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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 Metadata only [BMIM] [PF6] incorporation doubles CO2 selectivity of ZIF-8: elucidation of interactions and their consequences on performance(Amer Chemical Soc, 2016) N/A; N/A; N/A; N/A; N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Kınık, Fatma Pelin; Altıntaş, Çiğdem; Balcı, Volkan; Koyutürk, Burak; Uzun, Alper; Keskin, Seda; Master Student; Researcher; PhD Student; Master Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; 59917; 40548Experiments were combined with atomically detailed simulations and density functional theory (DFT) calculations to understand the effect of incorporation of an ionic liquid (IL), 1-n-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]), into a metal organic framework (MOF with a zeolitic imidazolate framework), ZIF-8, on the CO2 separation performance. The interactions between [BMIM] [PF6] and ZIF-8 were examined in deep detail, and their consequences on CO2/CH4, CO2/N-2, and CH4/N-2 separation have been elucidated by using experimental measurements complemented by DFT calculations and atomically detailed simulations. Results suggest that IL-MOF interactions strongly affect the gas affinity of materials at low pressure, whereas available pore volume plays a key role for gas adsorption at high pressures. Direct interactions between IL and MOF lead to at least a doubling of CO2/CH4 and CO2/N-2 selectivities of ZIF-8. These results provide opportunities for rational design and development of IL-incorporated MOFs with exceptional selectivity for target gas separation applications.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 Open Access A mixed-integer linear programming based training and feature selection method for artificial neural networks using piece-wise linear approximations(Elsevier, 2022) Şıldır, Hasan; Department of Chemical and Biological Engineering; Aydın, Erdal; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringArtificial Neural Networks (ANNs) may suffer from suboptimal training and test performance related issues not only because of the presence of high number of features with low statistical contributions but also due to their non-convex nature. This study develops piecewise-linear formulations for the efficient approximation of the non-convex activation and objective functions in artificial neural networks for optimal, global and simultaneous training and feature selection in regression problems. Such formulations include binary variables to account for the existence of the features and piecewise-linear approximations, which in turn, after one exact linearization step, calls for solving a mixed-integer linear programming problem with a global optimum guarantee because of convexity. Suggested formulation is implemented on two industrial case studies. Results show that efficient approximations are obtained through the usage of the method with only a few number of breakpoints. Significant feature space reduction is observed bringing about notable improvement in test accuracy.Publication Metadata only A model to predict maximum tolerable temperatures of metal-oxide-supported 1-n-butyl-3-methylimidazolium based ionic liquids(Elsevier, 2015) N/A; N/A; N/A; Department of Chemical and Biological Engineering; Akçay, Aslı; Babucci, Melike; Balci, Volkan; Uzun, Alper; Master Student; PhD Student; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; N/A; 59917The thermal stability limits of metal-oxide-supported ionic liquids (Its) with 1-n-butyl-3-methylimidazolium cation, [BMIM](+), on most commonly used metal-oxides, SiO2, TiO2, gamma-Al2O3, and MgO are determined. Data show that stability limits of bulk and metal-oxide-supported ILs linearly increase with increasing acidity of C2 proton on imidazolium ring, controlling the inter-ionic interaction strength. Moreover, data also show that the presence of metal-oxide lowers the stability limits considerably. This effect becomes more significant as the surface acidity of the metal-oxide decreases from SiO2 to MgO This decrease in stability limits with increasing point of zero charge (PZC) of metal-oxide indicates that the interaction between IL and metal-oxide becomes the dominant factor rather than the inter-ionic interactions. Based on these findings a simple mathematical expression was developed as a function of PZC and inter-ionic interaction strength probed by nu(C2H) to predict the stability limits of [BMIM](+)-based ILs immobilized on metal-oxides. Performance of the model was tested on several different ILs supported on different metal-oxides, including Fe2O3 and CeO2. Results show that the model successfully predicts the maximum operating or tolerable temperatures of supported-[BMIM](+)-based ILs with an average relative error less than 4.3%. We suggest that the model developed here can help to