Abstract
Biological systems, from individual cells to cells populations, to tissue and organs to whole organisms, are equipped in multiple positive and negative feedback control mechanisms. Knowledge of these mechanisms is crucial if we want to modify intracellular biochemical control systems (in genetic engineering), affect them during therapies, control populations metabolism or behavior (e.g. in bioreactors), or design artificial organs or devices supporting physiological processes. This work focuses on three aspects of interdisciplinary research in automatic control, biology and medicine: (i) modeling of physiological processes on a whole body level, aimed at supporting artificial organs development; (ii) using optimal control theory for designing anticancer therapy protocols and (iii) using simulation and analysis techniques for identification of complex intracellular regulatory mechanisms, aimed at expanding knowledge in this field.
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References
Pietribiasi, M., et al.: Modelling transcapillary transport of fluid and proteins in hemodialysis patients. PLoS One 11(8), e0159748 (2016). https://doi.org/10.1371/journal.pone.0159748
Waniewski, J., et al.: Changes of peritoneal transport parameters with time on dialysis: assessment with sequential peritoneal equilibration test. Int. J. Artif. Organs 40(11), 595–601 (2017). https://doi.org/10.5301/ijao.5000622
Rodbard, D.: Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol. Ther. 19(S3), S25–S37 (2017). https://doi.org/10.1089/dia.2017.0035
Aleppo, G., Webb, K.: Continuous glucose monitoring integration in clinical practice: a stepped guide to data review and interpretation. J. Diabetes Sci. Technol. 19 (2018). https://doi.org/10.1177/1932296818813581
Kim, J.H., Cowger, J.A., Shah, P.: The evolution of mechanical circulatory support. Cardiol. Clin. 36(4), 443–449 (2018). https://doi.org/10.1016/j.ccl.2018.06.011
Branson, R.D.: Automation of mechanical ventilation. Crit. Care Clin. 34(3), 383–394 (2018). https://doi.org/10.1016/j.ccc.2018.03.012
Ogurtsova, K., da Rocha Fernandes, J.D., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N.H., Cavan, D., Shaw, J.E., Makaroff, L.E.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128, 40–50 (2017). https://doi.org/10.1016/j.diabres.2017.03.024
Anderson, S.M., et al.: Multinational home use of closed-loop control is safe and effective. Diabetes Care 39(7), 1143–1150 (2016). https://doi.org/10.2337/dc15-2468
Buckingham, B., Ly, T.: Closed-loop control in type 1 diabetes. Lancet Diabetes Endocrinol. 4(3), 191–193 (2016). https://doi.org/10.1016/S2213-8587(16)00015-2
DeJournett, L., DeJournett, J.: In silico testing of an artificial-intelligence-based artificial pancreas designed for use in the intensive care unit setting. J. Diabetes Sci. Technol. 10(6), 1360–1371 (2016)
Trevitt, S., Simpson, S., Wood, A.: Artificial pancreas device systems for the closed-loop control of type 1 diabetes: what systems are in development? J. Diabetes Sci. Technol. 10(3), 714–23 (2016). https://doi.org/10.1177/1932296815617968
Ly, T.T., et al.: Automated overnight closed-loop control using a proportional-integral-derivative algorithm with insulin feedback in children and adolescents with type 1 diabetes at diabetes camp. Diabetes Technol. Ther. 18(6), 377–384 (2016). https://doi.org/10.1089/dia.2015.0431
Hovorka, R., et al.: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 25, 905–920 (2004)
Wang, Y., Xie, H., Jiang, X., Liu, B.: Intelligent closed-loop insulin delivery systems for ICU patients. IEEE J. Biomed. Health Inform. 18(1), 290–299 (2014). https://doi.org/10.1109/JBHI.2013.2269699
