The Experts below are selected from a list of 22197 Experts worldwide ranked by ideXlab platform
Gustavo Stolovitzky - One of the best experts on this subject based on the ideXlab platform.
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when the optimal is not the best parameter estimation in complex Biological Models
PLOS ONE, 2010Co-Authors: Diego Fernandez Slezak, C Suarez, Guillermo A Cecchi, G Marshall, Gustavo StolovitzkyAbstract:Background: The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical Models of cancer growth. These Models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor onditions may result in Biologically implausible values. Results: We discuss different methodological approaches to estimate parameters in complex Biological Models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. Conclusions: The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a Biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally. © 2010 Fernandez Slezak et al.
S Archer - One of the best experts on this subject based on the ideXlab platform.
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Measurement of nitric oxide in Biological Models
FASEB J, 1993Co-Authors: S ArcherAbstract:Nitric oxide (NO) is a small, gaseous, paramagnetic radical with a high affinity for interaction with ferrous hemoproteins such as soluble guanylate cyclase and hemoglobin. Interest in NO measurement increased exponentially with the discovery that NO or a related compound is the endothelium-derived relaxing factor (EDRF). In addition to being a potent endogenous vasodilator, NO has a role in inflammation, thrombosis, immunity, and neurotransmission. Measurement of NO is important as many of its effects (e.g., vasodilatation, inhibition of platelet aggregation) are similar to those of other substances produced by the endothelium, such as prostacyclin. NO is formed in small amounts in vivo and is rapidly destroyed by interaction with oxygen, making measurement difficult. A computerized search of the past five year's literature found NO measurements reported in fewer than 50 of 955 articles dealing with EDRF. Inhibitors of NO synthesis such as the arginine analogs or agents that inactivate NO, such as reduced hemoglobin, are commonly used as specific probes for NO, in vivo and in vitro; however, none of the NO inhibitors is completely specific. The most widely used assays use one of three strategies to detect NO: 1) NO is "trapped" by nitroso compounds, or reduced hemoglobin, forming a stable adduct that is detected by electron paramagnetic resonance (EPR) (detection threshold approximately 1 nmol); 2) NO oxidizes reduced hemoglobin to methemoglobin, which is detected by spectrophotometry (detection threshold approximately 1 nmol); 3) NO interacts with ozone producing light, "chemiluminescence" (detection threshold approximately 20 pmol). These assays can be performed to exclusively detect NO, or by adding acid and reducing agents to the sample, can measure NO and related oxides of nitrogen such as nitrite. Several new amperometric microelectrode assays offer the potential to measure smaller amounts of NO (10(-20) M), permitting NO measurement in intact issues and from single cells. This review describes the pharmacology and toxicology of NO and reviews the major techniques for measuring NO in Biological Models.
Stephen L Archer - One of the best experts on this subject based on the ideXlab platform.
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measurement of nitric oxide in Biological Models
The FASEB Journal, 1993Co-Authors: Stephen L ArcherAbstract:Nitric oxide (NO) is a small, gaseous, paramagnetic radical with a high affinity for interaction with ferrous hemoproteins such as soluble guanylate cyclase and hemoglobin. Interest in NO measurement increased exponentially with the discovery that NO or a related compound is the endothelium-derived relaxing factor (EDRF). In addition to being a potent endogenous vasodilator, NO has a role in inflammation, thrombosis, immunity, and neurotransmission. Measurement of NO is important as many of its effects (e.g., vasodilatation, inhibition of platelet aggregation) are similar to those of other substances produced by the endothelium, such as prostacyclin. NO is formed in small amounts in vivo and is rapidly destroyed by interaction with oxygen, making measurement difficult. A computerized search of the past five year's literature found NO measurements reported in fewer than 50 of 955 articles dealing with EDRF. Inhibitors of NO synthesis such as the arginine analogs or agents that inactivate NO, such as reduce...
Thao Dang - One of the best experts on this subject based on the ideXlab platform.
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HSCC - Parameter synthesis for polynomial Biological Models
Proceedings of the 17th international conference on Hybrid systems: computation and control - HSCC '14, 2014Co-Authors: Tommaso Dreossi, Thao DangAbstract:Parameter determination is an important task in the development of Biological Models. In this paper we consider parametric polynomial dynamical systems and address the following parameter synthesis problem: find a set of parameter values so that the resulting system satisfies a desired property. Our synthesis technique exploits the Bernstein polynomial representation to solve the synthesis problem using linear programming. We apply our framework to two case studies involving epidemic Models.
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HSB - Falsifying Oscillation Properties of Parametric Biological Models
arXiv: Logic in Computer Science, 2013Co-Authors: Thao Dang, Tommaso DreossiAbstract:We propose an approach to falsification of oscillation properties of parametric Biological Models, based on the recently developed techniques for testing continuous and hybrid systems. In this approach, an oscillation property can be specified using a hybrid automaton, which is then used to guide the exploration in the state and input spaces to search for the behaviors that do not satisfy the property. We illustrate the approach with the Laub-Loomis model for spontaneous oscillations during the aggregation stage of Dictyostelium.
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HSB - Analysis of parametric Biological Models with non-linear dynamics
Electronic Proceedings in Theoretical Computer Science, 2012Co-Authors: Romain Testylier, Thao DangAbstract:In this paper we present recent results on parametric analysis of Biological Models. The underlying method is based on the algorithms for computing trajectory sets of hybrid systems with polynomial dynamics. The method is then applied to two case studies of Biological systems: one is a cardiac cell model for studying the conditions for cardiac abnormalities, and the second is a model of insect nest-site choice.
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computing reachable states for nonlinear Biological Models
Theoretical Computer Science, 2011Co-Authors: Thao Dang, Colas Le Guernic, Oded MalerAbstract:In this paper, we describe reachability computation for continuous and hybrid systems and its potential contribution to the process of building and debugging Biological Models. We summarize the state-of-the-art for linear systems and then develop a novel algorithm for computing reachable states for nonlinear systems. We report experimental results obtained using a prototype implementation applied to several Biological Models. We believe these results constitute a promising contribution to the analysis of complex Models of Biological systems.
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computing reachable states for nonlinear Biological Models
Computational Methods in Systems Biology, 2009Co-Authors: Thao Dang, Colas Le Guernic, Oded MalerAbstract:In this paper we describe reachability computation for continuous and hybrid systems and its potential contribution to the process of building and debugging Biological Models. We then develop a novel algorithm for computing reachable states for nonlinear systems and report experimental results obtained using a prototype implementation. We believe these results constitute a promising contribution to the analysis of complex Models of Biological systems.
Quoc Dong Vu - One of the best experts on this subject based on the ideXlab platform.
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identification of parameter correlations for parameter estimation in dynamic Biological Models
BMC Systems Biology, 2013Co-Authors: Pu Li, Quoc Dong VuAbstract:Background One of the challenging tasks in systems biology is parameter estimation in nonlinear dynamic Models. A Biological model usually contains a large number of correlated parameters leading to non-identifiability problems. Although many approaches have been developed to address both structural and practical non-identifiability problems, very few studies have been made to systematically investigate parameter correlations.
