  Biologically-informed neural networks (BINNs), an extension of
physics-informed neural networks [1], are introduced and used to discover the
underlying dynamics of biological systems from sparse experimental data. In the
present work, BINNs are trained in a supervised learning framework to
approximate in vitro cell biology assay experiments while respecting a
generalized form of the governing reaction-diffusion partial differential
equation (PDE). By allowing the diffusion and reaction terms to be multilayer
perceptrons (MLPs), the nonlinear forms of these terms can be learned while
simultaneously converging to the solution of the governing PDE. Further, the
trained MLPs are used to guide the selection of biologically interpretable
mechanistic forms of the PDE terms which provides new insights into the
biological and physical mechanisms that govern the dynamics of the observed
system. The method is evaluated on sparse real-world data from wound healing
assays with varying initial cell densities [2].
