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Discovering adaptation-capable biological network structures using control-theoretic approaches
- Source :
- PLoS Computational Biology, Vol 18, Iss 1, p e1009769 (2022), PLoS Computational Biology
- Publication Year :
- 2022
- Publisher :
- Public Library of Science (PLoS), 2022.
-
Abstract
- Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.<br />Author summary Biological systems display a remarkable diversity of functionalities, many of which can be conceived as the response of a large network composed of small interconnecting modules. Unravelling the connection pattern, i.e. design principles, behind important biological functionalities is one of the most challenging problems in systems biology. One such phenomenon is perfect adaptation, which merits special attention owing to its universal presence ranging from chemotaxis in bacterial cells to calcium homeostasis in mammalian cells. The present work focuses on finding the design principles for perfect adaptation in the presence of a stair-case type disturbance. To this end, the current work proposes a systems-theoretic approach to deduce precise mathematical (hence structural) conditions that comply with the key performance parameters for adaptation. The approach is agnostic to the particularities of the reaction kinetics, underlining the dominant role of the topological structure on the response of the network. Notably, the design principles obtained in this work serve as the most strict necessary structural conditions for a network of any size to provide perfect adaptation.
- Subjects :
- Computer and Information Sciences
QH301-705.5
Information Theory
Adaptation, Biological
Gene Expression
Network Motifs
Research and Analysis Methods
Systems Science
Biochemistry
Models, Biological
Cellular and Molecular Neuroscience
Mathematical and Statistical Techniques
Industrial Engineering
DNA-binding proteins
Genetics
Gene Regulation
Control Theory
Biology (General)
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Peak Values
Feedback, Physiological
Transfer Functions
Ecology
Biology and life sciences
Directed Graphs
Proteins
Computational Biology
Eigenvalues
Control Engineering
Regulatory Proteins
Algebra
Computational Theory and Mathematics
Linear Algebra
Modeling and Simulation
Graph Theory
Physical Sciences
Signal Processing
Engineering and Technology
Synthetic Biology
Mathematical Functions
Network Analysis
Mathematics
Research Article
Transcription Factors
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 18
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....b9d23c4747b0e6f14497446b43cb4a0d