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BMC Systems Biology

Babita K Verma, Pushpavanam Subramaniam, Rajanikanth Vadigepalli
BACKGROUND: Liver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection...
January 16, 2019: BMC Systems Biology
Elaine R Reynolds, Ryan Himmelwright, Christopher Sanginiti, Jeffrey O Pfaffmann
BACKGROUND: The Notch signaling pathway is involved in cell fate decision and developmental patterning in diverse organisms. A receptor molecule, Notch (N), and a ligand molecule (in this case Delta or Dl) are the central molecules in this pathway. In early Drosophila embryos, these molecules determine neural vs. skin fates in a reproducible rosette pattern. RESULTS: We have created an agent-based model (ABM) that simulates the molecular components for this signaling pathway as agents acting within a spatial representation of a cell...
January 14, 2019: BMC Systems Biology
Yuqi Zhao, Deepali Jhamb, Le Shu, Douglas Arneson, Deepak K Rajpal, Xia Yang
BACKGROUND: Psoriasis is a complex multi-factorial disease, involving both genetic susceptibilities and environmental triggers. Genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS) have been carried out to identify genetic and epigenetic variants that are associated with psoriasis. However, these loci cannot fully explain the disease pathogenesis. METHODS: To achieve a comprehensive mechanistic understanding of psoriasis, we conducted a systems biology study, integrating multi-omics datasets including GWAS, EWAS, tissue-specific transcriptome, expression quantitative trait loci (eQTLs), gene networks, and biological pathways to identify the key genes, processes, and networks that are genetically and epigenetically associated with psoriasis risk...
January 14, 2019: BMC Systems Biology
Swamy Rakesh Adapa, Rachel A Taylor, Chengqi Wang, Richard Thomson-Luque, Leah R Johnson, Rays H Y Jiang
BACKGROUND: The lack of a continuous long-term in vitro culture system for Plasmodium vivax severely limits our knowledge of pathophysiology of the most widespread malaria parasite. To gain direct understanding of P. vivax human infections, we used Next Generation Sequencing data mining to unravel parasite in vivo expression profiles for P. vivax, and P. falciparum as comparison. RESULTS: We performed cloud and local computing to extract parasite transcriptomes from publicly available raw data of human blood samples...
January 11, 2019: BMC Systems Biology
Lili Wu, Hongli Wang, Qi Ouyang
BACKGROUND: Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing-response functions via synthetic biology...
January 11, 2019: BMC Systems Biology
Benjamín J Sánchez, Feiran Li, Eduard J Kerkhoven, Jens Nielsen
BACKGROUND: A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. RESULTS: SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution...
January 11, 2019: BMC Systems Biology
Hong Zeng, Aidong Yang
BACKGROUND: The formation of acetate by fast-growing Escherichia coli (E. coli) is a commonly observed phenomenon, often referred to as overflow metabolism. Among various studies that have been carried over decades, a recent work (Basan, M. et al. Nature 528, 99-104, 2015) suggested and validated that it is the differential proteomic efficiencies in energy biogenesis between fermentation and respiration that lead to the production of acetate at rapid growth conditions, as the consequence of optimally allocating the limited proteomic resource...
January 10, 2019: BMC Systems Biology
Laurence Yang, Ali Ebrahim, Colton J Lloyd, Michael A Saunders, Bernhard O Palsson
BACKGROUND: Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. RESULTS: We develop DynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. DynamicME correctly predicted the substrate utilization hierarchy on a mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles...
January 9, 2019: BMC Systems Biology
Guillermo de Anda-Jáuregui, Kai Guo, Brett A McGregor, Eva L Feldman, Junguk Hur
BACKGROUND: Aggregation of high-throughput biological data using pathway-based approaches is useful to associate molecular results to functional features related to the studied phenomenon. Biological pathways communicate with one another through the crosstalk phenomenon, forming large networks of interacting processes. RESULTS: In this work, we present the pathway crosstalk perturbation network (PXPN) model, a novel model used to analyze and integrate pathway perturbation data based on graph theory...
January 7, 2019: BMC Systems Biology
Sezin Kircali Ata, Le Ou-Yang, Yuan Fang, Chee-Keong Kwoh, Min Wu, Xiao-Li Li
BACKGROUND: Predicting disease causative genes (or simply, disease genes) has played critical roles in understanding the genetic basis of human diseases and further providing disease treatment guidelines. While various computational methods have been proposed for disease gene prediction, with the recent increasing availability of biological information for genes, it is highly motivated to leverage these valuable data sources and extract useful information for accurately predicting disease genes...
December 31, 2018: BMC Systems Biology
Jongsu Jun, Jungsoo Gim, Yongkang Kim, Hyunsoo Kim, Su Jong Yu, Injun Yeo, Jiyoung Park, Jeong-Ju Yoo, Young Youn Cho, Dong Hyeon Lee, Eun Ju Cho, Jeong-Hoon Lee, Yoon Jun Kim, Seungyeoun Lee, Jung-Hwan Yoon, Youngsoo Kim, Taesung Park
BACKGROUND: Discovering reliable protein biomarkers is one of the most important issues in biomedical research. The ELISA is a traditional technique for accurate quantitation of well-known proteins. Recently, the multiple reaction-monitoring (MRM) mass spectrometry has been proposed for quantifying newly discovered protein and has become a popular alternative to ELISA. For the MRM data analysis, linear mixed modeling (LMM) has been used to analyze MRM data. MSstats is one of the most widely used tools for MRM data analysis that is based on the LMMs...
December 31, 2018: BMC Systems Biology
Jian-Yu Shi, An-Qi Zhang, Shao-Wu Zhang, Kui-Tao Mao, Siu-Ming Yiu
BACKGROUND: During the identification of potential candidates, computational prediction of drug-target interactions (DTIs) is important to subsequent expensive validation in wet-lab. DTI screening considers four scenarios, depending on whether the drug is an existing or a new drug and whether the target is an existing or a new target. However, existing approaches have the following limitations. First, only a few of them can address the most difficult scenario (i.e., predicting interactions between new drugs and new targets)...
December 31, 2018: BMC Systems Biology
Dong Wang, Jie Li, Rui Liu, Yadong Wang
BACKGROUND: With the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods...
December 31, 2018: BMC Systems Biology
Quanya Liu, Peng Chen, Bing Wang, Jun Zhang, Jinyan Li
BACKGROUND: Hot spot residues are functional sites in protein interaction interfaces. The identification of hot spot residues is time-consuming and laborious using experimental methods. In order to address the issue, many computational methods have been developed to predict hot spot residues. Moreover, most prediction methods are based on structural features, sequence characteristics, and/or other protein features. RESULTS: This paper proposed an ensemble learning method to predict hot spot residues that only uses sequence features and the relative accessible surface area of amino acid sequences...
December 31, 2018: BMC Systems Biology
Yiwen Sun, Zexuan Zhu, Zhu-Hong You, Zijie Zeng, Zhi-An Huang, Yu-An Huang
BACKGROUND: MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification. RESULTS: Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity...
December 31, 2018: BMC Systems Biology
Yaping Wen, Guosheng Han, Vo V Anh
BACKGROUND: Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification. RESULTS: In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases...
December 31, 2018: BMC Systems Biology
Kun-Tze Chen, Hsin-Ting Shen, Chin Lung Lu
BACKGROUND: One of the important steps in the process of assembling a genome sequence from short reads is scaffolding, in which the contigs in a draft genome are ordered and oriented into scaffolds. Currently, several scaffolding tools based on a single reference genome have been developed. However, a single reference genome may not be sufficient alone for a scaffolder to generate correct scaffolds of a target draft genome, especially when the evolutionary relationship between the target and reference genomes is distant or some rearrangements occur between them...
December 31, 2018: BMC Systems Biology
Menglan Cai, Limin Li
BACKGROUND: Evaluating the significance for a group of genes or proteins in a pathway or biological process for a disease could help researchers understand the mechanism of the disease. For example, identifying related pathways or gene functions for chromatin states of tumor-specific T cells will help determine whether T cells could reprogram or not, and further help design the cancer treatment strategy. Some existing p-value combination methods can be used in this scenario. However, these methods suffer from different disadvantages, and thus it is still challenging to design more powerful and robust statistical method...
December 31, 2018: BMC Systems Biology
Ke Zhang, Wei Geng, Shuqin Zhang
BACKGROUND: Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms or laboratories. This motivates us to develop robust biomarker identification methods by integrating multiple datasets. METHODS: In this paper, we developed an integrative method for classification based on logistic regression...
December 31, 2018: BMC Systems Biology
Zhen Tian, Zhixia Teng, Shuang Cheng, Maozu Guo
BACKGROUND: Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network...
December 31, 2018: BMC Systems Biology
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