Motivation: Modelling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data, and on how well-suited the available data is to a particular modelling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modelling objective. However, implementation of OED is limited by currently available software tools that are not well-suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Results: Here we present the NLoed software package. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models, and facilitates modelling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modelling workflow. NLoed offers an accessible, modular, and flexible OED tool-set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLOED's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system. Availability: NLoed is available via pip from the PyPi repository; https://pypi.org/project/nloed/. Source code, documentation and examples can be found on Github at https://github.com/ingallslab/NLoed.
Near-optimal experimental design for model selection in systems biology
