(404c) AUMIC: An Automated Tool for Developing Dynamic Metabolic Models | AIChE

(404c) AUMIC: An Automated Tool for Developing Dynamic Metabolic Models


Song, H. - Presenter, Purdue University
Ramkrishna, D. - Presenter, Purdue University

We introduce an automated tool for developing dynamic metabolic models. A software is developed using MATLAB and named as AUMIC which stands for AUtomated tool for Metabolic modeling Integrated with the Cybernetic regulatory mechanisms. While AUMIC is designed mainly for constructing cybernetic models including lumped cybernetic model (LCM) (e.g., Kompala et al., 1986), hybrid cybernetic model (HCM) (Kim et al., 2008; Song et al., 2009), and lumped hybrid cybernetic model (L-HCM) (Song and Ramkrishna, 2010), it can also handle various other approaches to dynamic metabolic modeling such as lumped kinetic model (LKM) (e.g., Bijkerk and Hall, 1977), macroscopic bioreaction model (MBM) (Provost and Bastin, 2004), and dynamic flux balance analysis (dFBA) (Mahadevan et al., 2002) for comparative studies. LKM, MBM and dFBA can be considered as respective counterparts of LCM, HCM and L-HCM because of analogous features in each pair. In AUMIC, all of the foregoing approaches are subsumed as quasi-steady-state models which are formulated through consistent procedures within a unifying framework, i.e., network decomposition into elementary modes (EMs) using METATOOL (von Kamp and Schuster, 2006), EM classification and processing, and design of kinetics for uptake fluxes (and kinetics for enzyme synthesis). Moreover, AUMIC provides the parameter identification routine so that one can develop their own models using specific experimental data. Finally, the results are quickly analyzed using the post-processing module. Conveniently, all tasks in AUMIC are performed using graphical user interface. Continuing efforts are being made towards developing AUMIC as an even more effective and advanced tool and its first version will be released soon for public use.


Bijkerk AHE, Hall RJ. 1977. Mechanistic Model of Aerobic Growth of Saccharomyces-Cerevisiae. Biotechnology and Bioengineering 19(2):267-296

Kim JI, Varner JD, Ramkrishna D. 2008. A Hybrid Model of Anaerobic E. coli GJT001: Combination of Elementary Flux Modes and Cybernetic Variables. Biotechnology Progress 24(5):993-1006

Kompala DS, Ramkrishna D, Jansen NB, Tsao GT. 1986. Investigation of Bacterial-Growth on Mixed Substrates - Experimental Evaluation of Cybernetic Models. Biotechnology and Bioengineering 28(7):1044-1055

Mahadevan R, Edwards JS, Doyle FJ. 2002. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophysical Journal 83(3):1331-1340 Provost A, Bastin G. 2004. Dynamic metabolic modelling under the balanced growth condition. Journal of Process Control 14(7):717-728.

Song HS, Morgan JA, Ramkrishna D. 2009. Systematic Development of Hybrid Cybernetic Models: Application to Recombinant Yeast Co-consuming Glucose and Xylose, Biotechnology and Bioengineering 103(5): 984-102

Song HS, Ramkrishna D. 2010. Prediction of Metabolic Function from Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and Bioengineering 106(2): 271-284

von Kamp A, Schuster S. 2006. Metatool 5.0: fast and flexible elementary modes analysis. Bioinformatics 22(15):1930-1931