Signal propagation across layered biochemical networks
Biochemical reaction networks typically consist of a complicated structure with many interacting species and components. Techniques for the analysis of such complex systems commonly use decompositions into simpler subsystems. These decompositions are often modular, representing the state vector as a...
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers Inc.
2014
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| _version_ | 1797081325778239488 |
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| author | Prescott, T Papachristodoulou, A |
| author_facet | Prescott, T Papachristodoulou, A |
| author_sort | Prescott, T |
| collection | OXFORD |
| description | Biochemical reaction networks typically consist of a complicated structure with many interacting species and components. Techniques for the analysis of such complex systems commonly use decompositions into simpler subsystems. These decompositions are often modular, representing the state vector as a concatenation of component vectors. Without transformation, modular decompositions may lead to system parameters directly influencing the dynamics of many subsystems at once. When parameters are the control inputs, this complicates analysis and design. This paper investigates an alternative decomposition, termed layering, which partitions parameters between layers. This allows for hierarchical analysis, where the steady state response of the integrated system to the perturbation of a parameter is calculated in stages. The first stage is to calculate the local response of the steady state of a layer, considered in isolation from other layers; the second is to calculate the perturbed layer's effect on the others when connected back into the full system. This analysis results in a strategy for detecting the layered structure of a biochemical network based on preserving cycles of mass flow within layers. Additionally, by expressing how the local response propagates through the system we uncover the paths by which the direct control of a certain layer may indirectly control others, giving insights into how to exploit their dependencies. © 2014 American Automatic Control Council. |
| first_indexed | 2024-03-07T01:12:57Z |
| format | Conference item |
| id | oxford-uuid:8da7b420-4ecc-4046-925d-90257427a5f2 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T01:12:57Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | oxford-uuid:8da7b420-4ecc-4046-925d-90257427a5f22022-03-26T22:52:39ZSignal propagation across layered biochemical networksConference itemhttp://purl.org/coar/resource_type/c_5794uuid:8da7b420-4ecc-4046-925d-90257427a5f2Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers Inc.2014Prescott, TPapachristodoulou, ABiochemical reaction networks typically consist of a complicated structure with many interacting species and components. Techniques for the analysis of such complex systems commonly use decompositions into simpler subsystems. These decompositions are often modular, representing the state vector as a concatenation of component vectors. Without transformation, modular decompositions may lead to system parameters directly influencing the dynamics of many subsystems at once. When parameters are the control inputs, this complicates analysis and design. This paper investigates an alternative decomposition, termed layering, which partitions parameters between layers. This allows for hierarchical analysis, where the steady state response of the integrated system to the perturbation of a parameter is calculated in stages. The first stage is to calculate the local response of the steady state of a layer, considered in isolation from other layers; the second is to calculate the perturbed layer's effect on the others when connected back into the full system. This analysis results in a strategy for detecting the layered structure of a biochemical network based on preserving cycles of mass flow within layers. Additionally, by expressing how the local response propagates through the system we uncover the paths by which the direct control of a certain layer may indirectly control others, giving insights into how to exploit their dependencies. © 2014 American Automatic Control Council. |
| spellingShingle | Prescott, T Papachristodoulou, A Signal propagation across layered biochemical networks |
| title | Signal propagation across layered biochemical networks |
| title_full | Signal propagation across layered biochemical networks |
| title_fullStr | Signal propagation across layered biochemical networks |
| title_full_unstemmed | Signal propagation across layered biochemical networks |
| title_short | Signal propagation across layered biochemical networks |
| title_sort | signal propagation across layered biochemical networks |
| work_keys_str_mv | AT prescottt signalpropagationacrosslayeredbiochemicalnetworks AT papachristodouloua signalpropagationacrosslayeredbiochemicalnetworks |