Research

Optimizing 3D Bioprinting of Cancer Cells

We have developed novel strategies for bioprinting processes by presenting mathematical and computational frameworks to optimize outcomes both during and after bioprinting. 

  1. N. Moghimi, S.A. Hosseini, A.B. Dalan, D. Mohammadrezaei, A. Goldman, M. Kohandel, "Controlled tumour heterogeneity in a co-culture system by 3D bio-printed tumour-on-chip model," Nature Scientific Reports, 13 (1), 13648 (2023). 

  1. D. Mohammadrezaei, N. Moghimi, S. Vandvajdi, G. Powathil, S. Hamis, M. Kohandel, "Predicting and elucidating the post-printing behaviour of 3D printed cancer cells in hydrogel structures by integrating in-vitro and in-silico experiments," Nature Scientific Reports, 13 (1), 1211 (2023). 

  1. D. Mohammadrezaei, L. Podina, M. Kohandel, "Cell Viability Prediction and Optimization in Extrusion-Based Bioprinting via Neural Network-Based Bayesian Optimization Models," Biofabrication, In Press (2023). 

  1. N. Moghimi, S.A. Hosseini, M. Poudineh, M. Kohandel, "Recent advances on cancer-on-chip models: Development of 3D tumours and tumour microenvironment," Bioprinting, e00238 (2022). 

Systems Biology and Pharmacology-Informed Neural Networks

We integrate quantitative systems biology and pharmacology models with machine learning techniques to investigate cancer biology, drug response, and decision-making processes. 

  1. M. Przedborski, D. Sharon, S. Cathelin, S. Chan, M. Kohandel, "An integrative systems biology approach to overcome venetoclax resistance in acute myeloid leukemia," PLoS Computational Biology, 18 (9), e1010439 (2022). 

  1. B. Eastman, M. Przedborski, M. Kohandel, "Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy," Nature Scientific Reports, 11 (1), 1-17 (2021). 

  1. M. Przedborski, M. Smalley, S. Thiyagarajan, A. Goldman, M. Kohandel, "Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade," Nature Communications Biology, 4 (1), 1-15 (2021). 

  1. M. Smalley, M. Przedborski, S. Thiyagarajan, et al., "Integrating systems biology and an ex vivo human tumour model elucidates PD-1 blockade response dynamics," iScience, 23 (6), 101229 (2020). 

Cancer Tissue Heterogeneity and Adaptive Resistance to Chemotherapy

Our research explores mathematical and experimental approaches to uncover mechanisms of heterogeneity-driven adaptive resistance in cancer tissues, focusing on phenotypic plasticity and metabolic remodeling. 

  1. A. Goldman, et al., "Targeting tumour phenotypic plasticity and metabolic remodelling in adaptive cross-drug tolerance," Science Signaling, doi: 10.1126/scisignal.aas8779 (2019). 
  2. A. Goldman, et al., "Sequential administration of anti-cancer drugs overcomes adaptive resistance by targeting a vulnerable transition cell state during drug-induced phenotypic plasticity," Nature Communications, 6, doi 10.1038 (2015). Dhawan (former student) is a co-author. 
  3. A. Goldman, et al., "Rationally Designed 2-in-1 Nanoparticles Can Overcome Adaptive Resistance in Cancer," ACS Nano, 10, 5823 (2016). 
  4. A. Pandey, et al., "Sequential application of a cytotoxic nanoparticle and a PI3K inhibitor enhances antitumor efficacy," Cancer Research, 74 (3): 675-85 (2014).