It's about time : Analysing simplifying assumptions for modelling multi-step pathways in systems biology
(2020) In PLoS Computational Biology 16(6).- Abstract
Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics. Here, we perform a computational investigation of sequential multi-step pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a pathway leads to detectable signatures in model dynamics and its... (More)
Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics. Here, we perform a computational investigation of sequential multi-step pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a pathway leads to detectable signatures in model dynamics and its optimised parameters. An alternative assumption is suggested for simplifying such pathways. Rather than assuming a truncated number of pathway steps, we propose to use the assumption that the rates of information propagation along the pathway is homogeneous and, instead, letting the length of the pathway be a free parameter. We first focus on linear pathways that are sequential and have first-order kinetics, and we show how this assumption results in a three-parameter model that consistently outperforms its truncated rival and a delay differential equation alternative in recapitulating observed dynamics. We then show how the proposed assumption allows for similarly terse and effective models of non-linear pathways. Our results provide a foundation for well-informed decision making during model simplifications.
(Less)
- author
- Korsbo, Niklas and Jönsson, Henrik LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- in
- PLoS Computational Biology
- volume
- 16
- issue
- 6
- article number
- e1007982
- publisher
- Public Library of Science (PLoS)
- external identifiers
-
- pmid:32598362
- scopus:85087729038
- ISSN
- 1553-734X
- DOI
- 10.1371/journal.pcbi.1007982
- language
- English
- LU publication?
- yes
- id
- d5529a9f-0d46-4ede-8390-34f5fb5353d2
- date added to LUP
- 2020-07-22 11:39:04
- date last changed
- 2024-02-16 19:15:17
@article{d5529a9f-0d46-4ede-8390-34f5fb5353d2, abstract = {{<p>Thoughtful use of simplifying assumptions is crucial to make systems biology models tractable while still representative of the underlying biology. A useful simplification can elucidate the core dynamics of a system. A poorly chosen assumption can, however, either render a model too complicated for making conclusions or it can prevent an otherwise accurate model from describing experimentally observed dynamics. Here, we perform a computational investigation of sequential multi-step pathway models that contain fewer pathway steps than the system they are designed to emulate. We demonstrate when such models will fail to reproduce data and how detrimental truncation of a pathway leads to detectable signatures in model dynamics and its optimised parameters. An alternative assumption is suggested for simplifying such pathways. Rather than assuming a truncated number of pathway steps, we propose to use the assumption that the rates of information propagation along the pathway is homogeneous and, instead, letting the length of the pathway be a free parameter. We first focus on linear pathways that are sequential and have first-order kinetics, and we show how this assumption results in a three-parameter model that consistently outperforms its truncated rival and a delay differential equation alternative in recapitulating observed dynamics. We then show how the proposed assumption allows for similarly terse and effective models of non-linear pathways. Our results provide a foundation for well-informed decision making during model simplifications.</p>}}, author = {{Korsbo, Niklas and Jönsson, Henrik}}, issn = {{1553-734X}}, language = {{eng}}, number = {{6}}, publisher = {{Public Library of Science (PLoS)}}, series = {{PLoS Computational Biology}}, title = {{It's about time : Analysing simplifying assumptions for modelling multi-step pathways in systems biology}}, url = {{http://dx.doi.org/10.1371/journal.pcbi.1007982}}, doi = {{10.1371/journal.pcbi.1007982}}, volume = {{16}}, year = {{2020}}, }