The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle
Simple Summary
Abstract
1. Introduction
2. The Constrained Disorder Principle Platform
2.1. The Constrained Disorder Principle Provides a Platform for Using Noise
2.2. The Constructive Role of Noise: Diversifying Drug Administration Times and Dosages Creates a Random Environment and Helps Overcome Tolerance by Introducing Random Triggers to Cells and Biochemical Processes
2.3. Biological Noise from Genes to the Brain: The Correlation of Noise with Functionality and Malfunction
2.3.1. Genetic Variability Is Associated with Functionality
2.3.2. The Variability in Cell Structure Explains Its Functionality
2.3.3. Heart Rate Variability Represents the Significance of Fluctuations Within Certain Limitations
2.3.4. Blood Pressure Variability as a Biomarker for Disease
2.3.5. BMI Variability as an Independent Cardiovascular Risk Factor
2.3.6. Altered Gait Variability Can Serve as a Biomarker for Disease
2.3.7. The Brain Function Is Marked by Variability
2.3.8. Variability in Behavior Offers an Advantage for Better Adaptation to Noisy Environments
2.3.9. Aging Is Associated with Altered Variability Outside of the Normal Range
2.4. Variability in Tests: Distinguishing Technical Noise from Biological Noise
2.5. Models Must Consider the Variability Inherent in Biological Systems
2.6. A Role for Randomness in Evolution Processes
3. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
References
- Ilan, Y. The constrained disorder principle defines living organisms and provides a method for correcting disturbed biological systems. Comput. Struct. Biotechnol. J. 2022, 20, 6087–6096. [Google Scholar] [CrossRef] [PubMed]
- Ilan, Y. Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms. Biology 2024, 13, 830. [Google Scholar] [CrossRef] [PubMed]
- Ilan, Y. The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems. J. Pers. Med. 2025, 15, 10. [Google Scholar]
- Ilan, Y. Making use of noise in biological systems. Prog. Biophys. Mol. Biol. 2023, 178, 83–90. [Google Scholar] [CrossRef]
- Ilan, Y. Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks. Prog. Biophys. Mol. Biol. 2023, 180, 37–48. [Google Scholar] [CrossRef]
- Ilan, Y. The constrained-disorder principle defines the functions of systems in nature. Front. Netw. Physiol. 2024, 4, 1361915. [Google Scholar] [CrossRef]
- Sigawi, T.; Lehmann, H.; Hurvitz, N.; Ilan, Y. Constrained Disorder Principle-Based Second-Generation Algorithms Implement Quantified Variability Signatures to Improve the Function of Complex Systems. J. Bioinform. Syst. Biol. 2023, 6, 82–89. [Google Scholar] [CrossRef]
- Kessler, A.; Weksler-Zangen, S.; Ilan, Y. Role of the Immune System and the Circadian Rhythm in the Pathogenesis of Chronic Pancreatitis: Establishing a Personalized Signature for Improving the Effect of Immunotherapies for Chronic Pancreatitis. Pancreas 2020, 49, 1024–1032. [Google Scholar] [CrossRef]
- Ishay, Y.; Kolben, Y.; Kessler, A.; Ilan, Y. Role of circadian rhythm and autonomic nervous system in liver function: A hypothetical basis for improving the management of hepatic encephalopathy. Am. J. Physiol. Gastrointest. Liver Physiol. 2021, 321, G400–G412. [Google Scholar] [CrossRef]
- Kolben, Y.; Weksler-Zangen, S.; Ilan, Y. Adropin as a potential mediator of the metabolic system-autonomic nervous system-chronobiology axis: Implementing a personalized signature-based platform for chronotherapy. Obes. Rev. 2021, 22, e13108. [Google Scholar] [CrossRef]
- Kenig, A.; Kolben, Y.; Asleh, R.; Amir, O.; Ilan, Y. Improving Diuretic Response in Heart Failure by Implementing a Patient-Tailored Variability and Chronotherapy-Guided Algorithm. Front. Cardiovasc. Med. 2021, 8, 695547. [Google Scholar] [CrossRef] [PubMed]
- Azmanov, H.; Ross, E.L.; Ilan, Y. Establishment of an Individualized Chronotherapy, Autonomic Nervous System, and Variability-Based Dynamic Platform for Overcoming the Loss of Response to Analgesics. Pain Physician 2021, 24, 243–252. [Google Scholar] [PubMed]
- Potruch, A.; Khoury, S.T.; Ilan, Y. The role of chronobiology in drug-resistance epilepsy: The potential use of a variability and chronotherapy-based individualized platform for improving the response to anti-seizure drugs. Seizure 2020, 80, 201–211. [Google Scholar] [CrossRef] [PubMed]
- Isahy, Y.; Ilan, Y. Improving the long-term response to antidepressants by establishing an individualized platform based on variability and chronotherapy. Int. J. Clin. Pharmacol. Ther. 2021, 59, 768–774. [Google Scholar] [CrossRef]
- Khoury, T.; Ilan, Y. Introducing Patterns of Variability for Overcoming Compensatory Adaptation of the Immune System to Immunomodulatory Agents: A Novel Method for Improving Clinical Response to Anti-TNF Therapies. Front. Immunol. 2019, 10, 2726. [Google Scholar] [CrossRef]
- Khoury, T.; Ilan, Y. Platform introducing individually tailored variability in nerve stimulations and dietary regimen to prevent weight regain following weight loss in patients with obesity. Obes. Res. Clin. Pract. 2021, 15, 114–123. [Google Scholar] [CrossRef]
- Kenig, A.; Ilan, Y. A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment. Front. Physiol. 2019, 10, 1542. [Google Scholar] [CrossRef]
