Search

Non-small-cell lung cancer (NSCLC) is the predominant type of lung cancer in the world. Lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) are subtypes of NSCLC. We usually regard them as different disease due to their unique molecular characteristics, distinct cells of origin and dissimilar clinical response. However, the differences of genetic and epigenetic progression mechanism between LADC and LSCC are complicated to analyze. Therefore, we applied systems biology approaches and big databases mining to construct genetic and epigenetic networks (GENs) with next-generation sequencing data of LADC and LSCC. In order to obtain the real GENs, system identification and system order detection are conducted on gene regulatory networks (GRNs) and protein-protein interaction networks (PPINs) for each stage of LADC and LSCC. The core GENs were extracted via principal network projection (PNP). Based on the ranking of projection values, we got the core pathways in respect of KEGG pathway. Compared with the core pathways, we found significant differences between microenvironments, dysregulations of miRNAs, epigenetic modifications on certain signaling transduction proteins and target genes in each stage of LADC and LSCC. Finally, we proposed six genetic and epigenetic multiple-molecule drugs to target essential biomarkers in each progression stage of LADC and LSCC, respectively.

Background: Two big issues in the study of pathogens are determining how pathogens infect hosts and how the host defends itself against infection. Therefore, investigating host-pathogen interactions is important for understanding pathogenicity and host defensive mechanisms and treating infections.Methods: In this study, we used omics data, including time-course data from high-throughput sequencing, real-time polymerase chain reaction, and human microRNA (miRNA) and protein-protein interaction to construct an interspecies protein-protein and miRNA interaction (PPMI) network of human CD4+ T cells during HIV-1 infection through system modeling and identification.Results: By applying a functional annotation tool to the identified PPMI network at each stage of HIV infection, we found that repressions of three miRNAs, miR-140-5p, miR-320a, and miR-941, are involved in the development of autoimmune disorders, tumor proliferation, and the pathogenesis of T cells at the reverse transcription stage. Repressions of miR-331-3p and miR-320a are involved in HIV-1 replication, replicative spread, anti-apoptosis, cell proliferation, and dysregulation of cell cycle control at the integration/replication stage. Repression of miR-341-5p is involved in carcinogenesis at the late stage of HIV-1 infection.Conclusion: By investigating the common core proteins and changes in specific proteins in the PPMI network between the stages of HIV-1 infection, we obtained pathogenic insights into the functional core modules and identified potential drug combinations for treating patients with HIV-1 infection, including thalidomide, oxaprozin, and metformin, at the reverse transcription stage; quercetin, nifedipine, and fenbendazole, at the integration/replication stage; and staurosporine, quercetin, prednisolone, and flufenamic acid, at the late stage.

// Cheng-Wei Li 1 , Ping-Yao Chang 1 , Bor-Sen Chen 1 1 Laboratory of Control and Systems Biology, National Tsing Hua University, Hsinchu, Taiwan Correspondence to: Bor-Sen Chen, email: bschen@ee.nthu.edu.tw Keywords: DNA methylation, multiple potential drugs, hepatocarcinogenesis, miRNAs, principal network projection Received: February 19, 2016 Accepted: October 26, 2016 Published: November 04, 2016 ABSTRACT The mechanisms leading to the development and progression of hepatocellular carcinoma (HCC) are complicated and regulated genetically and epigenetically. The recent advancement in high-throughput sequencing has facilitated investigations into the role of genetic and epigenetic regulations in hepatocarcinogenesis. Therefore, we used systems biology and big database mining to construct genetic and epigenetic networks (GENs) using the information about mRNA, miRNA, and methylation profiles of HCC patients. Our approach involves analyzing gene regulatory networks (GRNs), protein-protein networks (PPINs), and epigenetic networks at different stages of hepatocarcinogenesis. The core GENs, influencing each stage of HCC, were extracted via principal network projection (PNP). The pathways during different stages of HCC were compared. We observed that extracellular signals were further transduced to transcription factors (TFs), resulting in the aberrant regulation of their target genes, in turn inducing mechanisms that are responsible for HCC progression, including cell proliferation, anti-apoptosis, aberrant cell cycle, cell survival, and metastasis. We also selected potential multiple drugs specific to prominent epigenetic network markers of each stage of HCC: lestaurtinib, dinaciclib, and perifosine against the NTRK2 , MYC , and AKT1 markers influencing HCC progression from stage I to stage II; celecoxib, axitinib, and vinblastine against the DDIT3 , PDGFB , and JUN markers influencing HCC progression from stage II to stage III; and atiprimod, celastrol, and bortezomib against STAT3 , IL1B , and NFKB1 markers influencing HCC progression from stage III to stage IV.

