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BMC Systems Biology

Yan-Feng Cao, Shi-Feng Wang, Xi Li, Yan-Ling Zhang, Yan-Jiang Qiao
BACKGROUND: Cancer is the second most common cause of death globally. The anticancer effects of Tanshinone IIA (Tan IIA ) has been confirmed by numerous researches. However, the underlying mechanism remained to be integrated in systematic format. Systems biology embraced the complexity of cancer; therefore, a system study approach was proposed in the present study to explore the anticancer mechanism of Tan IIA based on network pharmacology. METHOD: Agilent Literature Search (ALS), a text-mining tool, was used to pull protein targets of Tan IIA ...
October 29, 2018: BMC Systems Biology
Farrah Sadre-Marandi, Thabat Dahdoul, Michael C Reed, H Frederik Nijhout
BACKGROUND: There are large differences between men and women of child-bearing age in the expression level of 5 key enzymes in one-carbon metabolism almost certainly caused by the sex hormones. These male-female differences in one-carbon metabolism are greatly accentuated during pregnancy. Thus, understanding the origin and consequences of sex differences in one-carbon metabolism is important for precision medicine. RESULTS: We have created a mathematical model of hepatic one-carbon metabolism based on the underlying physiology and biochemistry...
October 24, 2018: BMC Systems Biology
T Conrad, O Kniemeyer, S G Henkel, T Krüger, D J Mattern, V Valiante, R Guthke, I D Jacobsen, A A Brakhage, S Vlaic, J Linde
BACKGROUND: Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration...
October 20, 2018: BMC Systems Biology
Adithya Sagar, Rachel LeCover, Christine Shoemaker, Jeffrey Varner
BACKGROUND: Mathematical modeling is a powerful tool to analyze, and ultimately design biochemical networks. However, the estimation of the parameters that appear in biochemical models is a significant challenge. Parameter estimation typically involves expensive function evaluations and noisy data, making it difficult to quickly obtain optimal solutions. Further, biochemical models often have many local extrema which further complicates parameter estimation. Toward these challenges, we developed Dynamic Optimization with Particle Swarms (DOPS), a novel hybrid meta-heuristic that combined multi-swarm particle swarm optimization with dynamically dimensioned search (DDS)...
October 12, 2018: BMC Systems Biology
Daniel Cook, Sirisha Achanta, Jan B Hoek, Babatunde A Ogunnaike, Rajanikanth Vadigepalli
BACKGROUND: Recent results from single cell gene and protein regulation studies are starting to uncover the previously underappreciated fact that individual cells within a population exhibit high variability in the expression of mRNA and proteins (i.e., molecular variability). By combining cellular network modeling, and high-throughput gene expression measurements in single cells, we seek to reconcile the high molecular variability in single cells with the relatively low variability in tissue-scale gene and protein expression and the highly coordinated functional responses of tissues to physiological challenges...
October 3, 2018: BMC Systems Biology
Xinyue Luo, Ruijie Song, Murat Acar
BACKGROUND: Gene-environment interactions are often mediated though gene networks in which gene expression products interact with other network components to dictate network activity levels, which in turn determines the fitness of the host cell in specific environments. Even though a gene network is the right context for studying gene-environment interactions, we have little understanding on how systematic genetic perturbations affects fitness in the context of a gene network. RESULTS: Here we examine the effect of combinatorial gene dosage alterations on gene network activity and cellular fitness...
September 26, 2018: BMC Systems Biology
Elliot Rowe, Bernhard O Palsson, Zachary A King
BACKGROUND: Flux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works. RESULTS: To meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization...
September 26, 2018: BMC Systems Biology
Kimberly C Olney, David B Nyer, Daniel A Vargas, Melissa A Wilson Sayres, Karmella A Haynes
BACKGROUND: Mounting evidence from genome-wide studies of cancer shows that chromatin-mediated epigenetic silencing at large cohorts of genes is strongly linked to a poor prognosis. This mechanism is thought to prevent cell differentiation and enable evasion of the immune system. Drugging the cancer epigenome with small molecule inhibitors to release silenced genes from the repressed state has emerged as a powerful approach for cancer research and drug development. Targets of these inhibitors include chromatin-modifying enzymes that can acquire drug-resistant mutations...
September 25, 2018: BMC Systems Biology
Andreas Kremling, Johannes Geiselmann, Delphine Ropers, Hidde de Jong
BACKGROUND: Carbon catabolite repression (CCR) controls the order in which different carbon sources are metabolised. Although this system is one of the paradigms of regulation in bacteria, the underlying mechanisms remain controversial. CCR involves the coordination of different subsystems of the cell - responsible for the uptake of carbon sources, their breakdown for the production of energy and precursors, and the conversion of the latter to biomass. The complexity of this integrated system, with regulatory mechanisms cutting across metabolism, gene expression, and signalling, has motivated important modelling efforts over the past four decades, especially in the enterobacterium Escherichia coli...
