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Introduction to Systems Biology
Abstract
The area of system biology is a combination of experimental large-scale measurement techniques and computational approaches to interrogate, model and understand life at the level of organismal system itself. The applications of system-wide approaches in understanding the factors that affect human health and diseases is opening a novel area with great prospects. In this chapter, the area of system biology has been described with the current status, and its impact on the individual and population. The application of system biology in developing a framework for modelling living systems and diseases is discussed. Further the utility of systems biology in molecular diagnostics, genetic techniques, deletion/duplication analysis, targeted variant analysis, DNA methylation, pathway-based biomarker analysis, gene interaction maps and disease genes identification etc. is described. These current knowledge of significant contribution in the field may stimulate further discussion and debates in other areas in addition to systems biology. In this way, the systems medicine will realize its full potential and promises within societal standards.
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The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
Individual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of ‘patients’ with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients’ heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic–pituitary–adrenal axis, we show that an integrated pipeline that combines machine learning (ML) and bifurcation analysis can be used to effectively and efficiently analyse the behaviors observed in populations of VPs. Compared with local sensitivity analyses, ML allows us to capture and analyse the contributions of simultaneous changes of multiple model parameters. Following up with bifurcation analysis, we are able to provide rigorous mechanistic insight regarding the influences of ML-identified parameters on the dynamical system’s behaviors. In this work, we illustrate the utility of this pipeline and suggest that its wider adoption will facilitate the use of VPs in the practice of systems pharmacology.
Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.
Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan.
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
Here, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166 rare monogenic diseases and linked them to 3163 causative genes, annotated with OMIM and Ensembl identifiers and HGNC symbols. The PubMed identifiers of the scientific publications, which for the first time described the rare diseases, and the publications, which found the genes causing the diseases were added using information from OMIM, PubMed, Wikipedia, whonamedit.com , and Google Scholar. The data are available under CC0 license as spreadsheet and as RDF in a semantic model modified from DisGeNET, and was added to Wikidata. This dataset relies on publicly available data and publications with a PubMed identifier, but by our effort to make the data interoperable and linked, we can now analyse this data. Our analysis revealed the timeline of rare disease and causative gene discovery and links them to developments in methods.
The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.
Most consensus recommendations for the genetic diagnosis of neurodevelopmental disorders (NDDs) do not include the use of next generation sequencing (NGS) and are still based on chromosomal microarrays, such as comparative genomic hybridization array (aCGH). This study compares the diagnostic yield obtained by aCGH and clinical exome sequencing in NDD globally and its spectrum of disorders. To that end, 1412 patients clinically diagnosed with NDDs and studied with aCGH were classified into phenotype categories: global developmental delay/intellectual disability (GDD/ID); autism spectrum disorder (ASD); and other NDDs. These categories were further subclassified based on the most frequent accompanying signs and symptoms into isolated forms, forms with epilepsy; forms with micro/macrocephaly and syndromic forms. Two hundred and forty-five patients of the 1412 were subjected to clinical exome sequencing. Diagnostic yield of aCGH and clinical exome sequencing, expressed as the number of solved cases, was compared for each phenotype category and subcategory. Clinical exome sequencing was superior than aCGH for all cases except for isolated ASD, with no additional cases solved by NGS. Globally, clinical exome sequencing solved 20% of cases (versus 5.7% by aCGH) and the diagnostic yield was highest for all forms of GDD/ID and lowest for Other NDDs (7.1% versus 1.4% by aCGH) and ASD (6.1% versus 3% by aCGH). In the majority of cases, diagnostic yield was higher in the phenotype subcategories than in the mother category. These results suggest that NGS could be used as a first-tier test in the diagnostic algorithm of all NDDs followed by aCGH when necessary.
RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied
to 18 samples from two human cell lines and the performance of the results was
evaluated. Raw gene expression signal was quantifed by non-parametric statistics to measure precision and accuracy. Diferential gene expression performance was estimated by testing 17 diferential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the diferent methods and procedures applied to the analysis of RNA-seq data, both for the quantifcation of the raw expression signal and for the diferential gene expression.
