Search

The liver, as a crucial metabolic organ, undergoes significant pathological changes during the aging process, which can have a profound impact on overall health. To gain a comprehensive understanding of these alterations, we employed data-driven approaches, along with biochemical methods, histology, and immunohistochemistry techniques, to systematically investigate the effects of aging on the liver. Our study utilized a well-established rat aging model provided by the National Institute of Aging. Systems biology approaches were used to analyze genome-wide transcriptomics data from liver samples obtained from young (4–5 months old) and aging (20–21 months old) Fischer 344 rats. Our findings revealed pathological changes occurring in various essential biological processes in aging livers. These included mitochondrial dysfunction, increased oxidative/nitrative stress, decreased NAD + content, impaired amino acid and protein synthesis, heightened inflammation, disrupted lipid metabolism, enhanced apoptosis, senescence, and fibrosis. These results were validated using independent datasets from both human and rat aging studies. Furthermore, by employing co-expression network analysis, we identified novel driver genes responsible for liver aging, confirmed our findings in human aging subjects, and pointed out the cellular localization of the driver genes using single-cell RNA-sequencing human data. Our study led to the discovery and validation of a liver-specific gene, proprotein convertase subtilisin/kexin type 9 (PCSK9), as a potential therapeutic target for mitigating the pathological processes associated with aging in the liver. This finding envisions new possibilities for developing interventions aimed to improve liver health during the aging process. [ABSTRACT FROM AUTHOR]

The comprehensive bioinformatics analysis of breast cancer exosomes revealed that HSP90AA1, CCT2, and ENO1 were novel hub genes in the giant protein–protein interaction network of 110 exosomal proteins. Exosomes and their cargo such as discrete proteins, nucleic acids, and lipids are having potential role in the pathophysiology of breast cancer (BC). This study showed that the identified hub genes were particularly abundant in GO and KEGG pathways relevant to the positive regulation of telomerase. In addition, these hub genes were found to be considerably overexpressed in breast adenocarcinoma patients compared to healthy controls, and further, this overexpression is linked to the poor prognosis in BC patients. Furthermore, the ROC analysis revealed that CCT2 gene has strong diagnostic and prognostic value for BC. Additionally, this in silico analysis found that the anticancer agents and HSP90 inhibitors such as ganetespib, retaspimycin, and tanespimycin would have considerable potential in the treatment of BC. Overall, this study findings imply that HSP90AA1, a molecular chaperon and CCT2, a chaperonin would serve as diagnostic and prognostic biomarkers, respectively, for BC. However, these findings need to be further confirmed by laboratory and clinical studies for validating their potential applications. [ABSTRACT FROM AUTHOR]

The delayed diagnosis of pancreatic cancer has resulted in rising mortality rate and low survival rate that can be circumvented using potent theranostics biomarkers. The treatment gets complicated with delayed detection resulting in lowered 5-year relative survival rate. In our present study, we employed systems biology approach to identify central genes that play crucial roles in tumor progression. Pancreatic cancer genes collected from various databases were used to construct a statistically significant interactome with 812 genes that was further analysed thoroughly using topological parameters and functional enrichment analysis. The significant genes in the network were then identified based on the maximum degree parameter. The overall survival analysis indicated through hazard ratio [HR] and gene expression [log Fold Change] across pancreatic adenocarcinoma revealed the critical role of FN1 [HR 1.4; log2(FC) 5.748], FGA [HR 0.78; log2(FC) 1.639] FGG [HR 0.9; log2(FC) 1.597], C3 [HR 1.1; log2(FC) 2.637], and QSOX1 [HR 1.4; log2(FC) 2.371]. The functional significance of the identified hub genes signified the enrichment of integrin cell surface interactions and proteoglycan syndecan-mediated cell signaling. The differential expression, low overall survival and functional significance of FN1 gene implied its possible role in controlling metastasis in pancreatic cancer. Furthermore, alternate splice variants of FN1 gene showed 10 protein coding transcripts with conserved cell attachment site and functional domains indicating the variants' potential role in pancreatic cancer. The strong association of the identified hub-genes can be better directed to design potential theranostics biomarkers for metastasized pancreatic tumor. [ABSTRACT FROM AUTHOR]