choose proper ILs that can tolerate the operating conditions of systems including ILs immobilized on metal oxides, such as in solid catalysts with ionic liquid layer (SCILL) or in supported ionic liquid phase (SILP) catalysts. (C) 2014 Elsevier Ltd. All rights reserved.Publication Open Access A multi-state coarse grained modeling approach for an intrinsically disordered peptide(American Institute of Physics (AIP) Publishing, 2017) Department of Chemical and Biological Engineering; N/A; Sayar, Mehmet; Dalgıçdır, Cahit; Ramezanghorbani, Farhad; Faculty Member; PhD Student; Department of Chemical and Biological Engineering; College of Engineering; Graduate School of Sciences and Engineering; 109820; N/A; N/AMany proteins display a marginally stable tertiary structure, which can be altered via external stimuli. Since a majority of coarse grained (CG) models are aimed at structure prediction, their success for an intrinsically disordered peptide's conformational space with marginal stability and sensitivity to external stimuli cannot be taken for granted. In this study, by using the LK alpha 14 peptide as a test system, we demonstrate a bottom-up approach for constructing a multi-state CG model, which can capture the conformational behavior of this peptide in three distinct environments with a unique set of interaction parameters. LK alpha 14 is disordered in dilute solutions; however, it strictly adopts the alpha-helix conformation upon aggregation or when in contact with a hydrophobic/hydrophilic interface. Our bottom-up approach combines a generic base model, that is unbiased for any particular secondary structure, with nonbonded interactions which represent hydrogen bonds, electrostatics, and hydrophobic forces. We demonstrate that by using carefully designed all atom potential of mean force calculations from all three states of interest, one can get a balanced representation of the nonbonded interactions. Our CG model behaves intrinsically disordered in bulk water, folds into an alpha-helix in the presence of an interface or a neighboring peptide, and is stable as a tetrameric unit, successfully reproducing the all atom molecular dynamics simulations and experimental results.Publication Open Access A new class of porous materials for efficient CO2 separation: ionic liquid/graphene aerogel composites(Elsevier, 2021) Department of Chemical and Biological Engineering; N/A; Department of Chemistry; Zeeshan, Muhammad; Yalçın, Kaan; Keskin, Seda; Uzun, Alper; Öztuna, Feriha Eylül Saraç; Ünal, Uğur; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Chemistry; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); Graduate School of Sciences and Engineering; College of Engineering; College of Sciences; N/A; N/A; 40548; 59917; N/A; 42079Here, we report a new post-synthesis modification strategy for functionalizing reduced graphene aerogels (rGAs) towards an exceptional CO2 separation performance. 1-N-butyl-3-methylimidazolium hexafluorophosphate ([BMIM][PF6]) was impregnated on a rGA, prepared by reducing GA at 700 degrees C, at various ionic liquid (IL) loadings of 5, 10, 30, and 50 wt%. The resulting composites were characterized in deep detail by X-ray photoelectron spectroscopy, X-ray diffraction, N-2 physical adsorption measurements, scanning electron microscopy, Fourier transform infrared and Raman spectroscopies, and thermogravimetric analysis. Results indicated the presence of interactions between the rGA surface and the anion of the IL, potentially improving the CO2 affinity. Volumetric gas adsorption measurements using these materials showed that the deposition of [BMIM][PF6] on rGA surface at an IL loading of 50 wt% boosts the CO2/CH4 selectivity by more than 20-times, exceeding an absolute value of 120, a remarkably higher CO2/CH4 selectivity compared to that of other functionalized materials under similar operating conditions. Tunability of both the IL structure and the surface characteristics of rGA offer a tremendous degree of flexibility for the rational design of these IL/rGA composites towards high performance in gas separation applications.Publication Open Access A novel method for PEGylation of chitosan nanoparticles through photopolymerization(Royal Society of Chemistry (RSC), 2019) Department of Chemical and Biological Engineering; Bozüyük, Uğur; Gökulu, İpek Simay; Doğan, Nihal Olcay; Kızılel, Seda; PhD Student; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; N/A; N/A; N/A; 28376An ultrafast and convenient method for PEGylation of chitosan nanoparticles has been established through a photopolymerization reaction between the acrylate groups of PEG and methacrylated-chitosan nanoparticles. The nanoparticle characteristics under physiological pH conditions were optimized through altered PEG chain length, concentration and duration of UV exposure. The method developed here has potential for clinical translation