Research Project: Glioma on a chip: Probing Glioma Cell Invasion and Gliomagenesis on a Multiplexed Chip
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Contributors
Funders
ID
EC.00131
Authors
Taşoğlu, Savaş
Faculty Member
Publications
Skin-on-a-chip technologies towards clinical translation and commercialization
(IOP Publishing Ltd, 2024) Dabbagh, Sajjad Rahmani; Dilmani, Asghari Sara; Sokullu, Emel; Tarar, Ceren; Taşoğlu, Savaş; Ismayilzada, Nilufar; Tokyay, Begüm Kübra; Abacı, Hasan Erbil; KUTTAM (Koç University Research Center for Translational Medicine); KUAR (KU Arçelik Research Center for Creative Industries); Graduate School of Sciences and Engineering; School of Medicine; Yes; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center; SCHOOL OF MEDICINE
Skin is the largest organ of the human body which plays a critical role in thermoregulation, metabolism (e.g. synthesis of vitamin D), and protection of other organs from environmental threats, such as infections, microorganisms, ultraviolet radiation, and physical damage. Even though skin diseases are considered to be less fatal, the ubiquity of skin diseases and irritation caused by them highlights the importance of skin studies. Furthermore, skin is a promising means for transdermal drug delivery, which requires a thorough understanding of human skin structure. Current animal and in vitro two/three-dimensional skin models provide a platform for disease studies and drug testing, whereas they face challenges in the complete recapitulation of the dynamic and complex structure of actual skin tissue. One of the most effective methods for testing pharmaceuticals and modeling skin diseases are skin-on-a-chip (SoC) platforms. SoC technologies provide a non-invasive approach for examining 3D skin layers and artificially creating disease models in order to develop diagnostic or therapeutic methods. In addition, SoC models enable dynamic perfusion of culture medium with nutrients and facilitate the continuous removal of cellular waste to further mimic the in vivo condition. Here, the article reviews the most recent advances in the design and applications of SoC platforms for disease modeling as well as the analysis of drugs and cosmetics. By examining the contributions of different patents to the physiological relevance of skin models, the review underscores the significant shift towards more ethical and efficient alternatives to animal testing. Furthermore, it explores the market dynamics of in vitro skin models and organ-on-a-chip platforms, discussing the impact of legislative changes and market demand on the development and adoption of these advanced research tools. This article also identifies the existing obstacles that hinder the advancement of SoC platforms, proposing directions for future improvements, particularly focusing on the journey towards clinical adoption.
Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling
(American Institute of Physics Inc., 2023) Ahmadpour, Abdollah; Taşoğlu, Savaş; Sarabi, Misagh Rezapour; Muradoğlu, Metin; Ural, Berk; İşgör, Pelin Kübra; Eren, Büşra Nimet; Department of Mechanical Engineering; KUTTAM (Koç University Research Center for Translational Medicine); KUAR (KU Arçelik Research Center for Creative Industries); Graduate School of Sciences and Engineering; KUIS AI (Koç University & İş Bank Artificial Intelligence Center); Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING; Research Center
Microneedle arrays are patches of needles at micro- and nano-scale, which are competent and versatile technologies that have been merged with microfluidic systems to construct more capable devices for biomedical applications, such as drug delivery, wound healing, biosensing, and sampling body fluids. In this paper, several designs and applications are reviewed. In addition, modeling approaches used in microneedle designs for fluid flow and mass transfer are discussed, and the challenges are highlighted.
