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groups:steuer:research [2017/02/20 14:30] – [Understanding Phototrophic Growth] steuergroups:steuer:research [2017/08/12 15:41] (current) – [Dynamics in Large-Scale Metabolic Networks] steuer
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 **Further reading:**  **Further reading:** 
-  * Westermark S and Steuer R (2016) **[[http://journal.frontiersin.org/article/10.3389/fbioe.2016.00095/|Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach.]]**  +  * Westermark S and Steuer R (2016) **[[http://journal.frontiersin.org/article/10.3389/fbioe.2016.00095/|Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach.]]** Front. Bioeng. Biotechnol. 4:95. doi: 10.3389/fbioe.2016.00095 
-Front. Bioeng. Biotechnol. 4:95. doi: 10.3389/fbioe.2016.00095 +  * Reimers AM, Knoop H, Bockmayr A, Steuer R.​ (2017) **[[https://www.ncbi.nlm.nih.gov/pubmed/28720699|Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth.]]** Proc Natl Acad Sci U S Apii201617508. doi: 10.1073/pnas.1617508114.
-  * Ruegen M, Bockmayr A, Steuer R.  (2015) **[[http://www.ncbi.nlm.nih.gov/pubmed/26496972|Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA]]** Sci Rep5:15247. doi: 10.1038/srep15247.+
   * H. Knoop, M. Gruendel, Y. Zilliges, R. Lehmann, S. Hoffmann, W. Lockau, R. Steuer. (2013) **[[http://www.ncbi.nlm.nih.gov/pubmed/23843751|Flux Balance Analysis of Cyanobacterial Metabolism: The metabolic network of Synechocystis sp. PCC 6803.]]** PLoS Comput Biol 9(6): e1003081. doi:10.1371/journal.pcbi.1003081 \\   * H. Knoop, M. Gruendel, Y. Zilliges, R. Lehmann, S. Hoffmann, W. Lockau, R. Steuer. (2013) **[[http://www.ncbi.nlm.nih.gov/pubmed/23843751|Flux Balance Analysis of Cyanobacterial Metabolism: The metabolic network of Synechocystis sp. PCC 6803.]]** PLoS Comput Biol 9(6): e1003081. doi:10.1371/journal.pcbi.1003081 \\
  
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-==== Dynamics in Large-Scale Metabolic Networks ====+==== The Nonlinear Dynamics of Metabolism ====
 {{groups/steuer/metabolism_control01.jpeg?nolink&150 }}  {{groups/steuer/metabolism_control01.jpeg?nolink&150 }} 
 One of the most challenging goals of computational systems biology is the development of large-scale kinetic models of cellular pathways. However, for most cellular networks, detailed kinetic modeling is not possible due to lack of knowledge kinetic parameters. To overcome some of these problems, we are interested in novel methods that allow the elucidation of large-scale metabolic networks in the face of uncertain and incomplete information. Recent work includes novel approaches that provide a bridge between stoichiometric analysis and explicit kinetic simulations. Without requiring knowledge about the explicit functional form of the kinetic rate equations and parameters, these methods seek to describe the possible dynamics of cellular networks. One of the most challenging goals of computational systems biology is the development of large-scale kinetic models of cellular pathways. However, for most cellular networks, detailed kinetic modeling is not possible due to lack of knowledge kinetic parameters. To overcome some of these problems, we are interested in novel methods that allow the elucidation of large-scale metabolic networks in the face of uncertain and incomplete information. Recent work includes novel approaches that provide a bridge between stoichiometric analysis and explicit kinetic simulations. Without requiring knowledge about the explicit functional form of the kinetic rate equations and parameters, these methods seek to describe the possible dynamics of cellular networks.