- Ilan, Y. Why targeting the microbiome is not so successful: Can randomness overcome the adaptation that occurs following gut manipulation? Clin. Exp. Gastroenterol. 2019, 12, 209–217. [Google Scholar] [CrossRef]
- Gelman, R.; Bayatra, A.; Kessler, A.; Schwartz, A.; Ilan, Y. Targeting SARS-CoV-2 receptors as a means for reducing infectivity and improving antiviral and immune response: An algorithm-based method for overcoming resistance to antiviral agents. Emerg. Microbes Infect. 2020, 9, 1397–1406. [Google Scholar] [CrossRef]
- Ishay, Y.; Potruch, A.; Schwartz, A.; Berg, M.; Jamil, K.; Agus, S.; Ilan, Y. A digital health platform for assisting the diagnosis and monitoring of COVID-19 progression: An adjuvant approach for augmenting the antiviral response and mitigating the immune-mediated target organ damage. Biomed. Pharmacother. 2021, 143, 112228. [Google Scholar] [CrossRef]
- Ilan, Y.; Spigelman, Z. Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance. Cancer Treat. Res. Commun. 2020, 25, 100240. [Google Scholar] [CrossRef] [PubMed]
- Hurvitz, N.; Azmanov, H.; Kesler, A.; Ilan, Y. Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases. Eur. J. Hum. Genet. 2021, 29, 1485–1490. [Google Scholar] [CrossRef]
- Ilan, Y. Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response. Front. Med. 2021, 8, 788777. [Google Scholar] [CrossRef]
- Gelman, R.; Berg, M.; Ilan, Y. A Subject-Tailored Variability-Based Platform for Overcoming the Plateau Effect in Sports Training: A Narrative Review. Int. J. Environ. Res. Public Health 2022, 19, 1722. [Google Scholar] [CrossRef]
- Azmanov, H.; Bayatra, A.; Ilan, Y. Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects. J. Pain Res. 2022, 15, 1051–1060. [Google Scholar] [CrossRef]
- Hurvitz, N.; Elkhateeb, N.; Sigawi, T.; Rinsky-Halivni, L.; Ilan, Y. Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management algorithms. Front. Aging 2022, 3, 1044038. [Google Scholar] [CrossRef]
- Kolben, Y.; Azmanov, H.; Gelman, R.; Dror, D.; Ilan, Y. Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections. Ann. Med. 2023, 55, 311–318. [Google Scholar] [CrossRef]
- Lehmann, H.; Arkadir, D.; Ilan, Y. Methods for Improving Brain-Computer Interface: Using A Brain-Directed Adjuvant and A Second-Generation Artificial Intelligence System to Enhance Information Streaming and Effectiveness of Stimuli. Int. J. Appl. Biol. Pharm. Technol. 2023, 14, 42–52. [Google Scholar] [CrossRef]
- Adar, O.; Hollander, A.; Ilan, Y. The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation. Adv. Respir. Med. 2023, 91, 350–367. [Google Scholar] [CrossRef]
- Ilan, Y. The Constrained Disorder Principle Accounts for The Structure and Function of Water as An Ultimate Biosensor and Bioreactor in Biological Systems. Int. J. Appl. Biol. Pharm. Technol. 2023, 14, 31–41. [Google Scholar] [CrossRef]
- Sigawi, T.; Hamtzany, O.; Shakargy, J.D.; Ilan, Y. The Constrained Disorder Principle May Account for Consciousness. Brain Sci. 2024, 14, 209. [Google Scholar] [CrossRef] [PubMed]
- Ilan, Y. Special Issue “Computer-Aided Drug Discovery and Treatment”. Int. J. Mol. Sci. 2024, 25, 2683. [Google Scholar] [CrossRef] [PubMed]
- Hurvitz, N.; Dinur, T.; Revel-Vilk, S.; Agus, S.; Berg, M.; Zimran, A.; Ilan, Y. A Feasibility Open-Labeled Clinical Trial Using a Second-Generation Artificial-Intelligence-Based Therapeutic Regimen in Patients with Gaucher Disease Treated with Enzyme Replacement Therapy. J. Clin. Med. 2024, 13, 3325. [Google Scholar] [CrossRef]
- Ilan, Y. Free Will as Defined by the Constrained Disorder Principle: A Restricted, Mandatory, Personalized, Regulated Process for Decision-Making. Integr. Psychol. Behav. Sci. 2024, 58, 1843–1875. [Google Scholar] [CrossRef]
- Ilan, Y. The Constrained Disorder Principle Defines Mitochondrial Variability and Provides A Platform for A Novel Mechanism for Improved Functionality of Complex Systems. Fortune J. Health Sci. 2024, 7, 338–347. [Google Scholar] [CrossRef]
- Ilan, Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front. Digit. Health 2020, 2, 569178. [Google Scholar] [CrossRef]
- March, S.; Nerurkar, N.; Jain, A.; Andrus, L.; Kim, D.; Whittaker, C.A.; Tan, E.K.W.; Thiberge, S.; Fleming, H.E.; Mancio-Silva, L.; et al. Autonomous circadian rhythms in the human hepatocyte regulate hepatic drug metabolism and inflammatory responses. Sci. Adv. 2024, 10, eadm9281. [Google Scholar] [CrossRef]
- Sugiyama, N.; Terry, F.E.; Gutierrez, A.H.; Hirano, T.; Hoshi, M.; Mizuno, Y.; Martin, W.; Yasunaga, S.i.; Niiro, H.; Fujio, K.; et al. Individual and population-level variability in HLA-DR associated immunogenicity risk of biologics used for the treatment of rheumatoid arthritis. Front. Immunol. 2024, 15, 1377911. [Google Scholar] [CrossRef]
- Ilan, Y. Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor. Int. J. Environ. Res. Public Health 2021, 18, 811. [Google Scholar] [CrossRef]
- Ilan, Y. Next-Generation Personalized Medicine: Implementation of Variability Patterns for Overcoming Drug Resistance in Chronic Diseases. J. Pers. Med. 2022, 12, 1303. [Google Scholar] [CrossRef]
- Hurvitz, N.; Ilan, Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from “Nice to Have” to Mandatory Systems. Clin. Pract. 2023, 13, 994–1014. [Google Scholar] [CrossRef] [PubMed]