Gallbladder cancer (GBC) is one of the most fatal malignancies of the biliary tract system and is ranked sixth among the neoplasms of the gastrointestinal tract. Gallstone disease (GSD) is considered the major risk factor for GBC. However, the underlying molecular mechanism of GBC pathogenesis from different stages of GSD is not yet clearly understood. We analyzed transcriptomic datasets of GBC with reference to GSD of three different follow-up periods, i.e., GBC vs. GSD3 (1–3 years), GBC vs. GSD5 (5–10 years), and GBC vs. GSD10 (more than 10 years). We identified overlapping and specific molecular signatures in GBC compared with GSD at three different follow-up periods. Using integrative network biology approaches, such as protein–protein interaction network analysis, transcriptional regulatory network analysis, and miRNA–target gene network analysis, we have identified a few hub genes. The hub genes identified from GBC vs. GSD3, GBC vs. GSD5, and GBC vs. GSD10 were directly or indirectly associated with cancer progression and initiation from GSD. Functional enrichment analysis indicated significant correlation between GSD and GBC pathogenesis. The identified hub genes can be used for future targeted validation to develop potential diagnostic, prognostic, or therapeutic biomarkers in GBC. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) in vitro culture systems using human induced pluripotent stem cells (hiPSCs) are useful tools to model neurodegenerative disease biology in physiologically relevant microenvironments. Though many successful biomaterials-based 3D model systems have been established for other neurogenerative diseases, such as Alzheimer’s disease, relatively few exist for Parkinson’s disease (PD) research. We employed tissue engineering approaches to construct a 3D silk scaffold-based platform for the culture of hiPSC-dopaminergic (DA) neurons derived from healthy individuals and PD patients harboring LRRK2 G2019S or GBA N370S mutations. We then compared results from protein, gene expression, and metabolic analyses obtained from two-dimensional (2D) and 3D culture systems. The 3D platform enabled the formation of dense dopamine neuronal network architectures and developed biological profiles both similar and distinct from 2D culture systems in healthy and PD disease lines. PD cultures developed in 3D platforms showed elevated levels of α-synuclein and alterations in purine metabolite profiles. Furthermore, computational network analysis of transcriptomic networks nominated several novel molecular interactions occurring in neurons from patients with mutations in LRRK2 and GBA. We conclude that the brain-like 3D system presented here is a realistic platform to interrogate molecular mechanisms underlying PD biology. [ABSTRACT FROM AUTHOR]

Introduction: Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools. Objective: Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data. Methods: IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis. Results and Conclusion: The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome. [ABSTRACT FROM AUTHOR]

Colorectal cancer is an important public health concern leading to significant cancer associate mortality. A vast majority of colon cancer arises from polyp which later follows adenoma, adenocarcinoma, and carcinoma sequence. This whole process takes several years to complete and recent genomic and proteomic technologies are identifying several targets involved in each step of polyp to carcinoma transformation in a large number of studies. Current text presents interaction network of targets involved in polyp to carcinoma transformation. In addition, important targets involved in each step according to network biological parameters are also presented. The functional overrepresentation analysis of each step targets and common top biological processes and pathways involved in carcinoma indicate several insights about this whole mechanism. Interaction networks indicate TP53, AKT1, GAPDH, INS, EGFR, and ALB as the most important targets commonly involved in polyp to carcinoma sequence. Though several important pathways are known to be involved in CRC, the central common involvement of PI3K-AKT indicates its potential for devising CRC management strategies. The common and central targets and pathways involved in polyp to carcinoma progression can shed light on its mechanism and potential management strategies. The data-driven approach aims to add valuable inputs to the mechanism of the years-long polyp-carcinoma sequence. [ABSTRACT FROM AUTHOR]