Systems biology is an approach to interrogate complex biological systems through large-scale quantification of numerous biomolecules. The immune system involves >1,500 genes/proteins in many interconnected pathways and processes, and a systems-level approach is critical in broadening our understanding of the immune response to vaccination. Changes in molecular pathways can be detected using high-throughput omics datasets (e.g., transcriptomics, proteomics, and metabolomics) by using methods such as pathway enrichment, network analysis, machine learning, etc. Importantly, integration of multiple omic datasets is becoming key to revealing novel biological insights. In this perspective article, we highlight the use of protein-protein interaction (PPI) networks as a multi-omics integration approach to unravel information flow and mechanisms during complex biological events, with a focus on the immune system. This involves a combination of tools, including: InnateDB, a database of curated interactions between genes and protein products involved in the innate immunity; NetworkAnalyst, a visualization and analysis platform for InnateDB interactions; and MetaBridge, a tool to integrate metabolite data into PPI networks. The application of these systems techniques is demonstrated for a variety of biological questions, including: the developmental trajectory of neonates during the first week of life, mechanisms in host-pathogen interaction, disease prognosis, biomarker discovery, and drug discovery and repurposing. Overall, systems biology analyses of omics data have been applied to a variety of immunology-related questions, and here we demonstrate the numerous ways in which PPI network analysis can be a powerful tool in contributing to our understanding of the immune system and the study of vaccines.
Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models.
This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein–protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.
Motivation:
Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.
Results:
ssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNAseq data, where the correct cell timing is recovered; and two TCGA datasets, where ssNPA patient clusters have significant survival differences. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches.
Availability:
http://www.benoslab.pitt.edu/Software/ssnpa/.
Supplementary information:
Supplementary data are available at Bioinformatics online.
Parkinson’s disease (PD) is a disabling neurodegenerative disease that manifests with resting tremor, bradykinesia, rigidity and postural instability. Since the discovery of microRNAs (miRNAs) in 1993, miRNAs have been shown to be important biological molecules involved in diverse processes to maintain normal cellular functions. Over the past decade, many studies have reported dysregulation of miRNA expressions in PD. Here, we identified 15 miRNAs from 34 reported screening studies that demonstrated dysregulation in the brain and/or neuronal models, cerebrospinal fluid (CSF) and blood. Specific miRNAs-of-interest that have been implicated in PD pathogenesis include miR-30, miR-29, let-7, miR-485 and miR-26. However, there are several challenges and limitations in drawing definitive conclusions due to the small sample size in clinical studies, varied laboratory techniques and methodologies and their incomplete penetrance of the blood–brain barrier. Developing an optimal delivery system and unravelling druggable targets of miRNAs in both experimental and human models and clinical validation of the results may pave way for novel therapeutics in PD.
Networks describe how parts associate with each other to form integrated systems which often have modular and hierarchical structure. In biology, network growth involves two processes, one that unifies and the other that diversifies. Here, we propose a biphasic (bow-tie) theory of module emergence. In the first phase, parts are at first weakly linked and associate variously. As they diversify, they compete with each other and are often selected for performance. The emerging interactions constrain their structure and associations. This causes parts to self-organize into modules with tight linkage. In the second phase, variants of the modules diversify and become new parts for a new generative cycle of higher level organization. The paradigm predicts the rise of hierarchical modularity in evolving networks at different timescales and complexity levels. Remarkably, phylogenomic analyses uncover this emergence in the rewiring of metabolomic and transcriptome-informed metabolic networks, the nanosecond dynamics of proteins, and evolving networks of metabolism, elementary functionomes, and protein domain organization.
Background:
The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem.
Results:
Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database.
Conclusions:
DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.
Purpose: Caffeic acid phenethyl ester (CAPE) is the main polyphenol extracted from honeybee propolis, which inhibits the growth of several kinds of tumor. This study aimed to assess the inhibitory effect of CAPE in nasopharyngeal carcinoma (NPC), evaluate the synergistic action of CAPE in radiotherapy sensitivity of NPC cell lines and further elucidate the possible molecular mechanism involved.
Materials and methods: CCK-8 assay was used to analyze cell proliferation ability. Colony formation assay was used to evaluate the clonogenic ability and radio-sensitiveness of NPC cells by CAPE treatment. Wound-healing and transwell assay were used to assess the motility of cells. The expression of key molecules of the epithelial–mesenchymal transition (EMT) was determined by western blot analysis and changes in radiation sensitivity were measured by colony-formation assay. cDNA microarray analysis was used to determine differentially expressed genes with and without CAPE treatment, with Gene Ontology enrichment of gene function and KEGG pathways determined. Cell cycle and apoptosis were detected by flow cytometry and western blot analysis.