- Sigawi, T.; Ilan, Y. Using Constrained-Disorder Principle-Based Systems to Improve the Performance of Digital Twins in Biological Systems. Biomimetics 2023, 8, 359. [Google Scholar] [CrossRef]
- Ilan, Y. Overcoming Compensatory Mechanisms toward Chronic Drug Administration to Ensure Long-Term, Sustainable Beneficial Effects. Mol. Ther. Methods Clin. Dev. 2020, 18, 335–344. [Google Scholar] [CrossRef]
- Bayatra, A.; Nasserat, R.; Ilan, Y. Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness Using a Personalized Second-generation Digital System. Curr. Pharm. Biotechnol. 2024, 25, 2078–2088. [Google Scholar] [CrossRef]
- Gelman, R.; Hurvitz, N.; Nesserat, R.; Kolben, Y.; Nachman, D.; Jamil, K.; Agus, S.; Asleh, R.; Amir, O.; Berg, M.; et al. A second-generation artificial intelligence-based therapeutic regimen improves diuretic resistance in heart failure: Results of a feasibility open-labeled clinical trial. Biomed. Pharmacother. 2023, 161, 114334. [Google Scholar] [CrossRef]
- Sigawi, T.; Gelman, R.; Maimon, O.; Yossef, A.; Hemed, N.; Agus, S.; Berg, M.; Ilan, Y.; Popovtzer, A. Improving the response to lenvatinib in partial responders using a Constrained-Disorder-Principle-based second-generation artificial intelligence-therapeutic regimen: A proof-of-concept open-labeled clinical trial. Front. Oncol. 2024, 14, 1426426. [Google Scholar] [CrossRef]
- Abdul-Rahman, F.; Tranchina, D.; Gresham, D. Fluctuating Environments Maintain Genetic Diversity through Neutral Fitness Effects and Balancing Selection. Mol. Biol. Evol. 2021, 38, 4362–4375. [Google Scholar] [CrossRef]
- Bitter, M.C.; Berardi, S.; Oken, H.; Huynh, A.; Lappo, E.; Schmidt, P.; Petrov, D.A. Continuously fluctuating selection reveals fine granularity of adaptation. Nature 2024, 634, 389–396. [Google Scholar] [CrossRef]
- Nuñez, J.G.; Paulose, J.; Möbius, W.; Beller, D.A. Range expansions across landscapes with quenched noise. Proc. Natl. Acad. Sci. USA 2024, 121, e2411487121. [Google Scholar] [CrossRef]
- Mas-Ponte, D.; Supek, F. Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation. Nucleic Acids Res. 2024, 52, 4393–4408. [Google Scholar] [CrossRef]
- Kemper, K.E.; Sidorenko, J.; Wang, H.; Hayes, B.J.; Wray, N.R.; Yengo, L.; Keller, M.C.; Goddard, M.; Visscher, P.M. Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nat. Commun. 2024, 15, 3776. [Google Scholar] [CrossRef]
- Nicol, P.B.; Paulson, D.; Qian, G.; Liu, X.S.; Irizarry, R.; Sahu, A.D. Robust identification of perturbed cell types in single-cell RNA-seq data. Nat. Commun. 2024, 15, 7610. [Google Scholar] [CrossRef]
- Marghi, Y.; Sumbul, U. Joint inference of discrete and continuous factors captures variability across and within cell types. Nat. Comput. Sci. 2024, 4, 733–734. [Google Scholar] [CrossRef]
- Marghi, Y.; Gala, R.; Baftizadeh, F.; Sümbül, U. Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. Nat. Comput. Sci. 2024, 4, 706–722. [Google Scholar] [CrossRef]
- Qian, J.; Bao, H.; Shao, X.; Fang, Y.; Liao, J.; Chen, Z.; Li, C.; Guo, W.; Hu, Y.; Li, A.; et al. Simulating multiple variability in spatially resolved transcriptomics with scCube. Nat. Commun. 2024, 15, 5021. [Google Scholar] [CrossRef]
- Martínez-Méndez, D.; Villarreal, C.; Huerta, L. Modeling uncertainty: The impact of noise in T cell differentiation. Front. Syst. Biol. 2024, 4, 1412931. [Google Scholar] [CrossRef]
- Shaikh, R.; Larson, N.J.; Kam, J.; Hanjaya-Putra, D.; Zartman, J.; Umulis, D.M.; Li, L.; Reeves, G.T. Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway. npj Syst. Biol. Appl. 2024, 10, 103. [Google Scholar] [CrossRef]
- Mitchison, T.; Kirschner, M. Dynamic instability of microtubule growth. Nature 1984, 312, 237–242. [Google Scholar] [CrossRef] [PubMed]
- Kirschner, M.W.; Mitchison, T. Microtubule dynamics. Nature 1986, 324, 621. [Google Scholar] [CrossRef]
- Ilan, Y. Randomness in microtubule dynamics: An error that requires correction or an inherent plasticity required for normal cellular function? Cell Biol. Int. 2019, 43, 739–748. [Google Scholar] [CrossRef]
- Ilan, Y. Microtubules: From understanding their dynamics to using them as potential therapeutic targets. J. Cell Physiol. 2019, 234, 7923–7937. [Google Scholar] [CrossRef] [PubMed]
- Ilan-Ber, T.; Ilan, Y. The role of microtubules in the immune system and as potential targets for gut-based immunotherapy. Mol. Immunol. 2019, 111, 73–82. [Google Scholar] [CrossRef] [PubMed]
- Forkosh, E.; Kenig, A.; Ilan, Y. Introducing variability in targeting the microtubules: Review of current mechanisms and future directions in colchicine therapy. Pharmacol. Res. Perspect. 2020, 8, e00616. [Google Scholar] [CrossRef]
- Ilan, Y. Microtubules as a potential platform for energy transfer in biological systems: A target for implementing individualized, dynamic variability patterns to improve organ function. Mol. Cell. Biochem. 2022, 478, 375–392. [Google Scholar] [CrossRef]
- Ilan, Y. Enhancing the plasticity, proper function and efficient use of energy of the Sun, genes and microtubules using variability. Clin. Transl. Discov. 2022, 2, e103. [Google Scholar] [CrossRef]
