Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics最新文献

{"title":"RNA-seq based mapping strategies to uncover heterogeneity in survival among Pancreatic Ductal Adenocarcinoma (PDAC) patients","authors":"Archana Bhardwaj, C. Josse, D. Van Daele, M. Chavez, K. van Steen","doi":"10.1145/3291757.3291761","DOIUrl":"https://doi.org/10.1145/3291757.3291761","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) is categorized as the seventh leading cause of cancer mortality in the world. Little is known about predictive markers for long-term survival. In this work, we performed a series of transcriptome computational analyses to better understand patient heterogeneity between longterm (LT) and short-term (ST) survivors. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first identified differentially expressed genes between LT/ST. The 216 predicted genes could be linked to multiple metabolic and cell cycle related pathways. Second, we performed unsupervised system biology approaches to obtain gene modules for our PDAC samples. In particular, important modules obtained via weighted gene co-expression network analysis (WGCNA) showed significant correlation with clinical features, including overall survival, tumour size, and tumour invasion. Third, we created individual-level perturbation profiles (PEEP) and found that both group-level and individual-level approaches indicated a change in secondary metabolic pathways and FoxO signalling pathways when comparing ST with LT patients. In addition, individual-level gene expression make-ups seemed to suggest a larger heterogeneity among long-term survivors compared to short-term survivors. In conclusion, despite the small sample size, but using testing strategies for small samples whenever possible, we have shown how the combination of multi-level information can give important clues towards PDAC prognosis and patient follow-up in personalized medicine.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125151518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Human Deubiquitinases Target Specificity by Network-based Analysis towards their Development as Therapeutics Target","authors":"Nurulisa Zulkifle, N. Zulkifli","doi":"10.1145/3291757.3291768","DOIUrl":"https://doi.org/10.1145/3291757.3291768","url":null,"abstract":"Deubiquitinases (DUBs), a component of protein ubiquitination, regulate many biological processes and cellular pathways critical for cell survival, proliferation, genome stability and transcriptional control that are closely related to disease such as cancer. DUBs rely on their interaction with target proteins in order to function within a particular pathway. However, there is currently little information on the DUBs' target protein. To observe the target distribution and specificity of the seven DUBs family (USP, UCH, MJD, OTU, JAMM, MINDY, ZUFSP), protein partners of every member in DUBs family were extracted from IntAct, MINT and IMEx databases and protein-protein interaction (PPI) network was constructed and analysed in Cytoscape, an open source software platform for visualising molecular interaction network. The PPI network of DUBs consists of 2328 nodes and 3409 edges and follows a power law distribution that corresponds to scale-free topology. From the PPI network, DUBs interaction is divided into: (1) DUBs-ubiquitin enzymes (e.g. E2 conjugating enzyme and E3 ligase), (2) DUBs-DUBs or also known as 'DUBbing DUBs' and (3) DUBs-other target proteins. We observed that only approximately 60% of proteins were the unique targets of each DUBs family, suggesting that target preference may not be conferred according to DUBs family as in the case of ubiquitin-linkage preference. Utilising the information from this study, it is anticipated that the potential of DUBs as diseases therapeutics target could be fully explored.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126799240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustered Network Topology of Regulatory System Strengthens the Cellular Robustness to Stress: a Case Study in a Resistant Cultivar of Cassava (Manihot esculenta Crantz) to Viral Infection","authors":"Thanakorn Jaemthaworn, Bhukrit Ruengsrichaiya, T. Saithong, Saowalak Kalapanuluk","doi":"10.1145/3291757.3291760","DOIUrl":"https://doi.org/10.1145/3291757.3291760","url":null,"abstract":"Robustness tradeoff is one of the essential properties of organisms required to withstand environmental perturbations. It is the balance of robustness and fragility of the biological systems in response to changes of surrounding environment. The property has been studied mostly based on modelling approach whereby the robustness is investigated via the impact of in silico perturbation on the system behaviors. Network topology properties, such as scale-free, small-world, and clustering coefficient, have also been introduced to infer the robustness of biological networks. In this work, we aimed to investigate the topological differences in transcriptional regulatory systems that would bring to the distinct robustness phenotype of an organism. Four gene co-expression networks (GCNs) were constructed to represent the transcriptional regulation of Namikonga (resistant) and Albert (susceptible) cassava cultivars under infection of cassava brown streak virus. The network topology analysis showed no significant differences among the GCNs. However, the distinction was observed in the topology of the group of genes having relatively high node degree. The results showed that the local clustering coefficients representing the average clustering coefficients of such particular genes with same degree in the GCNs of Namikonga were significantly higher than that of Albert. Moreover, GCNs of the susceptible cultivar adapted itself to increase the number of genes with high node degree after infection, whereas the resistant one performed inverse action. Taking all together, we finally proposed that the clustered network structure supports the robustness property of the resistant cultivar. Our findings provided the fundamental property required to improve robustness of the regulatory systems, resulted in resistant phenotypic trait. It would contribute to make a design rationale for crop improvement.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134301498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Docking Reveals Pitavastatin and Related Molecules Antagonize 1DHF and Its Pseudogene DHFR2 in Cancer Treatment","authors":"E. Kabir, Mohammad Kawsar Sharif Siam, N. Mustafa, S. M. Kabir","doi":"10.1145/3291757.3291763","DOIUrl":"https://doi.org/10.1145/3291757.3291763","url":null,"abstract":"One of the ways in which cancer may develop is via the folic acid biosynthetic pathway. Disruption of this pathway has a toxic effect on rapidly proliferating cells. The enzyme dihydrofolate reductase (DHFR), referred to, as 1DHF in this paper, is critical for DNA synthesis. This enzyme is highly expressed in cancer cells, making it a potential target for anti-cancer drug development. A positive correlation between cholesterol accumulation and cancer provides insight into further investigation of anti-cancer indication of statin drugs. In this study, drug repurposing, data science and other in silico computational methods have been utilized to find a potential statin antagonist of 1DHF and its functional pseudogene, DHFR2. The resolution of the structure of 1DHF retrieved from RCSB PDB was 2.3 Å. A protein similar to 1DHF was identified using protein BLAST. Furthermore, the structure of the protein, DHFR2 was validated with the help of z-scores, Errat results and Ramachandran plot. Docking of statin drugs with both the proteins were done using PyRx. The drug-protein interactions were identified using Discovery Studio. Among the potential statin drugs chosen, pitavastatin showed the highest binding affinities. The values were -12.7kcal/mol with 1DHF and -10kcal/mol with DHFR2. The important amino acids involved in the non-bonding interactions include THR56, TYR121, ILE7, VAL115, PHE31, PHE34, and LEU22 with 1DHF, and PHE32 and PRO27 with DHFR2. The study provides strong indication that pitavastatin could possibly reduce the risk of cancer development when administered as an anti-cancer drug. This study investigates statin drugs that targeted both 1DHF and DHFR2 in the treatment of cancer and found that pitavastatin demonstrated such effect.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"527 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123572436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RWNetSig","authors":"Youngbeen Moon, Daehwan Lee, Jaebum Kim","doi":"10.1145/3291757.3291762","DOIUrl":"https://doi.org/10.1145/3291757.3291762","url":null,"abstract":"Genetic disorders play an important role in cancer development. Especially, somatic variants, such as single nucleotide variations, insertions, deletions, and copy number variations, in specific genes, can promote tumorigenesis. Recently, NetSig has been developed that integrates existing protein interaction networks to predict cancer driver genes. However, NetSig is limited to use only directly interacting proteins. To address this issue, we developed a new statistic, called RWNetSig, that can be used to predict cancer driver genes by considering indirect as well as direct interactions in a network using the random walk with restart algorithm. In RWNetSig, indirectly interacting proteins are identified, the distance among them is calculated, the significance of their contribution to cancers is combined, and finally the RWNetSig score is calculated using the permutation statistical test. RWNetSig was evaluated and compared with Netsig using the Cosmic classic and the Cancer Gene Census datasets. We found that RWNetSig is superior to NetSig in identifying cancer driver genes. Our study reinforces the usefulness of network-based approaches in the field of the prediction of cancer driver genes. It will also contribute to gain new insight for deeper understanding of cancers.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Analysis of iGlur Delta-2 and other Depression Associated Proteins: Role of TRK, BDNF, CYP2B6, POLG, PICK1 for Early Detection and Treatment of Depression","authors":"Mohammad Kawsar Sharif Siam, E. Kabir, Partha Sanjana Jurashe, Mohammad Umer Sharif Shohan, Samiul Alam Rajib","doi":"10.1145/3291757.3291765","DOIUrl":"https://doi.org/10.1145/3291757.3291765","url":null,"abstract":"Psychiatric diseases relentlessly distress the ability of work also productivity of a person's life. Considering the phenotypic unpredictability amongst patients, it is very hard to understand the pathogenesis. The study aims to identify the pathways responsible for psychiatric disease, especially for depression. Initially 62 depression associated proteins were listed from the UniProt and then hub genes of those proteins were identified. For functional annotation analysis, proteins UniProt IDs were submitted to the Database for Annotation, Visualization and Integrated Discovery shortly known as DAVID. Gene ontologies, protein domains, and pathways were analyzed using the GO enrichment and KEGG. The functional annotation clustering identified a total of 150 GO terms clustered into 23 groups. The pathways, identified from the clustering and KEGG, were overlapped to construct a Protein Protein Interaction (PPI) network. Finally, the common pathways were separated and 300 selected anti-depressants drugs from 5 classes were docked with depression associated proteins such as iGluR DELTA-2 (PDB Id- 5KC8), Dopamine Receptor, D2 (PDB Id- 6CM4), Sodium Dependent Serotonin Transporter (PDB Id- 2KS9), Glutamate receptor ionotropic, NMDA 2B (PDB Id- 5EWL) etc. Six anti-depressant drugs such as sertraline carbamoyl, norethindrone, and aripiprazole had good binding affinities (-10.5, -10.4 and -9.3 respectively) with the proteins of interest. The study also revealed the possible biomarkers such as TRK, BDNF, CYP2B6, POLG, PICK1 suitable for early detection of depression. Building on these findings, more protein clusters can be identified to understand depression and its transduction targets to identify functional biomarkers for early diagnosis.