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Probabilistic three-dimensional fracture mechanics and its applications using FCPAS
Cracks can be seen in many engineering structures. It is important to determine the mechanical strength and life of the cracked structure or design a mechanical part with a damage tolerance approach. It is hard to determine the remaining life of machine parts exactly, since they include some uncertainties and variations in governing parameters of the problem, such as geometric dimensions and the variability of material properties and loading conditions. Therefore, for such problems, crack growth lives must be estimated by means of probabilistic approaches considering the variables that affect lives. In this study, for three-dimensional fatigue crack growth problems, a probabilistic crack growth life estimation procedure, which also involves Monte Carlo Simulations, was developed and validated by controlled laboratory experiments. The uncertainty in material properties affecting fatigue crack propagation life was determined using standard Compact Tension (CT) specimens machined from 7075-T6 aluminium alloy. Fatigue crack growth models for constant or variable amplitude loading in the literature were investigated and an improved model has been proposed. The data obtained from two-dimensional crack propagation tests were used in three-dimensional crack propagation simulations. A non-standard specimen made from Aluminium 7075-T6 has been designed for three-dimensional mode-I fatigue surface crack growth tests. Surface crack growth experiments under constant and variable amplitude loads were conducted using this specimen. Fatigue crack growth simulations were also carried out by considering the geometric tolerances of the specimen, the scatter of the fatigue crack growth-related material properties and the variability in loading. Experimental results were compared with simulations for different crack growth models, allowing validation of the proposed probabilistic fatigue crack growth methodology.
DMA verilerinin viskoelastik ana eğriye dönüştürülmesi ve Prony serisi bulunması
This thesis details the process of converting Dynamic Mechanical Analysis (DMA) data of a viscoelastic material at different temperatures into a master curve. The methodology involves shifting DMA data based on the Williams-Landel-Ferry (WLF) universal constants and iteratively adjusting the shift to construct a continuous master curve. A Prony series is then fitted to the master curve, providing insights into the relaxation modulus at various temperatures. For the iteration a numerical method has developed using MATLAB. This process involves a comparison between the numerical iteration and the manual shifting using excel which is a criterion for the numerical iteration. Fitting Prony series is executed using MATLAB algorithms, for this instance Least Squares Algorithm is used. The developed numerical method is verified by comparing the results to those predicted from a Finite Element Analysis of a brain tissue subjected to compression loading.
Farklı fiber konfigürasyonları için biyouyumlu mikroakışkan yapay akciğerlerin hesaplamalı pulsatil akış ve verimlilik analizi
Average-sized microfluidic artificial lungs consisting of rows and columns of fiber bundles with the different column to row aspect ratios (AR) are numerically analyzed for flow characteristics, maximum gas transfer performance, minimum pressure drop, and proper wall shear stress (WSS) values in terms of biocompatibility. The flow is fully laminar and assumed to be incompressible. The problem is solved with both Newtonian and Non-Newtonian Carreau models. The transport analysis is performed using a combined convection-diffusion model, and the numerical simulations are carried out with the finite element method. The inlet volumetric flow is modeled as a sinusoidal wave function to simulate the cardiac cycle and its effect on the device performance. The model is first validated with experimental studies in steady-state condition and compared with existing correlations for transient conditions. Then, the validated model is used for a parametric study in both steady and pulsatile flow conditions. The results show that increasing the aspect ratio in fiber configuration leads to converging gas transfer rate, higher pressure drop, and higher WSS. While determining the optimum configuration, the acceptable shear stress levels play a decisive role to ensure biocompatibility. Also, it is observed that the steady analysis underestimates the gas transfer for higher aspect ratios. The Newtonian model finds the pressure drop and shear stress values less than the Carreau model. In contrast, the oxygen transfer performance observed in the Newtonian model is overestimated approximately by 5\% compared to the Carreau model predictions.
