Optimizing neotissue growth inside perfusion bioreactors with respect to culture and labor cost: a multi-objective optimization study using evolutionary algorithms.
Mehrian, Mohammad; Geris, Liesbet
2020 • In Computer Methods in Biomechanics and Biomedical Engineering, 23 (7), p. 285-294
Algorithms; Bioreactors; Computer Simulation; Perfusion/instrumentation; Time Factors; Tissue Culture Techniques; Tissue Engineering/methods; 3D scaffold; Multi-objective optimization; bone tissue engineering; evolutionary algorithms; labor cost
Abstract :
[en] Tissue engineering is a fast progressing domain where solutions are provided for organ failure or tissue damage. In this domain, computer models can facilitate the design of optimal production process conditions leading to robust and economically viable products. In this study, we use a previously published computationally efficient model, describing the neotissue growth (cells + their extracellular matrix) inside 3D scaffolds in a perfusion bioreactor. In order to find the most cost-effective medium refreshment strategy for the bioreactor culture, a multi-objective optimization strategy was developed aimed at maximizing the neotissue growth while minimizing the total cost of the experiment. Four evolutionary optimization algorithms (NSGAII, MOPSO, MOEA/D and GDEIII) were applied to the problem and the Pareto frontier was computed in all methods. All algorithms led to a similar outcome, albeit with different convergence speeds. The simulation results indicated that, given the actual cost of the labor compared to the medium cost, the most cost-efficient way of refreshing the medium was obtained by minimizing the refreshment frequency and maximizing the refreshment amount.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Mehrian, Mohammad
Geris, Liesbet ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Language :
English
Title :
Optimizing neotissue growth inside perfusion bioreactors with respect to culture and labor cost: a multi-objective optimization study using evolutionary algorithms.
Publication date :
2020
Journal title :
Computer Methods in Biomechanics and Biomedical Engineering
ISSN :
1025-5842
eISSN :
1476-8259
Publisher :
Taylor & Francis, United Kingdom
Volume :
23
Issue :
7
Pages :
285-294
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 772418 - INSITE - Development and use of an integrated in silico-in vitro mesofluidics system for tissue engineering
Funders :
CER - Conseil Européen de la Recherche [BE] CE - Commission Européenne [BE]
Črepinšek M, Liu S-H, Mernik M., 2013. Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv. 45(3):35.
Deb K, Pratap A, Agarwal S, Meyarivan T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput. 6(2):182–197.
Dedieu S, Pibouleau L, Azzaro-Pantel C, Domenech S., 2003. Design and retrofit of multiobjective batch plants via a multicriteria genetic algorithm. Comput Chem Eng. 27(12):1723–1740.
Eberhart RC, Shi Y., 1998. Comparison between genetic algorithms and particle swarm optimization. Berlin, Heidelberg: Springer; p. 611–616.
Geris L, Guyot Y, Schrooten J, Papantoniou I., 2016. In silico regenerative medicine: how computational tools allow regulatory and financial challenges to be addressed in a volatile market. Interface Focus. 6(2). doi:10.1098/rsfs.2015.0105.
Guyot Y., 2015. A multiphysics multiscale computational framework for the simulation of perfusion bioreactor processes in bone tissue engineering [PhD thesis]. Université de Liège; p. 99–119.
Guyot Y, Luyten F, Schrooten J, Papantoniou I, Geris L., 2015. A three‐dimensional computational fluid dynamics model of shear stress distribution during neotissue growth in a perfusion bioreactor. Biotechnol Bioeng. 112(12):2591–2600.
Hadka D., 2015. “Platypus,” GitHub. [Online] [Accessed on 2019 Sep. 26]. http://github.com/Project-Platypus/Platypus.
Hassan R, Cohanim B, De Weck O, Venter G., 2005, April. A comparison of particle swarm optimization and the genetic algorithm. 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 18 April 2005 - 21 April 2005, Austin, Texas; p. 1897.
Heris SMK, Khaloozadeh H., 2011. Open-and closed-loop multiobjective optimal strategies for HIV therapy using NSGA-II. IEEE Trans Biomed Eng. 58(6):1678–1685.
Holland JH., 1975. Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
Horn J., 1997. Multicriterion decision making. In: Bäck T, Fogel DB, Michalewicz Z, editors. Handbook of evolutionary computation. Vol. 1. New York, Bristol: IOP Publishing and Oxford University Press; p. F1.9: 1–F1.9: 15.
Hossain S, Bergstrom D, Chen X., 2015. A mathematical model and computational framework for three‐dimensional chondrocyte cell growth in a porous tissue scaffold placed inside a bi‐directional flow perfusion bioreactor. Biotechnol Bioeng. 112(12):2601–2610.