Eren-Oruklu, M., Cinar, A., Quinnb, L., Smith, D.: Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes. J. Proc. Control 19, 1333–1346 (2009)
Marchetti, G., Barolo, M., Jovanovic, L., Zisser, H., Seborg, D.E.: A feedforward-feedback glucose control strategy for type 1 diabetes mellitus. J. Proc. Control 18(2), 149–162 (2008)
Palerm, C.C., Zisser, H., Jovanovic, L., Doyle, F.J.: A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes. J. Proc. Control 18(3–4), 258–265 (2008)
Fereydouneyan, F., Zare, A., Mehrshad, N.: Using a fuzzy controller optimized by a genetic algorithm to regulate blood glucose level in type 1 diabetes. J. Med. Eng. Technol. 35(5), 224–230 (2011). https://doi.org/10.3109/03091902.2011.569050
Fernandez de Canete, J., Gonzalez-Perez, S., Ramos-Diaz, J.C.: Artificial neural networks for closed loop control of in silico and ad hoc type 1 diabetes. Comput. Methods Programs Biomed. 106(1), 55–66 (2012). https://doi.org/10.1016/j.cmpb.2011.11.006
Rahaghi, F.N., Gough, D.A.: Blood glucose dynamics. Diabetes Technol. Ther. 10(2), 81–94 (2008). https://doi.org/10.1089/dia.2007.0256
Cameron, F.M., et al.: Closed-loop control without meal announcement in type 1 diabetes. Diabetes Technol. Ther. 19(9), 527–532 (2017). https://doi.org/10.1089/dia.2017.0078
Bally, L., et al.: Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol. 5(4), 261–270 (2017). https://doi.org/10.1016/S2213-8587(17)30001-3
Thabit, H., et al.: Closed-loop insulin delivery in inpatients with type 2 diabetes: a randomised, parallel-group trial. Lancet Diabetes Endocrinol. 5(2), 117–124 (2017). https://doi.org/10.1016/S2213-8587(16)30280-7
Ly, T.T., et al.: Automated hybrid closed-loop control with a proportional-integral-derivative based system in adolescents and adults with type 1 diabetes: individualizing settings for optimal performance. Pediatr. Diabetes 18(5), 348–355 (2017). https://doi.org/10.1111/pedi.12399
Zavitsanou, S., Mantalaris, A., Georgiadis, M.C., Pistikopoulos, E.N.: In silico closed-loop control validation studies for optimal insulin delivery in type 1 diabetes. IEEE Trans. Biomed. Eng. 62(10), 2369–2378 (2015). https://doi.org/10.1109/TBME.2015.2427991
Peyser, T., Dassau, E., Breton, M., Skyler, J.S.: The artificial pancreas: current status and future prospects in the management of diabetes. Ann. N. Y. Acad. Sci. 1311, 102–123 (2014). https://doi.org/10.1111/nyas.12431
Pinsker, J.E., et al.: Randomized crossover comparison of personalized MPC and PID control algorithms for the artificial pancreas. Diabetes Care 39(7), 1135–1142 (2016). https://doi.org/10.2337/dc15-2344
Christiansen, S.C., et al.: A review of the current challenges associated with the development of an artificial pancreas by a double subcutaneous approach. Diabetes Ther. 8(3), 489–506 (2017). https://doi.org/10.1007/s13300-017-0263-6
Blauw, H., Keith-Hynes, P., Koops, R., DeVries, J.H.: A review of safety and design requirements of the artificial pancreas. Ann. Biomed. Eng. 44(11), 3158–3172 (2016)
Akhlaghi, F., Matson, K.L., Mohammadpour, A.H., Kelly, M., Karimani, A.: Clinical pharmacokinetics and pharmacodynamics of antihyperglycemic medications in children and adolescents with type 2 diabetes mellitus. Clin Pharmacokinet. 56(6), 561–571 (2017). https://doi.org/10.1007/s40262-016-0472-6
Zarkovic, M., et al.: Variability of HOMA and QUICKI insulin sensitivity indices. Scand. J. Clin. Lab. Investig. 77(4), 295–297 (2017). https://doi.org/10.1080/00365513.2017.1306878
Dadiani, V., et al.: Physical activity capture technology with potential for incorporation into closed-loop control for type 1 diabetes. J. Diabetes Sci. Technol. 9(6), 1208–1216 (2015). https://doi.org/10.1177/1932296815609949
