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Publication Open Access 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients(Public Library of Science, 2019) Dinçer, Cansu; Kaya, Tuğba; Tunçbağ, Nurcan; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); College of Engineering; 26605; 8745Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in "patches" which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways, revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between each group and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.Publication Open Access 3D-printed microrobots from design to translation(Nature Portfolio, 2022) Department of Mechanical Engineering; N/A; Dabbagh, Sajjad Rahmani; Sarabi, Misagh Rezapour; Birtek, Mehmet Tuğrul; Sitti, Metin; Taşoğlu, Savaş; Faculty Member; Faculty Member; Department of Mechanical Engineering; KU Arçelik Research Center for Creative Industries (KUAR) / KU Arçelik Yaratıcı Endüstriler Uygulama ve Araştırma Merkezi (KUAR); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Health Sciences; Graduate School of Sciences and Engineering; School of Medicine; College of Engineering; N/A; N/A; N/A; N/A; 297104; 291971Microrobots have attracted the attention of scientists owing to their unique features to accomplish tasks in hard-to-reach sites in the human body. Microrobots can be precisely actuated and maneuvered individually or in a swarm for cargo delivery, sampling, surgery, and imaging applications. In addition, microrobots have found applications in the environmental sector (e.g., water treatment). Besides, recent advancements of three-dimensional (3D) printers have enabled the high-resolution fabrication of microrobots with a faster design-production turnaround time for users with limited micromanufacturing skills. Here, the latest end applications of 3D printed microrobots are reviewed (ranging from environmental to biomedical applications) along with a brief discussion over the feasible actuation methods (e.g., on- and off-board), and practical 3D printing technologies for microrobot fabrication. In addition, as a future perspective, we discussed the potential advantages of integration of microrobots with smart materials, and conceivable benefits of implementation of artificial intelligence (AI), as well as physical intelligence (PI). Moreover, in order to facilitate bench-to-bedside translation of microrobots, current challenges impeding clinical translation of microrobots are elaborated, including entry obstacles (e.g., immune system attacks) and cumbersome standard test procedures to ensure biocompatibility.Publication Open Access A cartridge based sensor array platform for multiple coagulation measurements from plasma(Royal Society of Chemistry (RSC), 2015) Bulut, Serpil; Yaralioglu, G. G.; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; Çakmak, Onur; Ermek, Erhan; Kılınç, Necmettin; Barış, İbrahim; Kavaklı, İbrahim Halil; Ürey, Hakan; PhD Student; Other; Researcher; Teaching Faculty; Faculty Member; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Sciences; N/A; 109991; N/A; 111629; 40319; 8579This paper proposes a MEMS-based sensor array enabling multiple clot-time tests for plasma in one disposable microfluidic cartridge. The versatile LoC (Lab-on-Chip) platform technology is demonstrated here for real-time coagulation tests (activated Partial Thromboplastin Time (aPTT) and Prothrombin Time (PT)). The system has a reader unit and a disposable cartridge. The reader has no electrical connections to the cartridge. This enables simple and low-cost cartridge designs and avoids reliability problems associated with electrical connections. The cartridge consists of microfluidic channels and MEMS microcantilevers placed in each channel. The microcantilevers are made of electroplated nickel. They are actuated remotely using an external electro-coil and the read-out is also conducted remotely using a laser. The phase difference between the cantilever oscillation and the coil drive is monitored in real time. During coagulation, the viscosity of the blood plasma increases resulting in a change in the phase read-out. The proposed assay was tested on human and control plasma samples for PT and aPTT measurements. PT and aPTT measurements from control plasma samples are comparable with the manufacturer's datasheet and the commercial reference device. The measurement system has an overall 7.28% and 6.33% CV for PT and aPTT, respectively. For further implementation, the microfluidic channels of the cartridge were functionalized for PT and aPTT tests by drying specific reagents in each channel. Since simultaneous PT and aPTT measurements are needed in order to properly evaluate the coagulation system, one of the most prominent features of the proposed assay is enabling parallel measurement of different coagulation parameters. Additionally, the design of the cartridge and the read-out system as well as the obtained reproducible results with 10 mu l of the plasma samples suggest an opportunity for a possible point-of-care application.
