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BMC Systems Biology

Alexander Lück, Verena Wolf
BACKGROUND: Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. RESULTS: We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data...
October 21, 2016: BMC Systems Biology
Katarzyna Jonak, Monika Kurpas, Katarzyna Szoltysek, Patryk Janus, Agata Abramowicz, Krzysztof Puszynski
No abstract text is available yet for this article.
October 21, 2016: BMC Systems Biology
Sayed-Rzgar Hosseini, Andreas Wagner
BACKGROUND: Biological systems are rife with examples of pre-adaptations or exaptations. They range from the molecular scale - lens crystallins, which originated from metabolic enzymes - to the macroscopic scale, such as feathers used in flying, which originally served thermal insulation or waterproofing. An important class of exaptations are novel and useful traits with non-adaptive origins. Whether such origins could be frequent cannot be answered with individual examples, because it is a question about a biological system's potential for exaptation...
October 21, 2016: BMC Systems Biology
Bram Thijssen, Tjeerd M H Dijkstra, Tom Heskes, Lodewyk F A Wessels
BACKGROUND: Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. RESULTS: We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers...
October 21, 2016: BMC Systems Biology
Sung-Hwan Cho, Sang-Min Park, Ho-Sung Lee, Hwang-Yeol Lee, Kwang-Hyun Cho
BACKGROUND: Colorectal cancer arises from the accumulation of genetic mutations that induce dysfunction of intracellular signaling. However, the underlying mechanism of colorectal tumorigenesis driven by genetic mutations remains yet to be elucidated. RESULTS: To investigate colorectal tumorigenesis at a system-level, we have reconstructed a large-scale Boolean network model of the human signaling network by integrating previous experimental results on canonical signaling pathways related to proliferation, metastasis, and apoptosis...
October 20, 2016: BMC Systems Biology
Sang-Mok Choo, Kwang-Hyun Cho
BACKGROUND: Boolean network modeling has been widely used to model large-scale biomolecular regulatory networks as it can describe the essential dynamical characteristics of complicated networks in a relatively simple way. When we analyze such Boolean network models, we often need to find out attractor states to investigate the converging state features that represent particular cell phenotypes. This is, however, very difficult (often impossible) for a large network due to computational complexity...
October 7, 2016: BMC Systems Biology
Katarzyna Jonak, Monika Kurpas, Katarzyna Szoltysek, Patryk Janus, Agata Abramowicz, Krzysztof Puszynski
BACKGROUND: Ataxia telangiectasia mutated (ATM) is a detector of double-strand breaks (DSBs) and a crucial component of the DNA damage response (DDR) along with p53 and NF- κB transcription factors and Wip1 phosphatase. Despite the recent advances in studying the DDR, the mechanisms of cell fate determination after DNA damage induction is still poorly understood. RESULTS: To investigate the importance of various DDR elements with particular emphasis on Wip1, we developed a novel mathematical model of ATM/p53/NF- κB pathways...
August 15, 2016: BMC Systems Biology
Tom S Weber, Mark Dukes, Denise C Miles, Stefan P Glaser, Shalin H Naik, Ken R Duffy
BACKGROUND: Cellular barcoding is a recently developed biotechnology tool that enables the familial identification of progeny of individual cells in vivo. In immunology, it has been used to track the burst-sizes of multiple distinct responding T cells over several adaptive immune responses. In the study of hematopoiesis, it revealed fate heterogeneity amongst phenotypically identical multipotent cells. Most existing approaches rely on ex vivo viral transduction of cells with barcodes followed by adoptive transfer into an animal, which works well for some systems, but precludes barcoding cells in their native environment such as those inside solid tissues...
June 30, 2016: BMC Systems Biology
Jason E McDermott, Hugh D Mitchell, Lisa E Gralinski, Amie J Eisfeld, Laurence Josset, Armand Bankhead, Gabriele Neumann, Susan C Tilton, Alexandra Schäfer, Chengjun Li, Shufang Fan, Shannon McWeeney, Ralph S Baric, Michael G Katze, Katrina M Waters
BACKGROUND: The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation...