Elektrik çift tabaka kapasitörlerinin kinetik ve denge özelliklerinin incelenmesi
The growing demand for efficient and scalable energy storage solutions has driven significant advancements in supercapacitor technology. This study investigates the kinetic and structural equilibrium characteristics of Electrical Double-Layer Capacitors (EDLCs) using molecular dynamics (MD) simulations to examine the effects of pore size, solvent dipole moment, and applied voltage on their performance. Twenty distinct systems were designed by combining two pore sizes (7.78 Å and 14.75 Å), five solvent dipole moments (ranging from 0.91 D to 6.18 D), and two applied voltages (1 V and 2 V). The objective is to elucidate how these parameters interact to optimize the energy and power densities of EDLCs. The results indicate a complex and sensitive relationship between pore size, electrode voltage, and capacitance. For systems with a 14.75 Å pore size, a positive correlation was observed between solvent dipole moment and capacitance across all dipole moments. However, in narrower pores (7.78 Å), this correlation reversed beyond a dipole moment of 2.88 D, revealing a nuanced dependence of capacitance on solvent polarity. Additionally, electrolyte behavior within the pores exhibited distinct spatial distributions: narrower pores led to denser ion layering, whereas wider pores facilitated more uniform ion dispersal. These findings provide deeper insights into EDLC dynamics, offering valuable guidelines for optimizing the design of next-generation supercapacitors.
Zaman serisi analizini etkili motif keşfi ile geliştirme ve motif keşfinin tahmin uygulamalarına entegre edilmesi
Time series motif discovery is a powerful technique for identifying recurring patterns in sequential data, offering valuable insights into diverse applications such as finance, healthcare, and climate science. This thesis presents an innovative framework for motif discovery in time series data, addressing key challenges in identifying recurring patterns of varying lengths and instances. The proposed methodology improves sensitivity and accuracy of motif detection by using Binary Integer Programming (BIP) for optimal motif selection and employing a heuristic algorithm for efficient pattern discovery, while integrating a dynamic feedback mechanism to refine parameters. Additionally, the study demonstrates how the discovered motifs can be applied to forecasting tasks by integrating them into a Transformer model pipeline augmented with motif embeddings, assessing whether motif-augmented predictions outperform simpler baselines. Extensive experiments on real-world financial datasets reveal that while the identified motifs can effectively capture recurring structures, incorporating them into advanced forecasting models does not necessarily lead to improved predictive performance. In particular, Transformers with or without motif embeddings underperform relative to basic econometric techniques. These findings underscore the complexity of leveraging motif information within deep learning frameworks for time series forecasting, suggesting that although motif discovery is valuable for understanding temporal patterns, more targeted modeling strategies or alternative machine learning approaches may be necessary to realize its full potential in predictive tasks.
Yüksek uzamsal-zamansal çözünürlüklü i̇kli̇m veri̇si̇ i̇le hi̇droloji̇k modelleme : Bozkurt (Kastamonu) örneği̇
This thesis integrates convection-permitting climate modeling with catchment-scale hydrological simulation to analyze the 2021 flash flood in Bozkurt, Kastamonu, and assess future flood risk under climate change. High-resolution precipitation and temperature data were produced using RegCM5 with a non-hydrostatic core, and used to drive the SWAT model in the Ezine Stream basin. Flash floods, triggered by short-duration intense rainfall and steep terrain, are among the most destructive hydrometeorological hazards. Bozkurt was selected due to its topography and vulnerability, with the August 2021 flood used as the reference event. The study aims to (1) simulate the 2021 flood, (2) evaluate runoff and streamflow under current and future conditions, and (3) assess flash flood potential. Results show that RegCM5–MOLOCH can capture convective rainfall patterns when configured with high resolution and proper domain setup, though some timing and spatial shifts occurred. SWAT was useful for general runoff trends but limited in flash flood timing. Future projections indicate an increase in short, intense rainfall events. The integrated modeling approach contributes to understanding local flood risks and supports future preparedness.