Results: CAPE suppressed the viability of NPC cell lines time- and dose-dependently. It induced apoptosis in NPC cells along with decreased expression of Bcl-XL and increased cleavage of PARP and expression of Bax. G1 phase arrest was induced by CAPE with ower expression of CDK4, CDK6, Rb and p-Rb. The migratory and invasive ability of NPC cells was decreased by the EMT pathway. The irradiation sensitivity of NPC cells was enhanced with CAPE treatment. CAPE specifically inhibited nuclear factor κB (NF-κB) signaling pathway by suppressing p65 subunit translocation from cytoplasm to nucleus. CAPE treatment was synergistic with chemotherapy and radiotherapy.
Conclusion: CAPE may inhibit the proliferation and metastasis of NPC cells but enhance radiosensitivity in NPC therapy by inhibiting the NF-κB pathway. CAPE could be a potential therapeutic compound for NPC therapy.
Despite exceptional experimental efforts to map out the human interactome, the continued data incompleteness limits our ability to understand the molecular roots of human disease. Computational tools offer a promising alternative, helping identify biologically significant, yet unmapped protein-protein interactions (PPIs). While link prediction methods connect proteins on the basis of biological or network-based similarity, interacting proteins are not necessarily similar and similar proteins do not necessarily interact. Here, we offer structural and evolutionary evidence that proteins interact not if they are similar to each other, but if one of them is similar to the other’s partners. This approach, that mathematically relies on network paths of length three (L3), significantly outperforms all existing link prediction methods. Given its high accuracy, we show that L3 can offer mechanistic insights into disease mechanisms and can complement future experimental efforts to complete the human interactome. Computational protein-protein interaction (PPI) prediction has the potential to complement experimental efforts to map interactomes. Here, the authors show that proteins tend to interact if one is similar to the other’s partners and that PPI prediction based on this principle is highly accurate.
For over 50 years Mendelian Inheritance in Man has chronicled the collective knowledge of the field of medical genetics. It initially cataloged the known X-linked, autosomal recessive and autosomal dominant inherited disorders, but grew to be the primary repository of curated information on both genes and genetic phenotypes and the relationships between them. Each phenotype and gene is given a separate entry assigned a stable, unique identifier. The entries contain structured summaries of new and important information based on expert review of the biomedical literature. OMIM.org provides interactive access to the knowledge repository, including genomic coordinate searches of the gene map, views of genetic heterogeneity of phenotypes in Phenotypic Series, and side-by-side comparisons of clinical synopses. OMIM.org also supports computational queries via a robust API. All entries have extensive targeted links to other genomic resources and additional references. Updates to OMIM can be found on the update list or followed through the MIMmatch service. Updated user guides and tutorials are available on the website. As of September 2018, OMIM had over 24,600 entries, and the OMIM Morbid Map Scorecard had 6,259 molecularized phenotypes connected to 3,961 genes.
Neurovascular integrity, including cerebral blood flow (CBF) and blood-brain barrier (BBB) function, plays a major role in determining cognitive capability. Recent studies suggest that neurovascular integrity could be regulated by the gut microbiome. The purpose of the study was to identify if ketogenic diet (KD) intervention would alter gut microbiome and enhance neurovascular functions, and thus reduce risk for neurodegeneration in young healthy mice (12-14 weeks old). Here we show that with 16 weeks of KD, mice had significant increases in CBF and P-glycoprotein transports on BBB to facilitate clearance of amyloid-beta, a hallmark of Alzheimer's disease (AD). These neurovascular enhancements were associated with reduced mechanistic target of rapamycin (mTOR) and increased endothelial nitric oxide synthase (eNOS) protein expressions. KD also increased the relative abundance of putatively beneficial gut microbiota (Akkermansia muciniphila and Lactobacillus), and reduced that of putatively pro-inflammatory taxa (Desulfovibrio and Turicibacter). We also observed that KD reduced blood glucose levels and body weight, and increased blood ketone levels, which might be associated with gut microbiome alteration. Our findings suggest that KD intervention started in the early stage may enhance brain vascular function, increase beneficial gut microbiota, improve metabolic profile, and reduce risk for AD.
Background
Several articles suggest that DNA methylation levels in blood relate to Parkinson’s disease (PD) but there is a need for a large-scale study that involves suitable population based controls. The purposes of the study were: (1) to study whether PD status is associated with DNA methylation levels in blood/saliva; (2) to study whether observed associations relate to blood cell types; and (3) to characterize genome-wide significant markers (“CpGs”) and clusters of CpGs (co-methylation modules) in terms of biological pathways.
Methods
In a population-based case control study of PD, we studied blood samples from 335 PD cases and 237 controls and saliva samples from another 128 cases and 131 controls. DNA methylation data were generated from over 486,000 CpGs using the Illumina Infinium array. We identified modules of CpGs (clusters) using weighted correlation network analysis (WGCNA).
Results
Our cross-sectional analysis of blood identified 82 genome-wide significant CpGs (including cg02489202 in LARS2 p = 8.3 × 10–11 and cg04772575 in ABCB9 p = 4.3 × 10–10). Three out of six PD related co-methylation modules in blood were significantly enriched with immune system related genes. Our analysis of saliva identified five significant CpGs. PD-related CpGs are located near genes that relate to mitochondrial function, neuronal projection, cytoskeleton organization, systemic immune response, and iron handling.
Conclusions
This study demonstrates that: (1) PD status has a profound association with DNA methylation levels in blood and saliva; and (2) the most significant PD-related changes reflect changes in blood cell composition. Overall, this study highlights the role of the immune system in PD etiology but future research will need to address the causal structure of these relationships.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-017-0466-5) contains supplementary material, which is available to authorized users.
Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism.
The exponential development of highly advanced scientific and medical research technologies throughout the past 30 years has arrived to the point where the high number of characterized molecular agents related to pathogenesis cannot be readily integrated or processed by conventional analytical approaches. Indeed, the realization that several moieties are signatures of disease has partly led to the increment of complex diseases being characterized. Scientists and clinicians can now investigate and analyse any individual dysregulations occurring within the genomic, transcriptomic, miRnomic, proteomic, and metabolomic levels thanks to currently available advanced technologies. However, there are drawbacks within this scientific brave new age in that only isolated molecular levels are individually investigated for their influence in affecting any particular health condition. Since their conception in 1992, systems biology/medicine focuses mainly on the perturbations of overall pathway kinetics for the consequent onset and/or deterioration of the investigated condition/s. Systems medicine approaches can therefore be employed for shedding light in multiple research scenarios, ultimately leading to the practical result of uncovering novel dynamic interaction networks that are critical for influencing the course of medical conditions. Consequently, systems medicine also serves to identify clinically important molecular targets for diagnostic and therapeutic measures against such a condition.
Purpose : To determine whether peri-implant crestal bone loss could be affected by systemic disease, primary ISQ value, implantation method (submerged vs. non-submerged), surface treatment, and bone density Materials and methods : Patients who underwent fixture installation from June 24, 2005 to October 23, 2008 at Seoul National University Bundang Hospital were evaluated. A total of 157 patients (male: 52, female: 85) had 346 fixtures installed. Among them, 49 patients had periapical radiographs taken 1 year after prostheses were first set. A total of 97 fixtures were implanted. In particular, 30 fixtures were installed in patients with systemic diseases such as diabetes mellitus, cardiovascular disease, hypertension, and liver disease. The immediate stability of implants was measured with Osstell tm . Implant surface treatment was classified into two groups (RBM, Cellnest (Anodized)), and bone density, into four groups (D1~D4). The bone resorption on the mesial and distal areas of fixtures was measured with periapical radiographs using the paralleling technique, and the mean value was calculated. The length determination program in IMPAX (AGFA, Belgium) was used. Results : At least 332 out of 346 (96%) installed GS implants were successfully osseointegrated 1 year after prostheses were first set. The mean value of the bone resorption of the installed GS implants was 0.44mm. The minimum value was 0mm, and the maximum value, 2.85mm. There was a statistically significant difference between the implantation methods (submerged, non-submerged) with regard to the amount of alveolar bone loss 1 year after prostheses were first set (p<0.05). Non-submerged implants showed less crestal bone loss. Note, however, that other variables had no correlation with crestal bone loss (p>0.05). Conclusion : There was a statistically significant difference between the 1-stage method and 2-stage method with regard to the amount of alveolar bone loss 1 year after prostheses were first set. Systemic disease, primary ISQ value, surface treatment, and bone density were not associated with alveolar bone loss. Other variables were assumed to have a correlation with alveolar bone loss.
