Biological Regulatory Networks Research Articles

RRP8, associated with immune infiltration, is a prospective therapeutic target in hepatocellular carcinoma

BackgroundRibosomal RNA Processing 8 (RRP8) is a nucleolar Rossman fold-like methyltransferase that exhibits increased expression in many malignant tumours. However, the role of RRP8 in hepatocellular carcinoma (HCC) is still uncertain. We explored the relationships between RRP8 and prognosis and immune infiltration, as well as the putative pathological function and mechanism of RRP8 in HCC.MethodsAnalysis of RRP8 expression across cancers was performed by using multiple databases. Associations between RRP8 expression and clinicopathological factors were further examined. Gene enrichment analysis was used to identify various putative biological activities and regulatory networks of RRP8 in HCC. The relationship between RRP8 expression and immune infiltration was confirmed by single-sample gene set enrichment analysis (ssGSEA). Univariate and multivariate Cox regression analyses were conducted to assess the impact of clinical variables on patient outcomes. Furthermore, a nomogram was constructed to estimate survival probability based on multivariate Cox regression analysis. Functional validation of RRP8 in HCC was performed with two different systems: doxycycline-inducible shRNA knockdown and CRISPR-Cas9 knockout.ResultsRRP8 was markedly overexpressed in HCC clinical specimens compared to adjacent normal tissues. Further analysis demonstrated that RRP8 was directly connected to multiple clinical characteristics and strongly associated with various immune markers in HCC. Moreover, elevated RRP8 expression indicated an unfavourable prognosis. Our functional studies revealed that both knockdown and knockout of RRP8 dramatically attenuated liver cancer cells to proliferate and migrate. Knockout of RRP8 decreased the phosphorylation of MEK1/2 and β-catenin-(Y654) signalling pathway components; downregulated downstream signalling effectors, including Cyclin D1 and N-cadherin; and upregulated E-cadherin.ConclusionsRRP8 is strongly implicated in immune infiltration and could be a potential therapeutic target in HCC.

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications.

Long non-coding RNAs (lncRNAs) are multifunctional and participate in a variety of biological processes and gene regulatory networks. The deregulation of lncRNAs has been extensively implicated in diverse human diseases, especially in cancers. Overwhelming evidence demonstrates that lncRNAs are essential to the pathophysiological processes of ovarian cancer (OC), acting as regulators involved in metastasis, cell death, chemoresistance, and tumor immunity. In this review, we illustrate the expanded functions of lncRNAs in the initiation and progression of OC and elaborate on the signaling pathways in which they pitch. Additionally, the potential clinical applications of lncRNAs as biomarkers in the diagnosis and treatment of OC were emphasized, cementing the bridge of communication between clinical practice and basic research.

What is special of “five” in biological regulatory networks?

What is special of “five” in biological regulatory networks?

The next decade of SET: from an oncoprotein to beyond.

This year marks the fourth decade of research into the protein SET, which was discovered in 1992. SET was initially identified as an oncoprotein, but later, it was shown to be a multifaceted protein involved in regulating numerous biological processes under both physiological and pathophysiological conditions. SET dysfunction is closely associated with diseases, such as cancer and Alzheimer's disease. With the increasing understanding of how SET works and how it is regulated in cells, targeting aberrant SET has emerged as a potential strategy for disease intervention. In this review, we present a comprehensive overview of the advancements in SET studies, encompassing its biological functions, regulatory networks, clinical implications, and pharmacological inhibitors. Furthermore, we provide insights into the future prospects of SET research, with a particular emphasis on its promising potential in the realm of immune modulation.

Systematic analysis of various RNA transcripts and construction of biological regulatory networks at the post-transcriptional level for chronic obstructive pulmonary disease

