Abstract
Bottom-up systems biology entails the construction of kinetic models of cellular pathways by collecting kinetic information on the pathway components (e.g., enzymes) and collating this into a kinetic model, based for example on ordinary differential equations. This requires integration and data transfer between a variety of tools, ranging from data acquisition in kinetics experiments, to fitting and parameter estimation, to model construction, evaluation and validation. Here, we present a workflow that uses the Python programming language, specifically the modules from the SciPy stack, to facilitate this task. Starting from raw kinetics data, acquired either from spectrophotometric assays with microtitre plates or from Nuclear Magnetic Resonance (NMR) spectroscopy time-courses, we demonstrate the fitting and construction of a kinetic model using scientific Python tools. The analysis takes place in a Jupyter notebook, which keeps all information related to a particular experiment together in one place and thus serves as an e-labbook, enhancing reproducibility and traceability. The Python programming language serves as an ideal foundation for this framework because it is powerful yet relatively easy to learn for the non-programmer, has a large library of scientific routines and active user community, is open-source and extensible, and many computational systems biology software tools are written in Python or have a Python Application Programming Interface (API). Our workflow thus enables investigators to focus on the scientific problem at hand rather than worrying about data integration between disparate platforms.
Highlights
With the inexorable advance of experimental techniques, the workload of researchers has begun shifting from data generation to data processing and analysis
We show that Python is well suited to performing the computational analyses required for experimental data processing, fitting of enzyme-kinetic parameters, construction of kinetic models, as well as model validation and further analysis
Enzyme-kinetic data were obtained in one of two ways: either, progress curves for a reaction or group of reactions were acquired with Nuclear Magnetic Resonance (NMR) spectroscopy, which were parametrised by fitting to a system of ordinary differential equations (ODEs) with the appropriate enzyme kinetic rate equations; or alternatively, initial-rate kinetics were performed on a single enzyme, typically with a spectrophotometric assay using microtitre plates, and fitted to a rate equation
Summary
With the inexorable advance of experimental techniques, the workload of researchers has begun shifting from data generation to data processing and analysis. It will become increasingly important for the systems biologist in the laboratory to utilise computational methods to improve data processing and visualisation of results. Computational systems biology presents the researcher with a powerful toolbox to integrate large kinetic datasets into models and eventually high resolution analyses of biological systems [1]. The top-down approach often involves constraint-based descriptive modelling where large datasets are used to infer relationships between parameters without necessarily understanding the underlying mechanisms [4]
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