Publication information: Journal of Korean Dental Science 2009;3(1):12-17.
Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.
Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.
To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.
We discuss to what extent disease transmission models provide reliable predictions. The concept of prediction is delineated as it is understood by modellers, and illustrated by some classic and recent examples. A precondition for a model to provide valid predictions is that the assumptions underlying it correspond to the reality, but such correspondence is always limited—all models are simplifications of reality. A central tenet of the modelling enterprise is what we may call the ‘robustness thesis’: a model whose assumptions approximately correspond to reality will make predictions that are approximately valid. To examine which of the predictions made by a model are trustworthy, it is essential to examine the outcomes of different models. Thus, if a highly simplified model makes a prediction, and if the same or a very similar prediction is made by a more elaborate model that includes some mechanisms or details that the first model did not, then we gain some confidence that the prediction is robust. An important benefit derived from mathematical modelling activity is that it demands transparency and accuracy regarding our assumptions, thus enabling us to test our understanding of the disease epidemiology by comparing model results and observed patterns. Models can also assist in decision-making by making projections regarding important issues such as intervention-induced changes in the spread of disease.
With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each 'P' in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine.
The recent and rapid advent of next-generation sequencing (NGS) has made this technology broadly available not only to researchers in various molecular and cellular biology fields but also to those in kidney disease. In this paper, we describe the usage of ChIP-seq (chromatin immunoprecipitation with sequencing) and RNA-seq for sample preparation and interpretation of raw data in the investigation of biological phenomenon in renal diseases. ChIP-seq identifies genome-wide transcriptional DNA-binding sites as well as histone modifications, which are known to regulate gene expression, in the intragenic as well as in the intergenic regions. With regard to RNA-seq, this process analyzes not only the expression level of mRNA but also splicing variants, non-coding RNA, and microRNA on a genome-wide scale. The combination of ChIP-seq and RNA-seq allows the clarification of novel transcriptional mechanisms, which have important roles in various kinds of diseases, including chronic kidney disease. The rapid development of these techniques requires an update on the latest information and methods of NGS. In this review, we highlight the merits and characteristics of ChIP-seq and RNA-seq and discuss the use of the genome-wide analysis in kidney disease.Kidney International advance online publication, 28 August 2013; doi:10.1038/ki.2013.321.
Gene set enrichment analysis (GSEA) is a statistical method to determine if predefined sets of genes are differentially expressed in different phenotypes. Predefined gene sets may be genes in a known metabolic pathway, located in the same cytogenetic band, sharing the same Gene Ontology category, or any user-defined set. In microarray experiments where no single gene shows statistically significant differential expression between phenotypes, GSEA has identified significant differentially expressed sets of genes, even where the average difference in expression between two phenotypes is only 20% for genes in the gene set. The gene set identified in the first GSEA analysis (oxidative phosphorylation genes differentially expressed in diabetic versus non-diabetic patients) was subsequently confirmed by independent laboratory studies published in the New England Journal of Medicine. Since the first paper on GSEA was published, many extensions and alternative methods have been described in the literature. In this paper, we describe the original GSEA algorithm, subsequent extensions and alternatives, results of some of the applications, some limitations of the methods and caveats for users, and possible future research directions. GSEA and related methods are complementary to conventional single-gene methods. Single gene methods work best when individual genes have large effects and there is small variance within the phenotype. GSEA is likely to be more powerful than conventional single-gene methods for studying the large number of common diseases in which many genes each make subtle contributions. It is a tool that deserves to be in the toolbox of bioinformatics practitioners.
We discuss some motivational challenges arising from the need to model and analyse complex biological systems at multiple scales (spatial and temporal), and present a biomodel engineering framework to address some of these issues within the context of multiscale Systems Biology. Our methodology is based on a structured family of Petri net classes which enables the investigation of a given system using various modelling abstractions: qualitative, stochastic, continuous and hybrid, optionally in a spatial context. We illustrate our approach with case studies demonstrating hierarchical flattening, treatment of space, and hierarchical organisation of space.
