Mahmut Kamil Aslan
+41 44 633 00 48
Kamil has received his B.Sc. degree from Bilkent University, Ankara, Turkey, and M.Sc. degree in from Middle East Technical University, Ankara, Turkey, both in Electrical and Electronics Engineering Department. Kamil joined deMello group as a PhD student in February 2018.
Image Guided Control for the Automation of Single Cell Microfluidic Platforms
One of the biggest challenges in modern biology is figuring out the decision-making processes of cells. Basic cell behaviors include sensing the environment with biosensors, processing data using an internal network and reaching a decision. Modeling the behavior of the cells under different environmental conditions is difficult due to their extremely complex internal molecular networks. However, the ability to model and track cells can be important in answering fundamental questions in cell biology. More specifically, tracking of single hematopoietic stem cells (HSCs) for downstream analysis holds a great potential to understand decision making mechanisms during differentiation process.
This project aims to understand the decision making process of the hematopoietic stem cells (HSCs) by tracking their lineage over generations at the single cell level. To achieve this, we will develop a microfluidic system that can trap single cell inside growth chambers, isolate sister cells after division and extract them for downstream transcriptome analysis. The whole system will incorporate image processing based automation. The experimental results obtained with the system will be used in constructing computational cell models.
Smartphone Based Real Time Flow Cytometry
Differentiating and quantifying heterogeneity of cells in a suspension still remains a main challenge in biology. Flow cytometry is a gold standard in cell sorting based on their unique morphological features. However, flow cytometry devices are generally bulky, not easily accessible and expensive. Recently, deep learning has become a promising technique by allowing quantification of the cell images based on their unique morphological features.
In this project, we integrate a microfluidic device with a smartphone for real-time quantification of the different cell types. Cells are focused and separated through microfluidic channel and imaged with a smartphone. Then, different cell types are classified in real-time by implementing machine learning based algorithm on the smartphone.