of chitosan nanoparticles. It also allows for the scalable and fast synthesis of nanoparticles with colloidal stability.Publication Open Access A systematic and efficient input selection method for artificial neural networks using mixed-integer nonlinear programming(Konya Teknik Üniversitesi, 2022) Şıldır, Hasan; Department of Chemical and Biological Engineering; Aydın, Erdal; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 311745Selection of input variables of the empirical models has vital effect on the prediction performance, reduced overfitting and reduced computational load. Various trials and error and sequential methods in the literature to deal with input selection for artificial neural networks (ANNs). However, these methods are not considered as automatic and systematic. This study proposes a novel and efficient mixed integer nonlinear programming-based approach to handle optimal input selection and the ANN training simultaneously for classification problems. Such selection uses binary (0-1) variables to represent the presence of the input variables and trains traditional continuous network weights simultaneously. Two classification case studies are given to demonstrate the advantages by using widely used data sets and statistical measures. The first data set is related to the characterization of the type of a tumor related to breast cancer, the second data set is about predicting the type of a biotechnological product using different features, the last one is related to heart failure prediction. Results show that better test performance can be achieved with optimally selected inputs, resulting in reduced overfitting. The proposed approach delivers a significant advantage during the design and training of the ANNs and is also applicable to other empirical models. / Ampirik modellerin girdi değişkenlerinin seçimi, tahmin performansı, azaltılmış fazla uydurma ve hesaplama yükünün azaltılması üzerinde önemli etkiye sahiptir. Literatürde yapay sinir ağları (YSA) için girdi seçimi ile ilgili çeşitli deneme yanılma yöntemleri mevcuttur ancak bu metodlar sistematik ve otomatik olarak kabul edilmemektedir. Bu çalışma, sınıflandırma problemleri için optimal girdi seçimi ve YSA eğitimini aynı anda ele almak için yeni ve verimli bir karma tamsayılı doğrusal olmayan programlama tabanlı bir yaklaşım önermektedir. Bu seçim, girdi değişkenlerinin varlığını temsil etmek için ikili (0-1) değişkenleri kullanır ve geleneksel sürekli ağ ağırlıklarını veya parametrelerini aynı anda eğitir. Yaygın olarak kullanılan veri setleri ve istatistiksel ölçümler kullanarak avantajları göstermek amacıyla üç sınıflandırma vaka çalışması sunulmuştur. Birinci veri seti meme kanseri ile ilgili tümörün tipin-in karakterizasyonu ile ilgili olup, ikinci veri seti ise farklı özellikler kullanılarak bir biyoteknolojik ürünün tipinin tahmin edilmesi ile ilgilidir, son veri seti ise kalp sağlığı ile ilgilidir. Sonuçlar, optimal olarak seçilen girdiler ile düşük fazla uydurma sayesinde daha iyi test performansının elde edilebileceğini göstermektedir. Önerilen yaklaşım, YSA'ların tasarımı ve eğitimi sırasında önemli bir avantaj sağlar ve diğer ampirik modellere de uygulanabilir.Publication Open Access Activation of the pleiotropic drug resistance pathway can promote mitochondrial DNA retention by fusion-defective mitochondria in saccharomyces cerevisiae(Genetics Society America (GSA), 2014) Department of Chemical and Biological Engineering; Dunn, Cory David; Mutlu, Nebibe; Garipler, Görkem; Akdoğan, Emel; Faculty Member; Department of Chemical and Biological Engineering; College of SciencesGenetic and microscopic approaches using Saccharomyces cerevisiae have identified many proteins that play a role in mitochondrial dynamics, but it is possible that other proteins and pathways that play a role in mitochondrial division and fusion remain to be discovered. Mutants lacking mitochondrial fusion are characterized by rapid loss of mitochondrial DNA. We took advantage of a petite-negative mutant that is unable to survive mitochondrial DNA loss to select for mutations that allow cells with fusion-deficient mitochondria to maintain the mitochondrial genome on fermentable medium. Nextgeneration sequencing revealed that all identified suppressor mutations not associated with known mitochondrial division components were localized to PDR1 or PDR3, which encode transcription factors promoting drug resistance. Further studies revealed that at least one, if not all, of these suppressor mutations dominantly increases resistance to known substrates of the pleiotropic drug resistance pathway. Interestingly, hyperactivation of this pathway did not significantly affect mitochondrial shape, suggesting that mitochondrial division was not greatly affected. Our results reveal an intriguing genetic connection between pleiotropic drug resistance and mitochondrial dynamics.