Aim: A systematic multimolecule drug design procedure is proposed for promoting hepatogenesis and liver regeneration. Materials & methods: Genome-wide microarray data including three hepatic conditions are obtained from the GEO database (GSE15238). System modeling and big data mining methods are used to construct real genome-wide genetic-and-epigenetic networks (GWGENs). Then, we extracted the core GWGENs by applying principal network projection on real GWGENs of normal, developing and regenerating livers, respectively. After that, we investigated the significant signal pathways and epigenetic modifications in the core GWGENs to identify potential biomarkers as drug targets. Result & conclusion: A multimolecule drug consisting of sulmazole, clofibrate, colchicine, furazolidone, nadolol, eticlopride and felbinac is proposed to target on novel biomarkers for promoting hepatogenesis and liver regeneration.

Thyroid cancer is the most common endocrine cancer. Particularly, papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. Up to now, there are few researches discussing the pathogenesis and progression mechanisms of PTC from the viewpoint of systems biology approaches. In this study, first we constructed the candidate genetic and epigenetic network (GEN) consisting of candidate protein&ndash, protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big database mining. Secondly, system identification and system order detection methods were applied to prune candidate GEN via next-generation sequencing (NGS) and DNA methylation profiles to obtain the real GEN. After that, we extracted core GENs from real GENs by the principal network projection (PNP) method. To investigate the pathogenic and progression mechanisms in each stage of PTC, core GEN was denoted in respect of KEGG pathways. Finally, by comparing two successive core signaling pathways of PTC, we not only shed light on the causes of PTC progression, but also identified essential biomarkers with specific gene expression signature. Moreover, based on the identified gene expression signature, we suggested potential candidate drugs to prevent the progression of PTC with querying Connectivity Map (CMap).

Clostridium difficile is the leading cause of nosocomial antibiotic-associated diarrhea, and the major etiologic agent of pseudomembranous colitis. In severe cases, C. difficile infection (CDI) can cause toxic megacolon, intestinal perforation, and death. The intestinal epithelium is the first tissue encountered in the adhesion and colonization of C. difficile, and serves as a physical defense barrier against infection. Despite the well-characterized cytotoxicity, few studies have investigated the genome-wide interplay between host cells and C. difficile. The aim of this study is to investigate the genetic and epigenetic molecular mechanisms between human colorectal epithelial Caco-2 cells and C. difficile during the early (0–60 min) and late stages (30–120 min) of infection. To investigate the cross-talk mechanisms during the progression of infection, we introduced a systems biology approach using big data mining, dynamic network modeling, a genome-wide data identification method, system order detection scheme, and principal network projection method (PNP). We focused on the construction of genome-wide genetic-and-epigenetic interspecies networks (GEINs) and subsequent extraction of host-pathogen core networks (HPNs) to investigate the progression of underlying host/pathogen genetic and epigenetic mechanisms from the early to late stages of CDI. Based on our results, we suggest that the cell wall proteins CD2787 and CD0237, which both play an important role in cell adhesion and pathogen defense mechanisms, can be considered as potential drug targets. In addition, the crucial proteins employed by C. difficile for sporulation, including CD1214, CD2629, and CD2643, can also be considered as potential drug targets since spore-mediated re-infection is a critical issue.

Until now, the theorems, analyses, and mechanisms of complicated systems, such as the ecosystem, biological systems, economic systems, and social systems, remain sectioned because their data and literature are subjected to fragmentation, distribution, and inconsistency. With increasing data (big data) and database expansion (big database), the method of integrating information to construct an explanatory pattern (or mechanistic models) to determine overall mechanisms is still a challenge. This explanatory pattern (or mechanistic models) is referred to as the big mechanism. Systems biology is centered at the heart of a new integrative 21st century paradigm of biology. In this chapter, the scope of the Big Mechanisms of systems biology is introduced. Further, we show how the block diagram can help resolve and describe the Big Mechanisms of systems biology via system modeling, system identification, and extensive data.

In order to improve the understanding of Big Mechanisms in systems biology, several procedures are described in this chapter to illustrate how to explore Big Mechanisms of systems biology through system modeling, system identification, and big database mining of high-throughput microarray data and next generation sequencing data. The procedures for extracting Big Mechanisms based on gene regulatory network (GRN), protein–protein interaction network (PPIN), integrated GRN and PPIN, and integrated genetic and epigenetic network are introduced in this chapter to offer scientists an useful method for enhancing their understanding of Big Mechanisms mediating life processes.