- Le Cunff, Y.; Chesneau, L.; Pastezeur, S.; Pinson, X.; Soler, N.; Fairbrass, D.; Mercat, B.; Rodriguez-Garcia, R.; Alayan, Z.; Abdouni, A.; et al. Unveiling inter-embryo variability in spindle length over time: Towards quantitative phenotype analysis. PLoS Comput. Biol. 2024, 20, e1012330. [Google Scholar] [CrossRef]
- Pecina, P.; Čunátová, K.; Kaplanová, V.; Puertas-Frias, G.; Šilhavý, J.; Tauchmannová, K.; Vrbacký, M.; Čajka, T.; Gahura, O.; Hlaváčková, M.; et al. Haplotype variability in mitochondrial rRNA predisposes to metabolic syndrome. Commun. Biol. 2024, 7, 1116. [Google Scholar] [CrossRef]
- Balazsi, G.; van Oudenaarden, A.; Collins, J.J. Cellular decision making and biological noise: From microbes to mammals. Cell 2011, 144, 910–925. [Google Scholar] [CrossRef]
- Borse, F.; Kičiatovas, D.; Kuosmanen, T.; Vidal, M.; Cabrera-Vives, G.; Cairns, J.; Warringer, J.; Mustonen, V. Quantifying massively parallel microbial growth with spatially mediated interactions. PLoS Comput. Biol. 2024, 20, e1011585. [Google Scholar] [CrossRef]
- Park, J.H.; Holló, G.; Schaerli, Y. From resonance to chaos by modulating spatiotemporal patterns through a synthetic optogenetic oscillator. Nat. Commun. 2024, 15, 7284. [Google Scholar] [CrossRef]
- Sarkar, T.; Krajnc, M. Graph topological transformations in space-filling cell aggregates. PLoS Comput. Biol. 2024, 20, e1012089. [Google Scholar] [CrossRef] [PubMed]
- Garcia-Gutierrez, E.; Monteoliva García, G.; Bodea, I.; Cotter, P.D.; Iguaz, A.; Garre, A. A secondary model for the effect of pH on the variability in growth fitness of Listeria innocua strains. Food Res. Int. 2024, 186, 114314. [Google Scholar] [CrossRef] [PubMed]
- Allaband, C.; Lingaraju, A.; Flores Ramos, S.; Kumar, T.; Javaheri, H.; Tiu, M.D.; Dantas Machado, A.C.; Richter, R.A.; Elijah, E.; Haddad, G.G.; et al. Time of sample collection is critical for the replicability of microbiome analyses. Nat. Metab. 2024, 6, 1282–1293. [Google Scholar] [CrossRef]
- Feldman, H.H.; Cummings, J.L.; Boxer, A.L.; Staffaroni, A.M.; Knopman, D.S.; Sukoff Rizzo, S.J.; Territo, P.R.; Arnold, S.E.; Ballard, C.; Beher, D.; et al. A framework for translating tauopathy therapeutics: Drug discovery to clinical trials. Alzheimer’s Dement. 2024, 20, 8129–8152. [Google Scholar] [CrossRef]
- Andrews, S.S.; Wiley, H.S.; Sauro, H.M. Design patterns of biological cells. BioEssays 2024, 46, 2300188. [Google Scholar] [CrossRef]
- Aviezer, N. Intelligent Design versus Evolution. Rambam Maimonides Med. J. 2010, 1, e0007. [Google Scholar] [CrossRef]
- Del Olmo, M.; Legewie, S.; Brunner, M.; Höfer, T.; Kramer, A.; Blüthgen, N.; Herzel, H. Network switches and their role in circadian clocks. J. Biol. Chem. 2024, 300, 107220. [Google Scholar] [CrossRef]
- Suen, J.Y.; Navlakha, S. A feedback control principle common to several biological and engineered systems. J. R. Soc. Interface 2022, 19, 20210711. [Google Scholar] [CrossRef]
- Hernesniemi, J.A.; Pukkila, T.; Molkkari, M.; Nikus, K.; Lyytikäinen, L.-P.; Lehtimäki, T.; Viik, J.; Kähönen, M.; Räsänen, E. Prediction of Sudden Cardiac Death with Ultra-Short-Term Heart Rate Fluctuations. JACC Clin. Electrophysiol. 2024, 10, 2010–2020. [Google Scholar] [CrossRef]
- Kinoshita, T.; Asai, T.; Ishigaki, T.; Suzuki, T.; Kambara, A.; Matsubayashi, K. Preoperative heart rate variability predicts atrial fibrillation after coronary bypass grafting. Ann. Thorac. Surg. 2011, 91, 1176–1181. [Google Scholar] [CrossRef]
- Smolkova, M.; Sekar, S.; Kim, S.H.; Sunwoo, J.; El-Dib, M. Using heart rate variability to predict neurological outcomes in preterm infants: A scoping review. Pediatr. Res. 2024. [Google Scholar] [CrossRef]
- Pedersen, M.V.; Renberg, A.F.V.; Christensen, J.K.; Andersen, H.B.; Andelius, T.C.K.; Kyng, K.J.; Andersen, M.; Henriksen, T.B. Lipopolysaccharide induced systemic inflammation and heart rate variability in a term newborn piglet model. Pediatr. Res. 2024, 97, 138–144. [Google Scholar] [CrossRef] [PubMed]
- Brandes-Aitken, A.; Hume, A.; Braren, S.; Werchan, D.; Zhang, M.; Brito, N.H. Maternal heart rate variability at 3-months postpartum is associated with maternal mental health and infant neurophysiology. Sci. Rep. 2024, 14, 18766. [Google Scholar] [CrossRef]
- Eren, N.; Onk, D.; Koç, A.; Kuyrukluyildiz, U.; Nalbant, R.A. The Effect of Intra-abdominal Pressure on Heart Rate Variability and Hemodynamics During Laparoscopic Cholecystectomy: A Prospective Observational Study. Cureus 2024, 16, e57890. [Google Scholar] [CrossRef]
- Budhi, R.B.; Singh, D. The Influence of Kapalabhati on Working Memory and Phasic Heart Rate Variability. Cureus 2024, 16, e61027. [Google Scholar] [CrossRef]
- Surana Gandhi, N.; Sorte, S.R.; Chatur, D.K.; Rathod, S.B. Anthropometric Predictors of Heart Rate Variability in Overweight Individuals: A Comparative Study. Cureus 2024, 16, e69434. [Google Scholar] [CrossRef]
- Amekran, Y.; El Hangouche, A.J. Effects of Exercise Training on Heart Rate Variability in Healthy Adults: A Systematic Review and Meta-analysis of Randomized Controlled Trials. Cureus 2024, 16, e62465. [Google Scholar] [CrossRef]
- Dominic, D.; Thirugnana Sambandam, S.; Anburaj, H.; Gopalakrishnan, N. Correlation Between Heart Rate Variability and Agility Scores of Elite Badminton Players: A Pilot Study. Cureus 2024, 16, e58267. [Google Scholar] [CrossRef]
- Arakaki, X.; Arechavala, R.J.; Choy, E.H.; Bautista, J.; Bliss, B.; Molloy, C.; Wu, D.A.; Shimojo, S.; Jiang, Y.; Kleinman, M.T.; et al. The connection between heart rate variability (HRV), neurological health, and cognition: A literature review. Front. Neurosci. 2023, 17, 1055445. [Google Scholar] [CrossRef]
- Morehouse, A.B.; Simon, K.C.; Chen, P.-C.; Mednick, S.C. Heart Rate Variability During REM Sleep is Associated with Reduced Negative Memory Bias. bioRxiv 2024. [Google Scholar] [CrossRef]
- Guendelman, S.; Kaltwasser, L.; Bayer, M.; Gallese, V.; Dziobek, I. Brain mechanisms underlying the modulation of heart rate variability when accepting and reappraising emotions. Sci. Rep. 2024, 14, 18756. [Google Scholar] [CrossRef] [PubMed]
- Krivosova, M.; Hutka, P.; Ondrejka, I.; Visnovcova, Z.; Funakova, D.; Hrtanek, I.; Ferencova, N.; Mlyncekova, Z.; Kovacova, V.; Macejova, A.; et al. Vortioxetine’s impact on the autonomic nervous system in depressed children and adolescents: Analysis of the heart rate variability. Sci. Rep. 2024, 14, 14442. [Google Scholar] [CrossRef] [PubMed]
- Schmalenberger, K.M.; Eisenlohr-Moul, T.A.; Jarczok, M.N.; Schneider, E.; Barone, J.C.; Thayer, J.F.; Ditzen, B. Associations of luteal phase changes in vagally mediated heart rate variability with premenstrual emotional changes. BMC Women’s Health 2024, 24, 448. [Google Scholar] [CrossRef]
- Książek, K.; Masarczyk, W.; Głomb, P.; Romaszewski, M.; Stokłosa, I.; Ścisło, P.; Dębski, P.; Pudlo, R.; Buza, K.; Gorczyca, P.; et al. Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data. Comput. Biol. Med. 2024, 176, 108544. [Google Scholar] [CrossRef]
- Delliaux, S.; Sow, A.K.; Echcherki, A.; Benyamine, A.; Gomes de Pinho, Q.; Brégeon, F.; Granel, B. Heart rate variability helps classify phenotype in systemic sclerosis. Sci. Rep. 2024, 14, 11151. [Google Scholar] [CrossRef]
- Attreed, A.; Morand, L.R.; Pond, D.C.; Sturmberg, J.P. The Clinical Role of Heart Rate Variability Assessment in Cognitively Impaired Patients and Its Applicability in Community Care Settings: A Systematic Review of the Literature. Cureus 2024, 16, e61703. [Google Scholar] [CrossRef]
- Reisinger, D.L.; Goodwin, M.S.; Horn, P.S.; Schmitt, L.M.; Coffman, M.C.; Shaffer, R.C. Examining the feasibility and utility of heart rate variability on intervention outcomes targeting emotion regulation in autism: A brief report. Sci. Rep. 2024, 14, 15409. [Google Scholar] [CrossRef]
- Lohman, T.; Sible, I.J.; Shenasa, F.; Engstrom, A.C.; Kapoor, A.; Alitin, J.P.M.; Gaubert, A.; Thayer, J.F.; Ferrer, F.; Nation, D.A. Reliability of beat-to-beat blood pressure variability in older adults. Sci. Rep. 2024, 14, 20197. [Google Scholar] [CrossRef]
- Iatridi, F.; Ekart, R.; Xagas, E.; Karkamani, E.; Karpetas, A.; Theodorakopoulou, M.P.; Devrikis, N.; Revela, I.; Papagianni, A.; Sarafidis, P. Dialysate sodium and short-term blood pressure variability in patients with intradialytic hypertension: A randomized crossover study. J. Hum. Hypertens. 2024, 38, 750–757. [Google Scholar] [CrossRef]
- Gupta, A.; Whiteley, W.N.; Godec, T.; Rostamian, S.; Ariti, C.; Mackay, J.; Whitehouse, A.; Janani, L.; Poulter, N.R.; Sever, P.S.; et al. Legacy benefits of blood pressure treatment on cardiovascular events are primarily mediated by improved blood pressure variability: The ASCOT trial. Eur. Heart J. 2024, 45, 1159–1169. [Google Scholar] [CrossRef]
- Cui, Y.; Ning, Y.-X.; Cai, J.-R.; Zhang, N.-N.; Chen, H.-S. Association of systolic blood pressure variability with remote ischemic conditioning in acute ischemic stroke. Sci. Rep. 2024, 14, 15562. [Google Scholar] [CrossRef]
- Tobushi, T.; Floras, J.S. Sleep Apnea, Autonomic Disturbances, and Blood Pressure Variability. Hypertension 2024, 81, 1837–1844. [Google Scholar] [CrossRef]
- Tully, P.J.; Tzourio, C. Psychiatric correlates of blood pressure variability in the elderly: The Three City cohort study. Physiol. Behav. 2017, 168, 91–97. [Google Scholar] [CrossRef]
- van Wingerden, A.-S.; Katsidoniotaki, M.; Haghighi, N.; Almonte, C.; Woolcock Martinez, H.; Valdes, E.; Castro, P.; Alian, A.; Booker, W.; Bello, N.; et al. Postpartum Blood Pressure Variability and Heart Rate Variability in Preeclampsia. Hypertension 2024, 81, 2510–2519. [Google Scholar] [CrossRef] [PubMed]
- Kario, K.; Kanegae, H.; Okawara, Y.; Tomitani, N.; Hoshide, S. Home Blood Pressure Variability Risk Prediction Score for Cardiovascular Disease Using Data From the J-HOP Study. Hypertension 2024, 81, 2173–2180. [Google Scholar] [CrossRef]
- Mizrahi, M.; Ben Ya’acov, A.; Ilan, Y. Gastric stimulation for weight loss. World J. Gastroenterol. 2012, 18, 2309–2319. [Google Scholar] [CrossRef]
- Goggins, E.; Mitani, S.; Tanaka, S. Clinical perspectives on vagus nerve stimulation: Present and future. Clin. Sci. 2022, 136, 695–709. [Google Scholar] [CrossRef]
- Capilupi, M.J.; Kerath, S.M.; Becker, L.B. Vagus Nerve Stimulation and the Cardiovascular System. Cold Spring Harb. Perspect. Med. 2020, 10, a034173. [Google Scholar] [CrossRef]
- Sigawi, T.; Israeli, A.; Ilan, Y. Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies’ Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders. Immunotargets Ther. 2024, 13, 525–539. [Google Scholar] [CrossRef]
