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Publication Metadata only Anomalies in the transcriptional regulatory network of the Yeast Saccharomyces cerevisiae(Elsevier, 2010) N/A; Department of Physics; Tuğrul, Murat; Kabakçıoğlu, Alkan; N/A; Faculty Member; Department of Physics; Graduate School of Sciences and Engineering; College of Sciences; N/A; 49854We investigate the structural and dynamical properties of the transcriptional regulatory network of the Yeast Saccharomyces cerevisiae and compare it with two "unbiased" ensembles: one obtained by reshuffling the edges and the other generated by mimicking the transcriptional regulation mechanism within the cell. Both ensembles reproduce the degree distributions (the first-by construction-exactly and the second approximately), degree-degree correlations and the k-core structure observed in Yeast. An exceptionally large dynamically relevant core network found in Yeast in comparison with the second ensemble points to a strong bias towards a collective organization which is achieved by subtle modifications in the network's degree distributions. We use a Boolean model of regulatory dynamics with various classes of update functions to represent in vivo regulatory interactions. We find that the Yeast's core network has a qualitatively different behavior, accommodating on average multiple attractors unlike typical members of both reference ensembles which converge to a single dominant attractor. Finally, we investigate the robustness of the networks and find that the stability depends strongly on the used function class. The robustness measure is squeezed into a narrower band around the order-chaos boundary when Boolean inputs are required to be nonredundant on each node. However, the difference between the reference models and the Yeast's core is marginal, suggesting that the dynamically stable network elements are located mostly on the peripherals of the regulatory network. Consistently, the statistically significant three-node motifs in the dynamical core of Yeast turn out to be different from and less stable than those found in the full transcriptional regulatory network.Publication Metadata only B-tensor: brain connectome tensor factorization for Alzheimer's disease(Institute of Electrical and Electronics Engineers (IEEE), 2021) Durusoy, Goktekin; Yildirm, Zerrin; Dal, Demet Yuksel; Ulasoglu-Yildiz, Cigdem; Kurt, Elif; Bayir, Gunes; Ozacar, Erhan; Ozarslan, Evren; Demirtas-Tatldede, Asl; Bilgic, Basar; Demiralp, Tamer; Gurvit, Hakan; Acar, Burak; Department of Physics; Kabakçıoğlu, Alkan; Faculty Member; Department of Physics; College of Sciences; 49854AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes to define a low-dimensional space via tensor factorization. We show on a cohort of 47 subjects, spanning the spectrum of dementia, that diagnosis with an accuracy of 77% to 100% is achievable in a 5D connectome space using different structural and functional connectome constructions in a uni-modal and multi-modal fashion. We further show that multi-modal tensor factorization improves the results suggesting complementary information in structure and function. A neurological assessment of the connectivity patterns identified largely agrees with prior knowledge, yet also suggests new associations that may play a role in the disease progress.Publication Metadata only Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins(Public Library Science, 2022) Wojciechowski, Jakub W.; Gasior-Glogowska, Marlena; Coustou, Virginie; Szulc, Natalia; Szefczyk, Monika; Kopaczynska, Marta; Saupe, Sven J.; Dyrka, Witold; Tekoğlu, Tahsin Emirhan; PhD Student; Graduate School of Sciences and Engineering; N/ANLR proteins are intracellular receptors constituting a conserved component of the innate immune system of cellular organisms. In fungi, NLRs are characterized by high diversity of architectures and presence of amyloid signaling. Here, we explore the diverse world of effector and signaling domains of fungal NLRs using state-of-the-art bioinformatic methods including MMseqs2 for fast clustering, probabilistic context-free grammars for sequence analysis, and AlphaFold2 deep neural networks for structure prediction. In addition to substantially improving the overall annotation, especially in basidiomycetes, the study identifies novel domains and reveals the structural similarity of MLKL-related HeLo- and Goodbye-like domains forming the most abundant superfamily of fungal NLR effectors. Moreover, compared to previous studies, we found several times more amyloid motif instances, including novel families, and validated aggregating and prion-forming properties of the most abundant of them in vitro and in vivo. Also, through an extensive in silico search, the NLR-associated amyloid signaling was identified in basidiomycetes. The emerging picture highlights similarities and differences in the NLR architectures and amyloid signaling in ascomycetes, basidiomycetes and other branches of life.Publication Metadata only Microcantilever based disposable viscosity sensor for serum and blood plasma measurements(Academic Press Inc Elsevier Science, 2013) N/A; Department of Mechanical Engineering; Department of Mechanical Engineering; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Çakmak, Onur; Elbüken, Çağlar; Ermek, Erhan; Mostafazadeh, Aref; Barış, İbrahim; Alaca, Burhanettin Erdem; Kavaklı, İbrahim Halil; Ürey, Hakan; PhD Student; Researcher; Faculty Member; Researcher; Teaching Faculty; Faculty Member; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Molecular Biology and Genetics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; College of Engineering; N/A; N/A; N/A; N/A; 111629; 115108; 40319; 8579This paper proposes a novel method for measuring blood plasma and serum viscosity with a microcantilever-based MEMS sensor. MEMS cantilevers are made of electroplated nickel and actuated remotely with magnetic field using an electro-coil. Real-time monitoring of cantilever resonant frequency is performed remotely using diffraction gratings fabricated at the tip of the dynamic cantilevers. Only few nanometer cantilever deflection is sufficient due to interferometric sensitivity of the readout. The resonant frequency of the cantilever is tracked with a phase lock loop (PLL) control circuit. The viscosities of liquid samples are obtained through the measurement of the cantilever's frequency change with respect to a reference measurement taken within a liquid of known viscosity. We performed measurements with glycerol solutions at different temperatures and validated the repeatability of the system by comparing with a reference commercial viscometer. Experimental results are compared with the theoretical predictions based on Sader's theory and agreed reasonably well. Afterwards viscosities of different Fetal Bovine Serum and Bovine Serum Albumin mixtures are measured both at 23 degrees C and 37 degrees C, body temperature. Finally the viscosities of human blood plasma samples taken from healthy donors are measured. The proposed method is capable of measuring viscosities from 0.86 cP to 3.02 cP, which covers human blood plasma viscosity range, with a resolution better than 0.04 cP. The sample volume requirement is less than 150 mu l and can be reduced significantly with optimized cartridge design. Both the actuation and sensing are carried out remotely, which allows for disposable sensor cartridges. (C) 2013 Published by Elsevier Inc.Publication Metadata only Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies(Oxford Univ Press, 2017) Nikolova, Olga; Moser, Russell; Kemp, Christopher; Margolin, Adam A.; Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Motivation: In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. Results: We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.Publication Metadata only Modeling reflex asymmetries with implicit delay differential equations(Elsevier, 1998) Mallet-Paret, J; Department of Mathematics; Atay, Fatihcan; Faculty Member; Department of Mathematics; College of Sciences; 253074Neuromuscular reflexes with time-delayed negative feedback, such as the pupil light reflex, have different rates depending on the direction of movement. This asymmetry is modeled by an implicit first-order delay differential equation in which the value of the rate constant depends on the direction of movement. Stability analyses are presented for the cases when the rate is: (1) an increasing and (2) a decreasing function of the direction of movement. It is shown that the stability of equilibria in these dynamical systems depends on whether the rate constant is a decreasing or increasing function. In particular, when the asymmetry has the shape of an increasing step function, it is possible to have stability which is independent of the value of the time delay or the steepness (i.e., gain) of the negative feedback. (C) 1998 Society for Mathematical Biology.Publication Metadata only On the uniqueness of epidemic models fitting a normalized curve of removed individuals(Springer Heidelberg, 2015) Bilge, Ayse Humeyra; Samanlioglu, Funda; Ergönül, Önder; Faculty Member; School of Medicine; 110398The susceptible-infected-removed (SIR) and the susceptible-exposed-infected-removed (SEIR) epidemic models with constant parameters are adequate for describing the time evolution of seasonal diseases for which available data usually consist of fatality reports. The problems associated with the determination of system parameters starts with the inference of the number of removed individuals from fatality data, because the infection to death period may depend on health care factors. Then, one encounters numerical sensitivity problems for the determination of the system parameters from a correct but noisy representative of the number of removed individuals. Finally as the available data is necessarily a normalized one, the models fitting this data may not be unique. We prove that the parameters of the (SEIR) model cannot be determined from the knowledge of a normalized curve of "Removed" individuals and we show that the proportion of removed individuals, , is invariant under the interchange of the incubation and infection periods and corresponding scalings of the contact rate. On the other hand we prove that the SIR model fitting a normalized curve of removed individuals is unique and we give an implicit relation for the system parameters in terms of the values of and , where is the steady state value of and and are the values of and its derivative at the inflection point of . We use these implicit relations to provide a robust method for the estimation of the system parameters and we apply this procedure to the fatality data for the H1N1 epidemic in the Czech Republic during 2009. We finally discuss the inference of the number of removed individuals from observational data, using a clinical survey conducted at major hospitals in Istanbul, Turkey, during 2009 H1N1 epidemic.Publication Metadata only Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning(Oxford University Press (OUP), 2019) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Dereli, Onur; Oğuz, Ceyda; Gönen, Mehmet; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 6033; 237468Motivation: Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results: We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used).Publication Metadata only Prediction of allosteric communication pathways in proteins(2022) Haliloğlu, Türkan; Hacısüleyman, Aysima; Department of Chemical and Biological Engineering; Erman, Burak; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 179997Motivation Allostery in proteins is an essential phenomenon in biological processes. In this article, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form. Results Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large (Bcl-xL), Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase (DHFR), HRas GTPase and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or pre-existence of some other functional states. Our model is computationally fast and simple and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality. Supplementary information are available at Bioinformatics online.Publication Metadata only Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces(Oxford Univ Press, 2005) N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Aytuna, Ali Selim; Gürsoy, Attila; Keskin, Özlem; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605Motivation: Elucidation of the full network of protein- protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Results: Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed.