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Anatomy of the Heart

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Second Order systems II

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.

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Related Experiment Video

Updated: Feb 11, 2026

Implantation of Total Artificial Heart in Congenital Heart Disease

Implantation of Total Artificial Heart in Congenital Heart Disease

Published on: July 18, 2014

Taking Systems Medicine to Heart.

Kalliopi Trachana¹, Rhishikesh Bargaje¹, Gustavo Glusman¹

¹From the Institute for Systems Biology, Seattle, WA (K.T., R.B., G.G., N.D.P., S.H., L.E.H.).

Circulation Research

|April 28, 2018

Summary

Systems medicine offers a holistic view of human physiology, focusing on network dynamics to understand health-to-disease transitions. This approach aids in early disease detection and developing personalized P4 medicine strategies.

Keywords:

cardiovascular disease chronic disease environmental exposure genomics systems biology

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Last Updated: Feb 11, 2026

Implantation of Total Artificial Heart in Congenital Heart Disease

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Published on: July 18, 2014

3D Whole-heart Myocardial Tissue Analysis

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Published on: April 12, 2017

Decellularization of Whole Human Heart Inside a Pressurized Pouch in an Inverted Orientation

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Published on: November 26, 2018

Area of Science:

Systems biology and computational approaches
Integrative physiology and network science
Translational medicine and P4 medicine

Background:

Human physiology involves complex, dynamically interacting networks.
Aging and environmental factors reduce resilience, leading to health-to-disease transitions.
These transitions involve qualitative shifts in biological network interactions.

Purpose of the Study:

To review systems medicine approaches for deciphering biological complexity.
To understand disease-perturbed network function and fundamental disease mechanisms.
To accelerate the development of personalized, predictive, preventive, and participatory (P4) medicine.

Main Methods:

Holistic analysis of physiological networks.
High-dimensional nonlinear reconstruction of health-to-disease transitions.
Innovative study designs, technologies, and data analysis.

Main Results:

Disease transitions are characterized by altered network dynamics.
Understanding these dynamics provides insights into disease mechanisms.
Systems medicine facilitates identification of early disease markers.

Conclusions:

Systems medicine is crucial for understanding complex physiological states.
It enables early detection of chronic diseases like cardiovascular disease.
Accelerating P4 medicine development is a key goal for systems medicine.