IET Systems Biology最新文献

{"title":"RYR2 mutation in non-small cell lung cancer prolongs survival via down-regulation of DKK1 and up-regulation of GS1-115G20.1: A weighted gene Co-expression network analysis and risk prognostic models","authors":"Wenjun Ren,&nbsp;Yongwu Li,&nbsp;Xi Chen,&nbsp;Sheng Hu,&nbsp;Wanli Cheng,&nbsp;Yu Cao,&nbsp;Jingcheng Gao,&nbsp;Xia Chen,&nbsp;Da Xiong,&nbsp;Hongrong Li,&nbsp;Ping Wang","doi":"10.1049/syb2.12038","DOIUrl":"10.1049/syb2.12038","url":null,"abstract":"<p><i>RYR2</i> mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into <i>RYR2</i> mutant group (<i>n</i> = 337) and <i>RYR2</i> wild group (<i>n</i> = 634), and established Kaplan-Meier curves. The results showed that <i>RYR2</i> mutant group lived longer than the wild group (<i>p</i> = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of <i>DKK1</i> and <i>GS1-115G20.1</i> expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high <i>DKK1</i> expression with <i>TP53</i>, <i>MTOR</i>, and <i>VEGF</i> expression. Several target miRNAs interacting with <i>GS1-115G20.1</i> were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with <i>RYR2</i> mutation may obtain better prognosis by down-regulating <i>DKK1</i> and up-regulating <i>GS1-115G20.1</i>.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 2","pages":"43-58"},"PeriodicalIF":2.3,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/2f/bc/SYB2-16-43.PMC8965387.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39955582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of the key genes and immune infiltrating cells determined by sex differences in ischaemic stroke through co-expression network module","authors":"Haipeng Xu,&nbsp;Yanzhi Ge,&nbsp;Yang Liu,&nbsp;Yang Zheng,&nbsp;Rong Hu,&nbsp;Conglin Ren,&nbsp;Qianqian Liu","doi":"10.1049/syb2.12039","DOIUrl":"10.1049/syb2.12039","url":null,"abstract":"<p>Stroke is one of the leading causes of patients' death and long-term disability worldwide, and ischaemic stroke (IS) accounts for nearly 80% of all strokes. Differential genes and weighted gene co-expression network analysis (WGCNA) in male and female patients with IS were compared. The authors used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) to analyse the distribution pattern of immune subtypes between male and female patients. In this study, 141 up-regulated and 61 down-regulated genes were gathered and distributed into five modules in response to their correlation degree to clinical traits. The criterion for Gene Ontology (GO) term and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway indicated that detailed analysis had the potential to enhance clinical prediction and to identify the gender-related mechanism. After that, the expression levels of hub genes were measured via the quantitative real-time PCR (qRT-PCR) method. Finally, CCL20, ICAM1 and PTGS2 were identified and these may be some promising targets for sex differences in IS. Besides, the hub genes were further verified by rat experiments. Furthermore, these CIBERSORT results showed that T cells CD8 and Monocytes may be the target for the treatment of male and female patients, respectively.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 1","pages":"28-41"},"PeriodicalIF":2.3,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39744883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and characterization of rectal cancer-related lncRNA-mRNA ceRNA network reveals prognostic biomarkers in rectal cancer","authors":"Guoying Cai,&nbsp;Meifei Sun,&nbsp;Xinrong Li,&nbsp;Junquan Zhu","doi":"10.1049/syb2.12035","DOIUrl":"10.1049/syb2.12035","url":null,"abstract":"<p>Rectal cancer is an important cause of cancer-related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA-mRNA pairs presented strong prognosis effects, such as <i>GPM6A</i>, <i>METTL24</i>, <i>SCN7A</i>, <i>HAND2-AS1</i> and <i>PDZRN4</i>. Then, the rectal cancer-related lncRNA-mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1-SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA-mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA-TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2-AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA-mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 6","pages":"192-204"},"PeriodicalIF":2.3,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675822/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39490334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-model fusion of classifiers for blood pressure estimation","authors":"Qi Ye,&nbsp;Bingo Wing-Kuen Ling,&nbsp;Nuo Xu,&nbsp;Yuxin Lin,&nbsp;Lingyue Hu","doi":"10.1049/syb2.12033","DOIUrl":"10.1049/syb2.12033","url":null,"abstract":"<p>Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi-model fusion of the classifiers. Here, the support vector machine, the random forest and the <i>K</i>-nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi-model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 6","pages":"184-191"},"PeriodicalIF":2.3,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/8c/00/SYB2-15-184.PMC8675793.