Building confidence in quantitative systems pharmacology models: An engineer's guide to exploring the rationale in model design and development: Building Confidence in QSP Models
Open accessAbstract
This tutorial promotes good practice for exploring the rationale of systems pharmacology models. A safety systems engineering inspired notation approach provides much needed rigor and transparency in development and application of models for therapeutic discovery and design of intervention strategies. Structured arguments over a model's development, underpinning biological knowledge, and analyses of model behaviors are constructed to determine the confidence that a model is fit for the purpose for which it will be applied.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A number of publications have recently proposed guidelines or recommendations on modeling practices; some focus on the design process of QSP models, whereas others suggest methods for assessing the quality and/or adequacy of QSP models in particular or models that support the MID3 paradigm in general.
“A number of publications have recently proposed guidelines or recommendations on modeling practices. Some of these focus on the design process of QSP models,26, 27, 28 whereas others suggest methods for assessing the quality and/or adequacy of QSP models in particular27, 28 or models that support the MID3 paradigm in general.29”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 37 · Cited by 5
Research works printed but not linked: 28
- [1]Alden, K., Andrews, P.S., Polack, F.A., Veiga‐Fernandes, H., Coles, M.C. & Timmis, J. Using argument notation to engineer biological simulations with increased confidence. J. R. Soc. Interface 12, 20141059 (2015). 25589574 PMC4345473
- [2]Kelly, T.P. Arguing safety a systematic approach to managing safety cases. PhD thesis, University of York, YCST 99/05 (1999).
- [3]Alden, K., Timmis, J., Andrews, P.S., Veiga‐Fernandes, H. & Coles, M.C. Pairing experimentation and computational modeling to understand the role of tissue inducer cells in the development of lymphoid organs. Front. Immunol. 3, 172 (2012). 22826707 PMC3399454
- [5]Denney, E., Pai, G. & Habli, I. Towards measurement of confidence in safety cases. < http://ieeexplore.ieee.org/document/6092593/?arnumber=6092593> (2011).
- [6]Fenn, J., Hawkins, R., Williams, P. & Kelly, T. The Safety of Systems: Proceedings of the Fifteenth Safety‐critical Systems Symposium, Bristol, UK, 13–15 February 2007. Springer London, London, 2007, ch. Safety Case Composition Using Contracts – Refinements based on Feedback from an Industrial Case Study, pp 133–146.
- [7]An, G. Agent‐based computer simulation and SIRS: building a bridge between basic science and clinical trials. Shock 16, 266– 273 (2001). 11580108
- [8]An, G. Concepts for developing a collaborative in silico model of the acute inflammatory response using agent‐based modeling. J. Crit. Care 21, 105– 110; discussion 110–111 (2006). 16616634
- [9]Ghetiu, T., Polack, F.A.C. & Bown, J. Argument‐driven validation of computer simulations – a necessity rather than an option. < http://ieeexplore.ieee.org/document/5617156/> (2010).
- [10]Polack, F.A.C., Droop, A., Garnett, P.R., Ghetiu, T. & Stepney, S. Simulation validation: exploring the suitability of a simulation of cell division and differentiation in the prostate CoSMoS 2011: Proceedings of the 2011 Workshop on Complex Systems Modelling and Simulation (2011).
- [11]Albergante, L., Timmis, J., Beattie, L. & Kaye, P.M. A Petri net model of granulomatous inflammation: implications for IL‐10 mediated control of Leishmania donovani infection. PLoS Comput. Biol. 9, e1003334 (2013). 24363630 PMC3867212
- [12]Alvar, J. et al Leishmaniasis worldwide and global estimates of its incidence. PLoS One 7, e35671 (2012). 22693548 PMC3365071
- [13]Kaye, P.M. et al The immunopathology of experimental visceral leishmaniasis. Immunol. Rev. 201, 239– 253 (2004). 15361245
- [14]Murray, H.W. Tissue granuloma structure‐function in experimental visceral leishmaniasis. Int. J. Exp. Pathol. 82, 249– 267 (2001). 11703536 PMC2517779
- [17]Koch I., Reisig W. & Schreiber F., eds. Modeling in Systems Biology: The Petri Net Approach (Springer, New York, 2011).