Publication Metadata only A class of bounded component analysis algorithms for the separation of both independent and dependent sources(IEEE-inst Electrical Electronics Engineers inc, 2013) Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 41624Bounded Component analysis (BCa) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. in this approach, under the practical source boundedness assumption, the widely used statistical independence assumption is replaced by a more generic domain separability assumption. This article introduces a geometric framework for the development of Bounded Component analysis algorithms. Two main geometric objects related to the separator output samples, namely Principal Hyper-Ellipsoid and Bounding Hyper-Rectangle, Are introduced. the maximization of the volume ratio of these objects, and its extensions, Are introduced as relevant optimization problems for Bounded Component analysis. the article also provides corresponding iterative algorithms for both real and complex sources. the numerical examples illustrate the potential advantage of the proposed BCa framework in terms of correlated source separation capability as well as performance improvement for short data records.Publication Open Access A computational multicriteria optimization approach to controller design for pysical human-robot interaction(Institute of Electrical and Electronics Engineers (IEEE), 2020) Tokatlı, Ozan; Patoğlu, Volkan; Department of Mechanical Engineering; Aydın, Yusuf; Başdoğan, Çağatay; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 125489Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.Publication Open Access A computational study of droplet-based bioprinting: effects of viscoelasticity(American Institute of Physics (AIP) Publishing, 2019) Taşoğlu, Savaş; Department of Mechanical Engineering; Nooranidoost, Mohammad; Izbassarov, Daulet; Muradoğlu, Metin; PhD Student; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; N/A; N/A; 46561Despite significant progress, cell viability continues to be a central issue in droplet-based bioprinting applications. Common bioinks exhibit viscoelastic behavior owing to the presence of long-chain molecules in their mixture. We computationally study effects of viscoelasticity of bioinks on cell viability during deposition of cell-loaded droplets on a substrate using a compound droplet model. The inner droplet, which represents the cell, and the encapsulating droplet are modeled as viscoelastic liquids with different material properties, while the ambient fluid is Newtonian. The model proposed by Takamatsu and Rubinsky ["Viability of deformed cells," Cryobiology 39(3), 243-251 (1999)] is used to relate cell deformation to cell viability. We demonstrate that adding viscoelasticity to the encapsulating droplet fluid can significantly enhance the cell viability, suggesting that viscoelastic properties of bioinks can be tailored to achieve high cell viability in droplet-based bioprinting systems. The effects of the cell viscoelasticity are also examined, and it is shown that the Newtonian cell models may significantly overpredict the cell viability.Publication Open Access A computational study of two-phase viscoelastic systems in a capillary tube with a sudden contraction/expansion(American Institute of Physics (AIP) Publishing, 2016) Department of Mechanical Engineering; Izbassarov, Daulet; Muradoğlu, Metin; PhD Student; Faculty Member; Department of Mechanical Engineering; College of Engineering; N/A; 46561Two-phase viscoelastic systems are computationally studied in a pressure-driven flow with a sudden contraction and expansion using a finite-difference/front-tracking method. The effects of viscoelasticity in drop and bulk fluids are investigated including high Weissenberg and Reynolds number cases up to Wi = 100 and Re = 100. The Finitely Extensible Non-linear Elastic-Chilcott and Rallison (FENE-CR) model is used to account for the fluid viscoelasticity. Extensive computations are performed to examine drop dynamics for a wide range of parameters. It is found that viscoelasticity interacts with drop interface in a non-monotonic and complicated way, and the two-phase viscoelastic systems exhibit very rich dynamics especially in the expansion region. At high Re, the drop undergoes large deformation in the contraction region followed by strong shape oscillations in the downstream of the expansion. For a highly viscous drop, a re-entrant cavity develops in the contraction region at the trailing edge which, in certain cases, grows and eventually causes encapsulation of ambient fluid. The re-entrant cavity formation is initiated at the entrance of the contraction and is highly influenced by the viscoelasticity. Compared to the corresponding straight channel case, the effects of viscoelasticity are reversed in the constricted channel: Viscoelasticity in drop/continuous phase hinders/enhances formation of the re-entrant cavity and entrainment of ambient fluid into main drop. Encapsulation of ambient fluid into main droplet may be another route to produce a compound droplet in microfluidic applications. (C) 2016 AIP Publishing LLC.Publication Open Access A divergence-free parametrization for dynamical dark energy(Institute of Physics (IOP) Publishing, 2015) Vazquez, J. Alberto; Department of Physics; Dereli, Tekin; Akarsu, Özgür; Faculty Member; Department of Physics; College of Sciences; 201358; N/AWe introduce a new parametrization for the dark energy, led by the same idea to the linear expansion of the equation of state in scale factor a and in redshift z, which diverges neither in the past nor future and contains the same number of degrees of freedom with the former two. We present constraints of the cosmological parameters using the most updated baryon acoustic oscillation (BAO) measurements along with cosmic microwave background (CMB) data and a recent reanalysis of Type Ia supernova (SN) data. This new parametrization allowed us to carry out successive observational analyses by decreasing its degrees of freedom systematically until ending up with a dynamical dark energy model that has the same number of parameters with ACDM. We found that the dark energy source with a dynamical equation of state parameter equal 2/3 at the early universe and -1 today fits the data slightly better than A.Publication Open Access A hybrid architecture for federated and centralized learning(Institute of Electrical and Electronics Engineers (IEEE), 2022) Elbir, Ahmet M.; Papazafeiropoulos, Anastasios K.; Kourtessis, Pandelis; Chatzinotas, Symeon; Department of Electrical and Electronics Engineering; Ergen, Sinem Çöleri; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 7211Many of the machine learning tasks rely on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) entailing huge communication overhead. To overcome this, federated learning (FL) has been suggested as a promising tool, wherein the clients send only the model updates to the PS instead of the whole dataset. However, FL demands powerful computational resources from the clients. In practice, not all the clients have sufficient computational resources to participate in training. To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them. Then, the model parameters are aggregated at the PS. To improve the efficiency of dataset transmission, we propose two different techniques: i) increased computation-per-client and ii) sequential data transmission. Notably, the HFCL frameworks outperform FL with up to 20% improvement in the learning accuracy when only half of the clients perform FL while having 50% less communication overhead than CL since all the clients collaborate on the learning process with their datasets.Publication Open Access A machine learning approach for implementing data-driven production control policies(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AGiven the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.