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1.
Phytomedicine ; 108: 154491, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36368285

RESUMO

BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for almost 85% of lung cancer-related deaths worldwide. Xihuang Pill (XHP) is a representative anticancer Chinese patented medicine used to treat NSCLC in China. However, to date, a systematic analysis of XHP's antitumour effects and its impact on the immune microenvironment has not been performed. PURPOSE: Based on the systems biology strategy and experimental validation, the present study aimed to investigate the pharmacological mechanisms involved in treating NSCLC with XHP. METHODS: A subcutaneous tumour model was established to evaluate XHP's tumour-inhibitory effect in BALB/c nude mice. RNA sequencing (RNA-seq) and bioinformatics analysis were conducted to identify differentially expressed genes (DEGs) and signalling pathways related to XHP treatment. Network analysis based on network pharmacology and protein-to-protein networks was applied to identify the compounds and genes targeted by XHP. External data from the TCGA-NSCLC cohort were used to verify the clinical significance of XHP-targeted genes in NSCLC. The expression of survival-related candidate genes after XHP treatment was verified via qPCR. The protein expression of calcium voltage-gated channel subunit alpha 1C (CACNA1C) in different NSCLC cell lines was analysed in the Human Protein Atlas database (HPA) and DepMap Portal. Using the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) algorithm and the single-sample gene set enrichment analysis (ssGSEA) algorithm uncovered the role of CACNA1C in the NSCLC tumour microenvironment (TME). RESULTS: XHP (2 g/kg/d) significantly inhibited the growth of transplanted A549 tumours. RNA-seq identified a total of 529 DEGs (189 upregulated and 340 downregulated). In addition, 542 GO terms, 41 significant KEGG pathways, 9 upregulated hallmarks pathways, and 18 downregulated hallmark pathways were enriched. These GO terms and signalling pathways were closely related to cell proliferation, immunity, energy metabolism, and the inflammatory response of NSCLC. In addition, XHP's network pharmacology analysis identified 301 compounds and 1,432 target genes. A comprehensive strategic analysis identified CACNA1C as a promising gene by which XHP targets and regulates the TME of NSCLC, benefiting patient survival. CACNA1C expression was positively correlated with both the immune score and stromal score but negatively correlated with the tumour purity score. Additionally, CACNA1C expression was significantly correlated with the infiltration levels of 15 types of immune cells and the expression levels of 6 well-known checkpoint genes. CONCLUSIONS: Our results show that by regulating the pathways associated with cell proliferation and immunity, XHP can suppress cancer cell growth in NSCLC. Additionally, XHP may increase the expression of CACNA1C to suppress immune cell infiltration and regulate the expression of checkpoint-related genes, thereby improving the overall survival of NSCLC patients.


Assuntos
Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Camundongos , Animais , Humanos , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Biologia de Sistemas , Camundongos Nus , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Regulação Neoplásica da Expressão Gênica , Microambiente Tumoral
2.
FASEB J ; 37(1): e22660, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36468661

RESUMO

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.


Assuntos
Descoberta de Drogas , Reposicionamento de Medicamentos , Mapas de Interação de Proteínas , Biologia de Sistemas , Conhecimento
3.
Hist Philos Life Sci ; 44(4): 55, 2022 Nov 03.
Artigo em Inglês | MEDLINE | ID: mdl-36326966

RESUMO

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard (2010) contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all mechanisms are more or less general (or abstract), and on the other, many (if not all) design principles are causal systems.


Assuntos
Biologia de Sistemas , Causalidade
4.
NPJ Syst Biol Appl ; 8(1): 42, 2022 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-36316338

RESUMO

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.