Cellular processes are governed and coordinated by a multitude of biopathways. A pathway can be viewed as a complex network of biochemical reactions. The dynamics of this network largely determines the functioning of the pathway. Hence the modeling and analysis of biochemical networks dynamics is an important problem and is an active area of research. Here we review quantitative models of biochemical networks based on ordinary differential equations (ODEs). We mainly focus on the parameter estimation and sensitivity analysis problems and survey the current methods for tackling them. In this context we also highlight a recently developed probabilistic approximation technique using which these two problems can be considerably simplified.
European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595–2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.
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.
More information: http://www.cancer-systemsbiology.org/.
This Teaching Resource provides lecture notes, slides, and a problem set for a set of three lectures from a course entitled "Systems Biology: Biomedical Modeling." The materials are from three separate lectures introducing applications of graph theory and network analysis in systems biology. The first lecture describes different types of intracellular networks, methods for constructing biological networks, and different types of graphs used to represent regulatory intracellular networks. The second lecture surveys milestones and key concepts in network analysis by introducing topological measures, random networks, growing network models, and topological observations from molecular biological systems abstracted to networks. The third lecture discusses methods for analyzing lists of genes and experimental data in the context of prior knowledge networks to make predictions.
Despite the recent progress in cancer management approaches, the mortality rate of
cancer is still growing and there are lots of challenges in the clinics in terms of novel
therapeutics. MicroRNAs (miRNA) are regulatory small noncoding RNAs and are
already confirmed to have a great role in regulating gene expression level by targeting
multiple molecules that affect cell physiology and disease development. Recently,
miRNAs have been introduced as promising therapeutic targets for cancer treatment.
Regulatory potential of tumor suppressor miRNAs, which enables regulation of entire
signaling networks within the cells, makes them an interesting option for developing
cancer therapeutics. In this regard, over recent decades, scientists have aimed at
developing powerful and safe targeting approaches to restore these suppressive
miRNAs in cancerous cells.
The present review summarizes the function of miRNAs in tumor development and
presents recent findings on how miRNAs have served as therapeutic agents against
cancer, with a special focus on tumor suppressor miRNAs (mimics). Moreover, the latest
investigations on the therapeutic strategies of miRNA delivery have been presented.
Repeat expansions cause more than 30 inherited disorders, predominantly neurogenetic. These can present with overlapping clinical phenotypes, making molecular diagnosis challenging. Single-gene or small-panel PCR-based methods can help to identify the precise genetic cause, but they can be slow and costly and often yield no result. Researchers are increasingly performing genomic analysis via whole-exome and whole-genome sequencing (WES and WGS) to diagnose genetic disorders. However, until recently, analysis protocols could not identify repeat expansions in these datasets. We developed exSTRa (expanded short tandem repeat algorithm), a method that uses either WES or WGS to identify repeat expansions. Performance of exSTRa was assessed in a simulation study. In addition, four retrospective cohorts of individuals with eleven different known repeat-expansion disorders were analyzed with exSTRa. We assessed results by comparing the findings to known disease status. Performance was also compared to three other analysis methods (ExpansionHunter, STRetch, and TREDPARSE), which were developed specifically for WGS data. Expansions in the assessed STR loci were successfully identified in WES and WGS datasets by all four methods with high specificity and sensitivity. Overall, exSTRa demonstrated more robust and superior performance for WES data than did the other three methods. We demonstrate that exSTRa can be effectively utilized as a screening tool for detecting repeat expansions in WES and WGS data, although the best performance would be produced by consensus calling, wherein at least two out of the four currently available screening methods call an expansion. Copyright © 2018 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.
Background:
The clinical application of next-generation sequencing technologies has offered a more comprehensive understanding of the mutational profile of tumor samples. The study was aimed to evaluate the feasibility of the NextDaySeq-Lung panel, which is an NGS-based assay for mutation analysis of key driver genes in lung cancer, in a clinical setting.
Methods:
A total of 138 FFPE samples of non-small cell lung cancer (NSCLC) were examined in parallel with assays developed on the next-generation sequencing (NGS), quantitative PCR (QPCR), and Sanger sequencing (Sanger) platforms for somatic mutations in EGFR, KRAS, PIK3CA, and BRAF. The assays with the three platforms were compared and analyzed.
