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Korsbo N, Jönsson H. It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS Comput Biol. 2020;16(6):e1007982doi: 10.1371/journal.pcbi.1007982.
Korsbo, N., & Jönsson, H. (2020). It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS computational biology, 16(6), e1007982. https://doi.org/10.1371/journal.pcbi.1007982
Korsbo, Niklas, and Jönsson, Henrik. "It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology." PLoS computational biology vol. 16,6 (2020): e1007982. doi: https://doi.org/10.1371/journal.pcbi.1007982
Korsbo N, Jönsson H. It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology. PLoS Comput Biol. 2020 Jun 29;16(6):e1007982. doi: 10.1371/journal.pcbi.1007982. eCollection 2020 Jun. PMID: 32598362; PMCID: PMC7351226.
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PLoS Comput Biol. 2020 Jun 29;16(6):e1007982. doi: 10.1371/journal.pcbi.1007982. eCollection 2020 Jun.

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

PLoS computational biology

Niklas Korsbo, Henrik Jönsson

Affiliations

  1. The Sainsbury Laboratory, University of Cambridge, Cambridge, United Kingdom.
  2. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
  3. Department of Astronomy and Theoretical Physics, Computational Biology and Biological Physics, Lund University, Lund, Sweden.

PMID: 32598362 PMCID: PMC7351226 DOI: 10.1371/journal.pcbi.1007982

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 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.

Conflict of interest statement

The authors have declared that no competing interests exist.

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