Computational modeling of the interactions between the maternal and fetal circulations in human pregnancy

Abstract

In pregnancy, fetal growth is supported by its placenta. In turn, the placenta is nourished by maternal blood, delivered from the uterus, in which the vasculature is dramatically transformed to deliver this blood an ever increasing volume throughout gestation. A healthy pregnancy is thus dependent on the development of both the placental and maternal circulations, but also the interface where these physically separate circulations come in close proximity to exchange gases and nutrients between mum and baby. As the system continually evolves during pregnancy, our understanding of normal vascular anatomy, and how this impacts placental exchange function is limited. Understanding this is key to improve our ability to understand, predict, and detect pregnancy pathologies, but presents a number of challenges, due to the inaccessibility of the pregnant uterus to invasive measurements, and limitations in the resolution of imaging modalities safe for use in pregnancy. Computational approaches provide an opportunity to gain new insights into normal and abnormal pregnancy, by connecting observed anatomical changes from high-resolution imaging to function, and providing metrics that can be observed by routine clinical ultrasound. Such advanced modeling brings with it challenges to scale detailed anatomical models to reflect organ level function. This suggests pathways for future research to provide models that provide both physiological insights into pregnancy health, but also are simple enough to guide clinical focus. We the review evolution of computational approaches to understanding the physiology and pathophysiology of pregnancy in the uterus, placenta, and beyond focusing on both opportunities and challenges.

Anin vitro, in uteroandin silicoframework of oxygen diffusion in intricate vascular networks of the placenta

“…Computational modelling of flow and diffusion in vasculature has been an emerging tool in overcoming challenges of traditional qualitative biology and providing novel, often counterintuitive, physical insights into previously unknown mechanisms of both health and disease 1,3,6,8 . When applied correctly, this tool holds immense diagnostic, prognostic and therapeutic potential in addressing modern era medical conundrums.…”

“…Computational experimentation is an emerging tool that is increasingly being leveraged to elucidate complex structure-function relationships in a number of biological systems such as the lungs 1,2 , placenta [3][4][5] , tumors 6,7 and others [8][9][10][11] . This in silico approach overcomes challenges of conventional experiments, allowing for novel measurements to be made or experimental interventions to be actioned, both of which would otherwise be difficult, if not impossible to achieve conventionally.…”

Placental blood flow sensing and regulation in fetal growth restriction

“…Correspondingly, a computational fluid dynamics model of the rat placenta found heterogenous FSS throughout the vascular network, with gradients at vessel bifurcations [ 14 ]. High fidelity in silico models of human fetoplacental haemodynamics will therefore be a valuable tool for providing metrics that can be correlated with in vivo fetoplacental assessment [ 15 ].…”

A Review of Biomechanics Analysis of the Umbilical–Placenta System With Regards to Diseases

“…To understand the hemodynamics and blood transport phenomena in the placenta, biomechanical computational modeling of network blood flow had been performed (Clark et al, 2015 , 2021 ; Junaid et al, 2017 ; Bappoo et al, 2021 ). One such multiscale modeling of placenta vasculature showed that the location of cord insertion and placenta shape with similar volume did not affect the overall placenta flow resistance, rather, the major determinants of placenta resistance were vessel sizes and numbers (Clark et al, 2015 ).…”

“…Bappoo et al recently proposed a robust framework for hemodynamics analysis in placental circulation (Bappoo et al, 2021 ). A comprehensive review on such modeling is provided by Clark et al (Clark et al, 2021 ).…”