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","authors":"","doi":"10.1145/3291757","DOIUrl":"https://doi.org/10.1145/3291757","url":null,"abstract":"","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"108 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131746976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of SNP Subsets for Severity of Beta-thalassaemia Classification Problem","authors":"Ek Thamwiwatthana, Kitsuchart Pasupa, S. Tongsima","doi":"10.1145/3291757.3291770","DOIUrl":"https://doi.org/10.1145/3291757.3291770","url":null,"abstract":"Single-nucleotide polymorphisms (SNPs) are important genetic variables that are very popular in Genome-wide association study at the present time. They are often used in studies related to genetic disorders. A distinctive trait of SNPs is that there are a lot of them since they are variables originated from various positions in a DNA sequence. Unfortunately, the number of samples investigated are usually far fewer than the number of SNPs and so an over-fitting often occurs when one wants to construct a predictive model for classifying a sample into a case or a control. This study investigated a dataset on beta-thalassemia, a common genetic disorder widely found in Thai population. The data in the set are divided into two groups: severe and mild groups. The aims of the study were to develop and evaluate methods for screening and ranking SNPs related to this disorder. The screening methods tested were Chi-squared test (χ2), Information Gain, and Gradient Boosting (GB). The SNPs that were screened in and selected were then used to construct a predictive model for classifying a sample to be either a severe or mild case. The model construction methods tested were Support Vector Machine (SVM), GB, and Naïve Bayes. Several combinations of a screening method and a model construction method were evaluated, and the evaluation results show that the best combination was χ2-SVM which used the number of selected SNPs of 10.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134257725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling and Verifying Dynamic Properties of Biological Neural Networks in Coq","authors":"Abdorrahim Bahrami, Elisabetta De Maria, A. Felty","doi":"10.1145/3291757.3291771","DOIUrl":"https://doi.org/10.1145/3291757.3291771","url":null,"abstract":"Formal verification has become increasingly important because of the kinds of guarantees that it can provide for software systems. Verification of models of biological and medical systems is a promising application of formal verification. Human neural networks have recently been emulated and studied as a biological system. Some recent research has been done on modelling some crucial neuronal circuits and using model checking techniques to verify their temporal properties. In large case studies, model checkers often cannot prove the given property at the desired level of generality. In this paper, we provide a model using the Coq Proof Assistant and prove properties concerning the dynamic behavior of some basic neuronal structures. Understanding the behavior of these modules is crucial because they constitute the elementary building blocks of bigger neuronal circuits. By using a proof assistant, we guarantee that the properties are true for any input values, any length of input, and any amount of time. With such a model, there is the potential to detect inactive regions of the human brain and to treat mental disorders. Furthermore, our approach can be generalized to the verification of other kinds of networks, such as regulatory, metabolic, or environmental networks.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129565227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative gene clusters analysis of Cordyceps militaris and related entomopathogenic fungi","authors":"Chayapat Wizaza, Warasinee Mujchariyakul, W. Vongsangnak, P. Patumcharoenpol, W. Kittichotirat","doi":"10.1145/3291757.3291759","DOIUrl":"https://doi.org/10.1145/3291757.3291759","url":null,"abstract":"Cordyceps militaris is an entomopathogenic fungus, which is being used as a biocontrol agent for insects as well as used as a fungal cell factory for pharmaceutical and industrial biotechnology. Comparative gene clusters analysis provides details on gene cluster function that can lead to a better understanding of evolution of fungal entomopathogenicity. In this study, we aimed to identify genome-specific and shared gene clusters together with their functions by comparative gene clusters analysis of C. militaris and other related entomopathogenic fungi. Initially, we retrieved the C. militaris and other related entomopathogenic fungal genome sequences, including two species of Cordyceps, three species of Metarhizium, Moelleriella libera, Isaria fumosorosea and Beauveria bassiana. Their genomes were then annotated for gene clusters associated with secondary metabolites by antiSMASH and SMURF tools together with manual curation. Accordingly, the results showed that 720 gene clusters were genome-specific and the remaining 391 gene clusters were shared between genomes. Of shared gene clusters, interestingly, we observed two gene clusters which were Huperzine A and Fumosorinone (2-pyridone alkaloid molecule) with orthologous genes between C. militaris and I. fumosorosea. This suggests that these species may have close evolutionary relationship than the other entomopathogenic fungi. This study provides useful information in gene clusters that are associated with secondary metabolite biosynthesis in C. militaris and related entomopathogenic fungi. It can be essential for metabolic analysis of this fungal species and can serve as a way to further develop in pharmacological and industrial fields in the future.","PeriodicalId":307264,"journal":{"name":"Proceedings of the 9th International Conference on Computational Systems-Biology and Bioinformatics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123490217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}