Assuntos
Algoritmos , Biologia de Sistemas , Biologia de Sistemas/métodos , Redes Reguladoras de Genes/genética , Transdução de Sinais/fisiologia
5.
Adv Exp Med Biol ; 1385: 1-22, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36352209

RESUMO

Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their cellular function has progressed continuously. Today, we have a good understanding of miRNA-mediated gene regulation, miRNA-mediated cross talk between genes including competing endogenous RNAs, and miRNA-mediated signaling transduction both in normal human physiology and in diseases.Besides, these noncoding RNAs have shown their value for clinical applications, especially in an oncological context. They can be used as reliable biomarkers for cancer diagnosis and prognosis and attract increasing attention as potential therapeutic targets. Many achievements made in the miRNA field are based on joint efforts from computational and molecular biologists. Systems biology approaches, which integrate computational and experimental methods, have played a fundamental role in uncovering the cellular functions of miRNAs.In this chapter, we review and discuss the role of miRNAs in oncology from a system biology perspective. We first describe biological facts about miRNA genetics and function. Next, we discuss the role of miRNAs in cancer progression and review the application of miRNAs in cancer diagnostics and therapy. Finally, we elaborate on the role that miRNAs play in cancer gene regulatory networks. Taken together, we emphasize the importance of systems biology approaches in our continued efforts to study miRNA cancer regulation.


Assuntos
MicroRNAs , Neoplasias , Humanos , MicroRNAs/genética , Biologia de Sistemas/métodos , Redes Reguladoras de Genes , Neoplasias/diagnóstico , Neoplasias/genética , Neoplasias/terapia , Regulação da Expressão Gênica , Biologia Computacional/métodos
6.
Sci Data ; 9(1): 678, 2022 11 08.
Artigo em Inglês | MEDLINE | ID: mdl-36347894

RESUMO

Recent advances in high-throughput experiments and systems biology approaches have resulted in hundreds of publications identifying "immune signatures". Unfortunately, these are often described within text, figures, or tables in a format not amenable to computational processing, thus severely hampering our ability to fully exploit this information. Here we present a data model to represent immune signatures, along with the Human Immunology Project Consortium (HIPC) Dashboard ( www.hipc-dashboard.org ), a web-enabled application to facilitate signature access and querying. The data model captures the biological response components (e.g., genes, proteins, cell types or metabolites) and metadata describing the context under which the signature was identified using standardized terms from established resources (e.g., HGNC, Protein Ontology, Cell Ontology). We have manually curated a collection of >600 immune signatures from >60 published studies profiling human vaccination responses for the current release. The system will aid in building a broader understanding of the human immune response to stimuli by enabling researchers to easily access and interrogate published immune signatures.


Assuntos
Software , Biologia de Sistemas , Vacinação , Humanos , Metadados
7.
BMC Bioinformatics ; 23(1): 503, 2022 Nov 24.
Artigo em Inglês | MEDLINE | ID: mdl-36434499

RESUMO

BACKGROUND: Building biological networks with a certain function is a challenge in systems biology. For the functionality of small (less than ten nodes) biological networks, most methods are implemented by exhausting all possible network topological spaces. This exhaustive approach is difficult to scale to large-scale biological networks. And regulatory relationships are complex and often nonlinear or non-monotonic, which makes inference using linear models challenging. RESULTS: In this paper, we propose a multi-layer perceptron-based differential equation method, which operates by training a fully connected neural network (NN) to simulate the transcription rate of genes in traditional differential equations. We verify whether the regulatory network constructed by the NN method can continue to achieve the expected biological function by verifying the degree of overlap between the regulatory network discovered by NN and the regulatory network constructed by the Hill function. And we validate our approach by adapting to noise signals, regulator knockout, and constructing large-scale gene regulatory networks using link-knockout techniques. We apply a real dataset (the mesoderm inducer Xenopus Brachyury expression) to construct the core topology of the gene regulatory network and find that Xbra is only strongly expressed at moderate levels of activin signaling. CONCLUSION: We have demonstrated from the results that this method has the ability to identify the underlying network topology and functional mechanisms, and can also be applied to larger and more complex gene network topologies.


Assuntos
Algoritmos , Redes Reguladoras de Genes , Redes Neurais de Computação , Biologia de Sistemas , Modelos Lineares
8.
BMC Genomics ; 23(1): 774, 2022 Nov 25.
Artigo em Inglês | MEDLINE | ID: mdl-36434498

RESUMO

BACKGROUND: Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs. RESULTS: The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10). CONCLUSION: Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes.


Assuntos
Ração Animal , Ingestão de Alimentos , Animais , Bovinos/genética , Ingestão de Alimentos/genética , Biologia de Sistemas , Marcadores Genéticos , Fenótipo
9.
Elife ; 112022 Nov 30.
Artigo em Inglês | MEDLINE | ID: mdl-36449390

RESUMO

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.


Assuntos
Pesquisa Biomédica , Proteômica , Humanos , Proteoma , Biologia de Sistemas , Descoberta de Drogas
10.
Sci Data ; 9(1): 722, 2022 11 24.
Artigo em Inglês | MEDLINE | ID: mdl-36433985

RESUMO

Plasmodium cynomolgi causes zoonotic malarial infections in Southeast Asia and this parasite species is important as a model for Plasmodium vivax and Plasmodium ovale. Each of these species produces hypnozoites in the liver, which can cause relapsing infections in the blood. Here we present methods and data generated from iterative longitudinal systems biology infection experiments designed and performed by the Malaria Host-Pathogen Interaction Center (MaHPIC) to delve deeper into the biology, pathogenesis, and immune responses of P. cynomolgi in the Macaca mulatta host. Infections were initiated by sporozoite inoculation. Blood and bone marrow samples were collected at defined timepoints for biological and computational experiments and integrative analyses revolving around primary illness, relapse illness, and subsequent disease and immune response patterns. Parasitological, clinical, haematological, immune response, and -omic datasets (transcriptomics, proteomics, metabolomics, and lipidomics) including metadata and computational results have been deposited in public repositories. The scope and depth of these datasets are unprecedented in studies of malaria, and they are projected to be a F.A.I.R., reliable data resource for decades.


Assuntos
Malária , Plasmodium cynomolgi , Animais , Interações Hospedeiro-Patógeno , Macaca mulatta , Plasmodium cynomolgi/fisiologia , Esporozoítos , Biologia de Sistemas , Zoonoses
11.
Front Immunol ; 13: 1052850, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36420258

RESUMO

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.


Assuntos
COVID-19 , Insuficiência Cardíaca , Humanos , Biologia de Sistemas , Biologia Computacional , SARS-CoV-2 , Terapia de Alvo Molecular , Insuficiência Cardíaca/genética
12.
Commun Biol ; 5(1): 1282, 2022 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-36418514

RESUMO

The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF-Regulatory Network Reconstruction using EXpression and Collaborative Filtering-a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.


Assuntos
Redes Reguladoras de Genes , Fatores de Transcrição , Humanos , Animais , Fatores de Transcrição/genética , Regulação da Expressão Gênica , Biologia de Sistemas , Drosophila/genética , Saccharomyces cerevisiae/genética , Expressão Gênica
13.
BMC Pulm Med ; 22(1): 437, 2022 Nov 23.
Artigo em Inglês | MEDLINE | ID: mdl-36419000

RESUMO

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log2 ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log2 ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.


Assuntos
Proteômica , Biologia de Sistemas , Humanos , Mostardeira , Progressão da Doença , Pulmão
14.
Toxins (Basel) ; 14(11)2022 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-36356011

RESUMO

A major trend in biomedical research has been an ideological shift away from studying individual components of an organism or biological process in isolation, and towards how those components function collectively, forming the basis of the field now known as systems biology [...].