- Almuwaqqat, Z.; Hui, Q.; Liu, C.; Zhou, J.J.; Voight, B.F.; Ho, Y.-L.; Posner, D.C.; Vassy, J.L.; Gaziano, J.M.; Cho, K.; et al. Long-Term Body Mass Index Variability and Adverse Cardiovascular Outcomes. JAMA Netw. Open 2024, 7, e243062. [Google Scholar] [CrossRef]
- Yu, E.A.; Bravo, M.D.; Avelino-Silva, V.I.; Bruhn, R.L.; Busch, M.P.; Custer, B. Higher intraindividual variability of body mass index is associated with elevated risk of COVID-19 related hospitalization and post-COVID conditions. Int. J. Obes. 2024, 48, 1711–1719. [Google Scholar] [CrossRef]
- Vaz, J.R.; Cortes, N.; Gomes, J.S.; Jordão, S.; Stergiou, N. Stride-to-stride fluctuations and temporal patterns of muscle activity exhibit similar responses during walking to variable visual cues. J. Biomech. 2024, 164, 111972. [Google Scholar] [CrossRef]
- Yano, S.; Nakamura, A.; Suzuki, Y.; Smith, C.E.; Nomura, T. Smartphone usage during walking decreases the positive persistency in gait cycle variability. Sci. Rep. 2024, 14, 16410. [Google Scholar] [CrossRef] [PubMed]
- Zhang, X.; Lu, H.; Fan, M.; Tian, W.; Wang, Y.; Cui, M.; Jiang, Y.; Suo, C.; Zhang, T.; Jin, L.; et al. Bidirectional mediation of bone mineral density and brain atrophy on their associations with gait variability. Sci. Rep. 2024, 14, 8483. [Google Scholar] [CrossRef]
- Jurkiewicz, T.; Delporte, L.; Revol, P.; Rossetti, Y.; Pisella, L. Effect of juggling expertise on pointing performance in peripheral vision. PLoS ONE 2024, 19, e0306630. [Google Scholar] [CrossRef]
- Sanders, R.H. Kinematics, coordination, variability, and biological noise in the prone flutter kick at different levels of a “learn-to-swim” programme. J. Sports Sci. 2007, 25, 213–227. [Google Scholar] [CrossRef]
- Goto, Y.; Kitajo, K. Selective consistency of recurrent neural networks induced by plasticity as a mechanism of unsupervised perceptual learning. PLoS Comput. Biol. 2024, 20, e1012378. [Google Scholar] [CrossRef]
- Zhu, Z.; Qi, Y.; Lu, W.; Feng, J. Learning to integrate parts for whole through correlated neural variability. PLoS Comput. Biol. 2024, 20, e1012401. [Google Scholar] [CrossRef]
- Aghi, K.; Schultz, R.; Newman, Z.L.; Mendonça, P.; Li, R.; Bakshinska, D.; Isacoff, E.Y. Synapse-to-synapse plasticity variability balanced to generate input-wide constancy of transmitter release. bioRxiv 2024. [Google Scholar] [CrossRef]
- Ilan, Y. Overcoming randomness does not rule out the importance of inherent randomness for functionality. J. Biosci. 2019, 44, 132. [Google Scholar] [CrossRef]
- Ilan, Y. Generating randomness: Making the most out of disordering a false order into a real one. J. Transl. Med. 2019, 17, 49. [Google Scholar] [CrossRef]
- Ilan, Y. Advanced Tailored Randomness: A Novel Approach for Improving the Efficacy of Biological Systems. J. Comput. Biol. 2020, 27, 20–29. [Google Scholar] [CrossRef]
- Ilan, Y. Order Through Disorder: The Characteristic Variability of Systems. Front. Cell Dev. Biol. 2020, 8, 186. [Google Scholar] [CrossRef]
- Shah, S.; Hembrook-Short, J.; Mock, V.; Briggs, F. Correlated variability and its attentional modulation depend on anatomical connectivity. Proc. Natl. Acad. Sci. USA 2024, 121, e2318841121. [Google Scholar] [CrossRef]
- Cui, Z.; Sato, T.; Jackson, A.; Jayarathna, S.; Itoh, M.; Yamani, Y. Gaze transition entropy as a measure of attention allocation in a dynamic workspace involving automation. Sci. Rep. 2024, 14, 23405. [Google Scholar] [CrossRef]
- Luo, J.; Qin, P.; Bi, Q.; Wu, K.; Gong, G. Individual variability in functional connectivity of human auditory cortex. Cereb. Cortex 2024, 34, bhae007. [Google Scholar] [CrossRef]
- Moia, S.; Chen, G.; Uruñuela, E.; Stickland, R.C.; Termenon, M.; Caballero-Gaudes, C.; Bright, M.G. Individual variability in the relationship between physiological and resting-state fMRI metrics. bioRxiv 2024. [Google Scholar] [CrossRef]
- Moore, J.J.; Genkin, A.; Tournoy, M.; Pughe-Sanford, J.L.; de Ruyter van Steveninck, R.R.; Chklovskii, D.B. The neuron as a direct data-driven controller. Proc. Natl. Acad. Sci. USA 2024, 121, e2311893121. [Google Scholar] [CrossRef]
- Ma, T.; Hermundstad, A.M. A vast space of compact strategies for effective decisions. Sci. Adv. 2024, 10, eadj4064. [Google Scholar] [CrossRef]
- Paramanick, S.; Biswas, A.; Soni, H.; Pal, A.; Kumar, N. Uncovering Universal Characteristics of Homing Paths using Foraging Robots. PRX Life 2024, 2, 033007. [Google Scholar] [CrossRef]
- Kliemann, D.; Galdi, P.; Water, A.L.V.D.; Egger, B.; Jarecka, D.; Adolphs, R.; Ghosh, S.S. Resting-State Functional Connectivity of the Amygdala in Autism: A Preregistered Large-Scale Study. Am. J. Psychiatry 2024, 181, 1076–1085. [Google Scholar] [CrossRef] [PubMed]
- Ojuri, B.; DeRonda, A.; Plotkin, M.; Mostofsky, S.H.; Rosch, K.S. The Impact of Sex on Cognitive Control in ADHD: Girls Slow to Inhibit, Boys Inhibit Less, and Both Show Higher Response Variability. J. Atten. Disord. 2024, 28, 1275–1288. [Google Scholar] [CrossRef]
- Kessler, F.; Frankenstein, J.; Rothkopf, C.A. Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties. Nat. Commun. 2024, 15, 5677. [Google Scholar] [CrossRef]
- Available online: https://news.marketersmedia.com/thechoicewheel-introduces-color-spin-wheel-a-fun-solution-to-decision-fatigue/89138142 (accessed on 1 December 2024).
