On the practical identifiability of microbial growth models incorporating Michaelis-Menten type nonlinearities

ABSTRACT:A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability and uncertainty of the prediction. The methodology is evaluated through development of a GC method for prediction of the heat of combustion ( ) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. Bootstrap method is found a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order groups contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter 2 identifiability issues, reporting of the 95%-confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models, in some cases it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95%-confidence interval. Last but not least, the newly developed GC model of the heat of combustion ( ) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other models reported in literature.

show abstract

“…less than 0.7 43,44 , and b) Parameter estimation errors (i.e. 95% confidence intervals) should be sufficiently low 45 .…”

Section: Parameter Identifiability Parameter Identifiability Is a Comentioning

confidence: 99%

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Such correlations suggest that several of the mathematical flux formulations were over-parameterized, given the relatively narrow ranges of metabolite concentrations observed in the perturbation simulations, which precluded the enzymes from taking on the full range of Michaelis-Menten behaviors. When substrate ranges are limited (e.g., to a < 4-fold range), simple linear flux formulations (e.g., flux = k [substrate]) may be preferable to representations based on Michaelis-Menten kinetics or biochemical systems theory (Holmberg, 1982;Savageau, 1969;Savageau, 1991). Alternatively, as shown in the Results section, when in vitro kinetic data are available, they can be used to tightly constrain K m values in Michaelis-Menten formulations, eliminating the problem of dramatic swings in V max to accommodate widely changing values of K m .…”

Section: Discussionmentioning

confidence: 99%

Diverse metabolic model parameters generate similar methionine cycle dynamics

Piazza

Xiao

Rabinowitz

et al. 2008

Journal of Theoretical Biology

View full text Add to dashboard Cite

Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine-cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.

show abstract

“…Such a large CI means that these parameters are unidentifiable and hence their values may not be reliable. 34,38,39 Faced with such an issue (actually commonly encountered in the field of engineering), GMoP requires a proper diagnosis of the unidentifiability to overcome the problem (e.g., experimental design or model-structure modification). 34,40 It should be mentioned that the study of Berendsen et al 21 recognized the importance of the identifiability issue during model building but did not provide a formal treatment of the issue, e.g., no parameter estimation quality was reported.…”

Section: Identifiability Of Biocatalysis Modelsmentioning

confidence: 99%

Application of modeling and simulation tools for the evaluation of biocatalytic processes: A future perspective

Sin

Woodley

Gernaey

2009

Biotechnology Progress

View full text Add to dashboard Cite

Modeling and simulation techniques have for some time been an important feature of biocatalysis research, often applied as a complement to experimental studies. In this short review, we report on the state-of-the-art process and kinetic modeling for biocatalysis with the aim of identifying future research needs. We have particularly focused on four aspects of modeling: (i) the model purpose, (ii) the process model boundary, (iii) the model structure, and (iv) the model identification procedure. First, one finds that most of the existing models describe biocatalyst behavior in terms of enzyme selectivity, mechanism, and reaction kinetics. More recently, work has focused on extending these models to obtain process flowsheet descriptions. Second, biocatalysis models remain at a relatively low level of complexity compared with the trends observed in other engineering disciplines. Hence, there is certainly room for additional development, i.e., detailed mixing and hydrodynamics, more process units (e.g., biorefinery). Third, biocatalysis models have been only partially subjected to formal statistical analysis. In particular, uncertainty analysis is needed to ascertain reliability of the predictions of the process model, which is necessary to make sound engineering decisions (e.g., the optimal process flowsheet, control strategy, etc). In summary, for modeling studies to be more mature and successful, one needs to introduce Good Modeling Practice and that asks for (i) a standardized and systematic guideline for model development, (ii) formal identifiability analysis, and (iii) uncertainty analysis. This will advance the utility of models in biocatalysis for more rigorous application within process design, optimization, and control strategy evaluation. V

show abstract

On the practical identifiability of microbial growth models incorporating Michaelis-Menten type nonlinearities

Cited by 263 publications

References 15 publications

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

A Comprehensive Methodology for Development, Parameter Estimation, and Uncertainty Analysis of Group Contribution Based Property Models—An Application to the Heat of Combustion

Diverse metabolic model parameters generate similar methionine cycle dynamics

Application of modeling and simulation tools for the evaluation of biocatalytic processes: A future perspective

Contact Info

Product

Resources

About