Personalized Tumor Growth Prediction Using Multiscale Modeling

2020-01-01
ÜNSAL, SERBÜLENT
Acar, Aybar Can
Itik, Mehmet
KABATAŞ, AYŞE
GEDİKLİ, ÖZNUR
ÖZDEMİR, FEYYAZ
TURHAN, KEMAL
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.
JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES

Suggestions

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...
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...
Model comparison for gynecological cancer datasets and selection of threshold value
Bahçivancı, Başak; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019)
Cancer is a very common system’s disease with its structural and functional complexities caused by high dimension and serious correlation of genes as well as sparsity of gene interactions. Hereby, different mathematical models have been suggested in the literature to unravel these challenges. Among many alternates, in this study we use the Gaussian graphical model, Gaussian copula graphical model and loop-based multivariate adaptive regression splines with/without interaction models due to their advantages ...
Inference of the stochastic MAPK pathway by modified diffusion bridge method
Purutçuoğlu Gazi, Vilda (2013-03-01)
The MAPK pathway is one of the well-known systems in oncogene researches of eukaryotes due to its important role in cell life. In this study, we perform the parameter estimation of a realistic MAPK system by using western blotting data. In inference, we use the modified diffusion bridge algorithm with data augmentation technique by modelling the realistically complex system via the Euler-Maruyama approximation. This approximation, which is the discretized version of the diffusion model, can be seen as an al...
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: https://hdl.handle.net/11511/63319.