NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology

Abstract

Modeling 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 are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling 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. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. 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 modeling 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 modeling 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.

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“…Ryan et al 42 provide the most recent review of BOED approaches. BOED is a research area applied in various domains, especially in biological and medical/pharmaceutical applications 43,44 . Due to its high computational cost, BOED was often not applicable in complex applications or limited to linear models 42 .…”

“…Due to its high computational cost, BOED was often not applicable in complex applications or limited to linear models 42 . It has recently become more popular with increasing computational power and open‐source implementations 43,45,46 . However, the computational effort remains high due to the computation of the utility function.…”