Assuntos
Pesquisa Biomédica , Biologia de Sistemas , Biologia de Sistemas/métodos , Pesquisa Biomédica/métodos , Previsões
15.
Front Immunol ; 13: 1014515, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36405707

RESUMO

The risk of active tuberculosis disease is 15-21 times higher in those coinfected with human immunodeficiency virus-1 (HIV) compared to tuberculosis alone, and tuberculosis is the leading cause of death in HIV+ individuals. Mechanisms driving synergy between Mycobacterium tuberculosis (Mtb) and HIV during coinfection include: disruption of cytokine balances, impairment of innate and adaptive immune cell functionality, and Mtb-induced increase in HIV viral loads. Tuberculosis granulomas are the interface of host-pathogen interactions. Thus, granuloma-based research elucidating the role and relative impact of coinfection mechanisms within Mtb granulomas could inform cohesive treatments that target both pathogens simultaneously. We review known interactions between Mtb and HIV, and discuss how the structure, function and development of the granuloma microenvironment create a positive feedback loop favoring pathogen expansion and interaction. We also identify key outstanding questions and highlight how coupling computational modeling with in vitro and in vivo efforts could accelerate Mtb-HIV coinfection discoveries.


Assuntos
Coinfecção , Infecções por HIV , HIV-1 , Tuberculose , Humanos , Biologia de Sistemas , Granuloma , Infecções por HIV/complicações
16.
Eur J Med Res ; 27(1): 251, 2022 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-36380355

RESUMO

BACKGROUND: Patients with non-alcoholic fatty liver disease (NAFLD) may be more susceptible to coronavirus disease 2019 (COVID-19) and even more likely to suffer from severe COVID-19. Whether there is a common molecular pathological basis for COVID-19 and NAFLD remains to be identified. The present study aimed to elucidate the transcriptional alterations shared by COVID-19 and NAFLD and to identify potential compounds targeting both diseases. METHODS: Differentially expressed genes (DEGs) for COVID-19 and NAFLD were extracted from the GSE147507 and GSE89632 datasets, and common DEGs were identified using the Venn diagram. Subsequently, we constructed a protein-protein interaction (PPI) network based on the common DEGs and extracted hub genes. Then, we performed gene ontology (GO) and pathway analysis of common DEGs. In addition, transcription factors (TFs) and miRNAs regulatory networks were constructed, and drug candidates were identified. RESULTS: We identified a total of 62 common DEGs for COVID-19 and NAFLD. The 10 hub genes extracted based on the PPI network were IL6, IL1B, PTGS2, JUN, FOS, ATF3, SOCS3, CSF3, NFKB2, and HBEGF. In addition, we also constructed TFs-DEGs, miRNAs-DEGs, and protein-drug interaction networks, demonstrating the complex regulatory relationships of common DEGs. CONCLUSION: We successfully extracted 10 hub genes that could be used as novel therapeutic targets for COVID-19 and NAFLD. In addition, based on common DEGs, we propose some potential drugs that may benefit patients with COVID-19 and NAFLD.


Assuntos
COVID-19 , MicroRNAs , Hepatopatia Gordurosa não Alcoólica , Humanos , Hepatopatia Gordurosa não Alcoólica/genética , Hepatopatia Gordurosa não Alcoólica/metabolismo , Redes Reguladoras de Genes , Biologia de Sistemas , Perfilação da Expressão Gênica , Biologia Computacional , COVID-19/genética , MicroRNAs/genética
17.
PLoS Comput Biol ; 18(11): e1010635, 2022 11.
Artigo em Inglês | MEDLINE | ID: mdl-36374853

