The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems

Overview

Journal J Pers Med

Date 2025 Jan 24

PMID 39852203

Authors

Yaron Ilan

Affiliations

Soon will be listed here.

Abstract

Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP, intrinsic unpredictability is mandatory for the dynamicity of biological systems under continuously changing internal and external perturbations. The present paper describes some of the parameters and challenges associated with uncertainty and randomness in biological systems and presents methods for quantifying them. Modeling biological systems necessitates accounting for the randomness, variability, and underlying uncertainty of systems in health and disease. The CDP provides a scheme for dealing with uncertainty in biological systems and sets the basis for using them. This paper presents the CDP-based second-generation artificial intelligence system that incorporates variability to improve the effectiveness of medical interventions. It describes the use of the digital pill that comprises algorithm-based personalized treatment regimens regulated by closed-loop systems based on personalized signatures of variability. The CDP provides a method for using uncertainties in complex systems in an outcome-based manner.

References

Sigawi T, Ilan Y . Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics (Basel). 2023; 8(4). PMC: 10452845. DOI: 10.3390/biomimetics8040359. View

Kandel I, Castelli M . Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset. Health Inf Sci Syst. 2021; 9(1):33. PMC: 8325732. DOI: 10.1007/s13755-021-00163-7. View

Usman M, Ojha S, Jha S, Chellappan D, Gupta G, Singh S . Biological databases and tools for neurological disorders. J Integr Neurosci. 2022; 21(1):41. DOI: 10.31083/j.jin2101041. View

Benford D, Halldorsson T, Jeger M, Knutsen H, More S, Naegeli H . Guidance on Uncertainty Analysis in Scientific Assessments. EFSA J. 2020; 16(1):e05123. PMC: 7009727. DOI: 10.2903/j.efsa.2018.5123. View

Ilan Y . Microtubules: From understanding their dynamics to using them as potential therapeutic targets. J Cell Physiol. 2018; 234(6):7923-7937. DOI: 10.1002/jcp.27978. View

Hass H, Kreutz C, Timmer J, Kaschek D . Fast integration-based prediction bands for ordinary differential equation models. Bioinformatics. 2015; 32(8):1204-10. DOI: 10.1093/bioinformatics/btv743. View

Mossman D, Berger J . Intervals for posttest probabilities: a comparison of 5 methods. Med Decis Making. 2002; 21(6):498-507. DOI: 10.1177/0272989X0102100608. View

DeSouza C, Legedza A, Sankoh A . An overview of practical approaches for handling missing data in clinical trials. J Biopharm Stat. 2010; 19(6):1055-73. DOI: 10.1080/10543400903242795. View

Abdar M, Samami M, Dehghani Mahmoodabad S, Doan T, Mazoure B, Hashemifesharaki R . Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Comput Biol Med. 2021; 135:104418. DOI: 10.1016/j.compbiomed.2021.104418. View

10.

Keating S, Waltemath D, Konig M, Zhang F, Drager A, Chaouiya C . SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol. 2020; 16(8):e9110. PMC: 8411907. DOI: 10.15252/msb.20199110. View

11.

Leggieri P, Liu Y, Hayes M, Connors B, Seppala S, OMalley M . Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng. 2021; 23:169-201. PMC: 8277735. DOI: 10.1146/annurev-bioeng-082120-022836. View

12.

Andrianakis I, Vernon I, McCreesh N, McKinley T, Oakley J, Nsubuga R . Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda. PLoS Comput Biol. 2015; 11(1):e1003968. PMC: 4288726. DOI: 10.1371/journal.pcbi.1003968. View

13.

van den Bosch O, Alvarez-Jimenez R, de Grooth H, Girbes A, Loer S . Breathing variability-implications for anaesthesiology and intensive care. Crit Care. 2021; 25(1):280. PMC: 8339683. DOI: 10.1186/s13054-021-03716-0. View

14.

Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Panahiazar M . A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Sci Data. 2019; 6(1):227. PMC: 6811630. DOI: 10.1038/s41597-019-0206-3. View

15.

Hartnack S, Budke C, Craig P, Jiamin Q, Boufana B, Campos-Ponce M . Latent-class methods to evaluate diagnostics tests for Echinococcus infections in dogs. PLoS Negl Trop Dis. 2013; 7(2):e2068. PMC: 3573084. DOI: 10.1371/journal.pntd.0002068. View

16.

Higdon D, Bowsher J, Johnson V, Turkington T, Gilland D, Jaszczak R . Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data. IEEE Trans Med Imaging. 1997; 16(5):516-26. DOI: 10.1109/42.640741. View

17.

Buiatti M, Longo G . Randomness and multilevel interactions in biology. Theory Biosci. 2013; 132(3):139-58. DOI: 10.1007/s12064-013-0179-2. View

18.

Venkatramanan S, Sadilek A, Fadikar A, Barrett C, Biggerstaff M, Chen J . Forecasting influenza activity using machine-learned mobility map. Nat Commun. 2021; 12(1):726. PMC: 7873234. DOI: 10.1038/s41467-021-21018-5. View

19.

Pimentel H, Bray N, Puente S, Melsted P, Pachter L . Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods. 2017; 14(7):687-690. DOI: 10.1038/nmeth.4324. View

20.

Hucka M, Bergmann F, Hoops S, Keating S, Sahle S, Schaff J . The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core. J Integr Bioinform. 2015; 12(2):266. PMC: 5451324. DOI: 10.2390/biecoll-jib-2015-266. View