Works Cited

The following is a list of all references cited throughout the MASSpy documentation.


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Bin Du, Daniel C. Zielinski, Erol S. Kavvas, Andreas Dräger, Justin Tan, Zhen Zhang, Kayla E. Ruggiero, Garri A. Arzumanyan, and Bernhard O. Palsson. Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Systems Biology, 10(1):40, 2016. URL:, doi:10.1186/s12918-016-0283-2.


Ali Ebrahim, Joshua A. Lerman, Bernhard O. Palsson, and Daniel R. Hyduke. Cobrapy: constraints-based reconstruction and analysis for python. BMC Systems Biology, 7(1):74, 2013. URL:, doi:10.1186/1752-0509-7-74.


Zachary B. Haiman, Daniel C. Zielinski, Yuko Koike, James T. Yurkovich, and Bernhard O. Palsson. Masspy: building, simulating, and visualizing dynamic biological models in python using mass action kinetics. PLOS Computational Biology, 17(1):e1008208–, 01 2021. URL:


Joshua J. Hamilton, Vivek Dwivedi, and Jennifer L. Reed. Quantitative assessment of thermodynamic constraints on the solution space of genome-scale metabolic models. Biophysical Journal, 105(2):512–522, 2021/01/25 2013. URL:, doi:10.1016/j.bpj.2013.06.011.


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Neema Jamshidi, Sharon J Wiback, and Bernhard Ø Palsson B. In silico model-driven assessment of the effects of single nucleotide polymorphisms (snps) on human red blood cell metabolism. Genome research, 12(11):1687–1692, 11 2002. URL:, doi:10.1101/gr.329302.


Kristian Jensen, Joao G.r. Cardoso, and Nikolaus Sonnenschein. Optlang: an algebraic modeling language for mathematical optimization. Journal of Open Source Software, 2(9):139, 2017. URL:, doi:10.21105/joss.00139.


Abhay Joshi and Bernhard O. Palsson. Metabolic dynamics in the human red cell: part i—a comprehensive kinetic model. Journal of Theoretical Biology, 141(4):515 – 528, 1989. URL:, doi:10.1016/S0022-5193(89)80233-4.


Abhay Joshi and Bernhard O. Palsson. Metabolic dynamics in the human red cell: part ii—interactions with the environment. Journal of Theoretical Biology, 141(4):529 – 545, 1989. URL:, doi:10.1016/S0022-5193(89)80234-6.


Abhay Joshi and Bernhard O. Palsson. Metabolic dynamics in the human red cell. part iii—metabolic reaction rates. Journal of Theoretical Biology, 142(1):41 – 68, 1990. URL:, doi:10.1016/S0022-5193(05)80012-8.


David E. Kaufman and Robert L. Smith. Direction choice for accelerated convergence in hit-and-run sampling. Operations Research, 46(1):84–95, 1998. URL:, doi:10.1287/opre.46.1.84.


Zachary A. King, Andreas Dräger, Ali Ebrahim, Nikolaus Sonnenschein, Nathan E. Lewis, and Bernhard O. Palsson. Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLOS Computational Biology, 11(8):1–13, 08 2015. URL:, doi:10.1371/journal.pcbi.1004321.


Anne Kümmel, Sven Panke, and Matthias Heinemann. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Molecular Systems Biology, 2(1):2006.0034, 2006. URL:, doi:10.1038/msb4100074.


Wout Megchelenbrink, Martijn Huynen, and Elena Marchiori. Optgpsampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks. PLOS ONE, 9(2):1–8, 02 2014. URL:, doi:10.1371/journal.pone.0086587.


Peter J. MULQUINEY, William A. BUBB, and Philip W. KUCHEL. Model of 2,3-bisphosphoglycerate metabolism in the human erythrocyte based on detailed enzyme kinetic equations1: in vivo kinetic characterization of 2,3-bisphosphoglycerate synthase/phosphatase using 13c and 31p nmr. Biochemical Journal, 342(3):567–580, 09 1999. URL:, doi:10.1042/bj3420567.


Bernhard Ø. Palsson. Systems Biology: Simulation of Dynamic Network States. Cambridge University Press, 2011. doi:10.1017/CBO9780511736179.


Jan Schellenberger, Richard Que, Ronan M T Fleming, Ines Thiele, Jeffrey D Orth, Adam M Feist, Daniel C Zielinski, Aarash Bordbar, Nathan E Lewis, Sorena Rahmanian, Joseph Kang, Daniel R Hyduke, and Bernhard Ø Palsson. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2.0. Nature Protocols, 6(9):1290–1307, 2011. URL:, doi:10.1038/nprot.2011.308.


James T. Yurkovich, Miguel A. Alcantar, Zachary B. Haiman, and Bernhard O. Palsson. Network-level allosteric effects are elucidated by detailing how ligand-binding events modulate utilization of catalytic potentials. PLOS Computational Biology, 14(8):1–16, 08 2018. URL:, doi:10.1371/journal.pcbi.1006356.