Hwang CL, Masud A., 1979. Multiple objective decision making, methods and applications: a state-of-the-art survey. In Collab. with SR Paidy and K. Yoon. New York (NY): Springer-verlag.
Kami D, Watakabe K, Yamazaki-Inoue M, Minami K, Kitani T, Itakura Y, Toyoda M, Sakurai T, Umezawa A, Gojo S., 2013. Large-scale cell production of stem cells for clinical application using the automated cell processing machine. BMC Biotechnol. 13(1):102.
Kennedy J., 2006. Swarm intelligence. Handbook of nature-inspired and innovative computing. Boston (MA): Springer. p. 187–219.
Kennedy RJ, Eberhart. 1995. Particle swarm optimisation. Proc. IEEE Int'l. Conf. on Neural Networks, IV, 1942–1948. Piscataway (NJ): IEEE Service Center.
Kim K-J, Smith RL., 2004. Parallel multiobjective evolutionary algorithms for waste solvent recycling. Ind Eng Chem Res. 43(11):2669–2679.
Kukkonen S, Lampinen J., 2005. GDE3: the third evolution step of generalized differential evolution. In (2005) IEEE congress on evolutionary computation (CEC 2015), Edinburgh, Scotland, September 2005, Vol I. IEEE Service Center. p. 443–450.
Li Z, Liu X, Duan X, Huang F., 2010. Comparative research on particle swarm optimisation and genetic algorithm. Comput Inf Sci. 3(1):120.
Mehrian M, Guyot Y, Papantoniou I, Olofsson S, Sonnaert M, Misener R, Geris L., 2018. Maximizing neotissue growth kinetics in a perfusion bioreactor: an in silico strategy using model reduction and Bayesian optimization. Biotechnol Bioeng. 115(3):617–629.
Nebro A. J, Durillo J. J, Garcia-Nieto J, Coello C. C, Luna F, Alba E., 2009, March. SMPSO: a new PSO-based metaheuristic for multi-objective optimization. In 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), Nashville, TN, USA, March 30 - April 2, 2009. IEEE; p. 66–73.
Olofsson S, Mehrian M, Calandra R, Geris L, Deisenroth M. P, Misener R., 2019. Bayesian multiobjective optimisation with mixed analytical and Black-Box functions: application to tissue engineering. IEEE Trans Biomed Eng. 66(3):727–739.
Panduro MA, Brizuela CA, Balderas LI, Acosta DA., 2009. A comparison of genetic algorithms, particle swarm optimisation and the differential evolution method for the design of scannable circular antenna arrays. Prog Electromagn Res B. 13:171–186.
Petrovski A, McCall J., 2001, March. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. International Conference on Evolutionary Multi-Criterion Optimization, Zurich, Switzerland. Heidelberg, Berlin: Springer; p. 531–545.
Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Rowinski PM., 2017. Swarm intelligence and evolutionary algorithms: performance versus speed. Inf Sci. 384:34–85.
Salter E, Goh B, Hung B, Hutton D, Ghone N, Grayson WL., 2011. Bone tissue engineering bioreactors: a role in the clinic? Tissue Eng Part B: Rev. 18(1):62–75.
Shimoni Y, Forsyth T, Srinivasan V, Szeto R., 2017. Reducing variability in cell-specific productivity in perfusion culture: a Case Study.253.
Sonnaert M, Papantoniou I, Bloemen V, Kerckhofs G, Luyten F, Schrooten J., 2017. Human periosteal‐derived cell expansion in a perfusion bioreactor system: proliferation, differentiation and extracellular matrix formation. J Tissue Eng Regen Med. 11(2):519–530.
Srijaya TC, Ramasamy TS, Kasim N., 2014. Advancing stem cell therapy from bench to bedside: lessons from drug therapies. J Transl Med. 12(1):243.
Steeves JD., 2015. Bench to bedside: challenges of clinical translation. Prog Brain Res. 218:227–239.
Storn R, Price K., 1997. Differential evolution–a simple and efficient heuristic for global optimisation over continuous spaces. J Global Optim. 11(4):341–359.
Suresh P, Basu PK., 2008. Improving pharmaceutical product development and manufacturing: impact on cost of drug development and cost of goods sold of pharmaceuticals. J Pharm Innov. 3(3):175–187.
Van Veldhuizen D. A, Lamont G. B., 1999, February. Multiobjective evolutionary algorithm test suites. In Symposium on Applied Computing, San Antonio, Texas, USA; Vol. 99, p. 351–357.
Zadeh L., 1963. Optimality and non-scalar-valued performance criteria. IEEE Trans Automat Contrib. 8(1):59–60.
Zhang Q, Li H., 2007. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput. 11(6):712–731.
