Parameter balancing: consistent parameter sets for kinetic metabolic models

Lubitz, Timo; Liebermeister, Wolfram

doi:10.1093/bioinformatics/btz129

Cited by 13 publications

(26 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the parameter values are refined by scanning the parameter space in various directions until a satisfactory outcome is achieved, i.e., the parameters minimized an objective function which is usually the weighted sum of squared distances between model simulations and the associated experimental values. An optimization algorithm must be used to find the best direction, along which the parameter values are changed to calculate a reduced objective function value within the imposed constraints, consistent with the non-equilibrium thermodynamics of biosystem [159,[209][210][211][212][213]. Of importance, a mechanistic kinetic metabolic model includes a set of nonlinear functions of the kinetic parameters and consequently forms a nonlinear optimization problem for which linear (or mixed integer linear) programming optimization methods perform poorly.…”

Section: Parameter Estimation Formulationmentioning

confidence: 99%

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Yasemi¹,

Jolicœur²

2021

Processes

View full text Add to dashboard Cite

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

show abstract

Section: Parameter Estimation Formulationmentioning

confidence: 99%

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Yasemi¹,

Jolicœur²

2021

Processes

View full text Add to dashboard Cite

show abstract

“…In real-life applications, if our priors and assumed noise levels are wrong, the reconstruction would be worse than suggested by our tests with artificial data. To obtain the realistic distributions of kinetic constant mentioned before, I started from known (or suspected) distributions (from [31,14], which relied on [8]), and adjusted them based on data. By visual inspection during parameter balancing, I noticed that some priors had to be changed, probably because kinetic constants in central metabolism are differently distributed than kinetic constants in metabolism in general.…”

Section: Parameter Identifiability and Choice Of Priorsmentioning

confidence: 99%

“…Methods for parameter fitting have been developed and benchmarked [18,19], and the question of parameter identifiability has been addressed [20]. parameter balancing [11,14], elasticity sampling [32], and enzyme cost minimisation [31], which I review in the discussion section.…”

Section: Introductionmentioning

confidence: 99%

“…It may be surprising that a simple convex estimation method exists. Model balancing relies on two insights: all fluxes must be predefined 13 , and logarithmic kinetic constants and metabolite concentration are the right variables for optimisation 14 . Model balancing builds on two other methods that share the same features and lead to convex optimality problems:…”

mentioning

confidence: 99%

“…Mathematically, parameter balancing resembles the component contribution method, which component contribution method[37] used to determine thermodynamic constants in eQuilibrator[35] 16. The equilibrium constants were not parameterised by standard chemical potentials µ • (as proposed in[14] for parameter balancing), but by independent equilibrium constants. This is convenient because we use a smaller set of independent variables and avoid non-identifiability (while the standard chemical potentials themselves are not in the centre of interest), and the same choice could be applied in parameter balancing 17.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

Liebermeister

Noor

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. In theory, kinetic constants can be fitted to measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, but these estimation problems are typically non-convex. This makes them hard to solve, especially if models are large. Here I assume that the metabolic fluxes are given and show that consistent kinetic constants, metabolite levels, and enzyme levels can then be found by solving a convex optimality problem. If logarithmic kinetic constants and metabolite concentrations are used as free variables and if Gaussian priors are employed, the posterior density is strictly convex. The resulting estimation method, called model balancing, can employ a wide range of rate laws, accounts for thermodynamic constraints on parameters, and considers the dependences between flux directions and metabolite concentrations through thermodynamic forces. It can be used to complete and adjust available data, to estimate in-vivo kinetic constants from omics data, or to construct plausible metabolic states with a predefined flux distribution. To demonstrate model balancing and to assess its practical use, I balance a model of E. coli central metabolism with artificial or experimental data. The tests show what information about kinetic constants can be extracted from omics data and reveal practical limits of estimating kinetic constants in vivo.Large models have been parameterised [21,22], and pipelines for model parameterization have been developed [23,24]. Finally, even if parameters are unknown, methods for parameter sampling and ensemble modelling allow to find plausible parameter sets [25] and to draw conclusions about possible dynamic behaviour [26].The key problem here is to obtain realistic, consistent values of kinetic constants. In-vivo values are hard to measure, and in-vitro values as proxies may be unreliable -or at least, this is hard to assess unless in-vivo values are known. So how can we infer model parameters from omics data? To obtain realistic model parameters and metabolic states, various types of measurement data must be combined. In an ideal case, in which kinetic constants and enzyme concentrations are precisely known, metabolite levels and fluxes could be computed by simulating the model. In another ideal case, in which enzyme levels, metabolite levels and fluxes are precisely known for a number of states, we can solve for the kinetic constants. In reality, we are in between these two cases: data of different types are available, but these data are incomplete and too noisy to be used directly in models. In practice, in model construction we may have different aims, for example (i) finding consistent kinetic constants in plausible ranges; (ii) adjusting and completing a data set of measured kinetic constants to obtain consistent model parameters; (iii) estimating in-vivo kinetic constants from omics data (measured enzyme levels, metabolite levels, and fluxes); (iv) completing and adju...

show abstract

Black‐box kinetic modeling of growth and citric acid production and estimation of ATP maintenance parameters forCandida oleophilaATCC20177

Tanıl

Nikerel

2020

Biotech and App Biochem

View full text Add to dashboard Cite

Mathematical modeling represents and predicts biological systems, explains underlying mechanisms, constituting one of the key focus points for fundamental and applied research to improve our understanding and to decrease costs. Organic acids are used in several industries such as monomers for bioplastics, food preservatives and additives, pharmaceuticals, and agriculture. Nonpetrochemical, sustainable production of organic acids is therefore of great interest. An important step in production of organic acids is the determination of growth and acid production dynamics, as the product itself may have direct and indirect inhibitory effects on the host's metabolism. The aim of this study it twofold: (i) to determine the parameters related to energetics of growth and production as growth (Kx ) and nongrowth associated (mATP ) maintenance constants and (ii) to set up and analyze an unstructured, black‐box kinetic model to describe the dynamics of the growth and production of citric acid by Candida oleophila ATCC20177 using published batch fermentation data. Kx and mATP were found to be 2.3 ± 1.7 and 5.25 ± 2.75, respectively, for the published P/O ratio of 1.45. The parameter sensitivities and correlations are determined using the Monte Carlo approach, and the final model is tested using chemostat data.

show abstract

Parameter balancing: consistent parameter sets for kinetic metabolic models

Cited by 13 publications

References 9 publications

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Model balancing: in search of consistent metabolic states and in-vivo kinetic constants

Black‐box kinetic modeling of growth and citric acid production and estimation of ATP maintenance parameters forCandida oleophilaATCC20177

Contact Info

Product

Resources

About