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

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.