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.
Highlights
Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds
Without including the optional model reduction step, we found that we could obtain a system of ordinary differential equations with balanced parameters for the largest systems within 1 hr, whilst it takes a matter of minutes to obtain smaller models
In this work we have introduced an automated computational pipeline that translates a genome-scale network of cellular metabolism into a parameterised set of ordinary differential equations that can simulate the dynamic behaviour of system components
Summary
Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Many of the favoured methods to model large metabolic networks relate system inputs (e.g. growth conditions) to phenotypic outputs (for example, growth rate or compound secretion) using directed graphs, i.e. internal dynamics are not directly considered [3, 4]. By using the quasisteady-state assumption, the metabolite concentrations are believed to be constant relative to the time taken for a cell to grow [4, 6]. This method requires data such as growth and production rates of the species under study [7, 8].
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