Personalized Tumor Growth Prediction Using Multiscale Modeling

Acar, Aybar Can
Itik, Mehmet
Purpose: Cancer is one of the most complex phenomena in biology and medicine. Extensive attempts have been made to work around this complexity. In this study, we try to take a selective approach; not modeling each particular facet in detail but rather only the pertinent and essential parts of the tumor system are simulated and followed by optimization, revealing specific traits. This leads us to a pellucid personalized model which is noteworthy as it closely approximates existing experimental results.


Integrated biomimetic scaffolds for soft tissue engineering
Güven, Sinan; Hasırcı, Nesrin; Department of Biotechnology (2006)
Tissue engineering has the potential to create new tissue and organs from cultured cells for transplantation. Biodegradable and biocompatible scaffolds play a vital role in the transfer of the cultured cells to a new tissue. Various scaffolds for soft tissue engineering have been developed, however there is not any structure totally mimicking the natural extracellular matrix (ECM), ready to use. In this study biodegradable and biocompatible scaffolds were developed from natural polymers by tissue engineerin...
Investigation of the drug resistance in capecitabine resistant hct-116, dabrafenib resistant ht-29, and sn-38 resistant ht-29 cell lines using cellular barcoding technology
Baygın, Rana Can; Acar, Ahmet; Department of Biology (2022-11-3)
Cancer is a complex disease, and understanding its biology holds great importance. Tumor cells exhibit hallmarks of cancer, including aberrantly activated signaling pathways and gain or loss genomic alterations that result in alterations in their differentiation programs, survival, proliferation, and programmed cell death. Developing resistance to therapeutic agents is one of the major problems in cancer therapies. Exploiting drug resistance using the principles of clonal evolution can pave the way to overc...
Development and Characterization of PEG-B-PCL Micelles Carrying Anticancer Agents
Işık, Gülhan; Tezcaner, Ayşen; Hasırcı, Nesrin; Department of Biotechnology (2022-2-9)
Cancer is a disease that decreases the quality of life. Many cancer drugs are either toxic or not effective due to their fast removal by reticuloendothelial system. Therefore, nano-sized drug delivery systems, especially the ones carrying the drugs directly to tumor, gained attention in the last decades. The aim of the study was to prepare nano-sized drug carrying micelles (drug conjugated and drug loaded) from methoxy polyethylene glycol-block-polycaprolactone (mPEG-b-PCL). In order to conjugate drugs, mPE...
Differential gene expression analysis in drug resistant multiple myeloma cell lines
Mutlu, Pelin; Gündüz, Ufuk; Department of Biology (2009)
The emergence of drug-resistance of tumor cells is a major complication for succesful chemotherapy. In this study, the molecular mechanisms of resistance to prednisone, vincristine and melphalan in multiple myeloma cell lines, RPMI-8226 and U-266 were investigated. Drug resistance was induced by application of the drugs by stepwise dose increments and confirmed by XTT cytotoxicity assay. Gene expression analysis demostrated that MDR1 gene is one of the most important factor causing the multidrug resistance ...
MTA-1 expression is associated with metastasis and epithelial to mesenchymal transition in colorectal cancer cells
Cagatay, Seda Tuncay; Cimen, Ismail; SAVAŞ, BERNA; Banerjee, Sreeparna (Springer Science and Business Media LLC, 2013-04-01)
Although metastasis associated protein 1 (MTA1) has been widely linked to tumor metastasis, the relevant mechanisms remain to be elucidated, especially in colorectal cancer (CRC). Here, we have investigated the link between MTA1, metastasis and epithelial-mesenchymal transition (EMT) in CRC. Eighteen normal colon tissues and 91 resected tumor samples were analyzed for MTA1 expression by immunohistochemistry (IHC). IHC indicated low or no nuclear MTA1 expression in the normal tissues and significantly higher...
Citation Formats
S. ÜNSAL et al., “Personalized Tumor Growth Prediction Using Multiscale Modeling,” JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES, pp. 347–363, 2020, Accessed: 00, 2020. [Online]. Available: