Summary: | Currently liver transplantation is the only treatment option for liver disease, but organ
availability cannot meet patient demand. Alternative regenerative therapies, including cell
transplantation, aim to modulate the injured microenvironment from inflammation and
scarring towards regeneration. The complexity of the liver injury response makes it challenging
to identify suitable therapeutic targets when relying on experimental approaches alone.
Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary
differential equation model of acute liver disease able to predict the host response to injury
and potential interventions. The Mdm2fl/fl mouse model of senescence-driven liver injury was
used to generate a quantitative dynamic characterisation of the key cellular players
(macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver
injury. This was qualitatively captured by the mathematical model. The mathematical model
was then used to predict injury outcomes in response to milder and more severe levels of
senescence-induced liver injury and validated with experimental in vivo data. In silico
experiments using the validated model were then performed to interrogate potential
approaches to enhance regeneration. These predicted that increasing the rate of macrophage
phenotypic switch or increasing the number of pro-regenerative macrophages in the system
will accelerate the rate of senescent cell clearance and resolution. These results showcase the
potential benefits of mechanistic mathematical modelling for capturing the dynamics of
complex biological systems and identifying therapeutic interventions that may enhance our
understanding of injury-repair mechanisms and reduce translational bottlenecks.
|