September 21, 2018: BMC Systems Biology
Michael A Rowland, Hannah Wear, Karen H Watanabe, Kurt A Gust, Michael L Mayo
BACKGROUND: A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals. RESULTS: We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals...
August 7, 2018: BMC Systems Biology
Minoo Ashtiani, Ali Salehzadeh-Yazdi, Zahra Razaghi-Moghadam, Holger Hennig, Olaf Wolkenhauer, Mehdi Mirzaie, Mohieddin Jafari
BACKGROUND: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins...
July 31, 2018: BMC Systems Biology
Håvard G Frøysa, Shirin Fallahi, Nello Blaser
BACKGROUND: The dynamics of biochemical networks can be modelled by systems of ordinary differential equations. However, these networks are typically large and contain many parameters. Therefore model reduction procedures, such as lumping, sensitivity analysis and time-scale separation, are used to simplify models. Although there are many different model reduction procedures, the evaluation of reduced models is difficult and depends on the parameter values of the full model. There is a lack of a criteria for evaluating reduced models when the model parameters are uncertain...
July 27, 2018: BMC Systems Biology
Rajat Anand, Dipanka Tanu Sarmah, Samrat Chatterjee
BACKGROUND: Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease. RESULTS: In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice...
July 25, 2018: BMC Systems Biology
Hooman Sedghamiz, Matthew Morris, Travis J A Craddock, Darrell Whitley, Gordon Broderick
BACKGROUND: The hypothalamic-pituitary-adrenal (HPA) axis is a central regulator of stress response and its dysfunction has been associated with a broad range of complex illnesses including Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS). Though classical mathematical approaches have been used to model HPA function in isolation, its broad regulatory interactions with immune and central nervous function are such that the biological fidelity of simulations is undermined by the limited availability of reliable parameter estimates...
July 17, 2018: BMC Systems Biology
Guan-Sheng Liu, Richard Ballweg, Alan Ashbaugh, Yin Zhang, Joseph Facciolo, Melanie T Cushion, Tongli Zhang
BACKGROUND: The yeast-like fungi Pneumocystis, resides in lung alveoli and can cause a lethal infection known as Pneumocystis pneumonia (PCP) in hosts with impaired immune systems. Current therapies for PCP, such as trimethoprim-sulfamethoxazole (TMP-SMX), suffer from significant treatment failures and a multitude of serious side effects. Novel therapeutic approaches (i.e. newly developed drugs or novel combinations of available drugs) are needed to treat this potentially lethal opportunistic infection...
July 17, 2018: BMC Systems Biology
Bob Strome, Ian Shenyen Hsu, Mitchell Li Cheong Man, Taraneh Zarin, Alex Nguyen Ba, Alan M Moses
BACKGROUND: The effort to characterize intrinsically disordered regions of signaling proteins is rapidly expanding. An important class of disordered interaction modules are ubiquitous and functionally diverse elements known as short linear motifs (SLiMs). RESULTS: To further examine the role of SLiMs in signal transduction, we used a previously devised bioinformatics method to predict evolutionarily conserved SLiMs within a well-characterized pathway in S. cerevisiae...
July 3, 2018: BMC Systems Biology
Bin Huang, Dongya Jia, Jingchen Feng, Herbert Levine, José N Onuchic, Mingyang Lu
BACKGROUND: One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks. RESULTS: We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters...
June 19, 2018: BMC Systems Biology
Robert W Smith, Rik P van Rosmalen, Vitor A P Martins Dos Santos, Christian Fleck
BACKGROUND: Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values...
June 19, 2018: BMC Systems Biology
Peter D Karp, Daniel Weaver, Mario Latendresse
BACKGROUND: Reaction gap filling is a computational technique for proposing the addition of reactions to genome-scale metabolic models to permit those models to run correctly. Gap filling completes what are otherwise incomplete models that lack fully connected metabolic networks. The models are incomplete because they are derived from annotated genomes in which not all enzymes have been identified. Here we compare the results of applying an automated likelihood-based gap filler within the Pathway Tools software with the results of manually gap filling the same metabolic model...
June 19, 2018: BMC Systems Biology
Abel Folch-Fortuny, Bas Teusink, Huub C J Hoefsloot, Age K Smilde, Alberto Ferrer
BACKGROUND: A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. RESULTS: Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios...
June 18, 2018: BMC Systems Biology
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