Article
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Linear and Nonlinear Modes & Data Signatures in Dynamic Systems Biology Models
Version 1
: Received: 26 July 2023 / Approved: 27 July 2023 / Online: 28 July 2023 (13:12:25 CEST)
A peer-reviewed article of this Preprint also exists.
DiStefano, J., III. Linear and Nonlinear Modes and Data Signatures in Dynamic Systems Biology Models. Appl. Sci. 2023, 13, 9772. DiStefano, J., III. Linear and Nonlinear Modes and Data Signatures in Dynamic Systems Biology Models. Appl. Sci. 2023, 13, 9772.
Abstract
The particulars of stimulus-response experiments done on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures – so that modal analysis can be used constructively in systems biology model building – for nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input-output data associated with a structural model – one that includes inputs and outputs explicitly – and for NL as well as linear biosystems. The methodology establishes model dimensionality (size, complexity) from particular data sets; helps select among multiple candidate models (model distinguishability); helps in designing new experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. For NL biosystems, results are not as comprehensive, similarly informative about their dominant dynamical properties, and unified with linear models on invariant 2-dimensional manifolds in phase space. Some automation of these highly technical aspects of biomodeling is also introduced.
Keywords
exponential modes; visible modes, hidden modes; data limitations; input-output data; mechanistic model; model distinguishability; invariant 2-dimensional manifolds
Subject
Engineering, Bioengineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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