Relevant Reading

These are some things you might like to read if you are interested in Maud.

Theory of Enzyme kinetics

  • Monod, J., Wyman, J., & Changeux, J. (1965). On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology, 12(1), 88–118.

    The original version of Maud’s allostery framework.

  • Popova, S. V., & Sel’kov, E. E. (1975). Generalization of the model by monod, wyman and changeux for the case of a reversible monosubstrate reaction. FEBS Letters, 53(3), 269–273.

    This paper sets out the generalised MWC framework that Maud uses to represent allosteric regulation.

  • Saa, P. A., & Nielsen, L. K. (2017). Formulation, construction and analysis of kinetic models of metabolism: A review of modelling frameworks. Biotechnology Advances, 35(8), 981–1003.

    A review paper covering different approaches to modelling metabolic networks.


  • Mahamkali, V., McCubbin, T., Beber, M., Marcellin, E., & Nielsen, L. K. (2020). multiTFA: a Python package for multi-variate Thermodynamics-based Flux Analysis. bioRxiv, (), 2020–12–01–407387.

  • Alberty, R. A. (2003). Thermodynamics of biochemical reactions. Hoboken, N.J: Wiley-Interscience.

    Chapter 4 explains how to adjust thermodynamic measurements to take into accound experimental conditions.

  • Noor, E., Haraldsd'ottir, Hulda S., Milo, R., & Fleming, R. M. T. (2013). Consistent Estimation of Gibbs Energy Using Component Contributions. PLoS Computational Biology, 9(7), 1003098.

    Original paper for the method behind equilibrator.

  • Du, B., Zielinski, D. C., & Palsson, B. O. (2018). Estimating Metabolic Equilibrium Constants: Progress and Future Challenges. Trends in Biochemical Sciences, 43(12), 960–969.

    Review paper discussing the problem of estimating thermodynamic parameters in general.

  • Du, B., Zhang, Z., Grubner, S., Yurkovich, J. T., Palsson, B. O., & Zielinski, D. C. (2018). Temperature-Dependent Estimation of Gibbs Energies Using an Updated Group-Contribution Method. Biophysical journal, 114(11), 2691–2702.

    Develops the component contribution method in several ways. There is a nice dataset in an excel sheet in the supplementary information.

Computational Models of metabolic networks

  • Shepelin, D., Machado, D., Nielsen, L. K., & Herrgaard, Markus J. (2020). Benchmarking kinetic models of Escherichia coli metabolism. bioRxiv, (), 2020–01–16–908921.

    A review paper that compares various computational models of E. coli

  • St. John, P., Strutz, J., Broadbelt, L. J., Tyo, K. E. J., & Bomble, Y. J. (2018). Bayesian inference of metabolic kinetics from genome-scale multiomics data. bioRxiv, (), .

    This paper takes a very similar approach to Maud, but represents reactions using lin-log kinetics.


  • Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C., Carpenter, B., Yao, Y., Kennedy, L., … (2020). Bayesian Workflow. arXiv:2011.01808 [stat], (), .

    A general discussion of the workflow that Maud is intended to be part of.


  • Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., … (2017). Stan: A Probabilistic Programming Language. Journal of Statistical Software, 76(1), 1–32.

    Introduces Stan and the ideas behind it

  • Margossian, C. C. (2019). A Review of automatic differentiation and its efficient implementation. WIREs Data Mining and Knowledge Discovery, 9(4), .

    Relatively recent and accessible discussion of automatic differentiation.

  • Carpenter, B., Hoffman, M. D., Brubaker, M., Lee, D., Li, P., & Betancourt, M. (2015). The Stan Math Library: Reverse-Mode Automatic Differentiation in C++. arXiv:1509.07164 [cs], (), .

    Explains Stan’s automatic differentiation implementation.