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30–60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing. [ABSTRACT FROM AUTHOR]

Background: COVID-19, a pandemic declared by WHO, has infected about 39.5 million and killed about 1.1 million people throughout the world. There is the urgent need of more studies to identify the novel drug targets and the drug candidates against it to handle the situation. Main body: To virtually screen various drugs against SARS-CoV-2, the scientists need the detail information about the various drug targets identified till date. The present review provides the information about almost all the drug targets, including structural and non-structural proteins of virus as well as host cell surface receptors, that can be used for virtual screening of drugs. Moreover, this review also focuses on the different network analysis tools that have been used for the identification of new drug targets and candidate repurposable drugs against SARS-CoV-2. Conclusion: This review provides important insights of various drug targets and the network analysis tools to young bioinformaticians and will help in creating pace to the drug repurposing strategy for COVID-19 disease. [ABSTRACT FROM AUTHOR]

Studies on how pancreatic islets respond under physiological and pathological conditions are obtained mostly based on the analysis of whole-islet transcriptome. However, the measurement from the whole islets quantifies the average behaviour of dominant cell types, thereby making it difficult to understand the cell-type-specific changes. Recently, the advent of single-cell RNA sequencing (scRNA-seq) technique has generated valuable resource on islet biology and type 2 diabetes (T2D). This provides an opportunity to understand the different cell types/states at both the network and individual gene expression levels. Here, we inferred the gene regulatory networks (GRNs) of pancreatic cells from publicly available scRNA-seq data in healthy and T2D using single-cell regulatory network inference and clustering workflow. Clustering of cells based on GRNs identifies endocrine and exocrine cells and multiple stable cell states in each alpha, beta and ductal cells. The phenotypic variations in cell states due to obesity and T2D are indistinguishable. Therefore, the trajectory of cells in pseudotime was constructed based on the cell-type-specific gene expression using Monocle2. The analysis shows that continuous spectrum of cell states exists with phenotypic-dependent branching and donor cell–cell variability in endocrine and exocrine cell types. We characterized the genes that give rise to bifurcation in the trajectory. Our study demonstrates that the network and trajectory inference approaches can be used to better understand the behaviour of pancreatic cells in health and disease. [ABSTRACT FROM AUTHOR]

We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors. [ABSTRACT FROM AUTHOR]

Abstract Post‐translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two‐hybrid system employing human protein kinases for the analyses of phospho‐tyrosine (pY)‐dependent PPIs in a direct experimental, large‐scale approach. We identified 292 mostly novel pY‐dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co‐immunoprecipitation experiments from mammalian cells. About one‐sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY‐PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY‐mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY‐dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY‐dependent PPIs dictate cellular cancer phenotypes.

The complex and interlinked cascade of events regulated by microRNAs (miRNAs), transcription factors (TF), and target genes highlight the multifactorial nature of ischemic stroke pathology. The complexity of ischemic stroke requires a wider assessment than the existing experimental research that deals with only a few regulatory components. Here, we assessed a massive set of genes, miRNAs, and transcription factors to build a miRNA-gene-transcription factor regulatory network to elucidate the underlying post-transcriptional mechanisms in ischemic stroke. Feed-forward loops (three-node, four-node, and novel five-node) were converged to establish regulatory relationships between miRNAs, TFs, and genes. The synergistic function of miRNAs in ischemic stroke was predicted and incorporated into a novel five-node feed-forward loop. Significant miRNA-TF pairs were identified using cumulative hypergeometric distribution. Two subnetworks were derived from the extensive miRNA-TF regulatory network and analyzed to predict the molecular mechanism relating the regulatory components. NFKB and STAT were identified to be the chief regulators of innate inflammatory and neuronal survival mechanisms, respectively. Exclusive novel interactions between miR-9 and miR-124 with TLX, BCL2, and HDAC4 were identified to explain the post-stroke induced neurogenesis mechanism. Therefore, this network-based approach to delineate miRNA, TF, and gene interactions might promote the development of effective therapeutics against ischemic stroke. [ABSTRACT FROM AUTHOR]