- Ilan, Y. Variability in exercise is linked to improved age-related dysfunctions: A potential role for the constrained-disorder principle-based second-generation artificial intelligence system. Res. Sq. 2023, rs-3. [Google Scholar] [CrossRef]
- Poganik, J.; Zhang, B.; Baht, G.; Tyshkovskiy, A.; Deik, A.; Kerepesi, C.; Yim, S.; Lu, A.; Haghani, A.; Gong, T.; et al. Biological age is increased by stress and restored upon recovery. Cell Metab. 2023, 35, 807–820. [Google Scholar] [CrossRef]
- Higgins-Chen, A.T.; Thrush, K.L.; Wang, Y.; Minteer, C.J.; Kuo, P.L.; Wang, M.; Niimi, P.; Sturm, G.; Lin, J.; Moore, A.Z.; et al. A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking. Nat. Aging 2022, 2, 644–661. [Google Scholar] [CrossRef]
- Tarkhov, A.; Denisov, K.; Fedichev, P. Aging Clocks, Entropy, and the Limits of Age-Reversal. bioRxiv 2022. [Google Scholar] [CrossRef]
- Hall, K.; Sacks, G.; Chandramohan, D.; Chow, C.; Wang, Y.-H.; Gortmaker, S.; Swinburn, B. Quantification of the effect of energy imbalance on bodyweight. Lancet 2011, 378, 826–837. [Google Scholar] [CrossRef]
- Schutte, A.E.; Kollias, A.; Stergiou, G.S. Blood pressure and its variability: Classic and novel measurement techniques. Nat. Rev. Cardiol. 2022, 19, 643–654. [Google Scholar] [CrossRef]
- Perevoshchikova, K.; Fedichev, P.O. Differential Responses of Dynamic and Entropic Aging Factors to Longevity Interventions. bioRxiv 2024. [Google Scholar] [CrossRef]
- How to Defeat Aging? Two Scientists Offer Their Visions. Available online: https://www.lifespan.io/news/how-to-defeat-aging-two-scientists-offer-their-visions/ (accessed on 1 December 2024).
- Tenchov, R.; Sasso, J.M.; Wang, X.; Zhou, Q.A. Aging Hallmarks and Progression and Age-Related Diseases: A Landscape View of Research Advancement. ACS Chem. Neurosci. 2024, 15, 1–30. [Google Scholar] [CrossRef] [PubMed]
- de Grey, A.D. Longevity Sticker Shock: The One Remaining Obstacle to Widespread Credentialed Support for Rejuvenation Biotechnology. Rejuvenation Res. 2015, 18, 201–202. [Google Scholar] [CrossRef] [PubMed]
- Lidsky, P.V.; Yuan, J.; Rulison, J.M.; Andino-Pavlovsky, R. Is Aging an Inevitable Characteristic of Organic Life or an Evolutionary Adaptation? Biochemistry. Biokhimiia 2022, 87, 1413–1445. [Google Scholar] [CrossRef] [PubMed]
- Tong, H.; Dwaraka, V.B.; Chen, Q.; Luo, Q.; Lasky-Su, J.A.; Smith, R.; Teschendorff, A.E. Quantifying the stochastic component of epigenetic aging. Nat. Aging 2024, 4, 886–901. [Google Scholar] [CrossRef]
- Meyer, D.H.; Schumacher, B. Aging clocks based on accumulating stochastic variation. Nat. Aging 2024, 4, 871–885. [Google Scholar] [CrossRef]
- Wen, W.; Grover, S.; Hazel, D.; Berning, P.; Baumgardt, F.; Viswanathan, V.; Tween, O.; Reinhart, R.M.G. Beta-band neural variability reveals age-related dissociations in human working memory maintenance and deletion. PLoS Biol. 2024, 22, e3002784. [Google Scholar] [CrossRef]
- Majumdar, G.; Yazin, F.; Banerjee, A.; Roy, D. Altered orbitofrontal cortex neural variability underlies idiosyncratic experiences during aging. bioRxiv 2024. [Google Scholar] [CrossRef]
- Hochholzer, W.; Ruff, C.T.; Mesa, R.A.; Mattimore, J.F.; Cyr, J.F.; Lei, L.; Frelinger, A.L.; Michelson, A.D.; Berg, D.D.; Angiolillo, D.J.; et al. Variability of Individual Platelet Reactivity Over Time in Patients Treated with Clopidogrel: Insights From the ELEVATE–TIMI 56 Trial. J. Am. Coll. Cardiol. 2014, 64, 361–368. [Google Scholar] [CrossRef]
- Todd, J.V.; Morgan, W.J.; Szczesniak, R.D.; Ostrenga, J.S.; O’Connell, O.J.; Cromwell, E.A.; Faro, A.; Jain, R. FEV1 Variability Predicts Lung Transplant or Mortality in Cystic Fibrosis Patients in the US. Ann. Am. Thorac. Soc. 2024, 21, 1416–1420. [Google Scholar] [CrossRef]
- Lv, H.; Sun, J.; Zhang, T.; Hui, Y.; Li, J.; Zhao, X.; Chen, S.; Liu, W.; Li, X.; Zhao, P.; et al. Associations of serum uric acid variability with neuroimaging metrics and cognitive decline: A population-based cohort study. BMC Med. 2024, 22, 256. [Google Scholar] [CrossRef]
- Vihinen, M. Generic model for biological regulation. F1000Research 2022, 11, 419. [Google Scholar] [CrossRef]
- Lin, Z.; Lu, Z.; Di, Z.; Tang, Y. Learning noise-induced transitions by multi-scaling reservoir computing. Nat. Commun. 2024, 15, 6584. [Google Scholar] [CrossRef]
- Terwijn, S.A. The Mathematical Foundations of Randomness. In The Challenge of Chance: A Multidisciplinary Approach from Science and the Humanities; Landsman, K., van Wolde, E., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 49–66. [Google Scholar]
- Altarabichi, M.G.; Nowaczyk, S.; Pashami, S.; Sheikholharam Mashhadi, P.; Handl, J. Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks. Inf. Sci. 2024, 667, 120500. [Google Scholar] [CrossRef]
- Akmal, S.; Chen, L.; Jin, C.; Raj, M.; Williams, R. Improved Merlin–Arthur Protocols for Central Problems in Fine-Grained Complexity. Algorithmica 2023, 85, 2395–2426. [Google Scholar] [CrossRef]
- Corchado, J.M.; López, S.; Garcia, R.; Chamoso, P. Generative Artificial Intelligence: Fundamentals. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 2023, 12, e31704. [Google Scholar] [CrossRef]
- Sherlekar, S.D. Parallel Computing Goes Mainstream. In Proceedings of the Contemporary Computing: 5th International Conference, IC3 2012, Noida, India, 6–8 August 2012; Springer: Berlin/Heidelberg, Germany, 2012; pp. 4–5. [Google Scholar]
- Huh, J.-H.; Higashi, T.; Sato, Y. Manipulating conductivity and noise for transitioning between stochastic and inverse stochastic resonances in liquid–crystal electroconvection. Sci. Rep. 2024, 14, 21821. [Google Scholar] [CrossRef]
- Hu, X.; Emery, B.A.; Khanzada, S.; Amin, H. DENOISING: Dynamic enhancement and noise overcoming in multimodal neural observations via high-density CMOS-based biosensors. Front. Bioeng. Biotechnol. 2024, 12, 1390108. [Google Scholar] [CrossRef]
- Lee, T.-W. Separating biological variance from noise by applying EM algorithm to modified General Linear Model. bioRxiv 2024. [Google Scholar] [CrossRef]
- Wu, X.; McDermott, M.; MacLean, A.L. Data-driven model discovery and model selection for noisy biological systems. bioRxiv 2024. [Google Scholar] [CrossRef]
- Available online: https://www.kcl.ac.uk/news/kings-physicist-to-build-analogue-computer-to-unlock-the-randomness-of-nature (accessed on 1 December 2024).
