Project
Real-time Control, Sensing and Learning in Systems and Synthetic Biology with Biomedical Applications
Synthetic biology has demonstrated the power of its approaches in many biomedical applications such as the synthesis of drug-delivery tools, the discovery of novel drugs, optogenetic systems based gene- and cell- therapies and so on. Modeling, identification, prediction and control are key components of both systems and synthetic biology and, thus, necessary for their convergence.
The availability of large datasets a priori is necessary for most of the state-of-the-art ML-based techniques in order to successfully tackle modeling, prediction, identification, and control problems. This makes them unsuitable for online implementation. The main objective is to present online learning-based techniques that predict/control the behavior of a biological system without a priori knowledge of the system dynamics or datasets.
Related Publications
[A13] Marquez, G., Dechiraju, H., Baniya, P., Li, H., Tebyani, M., Pansodtee, P., Jafari, M., Barbee, A., Orozco, J., Teodorescu M., Rolandi, M., & Gomez, M. (2023). Delivering biochemicals with precision using bioelectronic devices enhanced with feedback control, PLoS ONE, 19(5), e0298286.
[A12] Jafari, M., Marquez, G., Dechiraju, H., Gomez, M., & Rolandi, M. (2023). Merging machine learning and bioelectronics for closed-loop control of biological systems and homeostasis, Cell Reports Physical Science, 4(8), 101535.
[A11] Zlobina, K., Jafari, M., M., Rolandi, M., & Gomez, M. (2022). The role of machine learning in advancing precision medicine with feedback control, Cell Reports Physical Science, 3(11), 101149.
[A10] Carrión, H., Jafari, M., Yang, H. Y., Isseroff, R. R., Rolandi, M., Gomez, M., & Norouzi, N. (2022). HealNet - Self-supervised Acute Wound Heal-Stage Classification, In Machine Learning in Medical Imaging (pp. 446-455). Springer, Cham.
[A9] Sargent, B., Jafari, M., Marquez, G., Mehta, A., Sun, Y., Yang, H.Y., Zhu, K., Isseroff, R.R., Zhao, M., & Gomez, M. (2022). A machine learning based model accurately predicts cellular response to electric fields in multiple cell types, Scientific Reports, 12, 9912.
[A8] Jia, M., Jafari, M., Pansodtee, P., Teodorescu, M., Gomez, M., & Rolandi, M. (2022). A multi-ion electrophoretic pump for simultaneous on-chip delivery of H+, Na+, and Cl−, APL Materials, 10(4), 041112.
[A7] Carrión, H., Jafari, M., Bagood, M. D., Yang, H. Y., Isseroff, R. R., & Gomez, M. (2022). Automatic wound detection and size estimation using deep learning algorithms, PLOS Computational Biology, 18(3), e1009852.
[A6] Hosseini Jafari, B., Zlobina, K., Marquez, G., Jafari, M., Selberg, J., Jia, M., Rolandi, M., & Gomez, M. (2021). A feedback control architecture for bioelectronic devices with applications to wound healing, Journal of the Royal Society Interface, 18(185), 20210497.
[A5] Pansodtee, P., Selberg, J., Jia, M., Jafari, M., Dechiraju, H., Thomsen, T., Gomez, M., Rolandi, M., & Teodorescu, M. (2021). The multi-channel potentiostat: Development and evaluation of a scalable mini-potentiostat array for investigating electrochemical reaction mechanisms, PLoS ONE, 16(9), e0257167.
[A4] Selberg, J., Jafari, M., Bradley, C., Gomez, M., & Rolandi, M. (2020). Expanding biological control to bioelectronics with machine learning, APL Materials, 8(12), 120904.
[A3] Selberg, J., Jafari, M., Mathews, J., Jia, M., Pansodtee, P., Dechiraju, H., Wu, C., Cordero, A., Flora, A., Yonas, N., Jannetty, S., Diberardinis, M., Teodorescu, M., Levin, M., Gomez, M., & Rolandi, M. (2020). Machine Learning Driven Bioelectronics for Closed-loop Control of Cells, Advanced Intelligent Systems, 2(12), 2000140.
[A2] Jafari, M., Marquez, G., Selberg, J., Jia, M., Dechiraju, H., Pansodtee, P., Teodorescu, M., Rolandi, M., & Gomez, M. (2021). Feedback Control of Bioelectronic Devices Using Machine Learning, IEEE Control Systems Letters 5(4), 1133-1138.
[A1] Marquez, G., Johnson, B., Jafari, M., & Gomez, M. (2019, December). Online machine learning based predictor for biological systems. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 120-125). IEEE.