effectively eliminate the drug resistance caused by lenvatinib treatment and inhibit the proliferation of cancer cells. This study highlights that SOX4 may serve as a promising therapeutic target for tumor treatment.

From multivalued to Boolean functions: Preservation of soft nested canalization

Nested canalization (NC) is a property of Boolean functions which has been recently extended to multivalued functions. We study the effect of the Van Ham mapping (from multivalued to Boolean functions) on this property. We introduce the class of softly nested canalizing (SNC) multivalued functions, and prove that the Van Ham mapping sends SNC multivalued functions to NC Boolean functions. Since NC multivalued functions are SNC, this preservation property holds for NC multivalued functions as well. We also study the relevance of SNC functions in the context of gene regulatory network modelling.

Computing Minimal Boolean Models of Gene Regulatory Networks.

Models of gene regulatory networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understanding the variability observed in gene expression between different conditions. Arguably the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a priori. Our goal in this work is to create an algorithm that finds the network that fits the data optimally, and identify the network states that correspond to the noise-free data. We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. In addition, we extend our methodology into a heuristic that alleviates the computational complexity of the problem for datasets that are generated by single-cell RNA-Sequencing (scRNA-Seq). We demonstrate the effectiveness of these tools using simulated data, and in addition a publicly available scRNA-Seq dataset and the GRN that is associated with it. Our methodology will enable researchers to obtain a better understanding of the dynamics of GRNs and their biological role.

A single-cell genomic atlas for maturation of the human cerebellum during early childhood.

Inflammation early in life is a clinically established risk factor for autism spectrum disorders and schizophrenia, yet the impact of inflammation on human brain development is poorly understood. The cerebellum undergoes protracted postnatal maturation, making it especially susceptible to perturbations contributing to the risk of developing neurodevelopmental disorders. Here, using single-cell genomics of postmortem cerebellar brain samples, we characterized the postnatal development of cerebellar neurons and glia in 1 to 5 year-old children, comparing individuals who had died while experiencing inflammation to those who had died as a result of an accident. Our analyses revealed that inflammation and postnatal cerebellar maturation are associated with extensive, overlapping transcriptional changes primarily in two subtypes of inhibitory neurons: Purkinje neurons and Golgi neurons. Immunohistochemical analysis of a subset of these postmortem cerebellar samples revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those children who had experienced inflammation. Maturation-associated and inflammation-associated gene expression changes included genes implicated in neurodevelopmental disorders. A gene regulatory network model integrating cell type-specific gene expression and chromatin accessibility identified seven temporally specific gene networks in Purkinje neurons and suggested that inflammation may be associated with the premature downregulation of developmental gene expression programs.

Bifurcation control of a novel fractional-order gene regulatory network with incommensurate order and time delay

Gene regulatory networks play an extremely important role in human life activities. This paper presents a novel fractional-order gene regulatory network model (NFGRNM). The model employs incommensurate fractional orders and introduces time delay arising from the binding of miRNAs to RNA. Time delay often triggers instability such as oscillation and bifurcation, so in this paper we introduce a generalized hybrid control to control the unstable dynamical behavior induced by time delay. First we derive the NFGRNM using fractional order theory, after which the equilibrium point of the network model is calculated. Next, a systematic dynamics analysis of the NFGRNM is performed to derive sufficient conditions to generate the Hopf bifurcation. Most importantly, we introduce the extended hybrid control for the periodic oscillations generated by the time delay in the NFGRNM. Finally, numerical simulation results show that the order of the fractional order can have a great impact on the dynamical behavior of the network model and that the hybrid control has a significant role in delaying (or advancing) the Hopf bifurcation. Since infectious diseases such as SARS-Cov-2, influenza A, and HIV are still prevalent today, the study of gene regulatory networks not only promotes the study of the dynamics of complex networks but also has profound implications for the study of virus transmission in the host and various life activities in humans.

Automated pattern generation for swarm robots using constrained multi-objective genetic programming

Swarm robotic systems (SRSs), which are widely used in many fields, such as search and rescue, usually comprise a number of robots with relatively simple mechanisms collaborating to accomplish complex tasks. A challenging task for SRSs is to design local interaction rules for self-organization of robots that can generate adaptive patterns to entrap moving targets. Biologically inspired approaches such as gene regulatory network (GRN) models provide a promising solution to this problem. However, the design of GRN models for generating entrapping patterns relies on the expertise of designers. As a result, the design of the GRN models is often a laborious and tedious trial-and-error process. In this study, we propose a modular design automation framework for GRN models that can generate entrapping patterns. The framework employs basic network motifs to construct GRN models automatically without requiring expertise. To this end, a constrained multi-objective genetic programming is utilized to simultaneously optimize the structures and parameters of the GRN models. A multi-criteria decision-making approach is adopted to choose the preferred GRN model for generating the entrapping pattern. Comprehensive simulation results demonstrate that the proposed framework can obtain novel GRN models with simpler structures than those designed by human experts yet better performance in complex and dynamic environments. Proof-of-concept experiments using e-puck robots confirmed the feasibility and effectiveness of the proposed GRN models.

Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions.

Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.

Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells

Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.

Multi-omics analysis reveals the mechanism of calcium-reduced quality deterioration in mechanically injured green pepper fruit

Green pepper is regarded as one of the globally essential commercial crops with bright color and rich nutrition. However, green pepper is susceptible to mechanical injuries as a result of vibration, compression and impact events during postharvest handling and transportation. These injuries can induce physiological and biochemical abnormalities in fruit, thus accelerating fruit senescence and leading to economic losses. Calcium chloride (CaCl2) treatment is a safe, effective and economical method to maintain fruit quality during postharvest storage. Here, the impact of calcium treatment on the fruit quality of mechanically injured green pepper was investigated. The results indicated that calcium treatment maintained the sensory quality and hardness of green pepper fruit, inhibited redness development and elevation in the level of malondialdehyde (MDA). The combination of transcriptomics, metabolomics and assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) revealed treatment effects of the expression of key genes related to fruit color change, fruit softening, active oxygen metabolism and hormone response, as well as changes of metabolites concentrations, and chromatin accessibility changes. A construction of regulatory network model of calcium treatment on post-harvest mechanical injury of green pepper fruit was developed based on these effects. Overall, the study provides valuable insights into the impact of mechanical injury on fruit after harvest and its amelioration by calcium.

The Sordariomycetes: an expanding resource with Big Data for mining in evolutionary genomics and transcriptomics.

Advances in genomics and transcriptomics accompanying the rapid accumulation of omics data have provided new tools that have transformed and expanded the traditional concepts of model fungi. Evolutionary genomics and transcriptomics have flourished with the use of classical and newer fungal models that facilitate the study of diverse topics encompassing fungal biology and development. Technological advances have also created the opportunity to obtain and mine large datasets. One such continuously growing dataset is that of the Sordariomycetes, which exhibit a richness of species, ecological diversity, economic importance, and a profound research history on amenable models. Currently, 3,574 species of this class have been sequenced, comprising nearly one-third of the available ascomycete genomes. Among these genomes, multiple representatives of the model genera Fusarium, Neurospora, and Trichoderma are present. In this review, we examine recently published studies and data on the Sordariomycetes that have contributed novel insights to the field of fungal evolution via integrative analyses of the genetic, pathogenic, and other biological characteristics of the fungi. Some of these studies applied ancestral state analysis of gene expression among divergent lineages to infer regulatory network models, identify key genetic elements in fungal sexual development, and investigate the regulation of conidial germination and secondary metabolism. Such multispecies investigations address challenges in the study of fungal evolutionary genomics derived from studies that are often based on limited model genomes and that primarily focus on the aspects of biology driven by knowledge drawn from a few model species. Rapidly accumulating information and expanding capabilities for systems biological analysis of Big Data are setting the stage for the expansion of the concept of model systems from unitary taxonomic species/genera to inclusive clusters of well-studied models that can facilitate both the in-depth study of specific lineages and also investigation of trait diversity across lineages. The Sordariomycetes class, in particular, offers abundant omics data and a large and active global research community. As such, the Sordariomycetes can form a core omics clade, providing a blueprint for the expansion of our knowledge of evolution at the genomic scale in the exciting era of Big Data and artificial intelligence, and serving as a reference for the future analysis of different taxonomic levels within the fungal kingdom.

The Tolerance of Gut Commensal Faecalibacterium to Oxidative Stress Is Strain Dependent and Relies on Detoxifying Enzymes.

Obligate anaerobic bacteria in genus Faecalibacterium are among the most dominant taxa in the colon of healthy individuals and contribute to intestinal homeostasis. A decline in the abundance of this genus is associated with the occurrence of various gastrointestinal disorders, including inflammatory bowel diseases. In the colon, these diseases are accompanied by an imbalance between the generation and elimination of reactive oxygen species (ROS), and oxidative stress is closely linked to disruptions in anaerobiosis. In this work, we explored the impact of oxidative stress on several strains of faecalibacteria. An in silico analysis of complete genomes of faecalibacteria revealed the presence of genes encoding O2- and/or ROS-detoxifying enzymes, including flavodiiron proteins, rubrerythrins, reverse rubrerythrins, superoxide reductases, and alkyl peroxidase. However, the presence and the number of these detoxification systems varied greatly among faecalibacteria. These results were confirmed by O2 stress survival tests, in which we found that strains differed widely in their sensitivity. We showed the protective role of cysteine, which limited the production of extracellular O2•- and improved the survival of Faecalibacterium longum L2-6 under high O2 tension. In the strain F. longum L2-6, we observed that the expression of genes encoding detoxifying enzymes was upregulated in the response to O2 or H2O2 stress but with different patterns of regulation. Based on these results, we propose a first model of the gene regulatory network involved in the response to oxidative stress in F. longum L2-6. IMPORTANCE Commensal bacteria in the genus Faecalibacterium have been proposed for use as next-generation probiotics, but efforts to cultivate and exploit the potential of these strains have been limited by their sensitivity to O2. More broadly, little is known about how commensal and health-associated bacterial species in the human microbiome respond to the oxidative stress that occurs as a result of inflammation in the colon. In this work, we provide insights regarding the genes that encode potential mechanisms of protection against O2 or ROS stress in faecalibacteria, which may facilitate future advances in work with these important bacteria.

Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks.

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.

Self-Organization and Genomic Causality in Models of Morphogenesis.

The debate about what causes the generation of form and structure in embryological development goes back to antiquity. Most recently, it has focused on the divergent views as to whether the generation of patterns and form in development is a largely self-organized process or is mainly determined by the genome, in particular, complex developmental gene regulatory processes. This paper presents and analyzes pertinent models of pattern formation and form generation in a developing organism in the past and the present, with a special emphasis on Alan Turing's 1952 reaction-diffusion model. I first draw attention to the fact that Turing's paper remained, at first, without a noticeable impact on the community of biologists because purely physical-chemical models were unable to explain embryological development and often also simple repetitive patterns. I then show that from the year 2000 and onwards, Turing's 1952 paper was increasingly cited also by biologists. The model was updated to include gene products and now seemed able to account for the generation of biological patterns, though discrepancies between models and biological reality remained. I then point out Eric Davidson's successful theory of early embryogenesis based on gene-regulatory network analysis and its mathematical modeling that not only was able to provide a mechanistic and causal explanation for gene regulatory events controlling developmental cell fate specification but, unlike reaction-diffusion models, also addressed the effects of evolution and organisms' longstanding developmental and species stability. The paper concludes with an outlook on further developments of the gene regulatory network model.

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Landscape and perturbation of enhancer-driven core transcription regulatory circuits in cancer

Autoregulation of Transcription and Translation: A Qualitative Analysis

The regulation of both mRNA transcription and translation by down-stream gene products allows for a range of rich dynamical behaviours (e.g. homeostatic, oscillatory, excitability and intermittent solutions). Here, qualitative analysis is applied to an existing model of a gene regulatory network in which a protein dimer inhibits its own transcription and upregulates its own translation rate. It is demonstrated that the model possesses a unique steady state, conditions are derived under which limit cycle solutions arise and estimates are provided for the oscillator period in the limiting case of a relaxation oscillator. The analysis demonstrates that oscillations can arise only if mRNA is more stable than protein and the effect of nonlinear translation inhibition is sufficiently strong. Moreover, it is shown that the oscillation period can vary non-monotonically with transcription rate. Thus the proposed framework can provide an explanation for observed species-specific dependency of segmentation clock period on Notch signalling activity. Finally, this study facilitates the application of the proposed model to more general biological settings where post transcriptional regulation effects are likely important.

Exponential convergence in the mean-square sense of nonlocal stochastic almost automorphic genetic regulatory lattice networks

This paper first establishes the lattice model for nonlocal stochastic genetic regulatory networks with reaction diffusions by employing a mix of the finite difference and Mittag–Leffler time Euler difference techniques. Second, the existence of a unique bounded almost automorphic sequence in distribution and global mean-square exponential convergence to the achieved difference model are investigated. An illustrative example is used to show the feasible of the works of the current paper.

Transcriptome profiling of the Caenorhabditis elegans intestine reveals that ELT-2 negatively and positively regulates intestinal gene expression within the context of a gene regulatory network.

ELT-2 is the major transcription factor (TF) required for Caenorhabditis elegans intestinal development. ELT-2 expression initiates in embryos to promote development and then persists after hatching through the larval and adult stages. Though the sites of ELT-2 binding are characterized and the transcriptional changes that result from ELT-2 depletion are known, an intestine-specific transcriptome profile spanning developmental time has been missing. We generated this dataset by performing Fluorescence Activated Cell Sorting on intestine cells at distinct developmental stages. We analyzed this dataset in conjunction with previously conducted ELT-2 studies to evaluate the role of ELT-2 in directing the intestinal gene regulatory network through development. We found that only 33% of intestine-enriched genes in the embryo were direct targets of ELT-2 but that number increased to 75% by the L3 stage. This suggests additional TFs promote intestinal transcription especially in the embryo. Furthermore, only half of ELT-2's direct target genes were dependent on ELT-2 for their proper expression levels, and an equal proportion of those responded to elt-2 depletion with over-expression as with under-expression. That is, ELT-2 can either activate or repress direct target genes. Additionally, we observed that ELT-2 repressed its own promoter, implicating new models for its autoregulation. Together, our results illustrate that ELT-2 impacts roughly 20-50% of intestine-specific genes, that ELT-2 both positively and negatively controls its direct targets, and that the current model of the intestinal regulatory network is incomplete as the factors responsible for directing the expression of many intestinal genes remain unknown.