- Rashid, S.; Hamidi, S.Z.; Raza, M.A.; Shafique, R.; Alsubaie, A.S.; Elagan, S.K. Robustness and exploration between the interplay of the nonlinear co-dynamics HIV/AIDS and pneumonia model via fractional differential operators and a probabilistic approach. Sci. Rep. 2024, 14, 16922. [Google Scholar] [CrossRef]
- Yang, J.; Daily, N.J.; Pullinger, T.K.; Wakatsuki, T.; Sobie, E.A. Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments. PLoS Comput. Biol. 2024, 20, e1011806. [Google Scholar] [CrossRef]
- Maddu, S.; Chardès, V.; Shelley, M. Inferring biological processes with intrinsic noise from cross-sectional data. arXiv 2024, arXiv:2410.07501. [Google Scholar]
- Peretó, J.; Bada, J.L.; Lazcano, A. Charles Darwin and the origin of life. Orig. Life Evol. Biosph. 2009, 39, 395–406. [Google Scholar] [CrossRef] [PubMed]
- Radzvilavicius, A.; Blackstone, N. The evolution of individuality revisited. Biol. Rev. 2018, 93, 1620–1633. [Google Scholar] [CrossRef]
- Ellery, A. Self-replicating probes are imminent—Implications for SETI. Int. J. Astrobiol. 2022, 21, 212–242. [Google Scholar] [CrossRef]
- Buhrman, H.; Gulik, P.; Kelk, S.; Koolen, W.; Stougie, L. Some Mathematical Refinements Concerning Error Minimization in the Genetic Code. IEEE/ACM Trans. Comput. Biol. Bioinform. 2011, 8, 1358–1372. [Google Scholar] [CrossRef]
- Haddad, W.M. Thermodynamics: The Unique Universal Science. Entropy 2017, 19, 621. [Google Scholar] [CrossRef]
- Lineweaver, C.; Egan, C. Life, gravity and the second law of thermodynamics. Phys. Life Rev. 2008, 5, 225–242. [Google Scholar] [CrossRef]
- Abraham, J. Infinite Monkeys Typing the Human Genom; University of Louisiana: Lafayette, LA, USA, 2020. [Google Scholar] [CrossRef]
- El-Haj, M.; Kanovitch, D.; Ilan, Y. Personalized inherent randomness of the immune system is manifested by an individualized response to immune triggers and immunomodulatory therapies: A novel platform for designing personalized immunotherapies. Immunol. Res. 2019, 67, 337–347. [Google Scholar] [CrossRef]
- Ilan, Y. Beta-Glycosphingolipids as Mediators of Both Inflammation and Immune Tolerance: A Manifestation of Randomness in Biological Systems. Front. Immunol. 2019, 10, 1143. [Google Scholar] [CrossRef]
- Shabat, Y.; Lichtenstein, Y.; Ilan, Y. Short-Term Cohousing of Sick with Healthy or Treated Mice Alleviates the Inflammatory Response and Liver Damage. Inflammation 2021, 44, 518–525. [Google Scholar] [CrossRef] [PubMed]
- Rotnemer-Golinkin, D.; Ilan, Y. Personalized-Inherent Variability in a Time-Dependent Immune Response: A Look into the Fifth Dimension in Biology. Pharmacology 2022, 107, 417–422. [Google Scholar] [CrossRef]
- Latorre, A.; Moya, A. Role of Symbiosis in Evolution; Springer: New York, NY, USA, 2013; Volume 2, pp. 63–70. [Google Scholar]
- Why Free Will Doesn’t Exist, According to Robert Sapolsky. 2023. Available online: https://www.newscientist.com/article/2398369-why-free-will-doesnt-exist-according-to-robert-sapolsky/ (accessed on 1 December 2024).
- Yang, J.; Wang, X.; Carmona, C.P.; Wang, X.; Shen, G. Inverse relationship between species competitiveness and intraspecific trait variability may enable species coexistence in experimental seedling communities. Nat. Commun. 2024, 15, 2895. [Google Scholar] [CrossRef] [PubMed]
- Fabrèges, D.; Corominas-Murtra, B.; Moghe, P.; Kickuth, A.; Ichikawa, T.; Iwatani, C.; Tsukiyama, T.; Daniel, N.; Gering, J.; Stokkermans, A.; et al. Temporal variability and cell mechanics control robustness in mammalian embryogenesis. Science 2024, 386, eadh1145. [Google Scholar] [CrossRef]
- Werner, J.M.; Hover, J.; Gillis, J. Population variability in X-chromosome inactivation across 10 mammalian species. Nat. Commun. 2024, 15, 8991. [Google Scholar] [CrossRef]
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Ilan, Y. The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle. Biology 2025, 14, 349. https://doi.org/10.3390/biology14040349
Ilan Y. The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle. Biology. 2025; 14(4):349. https://doi.org/10.3390/biology14040349
Chicago/Turabian StyleIlan, Yaron. 2025. "The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle" Biology 14, no. 4: 349. https://doi.org/10.3390/biology14040349
APA StyleIlan, Y. (2025). The Relationship Between Biological Noise and Its Application: Understanding System Failures and Suggesting a Method to Enhance Functionality Based on the Constrained Disorder Principle. Biology, 14(4), 349. https://doi.org/10.3390/biology14040349