DMPy: a Python package for automated mathematical model construction of large-scale metabolic systems

Abstract

BackgroundModels of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed.ResultsIn this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics.ConclusionThe presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0584-8) contains supplementary material, which is available to authorized users.

Parameter balancing: consistent parameter sets for kinetic metabolic models

“…To make parameter balancing applicable, we provide software tools as well as Python and Matlab code. Our online interface allows modellers to balance model parameters with a few mouse clicks while the Python and Matlab packages can be integrated into modelling workflows (Smith et al , 2018; Stanford et al , 2013). Our implementation supports established standard formats: models are given in Systems Biology Markup Language format while the generic table format SBtab (Lubitz et al , 2016) is used for all other data.…”

Benchmarking kinetic models ofEscherichia colimetabolism

“…The development of kinetic models is presently an active field, in particular the automatic creation of kinetic models using predefined rules (39)(40)(41)(42)…”

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

“…Methods for parameter fitting have been developed and benchmarked [22,23], and the question of parameter identifiability has been addressed [24]. Large models have been parameterised [2,25], and workflows for model parameterization have been developed [26,27]. Finally, even if parameters are unknown, parameter sampling and ensemble modelling [28,29,30,31,32] allow to find plausible parameter sets [33] and to draw conclusions about dynamic behaviour independently of specific parameter values [34].…”

“…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.…”

“…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].…”