Abstract
Dementia is a disease that is characterized by the gradual loss of memory and cognition of patients due to the death of neurons. The future perspective is that the number of patients will increase, due to the aging of the population, reaching up to one third of the world population over 65 years. Alzheimer’s disease is the most common form of dementia and there is no medication to prevent or cure the disease. In this sense, the discovery of an efficient treatment for the disease is a real need, and the repositioning of drugs and in silico techniques can contribute to this purpose. Computational methods, such as Statistical Model Checking, which is a formal verification technique, contribute to this field of research, aiding to analyze the evolution of the protein and drugs interactions at a lower cost than the laboratory experiments. In this work, we present a model of the PI3K/AKT/mTOR pathway and we connected it with Tau and A\(\beta \), which are two important proteins that contribute to the evolution of Alzheimer’s disease. We analyzed the effect of rapamycin, an immunosuppressive drug, on those proteins. Our results show that this medicine has the potential to slow down one of the biological processes that causes neuronal death. In addition, we could show the formal model verification technique can be an efficient tool to design pharmacological strategies reducing experimental cost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agha, G., Palmskog, K.: A survey of statistical model checking. ACM Trans. Model. Comput. Simul. 28(1), 1–39 (2018)
Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994)
Bakir, M.E., Konur, S., Gheorghe, M., Krasnogor, N., Stannett, M.: Automatic selection of verification tools for efficient analysis of biochemical models. Bioinformatics 34, 3187–3195 (2018)
Baumgartner, G., Renner, K.H.: Humor in the elderly with dementia: development and initial validation of a behavioral observation system. Curr. Psychol., 1–14 (2019)
Bellozi, P.M.Q., et al.: NVP-BEZ235 (dactolisib) has protective effects in a transgenic mouse model of Alzheimer’s disease. Front. Pharmacol. 10, 1–11 (2019)
Bellozi, P.M.Q., et al.: Neuroprotective effects of the anticancer drug NVP-BEZ235 (dactolisib) on amyloid-\(\beta \) 1-42 induced neurotoxicity and memory impairment. Sci. Rep. 6, 25226 (2016)
örg Bormann, J., Lohse, J., Payer, M., Vezin, G.: Model checking in industrial hardware design. In: 32nd Design Automation Conference, pp. 298–303. IEEE (1995)
Bulychev, P., et al.: Monitor-based statistical model checking for weighted metric temporal logic. In: Bjørner, N., Voronkov, A. (eds.) LPAR 2012. LNCS, vol. 7180, pp. 168–182. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28717-6_15
Campos, S., Clarke, E., Marrero, W., Minea, M., Hiraishi, H.: Computing quantitative characteristics of finite-state real-time systems. In: Proceedings Real-Time Systems Symposium REAL-94, pp. 266–270. IEEE Comput. Soc. Press (1994)
Campos, S., Clarke, E.M., Minea, M.: Symbolic techniques for formally verifying industrial systems. Sci. Comput. Program. 29, 79–98 (1997)
Christensen, B.D.D.: Alzheimer’s disease: progress in the development of anti-amyloid disease-modifying therapies. CNS Spectr. 12(2), 113–123 (2007)
Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)
David, A., et al.: Statistical model checking for networks of priced timed automata. In: Fahrenberg, U., Tripakis, S. (eds.) FORMATS 2011. LNCS, vol. 6919, pp. 80–96. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24310-3_7
