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Article
Applying an inverse homeostasis perspective to simplify the design and implemention of robustly-nearly-homeostatic biological networks
David McMillen
University of Toronto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2676-5450
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This work is licensed under a CC BY 4.0 License. Read Full License
A nearly-homeostatic system is able to maintain the steady state system output close to a fixed set point, despite varying levels of environmental perturbation. Studying such systems enables the de- sign of synthetic cellular systems able to function consistently under a wide range of environmental conditions. Here we present the inverse homeostasis perspective, a novel approach to studying and de- signing nearly-homeostatic systems. It represents a graphical approach allowing visualization of how each regulatory parameter affects near-homeostatic performance while being more accessible than situation- specific, mathematically complex approaches. Given the difficulty of precise parameter measurement in biology, we focus on adjusting experimentally-determined steady state response curves to implement near- homeostatic systems without requiring explicit parameter measurements. One implication of the inverse homeostasis perspective is an illustration of the much stronger dependence of nearly-perfect integral con- trollers on the properties of the existing system being controlled, compared to the system-independence of perfect integral controllers.
Keywords
homeostasis, robustly-nearly-homeostatic biological network
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This preprint is under consideration at a Nature Portfolio Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Applying an inverse homeostasis perspective to simplify the design and implemention of robustly-nearly-homeostatic biological networks
David McMillen
University of Toronto
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2676-5450
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 30 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
A nearly-homeostatic system is able to maintain the steady state system output close to a fixed set point, despite varying levels of environmental perturbation. Studying such systems enables the de- sign of synthetic cellular systems able to function consistently under a wide range of environmental conditions. Here we present the inverse homeostasis perspective, a novel approach to studying and de- signing nearly-homeostatic systems. It represents a graphical approach allowing visualization of how each regulatory parameter affects near-homeostatic performance while being more accessible than situation- specific, mathematically complex approaches. Given the difficulty of precise parameter measurement in biology, we focus on adjusting experimentally-determined steady state response curves to implement near- homeostatic systems without requiring explicit parameter measurements. One implication of the inverse homeostasis perspective is an illustration of the much stronger dependence of nearly-perfect integral con- trollers on the properties of the existing system being controlled, compared to the system-independence of perfect integral controllers.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.