ArticlePLoS computational biology2020
It's about time: Analysing simplifying assumptions for modelling multi-step pathways in systems biology.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A scoping review of computational models on human glucose cerebral metabolism.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Physiology-informed regularisation enables training of universal differential equation systems for biological applications.PLoS computational biology · 2025Article
- Transcription factor competition facilitates self-sustained oscillations in single gene genetic circuits.PLoS computational biology · 2023Article
- The ups and downs of biological oscillators: a comparison of time-delayed negative feedback mechanisms.Journal of the Royal Society, Interface · 2023Article
- Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series.Sensors (Basel, Switzerland) · 2022Article
- Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number.Science advances · 2022Article
- Article
- Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.Bioinformatics (Oxford, England) · 2021Article
- A modular approach for modeling the cell cycle based on functional response curves.PLoS computational biology · 2021Article
- Memorizing environmental signals through feedback and feedforward loops.Current opinion in cell biology · 2021Review
- Focus on biosensors: Looking through the lens of quantitative biology.Quantitative plant biology · 2021Review
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Authors and funding
2 authors.
Funding
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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.
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