Results:
Compared with Sanger, NGS and QPCR assays have significant higher sensitivity, as Sanger failed to detect variants with mutation rates lower than 15%. Meanwhile, NGS and QPCR assays showed similar analytical sensitivity, specificity, and high concordance. In addition, the NGS assay exhibited advantages over QPCR in providing accurate information of allele sequence and mutation frequency, and detecting non-hotspot mutations.
Conclusions:
We reported the validation of NextDaySeq-Lung panel for mutation analysis in the clinical samples of lung cancer. The NGS assay has significant technical advantages over Sanger and QPCR assays. It shows good potential as a solid molecular diagnostics assay in the clinical setting.
Multi-hierarchical profiling may offer valuable insights into the structural stability and functional direction of biological networks in cellular development, pathological process and disease variation. Owing to the emergence of several new techniques, such as bioinformatics for omics data, structural biology and structural bioinformatics, the pace of network hierarchical research has accelerated a lot in recent years. Here, we discuss and compare the techniques available for quantifying multilevel hierarchies, with a focus on their features, capabilities and drawbacks when used for different applications. Then, we classify these methods into three types: topological spatial-scales, multilevel hierarchical control and feature ordering. We observe that challenges and limitations do exist in functional hierarchical identification. And, we also provide useful suggestions on how to analyze the dynamic data of complex network studies.
Although remarkable success has been achieved by traditional gene-mapping methods in locating genes associated with inherited human diseases, the resulting chromosomal regions are usually large, containing tens or even hundreds of genes. Therefore, it is indispensable to develop computational methods for the identification of genes that are truly responsible for diseases from candidate genes. To tackle this problem, several methods have been proposed to use both a phenotype similarity profile (phenome) and a proteinprotein interaction network (interactome) for the prioritization of candidate genes. The use of the phenome broadens the scope of applications of these methods for identifying disease-associated genes, and the use of the interactome provides a reliable measure of functional similarities between genes. These two data sources, together with carefully designed computational models, result in computational methods with superior performance in the prioritization of candidate genes for a given query disease of interest. In this paper, we review recent achievements of such computational methods that rely on the integration of the phenome and the interactome to prioritize candidate genes. We also summarize how similar methods can be readily used in identifying microRNAs that are potentially involved in complex diseases and discovering drugs that may target on disease-associated proteins. Finally, we discuss future prospects and challenges for the integration of multiple genomic data sources to systematically discover genes that underlie human diseases.
In the past decade, we have witnessed great advances in high-throughput genomic and proteomic profiling technologies, such as DNA microarrays, next-generation sequencing and mass spectrometry–based proteomics and metabolomics. These advances, capable of generating massive amounts of genomic, transcriptomic, proteomic, and metabolomic data, provide new opportunities to understand human diseases, identify potential biomarkers, and develop new treatments.1 This data-intensive paradigm has fundamentally transformed biomedical science and holds great promise for the betterment of human health.2
These high-throughput biotechnologies have also heightened the challenges of how to distill biological knowledge and novel insights from the sea of data. New computational approaches and statistical models are needed to effectively model and better interpret these high-dimensional, multiplatform data.1 Reductionism has led to tremendous success in molecular biology: we can now zoom in to study each individual genes or proteins, being able to study their composition and aberrations at the resolution of single nucleotide or amino acid and inquire their structural and chemical properties. However, genes and proteins in cells do not work in isolation: they communicate and coordinate with each other to carry out various biological functions. Systems approaches have become an important and promising alternative to unravel the mechanisms that orchestrate the activities of genes and proteins in cells. Systems approaches are particularly valuable to study complex diseases like cancers and cardiovascular diseases. Unlike many Mendelian diseases, where we can often pinpoint genetic culprits in single or a few genes, causes of complex diseases are multifaceted, involving various molecular aberrations and environmental factors.3 This complexity is further amplified by the interconnected nature of the biomolecules in the cells, which propagates these aberrations or erroneous signals throughout the system, thus posing a great challenge to elucidate the true causes and underlying mechanisms.
Biological networks provide a conceptual and intuitive framework …
Dynamic protein–protein interactions are involved in most physiological processes and, in particular, for the formation of multiprotein signaling complexes at transmembrane receptors, adapter proteins and effector molecules. Because the unregulated induction of signaling complexes has substantial clinical relevance, the investigation of these complexes is an active area of research. These studies strive to answer questions about the composition and function of multiprotein signaling complexes, along with the molecular mechanisms of their formation. In this review, the adapter protein, linker for activation of T cells (LAT), will be employed as a model to exemplify how signaling complexes are characterized using a range of techniques. The intensive investigation of LAT highlights how the systematic use of complementary techniques leads to an integrated understanding of the formation, composition and function of multiprotein signaling complexes that occur at receptors, adapter proteins and effector molecules.
Genomic technologies are reaching the point of being able to detect genetic variation in patients at high accuracy and reduced cost, offering the promise of fundamentally altering medicine. Still, although scientists and policy advisers grapple with how to interpret and how to handle the onslaught and ambiguity of genome-wide data, established and well-validated molecular technologies continue to have an important role, especially in regions of the world that have more limited access to next-generation sequencing capabilities. Here we review the range of methods currently available in a clinical setting as well as emerging approaches in clinical molecular diagnostics. In parallel, we outline implementation challenges that will be necessary to address to ensure the future of genetic medicine.
Importance of the field:
The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation.
Areas covered in this review:
This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself.
What the reader will gain:
The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained.
Take home message:
Systems thinking has now come of age enabling a 'bird's eye view' of the biological systems under study, at the same time allowing us to 'zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.
This unit describes the overview and strategies for molecular genetic diagnosis. Molecular genetic testing is typically used to detect changes in a single gene, and includes direct sequence analysis and detection of large deletion/duplication. Occasionally linkage analysis is still used when the gene is not known. Direct mutation analysis is possible if the gene responsible for a disorder has been identified. Genetic linkage analysis is used when polymorphic markers are available that are closely linked to a disease gene but the gene itself has not been cloned, or when it is difficult or impossible to detect mutations in the gene. For direct mutation analysis, the unit offers a review of the types of mutations that can be detected and the strategies that can be employed for their detection. The unit then describes the types of disorders that are best suited for linkage-based analysis and offers guidance for interpreting the data. The unit also overviews recent progress on high-throughput sequencing system and its possibility for its clinical application. Curr. Protoc. Hum. Genet. 76:9.1.1-9.1.7. © 2013 by John Wiley & Sons, Inc.
In order to better understand several cellular processes, it is helpful to study how various components make up the system. This systems perspective is supported by several modelling tools including network analysis. Networks of protein-protein interactions (PPI networks) offer a way to depict, visualize and quantify the functioning and relative importance of particular proteins in cell function. The toolkit of network analysis ranges from the local indices describing individual proteins (as network nodes) to global indicators of system architecture, describing the total interaction system (as the whole network). We briefly introduce some of these network indices and present a case study where the connectedness and potential functional relationships between certain disease proteins are inferred. We argue that network analysis can be used, in general, to improve databases, to infer novel functions, to quantify positional importance and to support predictions in pathogenesis studies. The systems perspective and network analysis can be of particular importance in studying diseases with complex molecular processes.
The metabolic network has become a hot topic in the area of system biology and flux-based analysis plays a very important
role in understanding the characteristics of organism metabolic networks. We review mainly the static methods for analyzing
metabolic networks such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on / off minimization
(ROOM), and dynamic flux balance analysis with linear quadratic regulator (DFBA-LQR). Then several kinds of commonly used
software for flux analysis are introduced briefly and compared with each other. Finally, we highlight the applications of
metabolic network flux analysis, especially its usage combined with other biological characteristics and its usage for drug
design. The idea of combining the analysis of metabolic networks and other biochemical data has been gradually promoted and
used in several aspects such as the combination of metabolic flux and the regulation of gene expression, the influence of
protein evolution caused by metabolic flux, the relationship between metabolic flux and the topological characteristics, the
optimization of metabolic engineering. More comprehensive and accurate properties of metabolic networks will be obtained by
integrating metabolic flux analysis, network topological characteristics and dynamic modeling.
Keywordsmetabolic network-flux balance analysis-minimization of metabolic adjustment-gene regulation-topological property