- [19]Combining software evidence arguments and assurance. Proceedings of ICSE‐2005: workshop on realising evidence‐based software engineering. < http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.3522> (2005).
- [21]Gazzinelli, R.T., Oswald, I.P., James, S.L. & Sher, A. IL‐10 inhibits parasite killing and nitrogen oxide production by IFN‐gamma‐activated macrophages. J. Immunol. 148, 1792– 1796 (1992). 1541819
- [22]Kane, M.M. & Mosser, D.M. The role of IL‐10 in promoting disease progression in leishmaniasis. J. Immunol. 166, 1141– 1147 (2001). 11145695
- [23]Verma, S. et al Quantification of parasite load in clinical samples of leishmaniasis patients: Il‐10 level correlates with parasite load in visceral leishmaniasis. PLoS One 5, 1– 8 (2010). PMC2852412 20404924
- [24]McCall, L.I. & Matlashewski, G. Involvement of the Leishmania donovani virulence factor A2 in protection against heat and oxidative stress. Exp. Parasitol. 132, 109– 115 (2012). 22691540
- [25]Cilfone, N.A., Kirschner, D.E. & Linderman, J.J. Strategies for efficient numerical implementation of hybrid multi‐scale agent‐based models to describe biological systems. Cell. Mol. Bioeng. 8, 119– 136 (2015). 26366228 PMC4564133
- [26]Hayden, E.C. Mozilla plan seeks to debug scientific code. Nature 501, 472 (2013). 24067690
- [27]Ince, D.C., Hatton, L. & Graham‐Cumming, J. The case for open computer programs. Nature 482, 485– 488 (2012). 22358837
- [28]Joppa, L.N. et al Computational science. Troubling trends in scientific software use. Science 340, 814– 815 (2013). 23687031
- [29]Rosenberg, L.H., Hammer, T.F. & Huffman, L.L. Requirements, testing, and metrics In 15th Annual Pacific Northwest Software Quality Conference. < https://www.researchgate.net/publication/228600621_Requirements_testing_and_metrics> (1998).
- [31]Alden, K., Read, M., Timmis, J., Andrews, P.S., Veiga‐Fernandes, H., & Coles, M. Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems. PLoS Comput. Biol. 9, e1002916 (2013). 23468606 PMC3585389
- [34]Barnes, N. Publish your computer code: it is good enough. Nature 467, 753 (2010). 20944687
- [35]Alden, K. & Read, M. Computing: scientific software needs quality control. Nature 502, 448 (2013). 24153289
- [36]Vinken, M. The adverse outcome pathway concept: a pragmatic tool in toxicology. Toxicology 312, 158– 165 (2013). 23978457
- Authors
- Timmis, J., Alden, K., Andrews, P., Clark, E., Nellis, A., Naylor, B., Coles, M., & Kaye, P.
- Affiliations
- University of York (GB)
- SimOmics Limited; York UK
- Corresponding author
- Timmis, J
- Venue
- CPT: Pharmacometrics & Systems Pharmacology
- Publisher
- Wiley
- Published
- 2017-02-09
- Volume
- 6
- Issue
- 3
- Pages
- 156-167
- ISSN
- 2163-8306 (ISSN-L)
- PMC
- PMC5351409
- OpenAlex
- W2551394389
- Cited by
- 5
- References
- 37
- Citations by year
- 2019: 4 · 2018: 1
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- CPT Pharmacometrics & Systems Pharmacology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Sunderland Repository (University of Sunderland) (submittedVersion)
- Funders
- Crack-IT Programme (NC/C013117/1, NC/C013205/1)
- MRC (G1000230)
- Royal Academy of Engineering
- The Royal Society
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Pharmaceutical studies and practices
- Keywords
- quantitative systems pharmacology, model confidence, model notation, model development, therapeutic discovery, model validation, biological knowledge integration
- MeSH terms
- Animals, Antiprotozoal Agents, Biomedical Engineering, Humans, Immunity, Cellular, Leishmaniasis, Visceral, Models, Biological, Systems Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
