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It's about time : Analysing simplifying assumptions for modelling multi-step pathways in systems biology

Korsbo, Niklas and Jönsson, Henrik LU (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.

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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}},
}