BackgroundAlthough chronic inflammation, oxidative stress, airway remodeling, and protease-antiprotease imbalance have been implicated in chronic obstructive pulmonary disease (COPD), the exact pathogenesis is still obscure. Gene transcription and post-transcriptional regulation have been taken into account as key regulators of COPD occurrence and development. Identifying the hub genes and constructing biological regulatory networks at the post-transcriptional level will help extend current knowledge on COPD pathogenesis and develop potential drugs.MethodsAll lung tissues from non-smokers (n = 6), smokers without COPD (smokers, n = 7), and smokers with COPD (COPD, n = 7) were collected to detect messenger RNA (mRNA), microRNA (miRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA) expression and identify the hub genes. Biological regulatory networks were constructed at the post-transcriptional level, including the RNA-binding protein (RBP)-hub gene interaction network and the competitive endogenous RNA (ceRNA) network. In addition, we assessed the composition and abundance of immune cells in COPD lung tissue and predicted potential therapeutic drugs for COPD. Finally, the hub genes were confirmed at both the RNA and protein levels.ResultsAmong the 20 participants, a total of 121169 mRNA transcripts, 1871 miRNA transcripts, 4244 circRNA transcripts, and 122130 lncRNA transcripts were detected. There were differences in the expression of 1561 mRNAs, 48 miRNAs, 33 circRNAs, and 545 lncRNAs between smokers and non-smokers, as well as 1289 mRNAs, 69 miRNAs, 32 circRNAs, and 433 lncRNAs between smokers and COPD patients. 18 hub genes were identified in COPD. TGF-β signaling and Wnt/β-catenin signaling may be involved in the development of COPD. Furthermore, the circRNA/lncRNA-miRNA-mRNA ceRNA networks and the RBP-hub gene interaction network were also constructed. Analysis of the immune cell infiltration level revealed that M2 macrophages and activated NK cells were increased in COPD lung tissues. Finally, we identified that the ITK inhibitor and oxybutynin chloride may be effective in treating COPD.ConclusionsWe identified several novel hub genes involved in COPD pathogenesis. TGF-β signaling and Wnt/β-catenin signaling were the most dysregulated pathways in COPD patients. Our study constructed post-transcriptional biological regulatory networks and predicted small-molecule drugs for the treatment of COPD, which enhanced the existing understanding of COPD pathogenesis and suggested an innovative direction for the therapeutic intervention of the disease.

A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer's disease.

Background: Alzheimer's Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD's prognosis. Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs'. Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes. Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Multi-Omics Techniques in Genetic Studies and Breeding of Forest Plants

In recent years, the ecological and economic values of forest plants have been gradually recognized worldwide. However, the growing global demand for new forest plant varieties with higher wood production capacity and better stress tolerance cannot be satisfied by conventional phenotype-based breeding, marker-assisted selection, and genomic selection. In the recent past, diverse omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, have been developed rapidly, providing powerful tools for the precision genetic breeding of forest plants. Genomics lays a solid foundation for understanding complex biological regulatory networks, while other omics technologies provide different perspectives at different levels. Multi-omics integration combines the different omics technologies, becoming a powerful tool for genome-wide functional element identification in forest plant breeding. This review summarizes the recent progress of omics technologies and their applications in the genetic studies on forest plants. It will provide forest plant breeders with an elementary knowledge of multi-omics techniques for future breeding programs.

Rate-independent Computation in Continuous Chemical Reaction Networks

Understanding the algorithmic behaviors that are in principle realizable in a chemical system is necessary for a rigorous understanding of the design principles of biological regulatory networks. Further, advances in synthetic biology herald the time when we will be able to rationally engineer complex chemical systems and when idealized formal models will become blueprints for engineering. Coupled chemical interactions in a well-mixed solution are commonly formalized as chemical reaction networks (CRNs). However, despite the widespread use of CRNs in the natural sciences, the range of computational behaviors exhibited by CRNs is not well understood. Here, we study the following problem: What functions f : ℝ k → ℝ can be computed by a CRN, in which the CRN eventually produces the correct amount of the “output” molecule, no matter the rate at which reactions proceed? This captures a previously unexplored but very natural class of computations: For example, the reaction X 1 + X 2 → Y can be thought to compute the function y = min ( x 1 , x 2 ). Such a CRN is robust in the sense that it is correct whether its evolution is governed by the standard model of mass-action kinetics, alternatives such as Hill-function or Michaelis-Menten kinetics, or other arbitrary models of chemistry that respect the (fundamentally digital) stoichiometric constraints (what are the reactants and products?). We develop a reachability relation based on a broad notion of “what could happen” if reaction rates can vary arbitrarily over time. Using reachability, we define stable computation analogously to probability 1 computation in distributed computing and connect it with a seemingly stronger notion of rate-independent computation based on convergence in the limit t → ∞ under a wide class of generalized rate laws. Besides the direct mapping of a concentration to a nonnegative analog value, we also consider the “dual-rail representation” that can represent negative values as the difference of two concentrations and allows the composition of CRN modules. We prove that a function is rate-independently computable if and only if it is piecewise linear (with rational coefficients) and continuous (dual-rail representation), or non-negative with discontinuities occurring only when some inputs switch from zero to positive (direct representation). The many contexts where continuous piecewise linear functions are powerful targets for implementation, combined with the systematic construction we develop for computing these functions, demonstrate the potential of rate-independent chemical computation.

G-protein signaling modulator 1 promotes colorectal cancer metastasis by PI3K/AKT/mTOR signaling and autophagy.