RESUMO

Visualization is a key recurring requirement for effective analysis of relational data. Biology is no exception. It is imperative to annotate and render biological models in standard, widely accepted formats. Finding graph-theoretical properties of pathways as well as identifying certain paths or subgraphs of interest in a pathway are also essential for effective analysis of pathway data. Given the size of available biological pathway data nowadays, automatic layout is crucial in understanding the graphical representations of such data. Even though there are many available software tools that support graphical display of biological pathways in various formats, there is none available as a service for on-demand or batch processing of biological pathways for automatic layout, customized rendering and mining paths or subgraphs of interest. In addition, there are many tools with fine rendering capabilities lacking decent automatic layout support. To fill this void, we developed a web service named SyBLaRS (Systems Biology Layout and Rendering Service) for automatic layout of biological data in various standard formats as well as construction of customized images in both raster image and scalable vector formats of these maps. Some of the supported standards are more generic such as GraphML and JSON, whereas others are specialized to biology such as SBGNML (The Systems Biology Graphical Notation Markup Language) and SBML (The Systems Biology Markup Language). In addition, SyBLaRS supports calculation and highlighting of a number of well-known graph-theoretical properties as well as some novel graph algorithms turning a specified set of objects of interest to a minimal pathway of interest. We demonstrate that SyBLaRS can be used both as an offline layout and rendering service to construct customized and annotated pictures of pathway models and as an online service to provide layout and rendering capabilities for systems biology software tools. SyBLaRS is open source and publicly available on GitHub and freely distributed under the MIT license. In addition, a sample deployment is available here for public consumption.


Assuntos
Software , Biologia de Sistemas , Biologia de Sistemas/métodos , Algoritmos , Modelos Biológicos
18.
Int J Mol Sci ; 23(20)2022 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-36292921

RESUMO

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.


Assuntos
Neoplasias , Animais , Neoplasias/genética , Fenótipo , Biologia de Sistemas
19.
NPJ Syst Biol Appl ; 8(1): 37, 2022 10 03.
Artigo em Inglês | MEDLINE | ID: mdl-36192551

RESUMO

Omics-based approaches have become increasingly influential in identifying disease mechanisms and drug responses. Considering that diseases and drug responses are co-expressed and regulated in the relevant omics data interactions, the traditional way of grabbing omics data from single isolated layers cannot always obtain valuable inference. Also, drugs have adverse effects that may impair patients, and launching new medicines for diseases is costly. To resolve the above difficulties, systems biology is applied to predict potential molecular interactions by integrating omics data from genomic, proteomic, transcriptional, and metabolic layers. Combined with known drug reactions, the resulting models improve medicines' therapeutical performance by re-purposing the existing drugs and combining drug molecules without off-target effects. Based on the identified computational models, drug administration control laws are designed to balance toxicity and efficacy. This review introduces biomedical applications and analyses of interactions among gene, protein and drug molecules for modeling disease mechanisms and drug responses. The therapeutical performance can be improved by combining the predictive and computational models with drug administration designed by control laws. The challenges are also discussed for its clinical uses in this work.


Assuntos
Proteômica , Biologia de Sistemas , Genômica/métodos , Humanos , Biologia de Sistemas/métodos
20.
PLoS Comput Biol ; 18(10): e1010651, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-36269772

RESUMO

Dynamical systems modeling, particularly via systems of ordinary differential equations, has been used to effectively capture the temporal behavior of different biochemical components in signal transduction networks. Despite the recent advances in experimental measurements, including sensor development and '-omics' studies that have helped populate protein-protein interaction networks in great detail, modeling in systems biology lacks systematic methods to estimate kinetic parameters and quantify associated uncertainties. This is because of multiple reasons, including sparse and noisy experimental measurements, lack of detailed molecular mechanisms underlying the reactions, and missing biochemical interactions. Additionally, the inherent nonlinearities with respect to the states and parameters associated with the system of differential equations further compound the challenges of parameter estimation. In this study, we propose a comprehensive framework for Bayesian parameter estimation and complete quantification of the effects of uncertainties in the data and models. We apply these methods to a series of signaling models of increasing mathematical complexity. Systematic analysis of these dynamical systems showed that parameter estimation depends on data sparsity, noise level, and model structure, including the existence of multiple steady states. These results highlight how focused uncertainty quantification can enrich systems biology modeling and enable additional quantitative analyses for parameter estimation.


Assuntos
Modelos Biológicos , Biologia de Sistemas , Biologia de Sistemas/métodos , Teorema de Bayes , Cinética , Transdução de Sinais/fisiologia
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