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Paper deals with an algorithm which allows the automatic selection of the best operating point of biological system. This task is one of the subjects of research in the field of metabolic engineering, which deals with control related issues, in particular, with modelling of biological phenomena, as well as, monitoring of the unstable states of biomass growth. The possibility of using specific biosensors and microfluidic system for monitoring, optimizing and controlling of a bioreactor is presented in this paper. To ensure proper experiment control of the bioreactor, a real-time measurement of parameters at the macroscale level and metabolic activity of microorganism cells at the microscale level are relevant. Therefore, much attention has been paid to the description and modelling of cyclical changes in metabolic states. For the determination of key process parameters, a micro- calorimeter for measuring the heat of reaction has been applied. The biosensor provides additional information, which is useful in development of an interface for monitoring the bioreactor by a decomposition of measure- ments including the scale of process. Finally, the paper discusses the problem of model selection describing the bioprocess at the microscale level.
bifurcation analysis, control of experiment, metabolic flux analysis, microcalorimetry measure- ments, multiscale modelling, unstable bioprocess
[1] Stephanopoulos, G. & Vallino, J.J., Network Rigidity and Metabolic Engineering in Metabolite Overproduction. Science 252, pp. 1675–1681, 1991. doi: http://dx.doi.org/10.1126/science.1904627
[2] Shimizu, H., Metabolic Engineering–Integrating Methodologies of Molecular Breeding and Bioprocess System Engineering. Journal of Bioscience and Bioengineering, 94, pp. 563–573, 2002. doi: http://dx.doi.org/10.1263/jbb.94.563
[3] Varner, J. & Ramkrishna, D., Metabolic Engineering from a Cybernetic Perspective.2. Qualitative Investigation of Nodal Architectures and Their Response to Genetic Perturbation. Biotechnology Progress, 15, pp. 426–438, 1999. doi: http://dx.doi.org/10.1021/bp990018h
[4] Choinski, D., Metzger, M., Nocon, W., Polakow, G. & Skupin, P., AI-Based Support for Experimentation in an Environmental Biotechnological Process. Lecture Notes in Computer Science, eds. C. Sombattheera et al. Springer-Verlag Berlin Heidelberg, pp. 155–166, 2012. doi: http://dx.doi.org/10.1007/978-3-642-35455-7_15
[5] Sin, G., Gujsasola, A., de Pauw, D.J.W., Baeza, J.A., Carrera J. & Vanrolleghem, P.A., A new Approach for Modeling Simultaneous Storage and Growth Processes for Activated Sludge Systems Under Aerobic Conditions. Biotechnology and Bioengineering, 92, pp. 600–613, 2005. doi: http://dx.doi.org/10.1002/bit.20741
[6] Karahan O., van Loosdrecht, M.C.M. & Orhon, D., Modeling the Utilization of Starch by Activated Sludge for Simultaneous Substrate Storage and Microbial Growth. Biotechnology and Bioengineering, 94, pp. 43–53, 2006. doi: http://dx.doi.org/10.1002/bit.20793
[7] Grady, C.P.L., Smets B.F. & Barbeau, D.S., Variability in kinetic parameter estimates: A review of possible causes and a proposed terminology. Water Research, 30, pp. 742–748, 1996. doi: http://dx.doi.org/10.1016/0043-1354(95)00199-9
[8] Ni, B.-J., Zeng, R.J., Fang, F., Xie, W.-M., Shen, G.-P. & Yu, H.-Q., Fractionating soluble microbial products in the activated sludge process. Water Research, 44, pp. 2292–2302, 2010. doi: http://dx.doi.org/10.1016/j.watres.2009.12.025
[9] Choinski, D., Metzger, M., Nocon, W., Polaków, G., Rozalowska, B. & Skupin, P., Cooperative Access to Hierarchical Data from Biotechnological Pilot-plant. Lecture Notes in Computer Science, ed. Y. Luo, Springer-Verlag Berlin Heidelberg, pp. 171–178, 2012. doi: http://dx.doi. org/10.1007/978-3-642-32609-7_24
[10] Skupin, P. & Metzger, M., The Application of Multi-Agent System in Monitoring and Control of Nonlinear Bioprocesses. Lecture Notes in Computer Science, eds. E. Corchado et al.,
Springer-Verlag Berlin Heidelberg, pp. 25–36, 2012. doi: http://dx.doi.org/10.1007/978-3642-28942-2_3
[11] Turek-Szytow, J., Choinski, D. & Miksch, K., Properties of the activated sludge after lipase bioaugmentation. Enironment Protection Enigineering, 33, pp. 211–219, 2007.
[12] Van Impe, J.F., Vercammen, D. & Van Derlinden E., Toward a next generation of predictive models: A systems biology primer. Food Control, 29, pp. 336–342, 2013. doi: http://dx.doi.
org/10.1016/j.foodcont.2012.06.019
[13] VanBriesen, J.M. & Evaluation of methods to predict bacterial yield using thermodynamics. Biodegradation, 13, pp. 171–190, 2002. doi: http://dx.doi.org/10.1023/a:1020887214879
[14] Hurley, K.D., Frederick, B.G, DeSistoa, W.J., van Heiningena, A.R.P. & Wheelera, M.C., Catalytic reaction characterization using micromachined nanocalorimeters. Applied Catalysis A: General, 390, pp. 84–93, 2010. doi: http://dx.doi.org/10.1016/j.apcata.2010.09.035
[15] Maskow, T., Kemp, R., Buchholz, F., Schubert, T., Kiesel, B. & Harms, H., What Heat is Telling Us about Microbial Conversions in Nature and Technology: From Chip- to Megacalorimetry. Microbial Biotechnology, 3, pp. 269–284, 2010. doi: http://dx.doi.org/10.1111/j.17517915.2009.00121.x
[16] Grady, C.P.L Jr., Daigger G.T. & Lim H.C., Biological Wastewater Treatment. Martin Dekker Publishing: New York, pp. 70–71, 1999.
[17] Cordier, J.L., Butsch, B.M., Birou, B. & von Stockar, U., The relationship between elemental composition and heat of combustion of microbial biomass. Applied Microbiology and Biotechnology, 25, pp. 305–312, 1987. doi: http://dx.doi.org/10.1007/bf00252538
[18] Dunn, I., Heinzle, E., Ingham, J. & Penosil, J., Biological Reaction Engineering :Dynamic Modelling Fundamentals with Simulation Examples. Wiley–VCH Verlag GmbH & Co. KGaA: Weinheim, pp. 91–92, 2005. doi: http://dx.doi.org/10.1002/3527603050
[19] Nelson, M.I. & Sidhu, H.S., Analysis of a chemostat model with variable yield coefficient. Journal of Mathematical Chemistry, 38, pp. 605–615, 2005. doi: http://dx.doi.org/10.1007/ s10910-005-6914-2
[20] Skupin, P., Simulation approach for detection of the self-sustained oscillations in continuous culture. Proc. of the 11th WSEAS Int. Conf. On Mathematics and Computers in Biology and Chemistry, eds. V. Monteanu, R. Raducanu, G., Dutica, A. Croitoru & V.E. Balas, Iasi, pp. 80–85, 2010.
[21] Ermentrout, B., Simulating, analyzing, and animating dynamical systems. A guide to XPPAUT for researchers and students. SIAM series Software Environments Tools, 2002. doi: http:// dx.doi.org/10.1137/1.9780898718195
[22] Pilyugin, S.S. & Waltman, P., Multiple limit cycles in the chemostat with variable yield. Mathematical Bioscience, 182, pp. 151–166, 2003. doi: http://dx.doi.org/10.1016/s00255564(02)00214-6
[23] Huang, X.C., Limit cycles in a continuous fermentation model. Journal of Mathematical Chemistry, 5, pp. 287–296, 1990. doi: http://dx.doi.org/10.1007/bf01166359