Hypoxia is a complex pathophysiological condition. The physiological and molecular responses to this stress have been extensively studied. However, the management of its ill effects still poses a challenge to clinicians. MicroRNAs (miRNAs) are short non-coding RNA molecules that control post-transcriptional gene expression. The regulatory role of miRNAs in hypoxic environments has been studied in many hypoxia-related disorders, however a comprehensive compilation and analysis of all data and the significance of miRNAs in hypoxia adaption is still lacking. This review summarizes the miRNAs related to various hypoxia-related disorders and highlights the computational approaches to study them. This would help in designing novel strategies toward efficient management of hypoxia-related disorders. [ABSTRACT FROM AUTHOR]

Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo. [ABSTRACT FROM AUTHOR]

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn't produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes. [ABSTRACT FROM AUTHOR]

Objective: To investigate the mechanisms of Panax notoginseng saponins (PNS) in treating coronary heart disease (CHD) by integrating gene interaction network and functional enrichment analysis. Methods: Text mining was used to get CHD and PNS associated genes. Gene-gene interaction networks of CHD and PNS were built by the GeneMANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identifified by network analysis. Results: The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene-gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusions: The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network. [ABSTRACT FROM AUTHOR]

It is well established that cardiac remodeling plays a pivotal role in the development of heart failure, a leading cause of death worldwide. Meanwhile, sympathetic hyperactivity is an important factor in inducing cardiac remodeling. Therefore, an in-depth understanding of beta-adrenoceptor signaling pathways would help to find better ways to reverse the adverse remodeling. Here, we reviewed five pathways, namely mitogen-activated protein kinase signaling, Gs-AC-cAMP signaling, Ca-calcineurin-NFAT/CaMKII-HDACs signaling, PI3K signaling and beta-3 adrenergic signaling, in cardiac remodeling. Furthermore, we constructed a cardiac-remodeling-specific regulatory network including miRNA, transcription factors and target genes within the five pathways. Both experimental and clinical studies have documented beneficial effects of beta blockers in cardiac remodeling; nevertheless, different blockers show different extent of therapeutic effect. Exploration of the underlying mechanisms could help developing more effective drugs. Current evidence of treatment effect of beta blockers in remodeling was also reviewed based upon information from experimental data and clinical trials. We further discussed the mechanism of how beta blockers work and why some beta blockers are more potent than others in treating cardiac remodeling within the framework of cardiac remodeling network. [ABSTRACT FROM AUTHOR]

Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality-lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality-lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality-lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases, Alzheimer's disease (AD) and Parkinson's disease (PD), constitute a major worldwide health problem. Several hypothesis have been put forth to elucidate the basis of onset and pathogenesis of AD and PD; however, till date, none of these seems to clearly elucidate the complex pathoetiology of these disorders. Notably, copper dyshomeostasis has been shown to underlie the pathophysiology of several neurodegenerative diseases including AD and PD. Numerous studies have concluded beyond doubt that imbalance in copper homeostatic mechanisms in conjunction with aging causes an acceleration in the copper toxicity elicited oxidative stress, which is detrimental to the central nervous system. Amyloid precursor protein and α-synuclein protein involved in AD and PD are copper binding proteins, respectively. In this review, we have discussed the possible association of copper metabolism proteins with AD and PD along with briefly outlining the expanding proportion of 'copper interactome' in human biology. Using network biology, we found that copper metabolism proteins, superoxide dismutase 1 and ceruloplasmin may represent direct and indirect link with AD and PD, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Epigenetics refers to changes in phenotype and gene expression that occur without alterations in DNA sequence. MicroRNAs are relatively recently discovered negative regulators of gene expression and act at the posttranscriptional level. Methods: This review summarizes epigenetic mechanisms of pulmonary hypertension, focusing on microRNAs related to pulmonary hypertension. Results: There are three major mechanisms of epigenetic regulation, including methylation of CpG islands, modification of histone proteins, and microRNAs. There may be an epigenetic component to pulmonary hypertension. These epigenetic abnormalities can be reversed therapeutically. Conclusions: By better integrating network biology with evolving technologies in cell culture and in vivo experimentation, we will better understand epigenetic mechanisms of pulmonary hypertension and identify more diagnostic and therapeutic targets in pulmonary hypertension. [ABSTRACT FROM AUTHOR]

