Evidence map›Paper›PMID 36084151›Full record

ArticlePLoS computational biology2022

Utility of constraints reflecting system stability on analyses for biological models.

Yoshiaki Kariya, Masashi Honma, Keita Tokuda, Akihiko Konagaya, Hiroshi Suzuki

Abstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yoshiaki KariyaDepartment of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0001-8227-2605
Masashi HonmaDepartment of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0001-8481-763X
Keita TokudaDepartment of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, Japan.ORCID 0000-0002-0275-5541
Akihiko KonagayaMolecular Robotics Research Institute, Limited, Kyowa Create Dai-ichi, Minato-ku, Tokyo, Japan.ORCID 0000-0001-6846-7449
Hiroshi SuzukiDepartment of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.ORCID 0000-0002-9730-3133

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulating complex biological models consisting of multiple ordinary differential equations can aid in the prediction of the pharmacological/biological responses; however, they are often hampered by the availability of reliable kinetic parameters. In the present study, we aimed to discover the properties of behaviors without determining an optimal combination of kinetic parameter values (parameter set). The key idea was to collect as many parameter sets as possible. Given that many systems are biologically stable and resilient (BSR), we focused on the dynamics around the steady state and formulated objective functions for BSR by partial linear approximation of the focused region. Using the objective functions and modified global cluster Newton method, we developed an algorithm for a thorough exploration of the allowable parameter space for biological systems (TEAPS). We first applied TEAPS to the NF-κB signaling model. This system shows a damped oscillation after stimulation and seems to fit the BSR constraint. By applying TEAPS, we found several directions in parameter space which stringently determines the BSR property. In such directions, the experimentally fitted parameter values were included in the range of the obtained parameter sets. The arachidonic acid metabolic pathway model was used as a model related to pharmacological responses. The pharmacological effects of nonsteroidal anti-inflammatory drugs were simulated using the parameter sets obtained by TEAPS. The structural properties of the system were partly extracted by analyzing the distribution of the obtained parameter sets. In addition, the simulations showed inter-drug differences in prostacyclin to thromboxane A2 ratio such that aspirin treatment tends to increase the ratio, while rofecoxib treatment tends to decrease it. These trends are comparable to the clinical observations. These results on real biological models suggest that the parameter sets satisfying the BSR condition can help in finding biologically plausible parameter sets and understanding the properties of biological systems.

Indexed as

NF-kappa BThromboxane A2AlgorithmsAnti-Inflammatory AgentsArachidonic AcidAspirinModels, BiologicalProstaglandins IAnti-Inflammatory AgentsArachidonic AcidAspirinNF-kappa BProstaglandins IThromboxane A2

Identifiers

PMID36084151
PMCPMC9491612

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.