Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture

Andrew Sinkoe; Arul Jayaraman; Juergen Hahn

Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture

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Author(s): Andrew Sinkoe^{1, 2} ; Arul Jayaraman³ ; Juergen Hahn^{1, 2, 4}
- Affiliations: 1: Department of Biomedical Engineering , Rensselaer Polytechnic Institute , 110 8th Street, Troy, New York , USA ;
  2: Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute , 110 8th Street, Troy, New York , USA ;
  3: McFerrin Department of Chemical Engineering , Texas A&M University , College Station, Texas , USA ;
  4: Department of Chemical and Biological Engineering , Rensselaer Polytechnic Institute , 110 8th Street, Troy, New York , USA
Source: Volume 12, Issue 6, December 2018, p. 241 – 246
DOI: 10.1049/iet-syb.2018.5014 , Print ISSN 1751-8849, Online ISSN 1751-8857

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 02/02/2018, Accepted 30/04/2018, Revised 11/04/2018, Published 15/06/2018

The isolation of T cells, followed by differentiation into Regulatory T cells (Tregs), and re-transplantation into the body has been proposed as a therapeutic option for inflammatory bowel disease. A key requirement for making this a viable therapeutic option is the generation of a large population of Tregs. However, cytokines in the local microenvironment can impact the yield of Tregs during differentiation. As such, experimental design is an essential part of evaluating the importance of different cytokine concentrations for Treg differentiation. However, currently only single, constant concentrations of the cytokines have been investigated. This work addresses this point by performing experimental design in silico which seeks to maximize the predicted induction of Tregs relative to Th17 cells, by selecting an optimal input function for the concentrations of TGF-β, IL-2, IL-6, and IL-23. While this approach sounds promising, the results show that only marginal improvements in the concentration of Tregs can be achieved for dynamic cytokine profiles as compared to optimal constant concentrations. Since constant concentrations are easier to implement in experiments, it is recommended for this particular system to keep the concentrations constant where IL-6 should be kept low and high concentrations of TGF-β, IL-2, and IL-23 should be used.