One approach to study the system-wide organization of biochemistry is to use statistical graph theory. In such heavily simplified methods, which disregard most of the dynamic aspects of biochemistry, one is faced with fundamental questions. One such question is how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In these graph representations, should the edges go between substrates and products, or substrates and substrates, or both? Should vertices represent substances or reactions? Different representations encode different information about the reaction system and affect network measures in different ways. This paper investigates which representation reflects the functional organization of the metabolic system in the best way, according to the defined criteria. Four different graph representations of metabolism (three where the vertices are metabolites, one where the vertices are reactions) are evaluated using data from different organisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments. It is found that a "substance network", where all metabolites participating in a reaction are connected, is better than others, evaluated with respect to both the functional overlap between modules and functions and to the number and identity of the identified currency metabolites. [ABSTRACT FROM AUTHOR]

This article describes recent developments in the design and implementation of various strategies towards the development of novel therapeutics using first principles from biology and chemistry. Strategies for multi-target therapeutics and network analysis with a focus on cancer and HIV are discussed. Methods for gene and siRNA delivery are presented along with challenges and opportunities for siRNA therapeutics. Advances in protein design methodology and screening are described, with a focus on their application to the design of antibody based therapeutics. Future advances in this area relevant to vaccine design are also mentioned. [ABSTRACT FROM AUTHOR]

Pluripotency is a hallmark feature of embryonic stem cells, but concomitantly expressed transcripts confound resolution of specific developmental signaling axes. Ratification of a molecular fingerprint critical to cardiogenesis mandated investigation of gene expression in stem-cell-derived cardiac precursors undergoing guided differentiation to define genomic networks responsible for cardiogenic progression. Upregulated transcripts organized into a discrete network with enhanced themes of “cardiovascular development” and “cellular movement”, while downregulated transcripts demonstrated significant overrepresentation of “oncogenesis” and “gene expression”. Ontological characterization of molecular functions revealed robust enrichment of binding factor classifications that bolstered upregulated themes of cardiovascular development and cellular movement, and significant nonstochastic overrepresentation of gene metabolism that drove oncogenic and gene expression network themes in downregulated transcripts. Collectively, these global adjustments engaged a systems biology repertoire switch towards functional specification. Thus, resolution of a precursor pedigree reveals coordinated themes that facilitate a nascent interactome which guides embryonic stem cell cardiopoiesis. [ABSTRACT FROM AUTHOR]

Genomic alterations lead to cancer complexity and form a major hurdle for comprehensive understanding of the molecular mechanisms underlying oncogenesis. In this review, we describe recent advances in studying cancer-associated genes from a systems biology point of view. The integration of known cancer genes onto protein and signaling networks reveals the characteristics of cancer genes within networks. This approach shows that cancer genes often function as network hub proteins which are involved in many cellular processes and form focal nodes in information exchange between many signaling pathways. Literature mining allows constructing gene-gene networks, in which new cancer genes can be identified. The gene expression profiles of cancer cells are used for reconstructing gene regulatory networks. By doing so, genes which are involved in the regulation of cancer progression can be picked up from these networks, after which their functions can be further confirmed in the laboratory. [ABSTRACT FROM AUTHOR]