Publishing reproducible dynamic kinetic models

Porubsky, Veronica L.; Smith, Lucian P.; Sauro, Herbert M.

doi:10.1093/bib/bbaa152

Cited by 5 publications

(4 citation statements)

References 83 publications

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“…It is widely recognized that there is a “reproducibility crisis” in science ( Baker, 2016 ) that includes computational science ( Mesirov, 2010 ; Peng, 2011 ; Stodden et al, 2016 ) and indeed computational modeling of biological systems ( Waltemath and Wolkenhauer, 2016 ; Mendes, 2018 ; Tiwari et al, 2021 ). I and others argue that reproducibility of results obtained from computer simulations of biological models (biomodels) could be enhanced by using open source software ( Ince et al, 2012 ; Mendes, 2018 ) that implement widely adopted standards ( Waltemath and Wolkenhauer, 2016 ; Blinov et al, 2021 ; Porubsky et al, 2021 ), which are part of various sets of rules proposed in the last 2 decades ( Le Novère et al, 2005 ; Waltemath et al, 2011a ; Lewis et al, 2016 ; Porubsky et al, 2020 ). Adoption of such practices, though, will only become widespread when enforced by publishers ( Schnell, 2018 ; Stodden et al, 2018 ) and funding agencies ( Yale Law School Roundtable Participants, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

“…Frontiers in Cell and Developmental Biology frontiersin.org 07 widely adopted standards (Waltemath and Wolkenhauer, 2016;Blinov et al, 2021;Porubsky et al, 2021), which are part of various sets of rules proposed in the last 2 decades (Le Novère et al, 2005;Waltemath et al, 2011a;Lewis et al, 2016;Porubsky et al, 2020). Adoption of such practices, though, will only become widespread when enforced by publishers (Schnell, 2018;Stodden et al, 2018) and funding agencies (Yale Law School Roundtable Participants, 2010).…”

Section: Discussionmentioning

confidence: 99%

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Reproducibility and FAIR principles: the case of a segment polarity network model

Mendes

2023

Front. Cell Dev. Biol.

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The issue of reproducibility of computational models and the related FAIR principles (findable, accessible, interoperable, and reusable) are examined in a specific test case. I analyze a computational model of the segment polarity network in Drosophila embryos published in 2000. Despite the high number of citations to this publication, 23 years later the model is barely accessible, and consequently not interoperable. Following the text of the original publication allowed successfully encoding the model for the open source software COPASI. Subsequently saving the model in the SBML format allowed it to be reused in other open source software packages. Submission of this SBML encoding of the model to the BioModels database enables its findability and accessibility. This demonstrates how the FAIR principles can be successfully enabled by using open source software, widely adopted standards, and public repositories, facilitating reproducibility and reuse of computational cell biology models that will outlive the specific software used.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Reproducibility and FAIR principles: the case of a segment polarity network model

Mendes

2023

Front. Cell Dev. Biol.

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“…Simulations were run in the Tellurium software [46] version 2.2.4.1, a Python environment for dynamical modeling of biological networks, along with its associated library for simulation of biological models, libRoadRunner [47]. This work supports reproducibility standards by making the models themselves, with complete code, available for download [48][49][50]. They were packaged with SED-ML Level 1 Version 4 [51], producing COMBINE files [52] that are available at our research group github site (https://github.com/saurolab-papers/).…”

Section: Methodsmentioning

confidence: 99%

Signal integration and integral feedback control with biochemical reaction networks

Andrews,

Kochen,

Smith

et al. 2024

Preprint

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Biochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a system's output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.

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“…Figure 1 illustrates the typical steps of a simulation experiment in systems biology. We and others have extensively reviewed the technical and cultural challenges to repeating each of these steps [8,9].…”

Section: Introductionmentioning

confidence: 99%

Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology

Blinov¹,

Gennari²,

Karr³

et al. 2021

Preprint

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Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been extensive standardization efforts. For example, Tiwari et al. recently found that they could only repeat 50% of published simulation results in systems biology. Toward improving the reproducibility of computational systems research, we identified several resources that investigators can leverage to make their research more accessible, executable, and comprehensible by others. In particular, we identified several domain standards and curation services, as well as powerful approaches pioneered by the software engineering industry that we believe many investigators could adopt. Together, we believe these approaches could substantially enhance the reproducibility of systems biology research. In turn, we believe enhanced reproducibility would accelerate the development of more sophisticated models that could inform precision medicine and synthetic biology.

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Publishing reproducible dynamic kinetic models

Cited by 5 publications

References 83 publications

Reproducibility and FAIR principles: the case of a segment polarity network model

Reproducibility and FAIR principles: the case of a segment polarity network model

Signal integration and integral feedback control with biochemical reaction networks

Practical Resources for Enhancing the Reproducibility of Mechanistic Modeling in Systems Biology

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