Design Principles for Biological Adaptation: A Systems and Control-Theoretic Treatment.

Autor:	Bhattacharya P; Department of Chemical Engineering, Indian Institute of Technology, Madras (IIT Madras), Chennai, India.; Robert Bosch Centre of Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India.; Initiative for Biological Science and Systems mEdicine (IBSE), IIT Madras, Chennai, India., Raman K; Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, Chennai, India. kraman@iitm.ac.in.; Robert Bosch Centre of Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India. kraman@iitm.ac.in.; Initiative for Biological Science and Systems mEdicine (IBSE), IIT Madras, Chennai, India. kraman@iitm.ac.in., Tangirala AK; Robert Bosch Centre of Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, Chennai, India. arunkt@iitm.ac.im.; Initiative for Biological Science and Systems mEdicine (IBSE), IIT Madras, Chennai, India. arunkt@iitm.ac.im.
Jazyk:	angličtina
Zdroj:	Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2024; Vol. 2760, pp. 35-56.
DOI:	10.1007/978-1-0716-3658-9_3
Abstrakt:	Establishing a mapping between (from and to) the functionality of interest and the underlying network structure (design principles) remains a crucial step toward understanding and design of bio-systems. Perfect adaptation is one such crucial functionality that enables every living organism to regulate its essential activities in the presence of external disturbances. Previous approaches to deducing the design principles for adaptation have either relied on computationally burdensome brute-force methods or rule-based design strategies detecting only a subset of all possible adaptive network structures. This chapter outlines a scalable and generalizable method inspired by systems theory that unravels an exhaustive set of adaptation-capable structures. We first use the well-known performance parameters to characterize perfect adaptation. These performance parameters are then mapped back to a few parameters (poles, zeros, gain) characteristic of the underlying dynamical system constituted by the rate equations. Therefore, the performance parameters evaluated for the scenario of perfect adaptation can be expressed as a set of precise mathematical conditions involving the system parameters. Finally, we use algebraic graph theory to translate these abstract mathematical conditions to certain structural requirements for adaptation. The proposed algorithm does not assume any particular dynamics and is applicable to networks of any size. Moreover, the results offer a significant advancement in the realm of understanding and designing complex biochemical networks. (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze:	MEDLINE
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