Computational Physiology

Computational physiology harnesses mathematical models and numerical simulation to bridge scales from molecular and cellular processes to whole-organ function and systemic dynamics. By encoding biophysical laws—governing fluid flow, electrical excitation, hormone secretion or tissue deformation—into computational frameworks, researchers can probe mechanisms that are difficult or impossible to access experimentally. Patient-specific approaches use anatomical reconstructions from medical imaging to generate personalised models of blood circulation or cardiac electrophysiology, while systems-level frameworks describe feedback in neuroendocrine axes or organ networks. Advances in high-performance computing and data assimilation allow models to be calibrated against clinical measurements, yielding predictive tools for virtual trials, therapy optimisation and mechanistic hypothesis testing across a range of physiological and pathophysiological conditions.

Research from Nature Portfolio

Recent studies have begun to resolve the origins of hormonal pulsatility by treating endocrine secretion as an ensemble of discrete impulses coupled with downstream delays. An impulsive time-delay differential equation model of the hypothalamic–pituitary–adrenal axis showed that intrinsic pituitary pulses alone can sustain ultradian oscillations, with adrenal delays modulating phase but not generating rhythms. In cardiovascular modelling, patient-specific lumped-parameter networks reconstructed from CT angiography have been demonstrated to reproduce coronary pressure and flow measurements across normal and stenotic segments, including estimates of fractional flow reserve without invasive instrumentation. Complementary work has synthesised non-invasive imaging and cuff pressure data into functional “cardiovascular avatars” that predict pressures and volumes in systemic circulation, enabling estimation of variables—such as intracardiac impedances or ventricular elastance—that cannot be measured directly in clinic.

Research from all publishers

Comparative studies of arterial haemodynamics have quantified the trade-offs between one-dimensional networks and three-dimensional fluid-structure simulations. When boundary conditions are consistently derived, reduced 1-D models reproduce global pressure and flow waveforms, guiding parameter selection for detailed 3-D analyses. Fluid–structure interaction simulations of tri-leaflet mechanical heart valves incorporating non-Newtonian blood rheology revealed cavitation onset at substantially lower pressures than previously assumed, with implications for leaflet design and durability. Investigations into image-based computational fluid dynamics of aortic flow have shown that assumed inlet velocity profiles markedly affect local wall shear stress and near-inlet velocities, whereas model predictions two diameters downstream remain robust—underlining the need for patient-specific boundary data when evaluating haemodynamic biomarkers.

Computational Physiology publication trend

The graph below shows the total number of articles in computational physiology across all publications each year (not limited to Nature Index journals).

Technical terms

Computational fluid dynamics (CFD): Numerical solution of the Navier–Stokes equations to predict flow velocities, pressures and shear stresses within biological conduits.

Lumped-parameter model: A zero-dimensional network in which vascular or organ compartments are represented by resistances, compliances and inertances to simulate global dynamics.

Impulsive time-delay differential equation: A mathematical formulation in which state changes occur as discrete pulses, and subsequent effects manifest after specified delays.

Fluid–structure interaction (FSI): Coupled simulation of fluid flow and deformable tissues or devices, integrating hemodynamics with mechanical compliance.

Wall shear stress (WSS): The tangential force per unit area exerted by flowing blood on the vessel wall, implicated in vascular remodelling and atherogenesis.

References

1. A systematic comparison between 1‐D and 3‐D hemodynamics in compliant arterial models. International Journal for Numerical Methods in Biomedical Engineering (2013).
2. The effect of inlet and outlet boundary conditions in image-based CFD modeling of aortic flow. BioMedical Engineering OnLine (2018).
3. FSI modeling and simulation of blood viscosity impacts on cavitation in mechanical heart valves. International Journal of Thermofluids (2024).
4. A patient-specific lumped-parameter model of coronary circulation. Scientific Reports (2018).
5. Bridging the gap between measurements and modelling: a cardiovascular functional avatar. Scientific Reports (2017).
6. Modeling pulsativity in the hypothalamic–pituitary–adrenal hormonal axis. Scientific Reports (2022).