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39374171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive investigation of RNA-sequencing dataset reveals the hub genes and molecular mechanisms of coronavirus disease 2019 acute respiratory distress syndrome","authors":"Wangsheng Deng,&nbsp;Jiaxing Zeng,&nbsp;Shunyu Lu,&nbsp;Chaoqian Li","doi":"10.1049/syb2.12034","DOIUrl":"10.1049/syb2.12034","url":null,"abstract":"<p>The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 6","pages":"205-218"},"PeriodicalIF":2.3,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/9f/4b/SYB2-15-205.PMC8441671.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39279057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable structure robust controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach","authors":"Mohamadreza Homayounzade","doi":"10.1049/syb2.12032","DOIUrl":"10.1049/syb2.12032","url":null,"abstract":"<p>Diabetes mellitus type 1 occurs when <math>\u0000 <mrow>\u0000 <mi>β</mi>\u0000 <mo>-</mo>\u0000 </mrow></math>cells in the pancreas are destroyed by the immune system. As a result, the pancreas cannot produce adequate insulin, and the glucose enters the cells to produce energy. To elevate the glycaemic concentration, sufficient amount of insulin should be taken orally or injected into the human body. Artificial pancreas is a device that automatically regulates the level of body insulin by injecting the requisite amount of insulin into the human body. A finite-time robust feedback controller based on the Extended Bergman Minimal Model is designed here. The controller is designed utilizing the backstepping approach and is robust against the unknown external disturbance and parametric uncertainties. The stability of the system is proved using the Lyapunov theorem. The controller is exponentially stable and hence provides the finite-time convergence of the blood glucose concentration to its desired magnitude. The effectiveness of the proposed control method is shown through simulation in MATLAB/Simulink environment via comparisons with previous studies.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 6","pages":"173-183"},"PeriodicalIF":2.3,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/38/c9/SYB2-15-173.PMC8675804.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39163460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage neural network prediction of chronic kidney disease","authors":"Hongquan Peng,&nbsp;Haibin Zhu,&nbsp;Chi Wa Ao Ieong,&nbsp;Tao Tao,&nbsp;Tsung Yang Tsai,&nbsp;Zhi Liu","doi":"10.1049/syb2.12031","DOIUrl":"10.1049/syb2.12031","url":null,"abstract":"<p>Accurate detection of chronic kidney disease (CKD) plays a pivotal role in early diagnosis and treatment. Measured glomerular filtration rate (mGFR) is considered the benchmark indicator in measuring the kidney function. However, due to the high resource cost of measuring mGFR, it is usually approximated by the estimated glomerular filtration rate, underscoring an urgent need for more precise and stable approaches. With the introduction of novel machine learning methodologies, prediction performance is shown to be significantly improved across all available data, but the performance is still limited because of the lack of models in dealing with ultra-high dimensional datasets. This study aims to provide a two-stage neural network approach for prediction of GFR and to suggest some other useful biomarkers obtained from the blood metabolites in measuring GFR. It is a composite of feature shrinkage and neural network when the number of features is much larger than the number of training samples. The results show that the proposed method outperforms the existing ones, such as convolutionneural network and direct deep neural network.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 5","pages":"163-171"},"PeriodicalIF":2.3,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/71/55/SYB2-15-163.PMC8675857.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39038177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constraint-based models for dominating protein interaction networks","authors":"Adel A. Alofairi,&nbsp;Emad Mabrouk,&nbsp;Ibrahim E. Elsemman","doi":"10.1049/syb2.12021","DOIUrl":"10.1049/syb2.12021","url":null,"abstract":"&lt;p&gt;The minimum dominating set (MDSet) comprises the smallest number of graph nodes, where other graph nodes are connected with at least one MDSet node. The MDSet has been successfully applied to extract proteins that control protein–protein interaction (PPI) networks and to reveal the correlation between structural analysis and biological functions. Although the PPI network contains many MDSets, the identification of multiple MDSets is an NP-complete problem, and it is difficult to determine the best MDSets, enriched with biological functions. Therefore, the MDSet model needs to be further expanded and validated to find constrained solutions that differ from those generated by the traditional models. Moreover, by identifying the critical set of the network, the set of nodes common to all MDSets can be time-consuming. Herein, the authors adopted the minimisation of metabolic adjustment (MOMA) algorithm to develop a new framework, called maximisation of interaction adjustment (MOIA). In MOIA, they provide three models; the first one generates two MDSets with a minimum number of shared proteins, the second model generates constrained multiple MDSets (&lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-MDSets), and the third model generates user-defined MDSets, containing the maximum number of essential genes and/or other important genes of the PPI network. In practice, these models significantly reduce the cost of finding the critical set and classifying the graph nodes. Herein, the authors termed the critical set as the &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-critical set, where &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; is the number of MDSets generated by the proposed model. Then, they defined a new set of proteins called the &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-critical set, where each node belongs to &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt; MDSets. This set has been shown to be as important as the &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-critical set and contains many essential genes, transcription factors, and protein kinases as the &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-critical set. The &lt;math&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;1&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;&lt;/math&gt;-critical set can be used to extend the search for drug target proteins. Based on the performance of the MOIA mod","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 5","pages":"148-162"},"PeriodicalIF":2.3,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38959855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long non-coding RNAs and their targets as potential biomarkers in breast cancer","authors":"Maryam Khalid,&nbsp;Rehan Zafar Paracha,&nbsp;Maryum Nisar,&nbsp;Sumaira Malik,&nbsp;Salma Tariq,&nbsp;Iqra Arshad,&nbsp;Amnah Siddiqa,&nbsp;Zamir Hussain,&nbsp;Jamil Ahmad,&nbsp;Amjad Ali","doi":"10.1049/syb2.12020","DOIUrl":"10.1049/syb2.12020","url":null,"abstract":"Abstract Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease‐specific biomarkers. Recently, long non‐coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post‐transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co‐expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co‐expressed lncRNAs and their cis‐ and trans‐regulating mRNA targets which include RP11‐108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 5","pages":"137-147"},"PeriodicalIF":2.3,"publicationDate":"2021-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38982766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bifurcation analyses and potential landscapes of a cortex-basal ganglia-thalamus model.","authors":"Chenri Yan,&nbsp;Quansheng Liu,&nbsp;Yuanhong Bi","doi":"10.1049/syb2.12018","DOIUrl":"https://doi.org/10.1049/syb2.12018","url":null,"abstract":"<p><p>The dynamics of cortical neuronal activity plays important roles in controlling body movement and is regulated by connection weights between neurons in a cortex-basal ganglia-thalamus (BGCT) loop. Beta-band oscillation of cortical activity is closely associated with the movement disorder of Parkinson's disease, which is caused by an imbalance in the connection weights of direct and indirect pathways in the BGCT loop. In this study, the authors investigate how the dynamics of cortical activity are modulated by connection weights of direct and indirect pathways in the BGCT loop under low dopamine levels through bifurcation analyses and potential landscapes. The results reveal that cortical activity displays rich dynamics under different connection weights, including one, two, or three stable steady states, one or two stable limit cycles, and the coexistence of one stable limit cycle with one stable steady state or two stable ones. For a low dopamine level, cortical activity exhibits oscillation for larger connection weights of direct and indirect pathways. The stability of these stable dynamics is explored by the potential landscapes.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"15 3","pages":"101-109"},"PeriodicalIF":2.3,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38880590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}