Inference of dynamical gene regulatory networks from single-cell data
  with physics informed neural networks
Maria Mircea Diego Garlaschelli Stefan Semrau
One of the main goals of developmental biology is to reveal the gene
regulatory networks (GRNs) underlying the robust differentiation of multipotent
progenitors into precisely specified cell types. Most existing methods to infer
GRNs from experimental data have limited predictive power as the inferred GRNs
merely reflect gene expression similarity or correlation. Here, we demonstrate,
how physics-informed neural networks (PINNs) can be used to infer the
parameters of predictive, dynamical GRNs that provide mechanistic understanding
of biological processes. Specifically we study GRNs that exhibit bifurcation
behavior and can therefore model cell differentiation. We show that PINNs
outperform regular feed-forward neural networks on the parameter inference task
and analyze two relevant experimental scenarios: 1. a system with cell
communication for which gene expression trajectories are available and 2.
snapshot measurements of a cell population in which cell communication is
absent. Our analysis will inform the design of future experiments to be
analyzed with PINNs and provides a starting point to explore this powerful
class of neural network models further.