Ben Brahim, N., Place, J., Renard, E., Breton, M.D.: Identification of main factors explaining glucose dynamics during and immediately after moderate exercise in patients with type 1 diabetes. J. Diabetes Sci. Technol. 9(6), 1185–1191 (2015). https://doi.org/10.1177/1932296815607864
Smieja, J., Galuszka, A.: Rule-based PID control of blood glucose level. In: Automatyzacja Procesów Dyskretnych. Teoria i zastosowania. t.II, pp. 223–232 (2018)
Kovatchev, B., Anderson, S., Heinemann, L., Clarke, W.: Comparison of the numerical and clinical accuracy of four continuous glucose monitors. Diabetes Care 31(6), 1160–1164 (2008). https://doi.org/10.2337/dc07-2401
Swierniak, A., Kimmel, M., Smieja, J., Puszynski, K., Psiuk-Maksymowicz, K.: System Engineering Approach to Planning Anticancer Therapies. Springer (2016). https://doi.org/10.1007/978-3-319-28095-0
Schaettler, H., Ledzewicz, U.: Optimal Control for Mathematical Models of Cancer Therapies. An Application of Geometric Methods. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2972-6
Swierniak, A., Kimmel, M., Smieja, J.: Mathematical modeling as a tool for planning anticancer therapy. Eur. J. Pharmacol. 625(1–3), 108–121 (2009). https://doi.org/10.1016/j.ejphar.2009.08.041
Swierniak, A., Polanski, A., Smieja, J.: Modelling growth of drug resistant cancer populations as the system with positive feedback. Math. Comput. Model. 37(11), 1245–1252 (2003)
Dolbniak, M., Smieja, J., Swierniak, A.: Structural sensitivity of control models arising in combined chemo-radiotherapy. In: Proceedings of the MMAR Conference, pp. 339–344 (2018)
Tyson, J.J., Novak, B.: Functional motifs in biochemical reaction networks. Ann. Rev. Phys. Chem. 61, 219–240 (2010)
Smieja, J.: Model based analysis of signaling pathways. Int. J. Appl. Math. Comp. Sci. 18(2), 139–145 (2008)
Kardynska, M., et al.: Quantitative analysis reveals crosstalk mechanisms of heat shock-induced attenuation of NF-\(\kappa \)B signaling at the single cell level. Plos Comp. Biol. 14(4), e1006130 (2018). https://doi.org/10.1371/journal.pcbi.1006130
Cruz, J.J.: Feedback Systems. McGraw-Hill, New York (1972)
Puszynski, K., Lachor, P., Kardynska, M., Smieja, J.: Sensitivity analysis of deterministic signaling pathways models. Bull. Pol. Acad. Sci. 60(3), 471–479 (2012)
Smieja, J., Kardynska, M., Jamroz, A.: The meaning of sensitivity functions in signaling pathways analysis. Discret. Contin. Dyn. Syst. Ser. B 10(8), 2697–2707 (2014). https://doi.org/10.3934/dcdsb.2014.19.2697
Kardyñska, M., Smieja, J.: Sensitivity analysis of signaling pathway models based on discrete-time measurements. Arch. Control Sci. 27(2), 239–250 (2017). https://doi.org/10.1515/acsc-2017-0015
Kardynska, M., Smieja, J.: Sensitivity analysis of signaling pathways in the frequency domain. In: Advances in Intelligent Systems and Computing, vol. 472, pp. 2194–5357 (2016). https://doi.org/10.1007/978-3-319-39904-1_25
Dolbniak, M., Kardynska, M., Smieja, J.: Sensitivity of combined chemo-and antiangiogenic therapy results in different models describing cancer growth. Discret. Contin. Dyn. Syst. Ser. B 23, 145–160 (2018). https://doi.org/10.3934/dcdsb.2018009
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Smieja, J. (2020). Automatic Control and Feedback Loops in Biology and Medicine. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2019. AUTOMATION 2019. Advances in Intelligent Systems and Computing, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-030-13273-6_1
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