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Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 41

Integrating Artificial Intelligence Techniques in Cell Mechanics

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DOI: http://dx.doi.org/10.15439/2024F4351

Citation: Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 41, pages 111–116 ()

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Abstract. The Artificial Intelligence (AI) and Machine Learning (ML) techniques have been revolutionizing many subjects. The AI-empowered methods such as Reinforcement Learning (RL) and Deep Learning (DL) have been employed for various aspects of cell mechanics. This work review state of art of AI and ML technologies that have been used to describe, analyze and predict the mechanics of cells as well as the use of numerical methods for cell mechanics. This review also consider the impact of utilizing physical constraints on the AI and ML models aiming at improved convergences during the training and validation phases. At the end, we will provide a statistical analysis of the reported studies and a discussion on the current challenges and future possibilities.

References

  1. A. F. Pegoraro, P. Janmey, and D. A. Weitz, “Mechanical properties of the cytoskeleton and cells,” Cold Spring Harbor perspectives in biology, vol. 9, no. 11, p. a022038, 2017.
  2. T. Svitkina, “The actin cytoskeleton and actin-based motility,” Cold Spring Harbor perspectives in biology, vol. 10, no. 1, p. a018267, 2018.
  3. R. Phillips, J. Kondev, J. Theriot, and H. Garcia, Physical biology of the cell. Garland Science, 2012.
  4. C. Battle, C. P. Broedersz, N. Fakhri, V. F. Geyer, J. Howard, C. F. Schmidt, and F. C. MacKintosh, “Broken detailed balance at mesoscopic scales in active biological systems,” Science, vol. 352, no. 6285, pp. 604–607, 2016.
  5. P. Romani, L. Valcarcel-Jimenez, C. Frezza, and S. Dupont, “Crosstalk between mechanotransduction and metabolism,” Nature Reviews Molecular Cell Biology, vol. 22, no. 1, pp. 22–38, 2021.
  6. M. Naeem, S. T. H. Rizvi, and A. Coronato, “A gentle introduction to reinforcement learning and its application in different fields,” IEEE access, vol. 8, pp. 209 320–209 344, 2020.
  7. M. Naeem, S. Bashir, Z. Ullah, and A. A. Syed, “A near optimal scheduling algorithm for efficient radio resource management in multi-user mimo systems,” Wireless Personal Communications, vol. 106, no. 3, pp. 1411–1427, 2019.
  8. M. Naeem, A. Coronato, Z. Ullah, S. Bashir, and G. Paragliola, “Optimal user scheduling in multi antenna system using multi agent reinforcement learning,” Sensors, vol. 22, no. 21, p. 8278, 2022.
  9. S. I. H. Shah, A. Coronato, M. Naeem, and G. De Pietro, “Learning and assessing optimal dynamic treatment regimes through cooperative imitation learning,” IEEE Access, vol. 10, pp. 78 148–78 158, 2022.
  10. S. I. H. Shah, M. Naeem, G. Paragliola, A. Coronato, and M. Pechenizkiy, “An ai-empowered infrastructure for risk prevention during medical examination,” Expert Systems with Applications, vol. 225, p. 120048, 2023.
  11. U. b. Khalid, M. Naeem, F. Stasolla, M. H. Syed, M. Abbas, and A. Coronato, “Impact of ai-powered solutions in rehabilitation process: Recent improvements and future trends,” International Journal of General Medicine, pp. 943–969, 2024.
  12. M. Cinque, A. Coronato, and A. Testa, “Dependable services for mobile health monitoring systems,” International Journal of Ambient Computing and Intelligence (IJACI), vol. 4, no. 1, pp. 1–15, 2012.
  13. C. J. Soelistyo, G. Vallardi, G. Charras, and A. R. Lowe, “Learning biophysical determinants of cell fate with deep neural networks,” Nature Machine Intelligence, vol. 4, no. 7, pp. 636–644, 2022.
  14. M. Cinque, A. Coronato, and A. Testa, “A failure modes and effects analysis of mobile health monitoring systems,” in Innovations and advances in computer, information, systems sciences, and engineering. Springer, 2012, pp. 569–582.
  15. R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. MIT press, 2018.
  16. M. Fiorino, M. Naeem, M. Ciampi, and A. Coronato, “Defining a metric-driven approach for learning hazardous situations,” Technologies, vol. 12, no. 7, p. 103, 2024.
  17. M. Jamal, Z. Ullah, M. Naeem, M. Abbas, and A. Coronato, “A hybrid multi-agent reinforcement learning approach for spectrum sharing in vehicular networks,” Future Internet, vol. 16, no. 5, p. 152, 2024.
  18. F. B. Mismar, J. Choi, and B. L. Evans, “A framework for automated cellular network tuning with reinforcement learning,” IEEE Transactions on Communications, vol. 67, no. 10, pp. 7152–7167, 2019.
  19. M. S. Schmitt, J. Colen, S. Sala, J. Devany, S. Seetharaman, A. Caillier, M. L. Gardel, P. W. Oakes, and V. Vitelli, “Machine learning interpretable models of cell mechanics from protein images,” Cell, vol. 187, no. 2, pp. 481–494, 2024.
  20. J. Ma, M. K. Yu, S. Fong, K. Ono, E. Sage, B. Demchak, R. Sharan, and T. Ideker, “Using deep learning to model the hierarchical structure and function of a cell,” Nature methods, vol. 15, no. 4, pp. 290–298, 2018.
  21. Z. Wang, Y. Xu, D. Wang, J. Yang, and Z. Bao, “Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement,” Nature machine intelligence, vol. 4, no. 1, pp. 73–83, 2022.
  22. H. Zheng, W. Xie, K. Wang, and Z. Li, “Opportunities of hybrid model-based reinforcement learning for cell therapy manufacturing process control,” arXiv preprint https://arxiv.org/abs/2201.03116, 2022.
  23. Z. Wang, D. Wang, C. Li, Y. Xu, H. Li, and Z. Bao, “Deep reinforcement learning of cell movement in the early stage of c. elegans embryogenesis,” Bioinformatics, vol. 34, no. 18, pp. 3169–3177, 2018.
  24. Z. Wang, D. Wang, C. Li, Y. Xu, H. Li, and Z. Bao, “Modeling cell migration with convolutional neural network and deep reinforcement learning,” Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), Tech. Rep., 2019.
  25. P. C. Kinnunen, K. K. Ho, S. Srivastava, C. Huang, W. Shen, K. Garikipati, G. D. Luker, N. Banovic, X. Huan, J. J. Linderman et al., “Integrating inverse reinforcement learning into data-driven mechanistic computational models: a novel paradigm to decode cancer cell heterogeneity,” Frontiers in Systems Biology, vol. 4, p. 1333760, 2024.
  26. A. Procopio, G. Cesarelli, L. Donisi, A. Merola, F. Amato, and C. Cosentino, “Combined mechanistic modeling and machine-learning approaches in systems biology–a systematic literature review,” Computer methods and programs in biomedicine, p. 107681, 2023.
  27. A. Zaritsky, A. Jamieson, E. Welf, A. Nevarez, J. Cillay, U. Eskiocak, B. Cantarel, and G. Danuser, “Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma, cell systems 12 (2021).”
  28. D. Nguyen, L. Tao, H. Ye, and Y. Li, “Machine learning-based prediction for single-cell mechanics,” Mechanics of Materials, vol. 180, p. 104631, 2023.
  29. H. Li, D. Matsunaga, T. S. Matsui, H. Aosaki, G. Kinoshita, K. Inoue, A. Doostmohammadi, and S. Deguchi, “Wrinkle force microscopy: a machine learning based approach to predict cell mechanics from images,” Communications biology, vol. 5, no. 1, p. 361, 2022.
  30. K. S. Wong, X. Zhong, C. S. L. Low, and P. Kanchanawong, “Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses,” Scientific Reports, vol. 12, no. 1, p. 15329, 2022.
  31. E. Moen, D. Bannon, T. Kudo, W. Graf, M. Covert, and D. Van Valen, “Deep learning for cellular image analysis,” Nature methods, vol. 16, no. 12, pp. 1233–1246, 2019.
  32. S. Baar, M. Kuragano, K. Tokuraku, and S. Watanabe, “Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images,” Scientific Reports, vol. 12, no. 1, p. 16884, 2022.
  33. S. Rinner, A. Trentino, H. Url, F. Burger, J. von Lautz, B. Wolfrum, and F. Reinhard, “Detection of cellular micromotion by advanced signal processing,” Scientific Reports, vol. 10, no. 1, p. 20078, 2020.
  34. H. Aragaki, K. Ogoh, Y. Kondo, and K. Aoki, “Lim tracker: a software package for cell tracking and analysis with advanced interactivity,” Scientific Reports, vol. 12, no. 1, p. 2702, 2022.
  35. H. Cui, C. Wang, H. Maan, K. Pang, F. Luo, N. Duan, and B. Wang, “scgpt: toward building a foundation model for single-cell multi-omics using generative ai,” Nature Methods, pp. 1–11, 2024.
  36. J. Wang, D. Agarwal, M. Huang, G. Hu, Z. Zhou, C. Ye, and N. R. Zhang, “Data denoising with transfer learning in single-cell transcriptomics,” Nature methods, vol. 16, no. 9, pp. 875–878, 2019.
  37. A. Shree, M. K. Pavan, and H. Zafar, “scdreamer for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier,” Nature Communications, vol. 14, no. 1, p. 7781, 2023.
  38. S. A. Khan, A. Maillo, V. Lagani, R. Lehmann, N. A. Kiani, D. Gomez-Cabrero, and J. Tegner, “Reusability report: Learning the transcriptional grammar in single-cell rna-sequencing data using transformers,” Nature Machine Intelligence, vol. 5, no. 12, pp. 1437–1446, 2023.
  39. V. Fazio, N. M. Pugno, O. Giustolisi, and G. Puglisi, “Physically based machine learning for hierarchical materials,” Cell Reports Physical Science, vol. 5, no. 2, 2024.
  40. M. Alber, A. Buganza Tepole, W. R. Cannon, S. De, S. Dura-Bernal, K. Garikipati, G. Karniadakis, W. W. Lytton, P. Perdikaris, L. Petzold et al., “Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences,” NPJ digital medicine, vol. 2, no. 1, p. 115, 2019.
  41. F. Regazzoni, L. Dedè, and A. Quarteroni, “Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics,” Computer Methods in Applied Mechanics and Engineering, vol. 370, p. 113268, 2020.