Navegando por Palavras-chave "systems biology"

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    Network Analysis to Risk Stratify Patients With Exercise Intolerance
    (Lippincott Williams & Wilkins, 2018) Oldham, William M.; Oliveira, Rudolf Krawczenko Feitoza de [UNIFESP]; Wang, Rui-Sheng; Opotowsky, Alexander R.; Rubins, David M.; Hainer, Jon; Wertheim, Bradley M.; Alba, George A.; Choudhary, Gaurav; Tornyos, Adrienn; MacRae, Calum A.; Loscalzo, Joseph; Leopold, Jane A.; Waxman, Aaron B.; Olschewski, Horst; Kovacs, Gabor; Systrom, David M.; Maron, Bradley A.
    Rationale: Current methods assessing clinical risk because of exercise intolerance in patients with cardiopulmonary disease rely on a small subset of traditional variables. Alternative strategies incorporating the spectrum of factors underlying prognosis in at-risk patients may be useful clinically, but are lacking. Objective: Use unbiased analyses to identify variables that correspond to clinical risk in patients with exercise intolerance. Methods and Results: Data from 738 consecutive patients referred for invasive cardiopulmonary exercise testing at a single center (2011-2015) were analyzed retrospectively (derivation cohort). A correlation network of invasive cardiopulmonary exercise testing parameters was assembled using vertical bar r vertical bar>0.5. From an exercise network of 39 variables (ie, nodes) and 98 correlations (ie, edges) corresponding to P<9.5e(-46) for each correlation, we focused on a subnetwork containing peak volume of oxygen consumption (pVo(2)) and 9 linked nodes. K-mean clustering based on these 10 variables identified 4 novel patient clusters characterized by significant differences in 44 of 45 exercise measurements (P<0.01). Compared with a probabilistic model, including 23 independent predictors of pVo(2) and pVo(2) itself, the network model was less redundant and identified clusters that were more distinct. Cluster assignment from the network model was predictive of subsequent clinical events. For example, a 4.3-fold (P<0.0001
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    Toward Community Standards and Software for Whole-Cell Modeling
    (Ieee-Inst Electrical Electronics Engineers Inc, 2016) Waltemath, Dagmar; Karr, Jonathan R.; Bergmann, Frank T.; Chelliah, Vijayalakshmi; Hucka, Michael; Krantz, Marcus; Liebermeister, Wolfram; Mendes, Pedro; Myers, Chris J.; Pir, Pinar; Alaybeyoglu, Begum; Aranganathan, Naveen K.; Baghalian, Kambiz; Bittig, Arne T.; Burke, Paulo Eduardo Pinto [UNIFESP]; Cantarelli, Matteo; Chew, Yin Hoon; Costa, Rafael S.; Cursons, Joseph; Czauderna, Tobias; Goldberg, Arthur P.; Gomez, Harold F.; Hahn, Jens; Hameri, Tuure; Gardiol, Daniel F. Hernandez; Kazakiewicz, Denis; Kiselev, Ilya; Knight-Schrijver, Vincent; Knuepfer, Christian; Koenig, Matthias; Lee, Daewon; Lloret-Villas, Audald; Mandrik, Nikita; Medley, J. Kyle; Moreau, Bertrand; Naderi-Meshkin, Hojjat; Palaniappan, Sucheendra K.; Priego-Espinosa, Daniel; Scharm, Martin; Sharma, Mahesh; Smallbone, Kieran; Stanford, Natalie J.; Song, Je-Hoon; Theile, Tom; Tokic, Milenko; Tomar, Namrata; Toure, Vasundra; Uhlendorf, Jannis; Varusai, Thawfeek M.; Watanabe, Leandro H.; Wendland, Florian; Wolfien, Markus; Yurkovich, James T.; Zhu, Yan; Zardilis, Argyris; Zhukova, Anna; Schreiber, Falk
    Objective: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. Methods: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. Results: Our analysis revealed several challenges to representing WC models using the current standards. Conclusion: We, therefore, propose several new WC modeling standards, software, and databases. Significance: We anticipate that these new standards and software will enable more comprehensive models.