Kinetic models of metabolic pathways have grown in size and complexity as computer technology has improved to a point where whole cells may now be modelled. This growth, however, has made the extraction of information from these models increasingly difficult. While various tools exist for the analysis of such models, currently it is difficult to apply and combine these tools without a background in computer programming and the underlying mathematics of models. Here we set out to develop an integrated, software-based analysis framework which combines various analysis tools into a single workflow that can yield meaningful results in the hands of both experts and novices.
To develop and integrate these tools we utilised the Python programming language due to its extensive ecosystem of open-source scientific libraries . The Python Simulator for Cellular Systems (PySCeS) , previously developed in our group, was used as a base for the development. Thus far we have developed SymCA for symbolic metabolic control analysis and RateChar for generalised supply-demand analysis (GSDA):
In symbolic control analysis the systemic properties (control coefficients) of a metabolic system are expressed in terms of the local properties (elasticity coefficients) of the system . These expressions are generated by inversion of the E matrix which contains structural and kinetic information of the metabolic system . SymCA uses Sympy to perform this inversion and to factorise the terms of the control coefficient expressions in order to perform control pattern analysis.
GSDA can be used to determine points of functional differentiation in a metabolic pathway and to quantify the importance of various routes of regulation of an intermediate with a reaction block . RateChar provides a simple interface to perform parameter scans for intermediates in metabolic systems using functionality provided by PySCeS. Matplotlib is used to visualise the results of these parameter scans as rate characteristic plots.
The combination of these analysis tools will be presented as a framework whereby metabolic models can be studied in terms of their control, regulation and function in a fine-grained manner. It also lowers the skill barrier of entry for the analysis of metabolic models by abstraction of programming and mathematical concepts into simple yet powerful functions.
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