2016: BMC Systems Biology
David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Reinhard Laubenbacher
BACKGROUND: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation...
2016: BMC Systems Biology
Edwin F Juarez, Roy Lau, Samuel H Friedman, Ahmadreza Ghaffarizadeh, Edmond Jonckheere, David B Agus, Shannon M Mumenthaler, Paul Macklin
BACKGROUND: The increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge. RESULTS: CellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training...
2016: BMC Systems Biology
Stanislav Sokolenko, Marco Quattrociocchi, Marc G Aucoin
BACKGROUND: The estimation of intracellular flux through traditional metabolic flux analysis (MFA) using an overdetermined system of equations is a well established practice in metabolic engineering. Despite the continued evolution of the methodology since its introduction, there has been little focus on validation and identification of poor model fit outside of identifying "gross measurement error". The growing complexity of metabolic models, which are increasingly generated from genome-level data, has necessitated robust validation that can directly assess model fit...
2016: BMC Systems Biology
Poonam Phalak, Jin Chen, Ross P Carlson, Michael A Henson
BACKGROUND: Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia...
2016: BMC Systems Biology
David M Budden, Edmund J Crampin
BACKGROUND: Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory...
2016: BMC Systems Biology
Maryam Nazarieh, Andreas Wiese, Thorsten Will, Mohamed Hamed, Volkhard Helms
BACKGROUND: Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs...
2016: BMC Systems Biology
Xun Yue, Xing Guo Li, Xin-Qi Gao, Xiang Yu Zhao, Yu Xiu Dong, Chao Zhou
BACKGROUND: Phytohormone synergies and signaling interdependency are important topics in plant developmental biology. Physiological and genetic experimental evidence for phytohormone crosstalk has been accumulating and a genome-scale enzyme correlation model representing the Arabidopsis metabolic pathway has been published. However, an integrated molecular characterization of phytohormone crosstalk is still not available. RESULTS: A novel modeling methodology and advanced computational approaches were used to construct an enzyme-based Arabidopsis phytohormone crosstalk network (EAPCN) at the biosynthesis level...
2016: BMC Systems Biology
Suleyman Vural, Xiaosheng Wang, Chittibabu Guda
BACKGROUND: The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. Several classification strategies based on ER/PR/HER2 expression or the expression profiles of a panel of genes have helped, but such methods often produce misleading results due to their dynamic nature. In contrast, somatic DNA mutations are relatively stable and lead to initiation and progression of many sporadic cancers...
2016: BMC Systems Biology
Zhongming Zhao, Yunlong Liu, Yufei Huang, Kun Huang, Jianhua Ruan
The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13-15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology...
2016: BMC Systems Biology
Sujuan Wu, Junyi Li, Mushui Cao, Jing Yang, Yi-Xue Li, Yuan-Yuan Li
BACKGROUND: Glioma is the most common brain tumor and it has very high mortality rate due to its infiltration and heterogeneity. Precise classification of glioma subtype is essential for proper therapeutic treatment and better clinical prognosis. However, the molecular mechanism of glioma is far from clear and the classical classification methods based on traditional morphologic and histopathologic knowledge are subjective and inconsistent. Recently, classification methods based on molecular characteristics are developed with rapid progress of high throughput technology...
2016: BMC Systems Biology
Rongjie Liu, Chunjiang Qian, Shuqian Liu, Yu-Fang Jin
BACKGROUND: Driving Boolean networks to desired states is of paramount significance toward our ultimate goal of controlling the progression of biological pathways and regulatory networks. Despite recent computational development of controllability of general complex networks and structural controllability of Boolean networks, there is still a lack of bridging the mathematical condition on controllability to real boolean operations in a network. Further, no realtime control strategy has been proposed to drive a Boolean network...
2016: BMC Systems Biology
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