Innovative drug design focuses on new drug targets. Unlike magic bullets targeting a single disease-causing molecule, a systems drug design is proposed in this chapter to develop multiple drug targets for multiple-target cocktail therapies. First, based on computational models, a systems drug discovery scheme is introduced using database mining and high-throughput data, including the validation of drug targets via cross-referencing of experimental data, pharmacogenomics, polymorphism haplotyping, and the understanding of drug metabolism and toxicity. Next, based on the carcinogenesis relevance value, we investigated the common core and specific network marker of four cancers for multiple drug targets with the aim of suggesting potential and relevant medical therapy for cancer. Finally, based on 28 significant proteins of core network markers in the carcinogenesis of four cancers, a systems drug design mechanism is introduced to select multiple drug targets for multiple-target cocktail therapies.

Regeneration is an intriguing process, but the molecular and cellular basis of this process is not fully understood. Biologists have found that embryonic development, regeneration, and cancer share a number of common regulation pathways. Studies of protein–protein interactions (PPIs) and modulations of the pathways may improve the understanding of embryonic development, cancer, and regeneration. Since stem cells are key factors in development, regeneration, and cancer, misregulation in the differentiation processes of stem cells can lead to pathological conditions, such as cancer. In this chapter, the systems mechanisms of three differentiation processes of stem cells are discussed, first based on the PPI network and functional module. Next, the cerebella regeneration-related pathways and their crosstalk after traumatic brain injury in zebrafish are introduced, to investigate the phenotypes and the responses to the second injury that involves the regeneration mechanisms of the cerebella wound healing PPI network, via dynamic modeling and big database mining. Pathway enrichment and ontological analysis are incorporated into a refined network to highlight the main molecular scheme of cerebella wound healing. Finally, we investigate the carcinogenic mechanisms and regeneration mechanism using genetic and epigenetic cell cycle networks between cancer cells and embryonic stem cells by comparing the mechanisms underlying cell cycle progression in stem cells and cancer cells through next generation sequencing (NGS) data and dynamic models.

Cancer is a complex and heterogeneous disease with high levels of robustness against various therapeutic interventions. In industrialized countries, cancer is one of major causes of death. Given the complex, heterogeneous, and evolving nature of cancer, it is essential for a system-oriented view to be adopted for an indepth understanding. Systems biology approaches may complement experimental approaches for investigating the complex tumorigenesis mechanisms of cancer. In this chapter, we first construct and clarify the dynamic gene regulatory network of the cancer cell cycle using a dynamic model and microarray data. Next, we investigate tumorigenesis mechanisms in a cancer-perturbed protein–protein interaction network. A network-based biomarker is also introduced for molecular function investigation and the diagnosis of lung cancer. Finally, network-based biomarkers of bladder cancer are investigated based on a genome-wide genetic and epigenetic network through database mining and NGS data identification. Since epigenetic regulation, microRNA (miRNA) regulation, and DNA methylation are associated with carcinogenesis and the development of cancer, it is more appealing to construct an integrated genetic and epigenetic network (IGEN) for cancer to investigate the Big Mechanisms of carcinogenesis and development of cancer based on a genome-wide scale including protein interactions, transcription, miRNA regulation, and DNA methylation. In Section 9.4, network markers of bladder cancer based on a genome-wide IGEN are derived from system modeling and identification, big database mining, and NGS data.

Aging is an inevitable part of life for humans, and slowing down the aging process has become a main focus of human endeavor. Here, we applied a systems biology approach to construct protein-protein interaction networks, gene regulatory networks, and epigenetic networks, i.e. genetic and epigenetic networks (GENs), of elderly individuals and young controls. We then compared these GENs to extract aging mechanisms using microarray data in peripheral blood mononuclear cells, microRNA (miRNA) data, and database mining. The core GENs of elderly individuals and young controls were obtained by applying principal network projection to GENs based on Principal Component Analysis. By comparing the core networks, we identified that to overcome the accumulated mutation of genes in the aging process the transcription factor JUN can be activated by stress signals, including the MAPK signaling, T-cell receptor signaling, and neurotrophin signaling pathways through DNA methylation of BTG3, G0S2, and AP2B1 and the regulations of mir-223 let-7d, and mir-130a. We also address the aging mechanisms in old men and women. Furthermore, we proposed that drugs designed to target these DNA methylated genes or miRNAs may delay aging. A multiple drug combination comprising phenylalanine, cholesterol, and palbociclib was finally designed for delaying the aging process.

The interplay between entropy and robustness of gene network is a core mechanism of systems biology. The entropy is a measure of randomness or disorder of a physical system due to random parameter fluctuation and environmental noises in gene regulatory networks. The robustness of a gene regulatory network, which can be measured as the ability to tolerate the random parameter fluctuation and to attenuate the effect of environmental noise, will be discussed from the robust H∞ stabilization and filtering perspective. In this review, we will also discuss their balancing roles in evolution and potential applications in systems and synthetic biology.