David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B.: UPPAAL SMC tutorial. Int. J. Softw. Tools Technol. Transf. 17(4), 397–415 (2015). https://doi.org/10.1007/s10009-014-0361-y
De Strooper, B., Vassar, R., Golde, T.: The secretases: enzymes with therapeutic potential in Alzheimer disease. Nat. Rev. Neurol. 6(2), 99–107 (2010)
Dorvash, M., et al.: Dynamic modeling of signal transduction by mTOR complexes in cancer. J. Theor. Biol. 483, 109992 (2019)
Durães, F., Pinto, M., Sousa, E.: Old drugs as new treatments for neurodegenerative diseases. Pharmaceuticals 11(2), 1–21 (2018)
Ferreira, B., et al.: Intelligent service to perform overtaking in vehicular networks. In: Proceedings - IEEE Symposium on Computers and Communications 2016-Febru, pp. 669–676 (2016)
Gabbouj, S., et al.: Altered insulin signaling in Alzheimer’s disease brain - special emphasis on PI3K-Akt pathway. Front. Neurosci. 13, 1–8 (2019)
Goltsov, A., Tashkandi, G., Langdon, S.P., Harrison, D.J., Bown, J.L.: Kinetic modelling of in vitro data of PI3K, mTOR1, PTEN enzymes and on-target inhibitors Rapamycin, BEZ235, and LY294002. Eur. J. Pharm. Sci. 97, 170–181 (2017)
Gong, H., Zuliani, P., Clarke, E.M.: Model checking of a diabetes-cancer model. AIP Conf. Proc. 1371, 234–243 (2011)
Hao, W., Friedman, A.: Mathematical model on Alzheimer’s disease. BMC Syst. Biol. 10(1), 108 (2016)
Heras-Sandoval, D., Pérez-Rojas, J.M., Hernández-Damián, J., Pedraza-Chaverri, J.: The role of PI3K/AKT/mTOR pathway in the modulation of autophagy and the clearance of protein aggregates in neurodegeneration. Cell. Signal. 26(12), 2694–2701 (2014)
Hurd, M.D., Martorell, P., Delavande, A., Mullen, K.J., Langa, K.M.: Monetary costs of dementia in the United States. N. Engl. J. Med. 368(14), 1326–1334 (2013)
Konur, S., Dixon, C., Fisher, M.: Analysing robot swarm behaviour via probabilistic model checking. Robot. Auton. Syst. 60(2), 199–213 (2012)
Kubota, H., et al.: Temporal coding of insulin action through multiplexing of the AKT pathway. Mol. Cell 46(6), 820–832 (2012)
Kwiatkowska, M., Norman, G., Parker, D.: Stochastic model checking. In: Bernardo, M., Hillston, J. (eds.) SFM 2007. LNCS, vol. 4486, pp. 220–270. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72522-0_6
Le Novère, N.: Quantitative and logic modelling of molecular and gene networks. Nat. Rev. Genet. 16, 146–158 (2015)
Lee, Y.S., Chow, W.N.V., Lau, K.F.: Phosphorylation of FE65 at threonine 579 by GSK3\(\beta \) stimulates amyloid precursor protein processing. Sci. Rep. 7(1), 1–10 (2017)
Levine, B., Kroemer, G.: Autophagy in the pathogenesis of disease. Cell 132(1), 27–42 (2008)
Li, J., Kim, S.G., Blenis, J.: Rapamycin: one drug, many effects (2014)
Lin, A.L., et al.: Rapamycin rescues vascular, metabolic and learning deficits in apolipoprotein E4 transgenic mice with pre-symptomatic Alzheimer’s disease. J. Cereb. Blood Flow Metab. 37(1), 217–226 (2017)
Liu, Y., et al.: Rapamycin decreases Tau phosphorylation at Ser214 through regulation of cAMP-dependent kinase. Neurochem. Int. 62(4), 458–467 (2013)
Llorens-Martín, M., Jurado, J., Hernández, F., Ávila, J.: GSK-3\(\beta \), a pivotal kinase in Alzheimer disease. Front. Mol. Neurosci. 7, 1–11 (2014)
Majd, S., Power, J., Majd, Z.: Alzheimer’s disease and cancer: when two monsters cannot be together. Front. Neurosci. 13, 1–11 (2019)
McMillan, K.L.: A methodology for hardware verification using compositional model checking. Sci. Comput. Program. 37(1–3), 279–309 (2000)
Ozcelik, S., et al.: Rapamycin attenuates the progression of Tau pathology in P301S Tau transgenic mice. PLoS ONE 8(5), 2–8 (2013)