References

1. 1)
  - 9. Himmel, M.E., Yao, Y., Orban, P.C., et al: ‘Regulatory T-cell therapy for inflammatory bowel disease: more questions than answers’, Immunology, 2012, 136, (2), pp. 115–122.
2. 2)
  - 27. Chelliah, V., Laibe, C., Le Novère, N. ‘Biomodels database: a repository of mathematical models of biological processes’, Methods Mol. Biol., 2013, 1021, pp. 189–199.
3. 3)
  - 16. Morrison, P.J., Ballantyne, S.J., Kullberg, M.C.: ‘Interleukin-23 and T help 17-type responses in intestinal inflammation: from cytokines to T-cell plasticity’, Immunology, 2011, 133, (4), pp. 397–408.
4. 4)
  - 3. Nathan, C.: ‘Points of control in inflammation’, Nature, 2002, 420, (6917), pp. 846–852.
5. 5)
  - 22. Montgomery, D.C.: ‘Design and analysis of experiments’ (John Wiley & Sons, Hoboken, 1976, 5th edn. 2001).
6. 6)
  - 21. Walter, E., Pronzato, L.: ‘Qualitative and quantitative experiment design for phenomenological models – a survey’, Automatica, 1990, 26, (2), pp. 195–213.
7. 7)
  - 25. Jones, J.A., Vernacchio, V.R., Sinkoe, A.L., et al: ‘Experimental and computational optimization of an Escherichia coli co-culture for the efficient production of flavonoids’, Metab. Eng., 2016, 35, pp. 55–63.
8. 8)
  - 23. Fedorov, V.V.: ‘Theory of optimal experiments’ (Academic Press, New York, 1972).
9. 9)
  - 15. Shale, M., Shiering, C., Powrie, F.: ‘CD4+ T cell subsets in intestinal inflammation’, Immunol. Rev., 2013, 252, (1), pp. 164–184.
10. 10)
  - 24. Silvey, S.D.: ‘Optimal design, an introduction to the theory for parameter estimation’ (Chapman and Hall, London, 1980).
11. 11)
  - 13. Steinmeyer, S., Howsmon, D.P., Alaniz, R.C., et al: ‘Empirical modeling of T cell activation predicts interplay of host cytokines and bacterial indole’, Biotechnol. Bioeng., 2017, 114, (11), pp. 2660–2667.
12. 12)
  - 6. Bettelli, E., Carrier, Y., Gao, W., et al: ‘Reciprocal developmental pathways for the generation of pathogenic effector Th17 and regulatory T cells’, Nature, 2006, 441, (7090), pp. 235–238.
13. 13)
  - 1. El-Gabalawy, H., Guenther, L.C., Bernstein, C.N.: ‘Epidemiology of immune-mediated inflammatory diseases: incidence, prevalence, natural history, and comorbidities’, J. Rheumatol., 2010, 85, pp. 2–10.
14. 14)
  - 20. Sakaguchi, S., Miyara, M., Costantino, C.M., et al: ‘FOXP3+ regulatory t cells in the human immune system’, Nat. Rev. Immunol., 2010, 10, (7), pp. 490–500.
15. 15)
  - 7. Weaver, C.T., Elson, C.O., Fouser, L.A., et al: ‘The Th17 pathway and inflammatory diseases of the intestines, lungs, and skin’, Annu. Rev. Pathol., 2013, 8, (1), pp. 477–512.
16. 16)
  - 10. Roncarolo, M.G., Battaglia, M.: ‘Regulatory T-cell immunotherapy for tolerance to self antigens and alloantigens in humans’, Nat. Rev. Immunol., 2007, 7, (8), pp. 585–598.
17. 17)
  - 5. Carbo, A., Hontecillas, R., Kronsteiner, B., et al: ‘Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity’, PLoS Comput. Biol., 2013, 9, (4), p. e1003027.
18. 18)
  - 31. Wächter, A., Biegler, L.T.: ‘On the implementation of a primal-dual interior point filter line search algorithm for large-scale nonlinear programming’, Math. Program., 2006, 106, (1), pp. 25–57.
19. 19)
  - 4. Eastaff-Leung, N., Mabarrack, N., Barbour, A., et al: ‘FOXP3+ regulatory T cells, Th17 effector cells, and cytokine environment in inflammatory bowel disease’, J. Clin. Immunol., 2009, 30, (1), pp. 80–89.
20. 20)
  - 17. Veldhoen, M., Hocking, R.J., Atkins, C.J., et al: ‘TGFβ in the context of an inflammatory cytokine milieu supports de novo differentiation of IL-17-producing T cells’, Immunity, 2006, 24, (2), pp. 179–189.
21. 21)
  - 28. Matlab 2015a. The MathWorks, Inc., Natick, MA, United States.
22. 22)
  - 11. Fischbach, M.A., Bluestone, J.A., Lim, W.A.: ‘Cell-based therapeutics: the next pillar of medicine’, Sci. Transl. Med., 2013, 5, (179).
23. 23)
  - 30. Chu, Y., Jayaraman, A., Hahn, J.: ‘Parameter sensitivity analysis of IL-6 signalling pathways’, IET Syst. Biol., 2007, 1, (6), pp. 342–352.
24. 24)
  - 12. Hippen, K.L., Merkel, S.C., Schirm, D.K., et al: ‘Generation and large-scale expansion of human inducible regulatory T cells that suppress graft-versus-host disease’, Am. J. Transpl., 2011, 11, (6), pp. 1148–1157.
25. 25)
  - 26. Sinkoe, A., Hahn, J.: ‘Optimal experimental design for parameter estimation of an IL-6 signaling model’, Processes, 2017, 5, (3), p. 49.
26. 26)
  - 29. Almquist, J., Leander, J., Jirstrand, M.: ‘Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood’, J. Pharmacokinet. Pharmacodyn., 2015, 42, (3), pp. 191–209.
27. 27)
  - 19. Dai, W., Word, D.P., Hahn, J.: ‘Modeling and dynamic optimization of fuel-grade ethanol fermentation using fed-batch process’, Control Eng. Pract., 2014, 22, pp. 231–241.
28. 28)
  - 18. Yamane, H., Paul, W.E.: ‘Early signaling events that underlie fate decisions of naive CD4+ T cells toward distinct T-helper cell subsets’, Immunol. Rev., 2013, 252, (1), pp. 12–23.
29. 29)
  - 14. Baecher-Allan, C., Hafler, D.A.: ‘Human regulatory T cells and their role in autoimmune disease’, Immunol. Rev., 2006, 212, (1), pp. 203–216.
30. 30)
  - 2. Sakaguchi, S., Yamaguchi, T., Nomura, T., et al: ‘Regulatory T cells and immune tolerance’, Cell, 2008, 133, (5), pp. 775–787.
31. 31)
  - 8. Zhou, L., Lopes, J.E., Chong, M.M.W., et al: ‘TGF-β-induced FOXP3 inhibits Th17 cell differentiation by antagonizing RORγt function’, Nature, 2008, 453, (7192), pp. 236–240.

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Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture

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