Biomedical optical fibers
(Royal Society of Chemistry, 2021) Öztürk, Ece; Sarabi, Misagh Rezapour; Taşoğlu, Savaş; Jiang, Nan; Yetişen, Ali K.; Department of Mechanical Engineering; KUAR (KU Arçelik Research Center for Creative Industries); KUTTAM (Koç University Research Center for Translational Medicine); School of Medicine; Yes; College of Engineering; Research Center; SCHOOL OF MEDICINE
Optical fibers with the ability to propagate and transfer data via optical signals have been used for decades in medicine. Biomaterials featuring the properties of softness, biocompatibility, and biodegradability enable the introduction of optical fibers' uses in biomedical engineering applications such as medical implants and health monitoring systems. Here, we review the emerging medical and health-field applications of optical fibers, illustrating the new wave for the fabrication of implantable devices, wearable sensors, and photodetection and therapy setups. A glimpse of fabrication methods is also provided, with the introduction of 3D printing as an emerging fabrication technology. The use of artificial intelligence for solving issues such as data analysis and outcome prediction is also discussed, paving the way for the new optical treatments for human health.
ML-automated microfluidic circuit design
(American Association for the Advancement of Science, 2026) Birtek, Mehmet Tuğrul; Choukri, Abdullah Ahmed; Taşoğlu, Savaş; Choukri, Abdullah Ahmed; Birtek, Mehmet Tuğrul; Ozcan, Aydogan; Department of Industrial Engineering; Department of Mechanical Engineering; KUTTAM (Koç University Research Center for Translational Medicine); KUIS AI (Koç University & İş Bank Artificial Intelligence Center); KUAR (KU Arçelik Research Center for Creative Industries); Yes; College of Engineering; Research Center
Microfluidics enable high-precision and cost-effective processing of biological and chemical substances. However, designing and fabricating microfluidic chips typically requires substantial expertise and numerous design iterations, posing considerable barriers to entry for nonexperts. We introduce mu FluidicGenius (mu FG), an open-access, machine learning (ML)-augmented design tool that enables nonexpert users to rapidly create functional microfluidic circuits. Users simply define the spatial placement of reservoirs, specify the channel connections between them, and assign desired flow rates through this layout. Leveraging a hybrid algorithmic framework that integrates ML models with mathematical modeling, mu FG automatically generates spatially coded maze structures that implement the precise fluidic resistances needed to meet the target flow distribution. These resistive elements are optimized to fit within the available geometry and can reproduce complex flow profiles, such as physiologically relevant flow rates in multi-organ-on-chip platforms. The resulting microfluidic designs are directly exportable for three-dimensional printing. Experimental validation demonstrates that mu FG-generated circuits reproduce target flow distributions with 90% accuracy. By streamlining and automating microfluidic circuit creation, mu FG not only lowers the barrier to entry for nonexperts but also showcases a principled and efficient application of ML to fluidic system design, enabling rapid and customizable development of complex microfluidic architectures.
Machine learning-enabled optimization of interstitial fluid collection via a sweeping microneedle design
(American Chemical Society, 2023) Aydın, Erdal; Tarar, Ceren; Taşoğlu, Savaş; Yetişen, Ali K.; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; KUTEM (Koç University Tüpraş Energy Center); KUIS AI (Koç University & İş Bank Artificial Intelligence Center); KUTTAM (Koç University Research Center for Translational Medicine); KUAR (KU Arçelik Research Center for Creative Industries); Graduate School of Sciences and Engineering; Yes; College of Engineering; Research Center; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Microneedles (MNs)allow for biological fluid sampling and drugdelivery toward the development of minimally invasive diagnosticsand treatment in medicine. MNs have been fabricated based on empiricaldata such as mechanical testing, and their physical parameters havebeen optimized through the trial-and-error method. While these methodsshowed adequate results, the performance of MNs can be enhanced byanalyzing a large data set of parameters and their respective performanceusing artificial intelligence. In this study, finite element methods(FEMs) and machine learning (ML) models were integrated to determinethe optimal physical parameters for a MN design in order to maximizethe amount of collected fluid. The fluid behavior in a MN patch issimulated with several different physical and geometrical parametersusing FEM, and the resulting data set is used as the input for MLalgorithms including multiple linear regression, random forest regression,support vector regression, and neural networks. Decision tree regression(DTR) yielded the best prediction of optimal parameters. ML modelingmethods can be utilized to optimize the geometrical design parametersof MNs in wearable devices for application in point-of-care diagnosticsand targeted drug delivery.