Patel, A.N., Jhamandas, J.H.: Neuronal receptors as targets for the action of amyloid-beta protein (a [beta]) in the brain. Expert. Rev. Mol. Med. 14 (2012)
Pezze, P.D., et al.: A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation. Sci. Signal. 5(217), 1–18 (2012)
Proctor, C.J., Gray, D.A.: GSK3 and p53 - is there a link in Alzheimer’s disease? Mol. Neurodegener. 5(1), 1–15 (2010)
Ryu, S.H., et al.: Incidence and course of depression in patients with Alzheimer’s disease. Psychiatry Investig. 14(3), 271 (2017)
Saxton, R.A., Sabatini, D.M.: mTOR signaling in growth, metabolism, and disease. Cell 168(6), 960–976 (2017)
Selles, M.C., Oliveira, M.M., Ferreira, S.T.: Brain inflammation connects cognitive and non-cognitive symptoms in Alzheimer’s disease. J. Alzheimer’s Dis. 64(s1), S313–S327 (2018)
Siegel, G.J.: Basic Neurochemistry: Molecular, Cellular, and Medical Aspects, 7th edn. Elsevier, Amsterdam (2006)
Siman, R., Cocca, R., Dong, Y.: The mTOR inhibitor rapamycin mitigates perforant pathway neurodegeneration and synapse loss in a mouse model of early-stage Alzheimer-type tauopathy. PLoS ONE 10(11), 1–21 (2015)
Singh, A.K., Kashyap, M.P., Tripathi, V.K., Singh, S., Garg, G., Rizvi, S.I.: Neuroprotection through rapamycin-induced activation of autophagy and PI3K/Akt1/mTOR/CREB signaling against amyloid-\(\beta \)-induced oxidative stress, synaptic/neurotransmission dysfunction, and neurodegeneration in adult rats. Mol. Neurobiol. 54(8), 5815–5828 (2017)
Spilman, P., et al.: Inhibition of mTOR by rapamycin abolishes cognitive deficits and reduces amyloid-\(\beta \) levels in a mouse model of Alzheimer’s disease. PLoS ONE 5(4), 1–8 (2010)
Sulaimanov, N., Klose, M., Busch, H., Boerries, M.: Understanding the mTOR signaling pathway via mathematical modeling. WIREs Syst. Biol. Med. 9 (2017)
Tenazinha, N., Vinga, S.: A survey on methods for modeling and analyzing integrated biological networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(4), 943–958 (2011)
Varusai, T.M., Nguyen, L.K.: Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR. Sci. Rep. 8(1), 1–14 (2018)
Vilallonga, G.D., De Almeida, A.C.G., Ribeiro, K.T., Campos, S.V., Rodrigues, A.M.: Hypothesized diprotomeric enzyme complex supported by stochastic modelling of palytoxin-induced Na/K pump channels. R. Soc. Open Sci. 5(3) (2018)
Wang, J., Gu, B.J., Masters, C.L., Wang, Y.J.: A systemic view of Alzheimer disease - Insights from amyloid-\(\beta \) metabolism beyond the brain. Nat. Rev. Neurol. 13(10), 612–623 (2017)
Wang, Q., Clarke, E.M.: Formal methods for biological systems : languages, algorithms, and applications. Ph.D. thesis, Carnegie Mellon University (2016)
Younes, H.L.S.: Verification and Planning for Stochastic Processes with Asynchronous Events (2005)
Zuliani, P.: Statistical model checking for biological applications. Int. J. Softw. Tools Technol. Transf. 17(4), 527–536 (2014)
Acknowledgments
The authors would like to thank Fundação de Amparo à Pesquisa de Minas Gerais – FAPEMIG, Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superios – CAPES for partially funding this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fernandes, H.R., Gomes, G.F., de Oliveira, A.C.P., Campos, S.V.A. (2020). Statistical Model Checking in Drug Repurposing for Alzheimer’s Disease. In: Carvalho, G., Stolz, V. (eds) Formal Methods: Foundations and Applications. SBMF 2020. Lecture Notes in Computer Science(), vol 12475. Springer, Cham. https://doi.org/10.1007/978-3-030-63882-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-63882-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63881-8
Online ISBN: 978-3-030-63882-5
eBook Packages: Computer ScienceComputer Science (R0)