Background Epigenetics has been investigated in cancer initiation, and development, especially, since the appearance of epigenomics. Epigenetics may be defined as the mechanisms that lead to heritable changes in gene function and without affecting the sequence of genome. These mechanisms explain how individuals with the same genotype produce phenotypic differences in response to environmental stimuli. Recently, with the accumulation of high-throughput next-generation sequencing (NGS) data, a key goal of systems biology is to construct networks for different cellular levels to explore whole cellular mechanisms. At present, there is no satisfactory method to construct an integrated genetic and epigenetic cellular network (IGECN), which combines NGS omics data with gene regulatory networks (GRNs), microRNAs (miRNAs) regulatory networks, protein-protein interaction networks (PPINs), and epigenetic regulatory networks of methylation using high-throughput NGS data. Results We investigated different kinds of NGS omics data to develop a systems biology method to construct an integrated cellular network based on three coupling models that describe genetic regulatory networks, protein–protein interaction networks, microRNA (miRNA) regulatory networks, and methylation regulation. The proposed method was applied to construct IGECNs of gastric cancer and the human immune response to human immunodeficiency virus (HIV) infection, to elucidate human defense response mechanisms. We successfully constructed an IGECN and validated it by using evidence from literature search. The integration of NGS omics data related to transcription regulation, protein-protein interactions, and miRNA and methylation regulation has more predictive power than independent datasets. We found that dysregulation of MIR7 contributes to the initiation and progression of inflammation-induced gastric cancer; dysregulation of MIR9 contributes to HIV-1 infection to hijack CD4+ T cells through dysfunction of the immune and hormone pathways; dysregulation of MIR139-5p, MIRLET7i, and MIR10a contributes to the HIV-1 integration/replication stage; dysregulation of MIR101, MIR141, and MIR152 contributes to the HIV-1 virus assembly and budding mechanisms; dysregulation of MIR302a contributes to not only microvesicle-mediated transfer of miRNAs but also dysfunction of NF-κB signaling pathway in hepatocarcinogenesis. Conclusion The coupling dynamic systems of the whole IGECN can allow us to investigate genetic and epigenetic cellular mechanisms via omics data and big database mining, and are useful for further experiments in the field of systems and synthetic biology.

In general, it is very difficult to measure the information flow in a cellular network directly. In this study, based on an information flow model and microarray data, we measured the information flow in cellular networks indirectly by using a systems biology method. First, we used a recursive least square parameter estimation algorithm to identify the system parameters of coupling signal transduction pathways and the cellular gene regulatory network (GRN). Then, based on the identified parameters and systems theory, we estimated the signal transductivities of the coupling signal transduction pathways from the extracellular signals to each downstream protein and the information transductivities of the GRN between transcription factors in response to environmental events. According to the proposed method, the information flow, which is characterized by signal transductivity in coupling signaling pathways and information transductivity in the GRN, can be estimated by microarray temporal data or microarray sample data. It can also be estimated by other high-throughput data such as next-generation sequencing or proteomic data. Finally, the information flows of the signal transduction pathways and the GRN in leukemia cancer cells and non-leukemia normal cells were also measured to analyze the systematic dysfunction in this cancer from microarray sample data. The results show that the signal transductivities of signal transduction pathways change substantially from normal cells to leukemia cancer cells.

We use a systems biology approach to construct protein-protein interaction networks (PPINs) for early and late stage bladder cancer. By comparing the networks of these two stages, we find that both networks showed very significantly different mechanisms. To obtain the differential network structures between cancer and noncancer PPINs, we constructed cancer PPIN and noncancer PPIN network structures for the two bladder cancer stages using microarray data from cancer cells and their adjacent noncancer cells, respectively. With their carcinogenesis relevance values (CRVs), we identified 152 and 50 significant proteins and their PPI networks (network markers) for early and late stage bladder cancer by statistical assessment. To investigate the evolution of network biomarkers in the carcinogenesis process, primary pathway analysis showed that the significant pathways of early stage bladder cancer are related to ordinary cancer mechanisms, while the ribosome pathway and spliceosome pathway are most important for late stage bladder cancer. Their only intersection is the ubiquitin mediated proteolysis pathway in the whole stage of bladder cancer. The evolution of network biomarkers from early to late stage can reveal the carcinogenesis of bladder cancer. The findings in this study are new clues specific to this study and give us a direction for targeted cancer therapy, and it should be validated in vivo or in vitro in the future.

Background Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology. Methods In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins. Results We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy. Conclusions Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy.