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Publication Metadata only Mathematical modeling of Behçet's disease: a dynamical systems approach(World Scientific Publ Co Pte Ltd, 2015) Gül, Ahmet; N/A; N/A; Department of Chemical and Biological Engineering; Department of Electrical and Electronics Engineering; Erdem, Cemal; Bozkurt, Yasemin; Erman, Burak; Demir, Alper; Master Student; PhD Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Electrical and Electronics Engineering; N/A; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 179997; 3756Behcet's Disease (BD) is a multi-systemic, auto-inflammatory disorder that is characterized by recurrent episodes of inflammatory manifestations affecting skin, mucosa, eyes, blood vessels, joints and several other organs. BD is classified as a multifactorial disease with an important contribution of genetics. Genetic studies suggest that there is a strong association of BD with a Class I major histocompatibility complex antigen, named HLA-B*51, along with several other weaker associations with genes encoding proteins involved in inflammation. However, pathogenic mechanisms associated with these genetic variations and their interactions with the environment have not been elucidated yet. In this paper, we present a mathematical model for BD based on a dynamical systems perspective that captures especially the relapsing nature of the disease. We propose a disease progression mechanism and construct a model, in the form of coupled ordinary differential equations (ODEs), which reveals the occurrence pattern of the disease in the population. According to our model, the disease has three distinct modes describing different phenotypes of people carrying HLA-B*51 tissue antigen, namely, the Healthy Carrier, the Potential Patient and the Active Patient. We herein present an exemplary mathematical model for BD, for the first time in the literature, that concisely captures the actions of many cell types together with genetic and environmental effects. The proposed model provides insight into this complex inflammatory disease which may lead to identification of new tools for its treatment and prevention.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 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.