Colorectal cancer is the second most common malignant tumor worldwide. A deeper insight into the mechanisms underlying colorectal cancer metastasis is urgently needed. G-protein signaling modulator 1 and autophagy play critical roles in tumor migration and invasion. However, the biological functions and regulatory networks of G-protein signaling modulator 1 and autophagy have not yet been fully studied. We performed immunohistochemistry and clinic-pathological characteristic analysis in 328 human colorectal cancer specimens to identify the clinical role of G-protein signaling modulator 1 in colorectal cancer. An in vitro coculture system and a tumor metastasis mouse model were used to explore the biological function of G-protein signaling modulator 1 on tumor metastasis. Autophagic flux detection like GFP-LC3B signal immunofluorescence and electron microscope observation of autophagic vesicles and confocal microscope detection were used to gain insights into the underlying role of G-protein signaling modulator 1 in autophagy. We found that G-protein signaling modulator 1 was abundantly expressed in colorectal cancer tissues and was associated with lymph node metastasis and poor prognosis. Furthermore, our bioinformatic and functional studies demonstrated that G-protein signaling modulator 1 significantly promoted cell migration and invasion, both in vitro and in vivo. Mechanistically, we demonstrated that G-protein signaling modulator 1 could promote colorectal cancer cell migration and invasion and inhibit autophagy and by activating the PI3K/AKT/mTOR pathway. We proposed that G-protein signaling modulator 1 promotes colorectal cancer metastasis by modulating autophagy through the PI3K/AKT/mTOR pathway.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models.

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

Autonomous Chemistry Enabling Environment-Adaptive Electrochemical Energy Storage Devices

Autonomous Chemistry Enabling Environment-Adaptive Electrochemical Energy Storage Devices

Identification of critical genes and molecular pathways in COVID-19 myocarditis and constructing gene regulatory networks by bioinformatic analysis.

BackgroundThere is growing evidence of a strong relationship between COVID-19 and myocarditis. However, there are few bioinformatics-based analyses of critical genes and the mechanisms related to COVID-19 Myocarditis. This study aimed to identify critical genes related to COVID-19 Myocarditis by bioinformatic methods, explore the biological mechanisms and gene regulatory networks, and probe related drugs.MethodsThe gene expression data of GSE150392 and GSE167028 were obtained from the Gene Expression Omnibus (GEO), including cardiomyocytes derived from human induced pluripotent stem cells infected with SARS-CoV-2 in vitro and GSE150392 from patients with myocarditis infected with SARS-CoV-2 and the GSE167028 gene expression dataset. Differentially expressed genes (DEGs) (adjusted P-Value <0.01 and |Log2 Fold Change| ≥2) in GSE150392 were assessed by NetworkAnalyst 3.0. Meanwhile, significant modular genes in GSE167028 were identified by weighted gene correlation network analysis (WGCNA) and overlapped with DEGs to obtain common genes. Functional enrichment analyses were performed by using the "clusterProfiler" package in the R software, and protein-protein interaction (PPI) networks were constructed on the STRING website (https://cn.string-db.org/). Critical genes were identified by the CytoHubba plugin of Cytoscape by 5 algorithms. Transcription factor-gene (TF-gene) and Transcription factor-microRibonucleic acid (TF-miRNA) coregulatory networks construction were performed by NetworkAnalyst 3.0 and displayed in Cytoscape. Finally, Drug Signatures Database (DSigDB) was used to probe drugs associated with COVID-19 Myocarditis.ResultsTotally 850 DEGs (including 449 up-regulated and 401 down-regulated genes) and 159 significant genes in turquoise modules were identified from GSE150392 and GSE167028, respectively. Functional enrichment analysis indicated that common genes were mainly enriched in biological processes such as cell cycle and ubiquitin-protein hydrolysis. 6 genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) were identified as critical genes. TF-gene interactions and TF-miRNA coregulatory network were constructed successfully. A total of 10 drugs, (such as Etoposide, Methotrexate, Troglitazone, etc) were considered as target drugs for COVID-19 Myocarditis.ConclusionsThrough bioinformatics method analysis, this study provides a new perspective to explore the pathogenesis, gene regulatory networks and provide drug compounds as a reference for COVID-19 Myocarditis. It is worth highlighting that critical genes (CDK1, KIF20A, PBK, KIF2C, CDC20, UBE2C) may be potential biomarkers and treatment targets of COVID-19 Myocarditis for future study.

Influence maximization in Boolean networks

The optimization problem aiming at the identification of minimal sets of nodes able to drive the dynamics of Boolean networks toward desired long-term behaviors is central for some applications, as for example the detection of key therapeutic targets to control pathways in models of biological signaling and regulatory networks. Here, we develop a method to solve such an optimization problem taking inspiration from the well-studied problem of influence maximization for spreading processes in social networks. We validate the method on small gene regulatory networks whose dynamical landscapes are known by means of brute-force analysis. We then systematically study a large collection of gene regulatory networks. We find that for about 65% of the analyzed networks, the minimal driver sets contain less than 20% of their nodes.

Abstract 5316: Risk of cognitive impairment associated with androgen deprivation therapy in prostate cancer

Abstract Androgen deprivation therapy (ADT) is a commonly used clinical treatment for non-metastatic and metastatic hormone-sensitive prostate cancer. Long-term ADT treatment results in adverse side-effects in patients including depression, frailty, cognitive impairment and dementia. Studies have reported increased levels of pro-inflammatory cytokines and inflammatory markers in older cancer patients, however, the relationship between inflammatory biomarkers and the severity of cognition in prostate cancer patients under ADT has not been investigated. We sought to identify peripheral biomarkers that could provide links between the mental changes and major pathological mechanisms responsible for the development of cognition in these patients. For these studies, gene expression data (GSE69223) of 30 matched malignant and non-malignant prostate tissue samples from 15 prostate cancer patients receiving neoadjuvant antiandrogen therapy before prostatectomy, were compared in parallel with postmortem brain tissue samples of Parkinson's and Alzheimer patients as additional neurological diagnosis. IPA analysis was performed in the context of known biological response and regulatory networks. Fisher’s exact test for each network was converted to a score of −log10 (p-value). Further validation was performed in BT142-neural cells and M059K-glial cells by qRT-PCR with and without antiandrogen (enzalutamide) treatment. A total of 1952 DEGs were identified in postmortem brain tissue specimens, and 101 DEGs were identified in prostate cancer patients receiving ADT before surgery. IPA analysis revealed 33 commonly expressed genes with changes in cytokine-cytokine signaling network overlapped in both patient cohorts. Pathway analysis showed that IL17 signaling pathway, regulation of cytokine production and changes in T-cell subsets by IL-17A and IL-17F were overrepresented. Furthermore, lipopolysaccharide, tumor necrosis factor and toll-like receptors were identified as upstream transcriptional regulators of these signaling pathways. Gene expression of pro-inflammatory cytokines viz. LIFR, IL1RN, IL6, IL10 and LIF were increased in both neural and glial cells treated with enzalutamide, compared to non-enzalutamide treated cells. Our results suggest that changes in cytokine signaling under the influence of ADT in prostate cancer patients may linked with cognitive impairment presenting new areas for diagnostic and therapeutic development in combating brain deficits. Citation Format: Shiv Verma, Eswar Shankar, Viabhav Singh, Sanjay Gupta. Risk of cognitive impairment associated with androgen deprivation therapy in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5316.

Behavioural variation among workers promotes feed-forward loops in a simulated insect colony.

Coordinated responses in eusocial insect colonies arise from worker interaction networks that enable collective processing of ecologically relevant information. Previous studies have detected a structural motif in these networks known as the feed-forward loop, which functions to process information in other biological regulatory networks (e.g. transcriptional networks). However, the processes that generate feed-forward loops among workers and the consequences for information flow within the colony remain largely unexplored. We constructed an agent-based model to investigate how individual variation in activity and movement shaped the production of feed-forward loops in a simulated insect colony. We hypothesized that individual variation along these axes would generate feed-forward loops by driving variation in interaction frequency among workers. We found that among-individual variation in activity drove over-representation of feed-forward loops in the interaction networks by determining the directionality of interactions. However, despite previous work linking feed-forward loops with efficient information transfer, activity variation did not promote faster or more efficient information flow, thus providing no support for the hypothesis that feed-forward loops reflect selection for enhanced collective functioning. Conversely, individual variation in movement trajectory, despite playing no role in generating feed-forward loops, promoted fast and efficient information flow by linking together otherwise unconnected regions of the nest.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co-expression network analysis.

AimWhole transcriptome analysis was conducted to identify differentially expressed RNAs and regulatory networks associated with papillary thyroid carcinoma (PTC).MethodsA weighted gene co‐expression network analysis based on high‐throughput sequencing data for six pairs of PTC and adjacent tissue samples was conducted to understand the biological functions and regulatory networks involving long non‐coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs).ResultsWe detected 131, 338, 31, and 556 differentially expressed circRNAs, lncRNAs, miRNAs, and mRNAs, respectively. We identified modules that were significantly positively and negatively related to cancer and lymph node metastasis. Gray and turquoise modules were positively correlated with cancer phenotypes (p < 0.05), whereas yellow, brown, and blue modules were negatively correlated with cancer (p < 0.05). Gray module was positively correlated with lateral lymph node metastasis (p = 0.02). Kaplan–Meier analyses revealed that the levels of transmembrane protein 63C (TMEM63C), lysyl oxidase‐like 1 (LOXL1), collagen type V alpha 1 chain (COL5A1), ADAM metalloproteinase with thrombospondin type I motif 2 (ADAMTS2), and LysM‐domain containing 3 (LYSMD3) were significantly associated with overall survival (p < 0.05). Significant increase in the expression of COL5A1 and LOXL1 in tumor tissues was validated by quantitative real‐time polymerase chain reaction (p < 0.05). COL5A1 and LOXL1 promoted PTC cell growth and invasion in vitro.ConclusionsWe identified COL5A1 and LOXL1 as potential prognostic biomarkers, providing new insights into the occurrence and progression of PTC.

Topologies of synthetic gene circuit for optimal fold change activation in bacterial biosensors

Biological regulatory networks in nature comprise feedforward and feedback loops allowing cells to perform sophisticated tasks such as DNA repair, cell division, and apoptosis. During the past decades, network motifs have been introduced into living cells to scale–up the computational complexity of synthetic gene circuits. For example, mutual inhibition was the first network that has been implemented in Escherichia coli cells to create a bistable switch. Recently, an incoherent feedforward loop and integral feedback controller have been applied to achieve robust adaptation in Escherichia coli cells.

Exosomal lncRNA and mRNA profiles in polycystic ovary syndrome: bioinformatic analysis reveals disease-related networks

Research questionWhat is the role of exosomal lncRNAs and mRNAs profiles and their interaction networks in regulating the development of polycystic ovary syndrome (PCOS)? DesignLncRNA microarray was used to analyse the expression profiles of lncRNA and mRNA in follicular fluid exosomes from three patients with polycystic ovary syndrome (PCOS) and three control women. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to explore biological functions of exosomal mRNAs in PCOS. Six PCOS-related genes were selected, and coding-non-coding gene co-expression (CNC) networks and competing endogenous RNA (ceRNA) networks analysis were combined to reveal lncRNA–miRNA–mRNA interaction networks. ResultsA total of 5373 differentially expressed exosomal lncRNAs and 3381 differentially expressed exosomal mRNAs were identified (fold change ≥2 and P < 0.05). Gene ontology analysis indicated that 14 terms of biological process were related to reproductive development. KEGG pathway analysis revealed PCOS-related pathways, such as MAPK signalling pathway and some inflammation-related signalling pathways. Interaction networks of lncRNAs, miRNAs and six PCOS-related genes (IRS1, CYP11A1, BMP6, FSHR, WNT4 and CYP19A1) were constructed. The CNC networks and ceRNA networks analysis uncovered some novel PCOS-related exosomal lncRNA-miRNA-mRNA regulatory networks. ConclusionsDifferential expression profiles of exosomal lncRNAs and mRNAs were identified, and some PCOS-related biological processes and regulatory networks were indicated. LncRNAs and mRNAs in follicular fluid exosomes may play important roles in the follicular development of PCOS.

Critical dynamics in biological Boolean networks follows from symmetric response to input genes

A recent observation on an extensive collection of biological gene regulatory networks suggests that the regulatory dynamics is tuned to remain close to the order-chaos boundary in the Lyapunov sense [1]. We here investigate, from a mathematical perspective, the structural/functional constraints which give rise to such accumulation around criticality in these systems. While the role of canalizing functions in this respect is well established, we find that critical sensitivity to small input variations also follows from an over-abundance of symmetrical inputs, i.e. regulatory genes invoking identical or complementary responses on their common target. A random network ensemble constructed to have the same distribution of symmetric inputs as in the above collection of biological networks captures the dependence of the sensitivity on mean activity bias, a nontrivial characteristic which the canalizing ensemble fails to fully reproduce.

A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature.

A biological pathway or regulatory network is a collection of molecular regulators which can activate the changes in cellular processes leading to an assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is given much attention as the scientific literature contains a rich set of linguistic features to extract biological associations between genes and proteins. These associations can be united to construct biological networks. Here, we present a brief overview about various biological pathways, biomedical text resources/corpora for network construction and state-of-the-art existing methods for network construction followed by our hybridtext mining protocol for extracting pathways and regulatory networks from biomedical literature.