Karadeniz kıyılarında gözlenen yağış ekstremleri ve öngörülen etkileri
Climate change has increased extreme precipitation events worldwide, leading to significant economic losses in Black Sea coastal countries (Bulgaria, Georgia, Romania, Russia, Türkiye, and Ukraine). This study investigates the relationship between the return periods of such events (2000–2024) and the resulting economic losses, while also projecting future impacts. Strong and significant relationships were found for Türkiye, Bulgaria, and Romania, while analyses for Russia and Ukraine were limited due to data gaps. For future projections, extreme precipitation trends for 2030–2060 were evaluated using the MPI-ESM1-2-LR climate model under the SSP5-8.5 scenario (CMIP6). Even small increases in rainfall are shown to cause significant economic consequences. The findings provide a foundation for disaster management, infrastructure planning, and regional climate adaptation policies
Comparison of the national-contributed emissions with the climate trace's global emissions inventory
Accurate and transparent greenhouse gas (GHG) inventories are vital for tracking global climate commitments. With advances in satellite technologies and AI-integrated platforms, alternative inventories like Climate TRACE have emerged as complements or replacements for traditional systems such as the UNFCCC. This study compares GHG emissions reported by Climate TRACE and UNFCCC country submissions for 2015–2021. Eight countries—Australia, Belgium, Brazil, Germany, Japan, Poland, Turkey, and the United States—were selected for their data availability and regional diversity. Percentage differences between the two datasets were calculated for 13 sectors, and methodological consistencies were assessed using Principal Component Analysis (PCA) and K-means clustering. A heatmap visualization illustrated percentage differences across sub-sectors, highlighting areas of agreement and divergence. Results show substantial differences across most countries and sectors, with Climate TRACE often reporting higher emissions. The Waste sector and certain LULUCF sub-sectors had the largest discrepancies, largely due to methodological, scope, and data granularity differences. Climate TRACE's satellite- and AI-based methods—such as flaring inefficiency models, wastewater plant detection, and forest degradation monitoring—contributed to these gaps. Conversely, sectors like Enteric Fermentation in some countries (e.g., Belgium) showed higher alignment where Climate TRACE applied standard IPCC methodologies. The findings underscore how methodological heterogeneity—between and within inventories—affects comparability, highlighting the need for transparent methods and consistent sectoral definitions to improve global climate accountability.
Büyük depremleri takip eden artçı şok örüntülerinin derin öğrenme ile modellenmesi
This thesis presents data-driven modeling strategies for predicting the spatial distribution, number, and magnitude of aftershocks following major earthquakes. To overcome the limitations of classical physics-based approaches, alternative model structures were developed for both classification and regression tasks using machine learning methods such as artificial neural networks, the XGBoost algorithm, and kernel-based probabilistic modeling. The study systematically evaluated various input combinations—including static components derived from stress tensors, neighborhood information, gradient-based derivative quantities, spatial coordinates, and distance to the epicenter—and additionally applied oversampling and undersampling strategies to mitigate class imbalance, while employing kernel-based continuous labeling to model the spatial continuity of aftershock probabilities. Through this comprehensive experimental analysis, the prediction performances of aftershock occurrence, count, and magnitude were compared across different data structures and modeling strategies, demonstrating the potential of stress-based machine learning models in aftershock forecasting. The findings indicate that kernel-based probabilistic models provide a more realistic and spatially coherent representation of aftershock regions, XGBoost enhances discrimination capability in classification tasks, and multilayer neural networks yield more balanced performance in numerical regression tasks. Overall, the results show that static features derived from stress tensors can successfully capture large-scale spatial patterns of aftershock behavior, yet modeling fine-scale variability will require richer temporal and geological datasets in future research.
Melanopsin ve cryptochrome proteinleri arasındaki etkileşimin hesaplamalı araştırması
Circadian rhythms are oscillations in the biochemical, physiological, and behavioralfunctions of organisms that occur with a periodicity of approximately 24 hours. Inmammals, circadian rhythm is generated by a molecular clock. The molecular clock,which is located at suprachiasmatic nuclei (SCN) part of brain, is synchronized byenvironmental light-dark cycle.The cryptochromes are the mammalian circadian photoreceptors; they absorb light andtransmit the signal to the molecular clock. The cryptochromes and melanopsin (andpossibly other opsin family pigments) have been proposed as circadian photoreceptorpigments that exist in the inner retina. Experimental studies imply that there is mostprobably an interaction between melanopsin and cryptochromes for molecular clock tofunction normally. In order to uncover this interaction; the tertiary structures ofMelanopsin and Cryptochrome; the possible interaction between those two proteins havebeen predicted by usage of different computational means.The results of this study imply that mammalian Melanopsin and Cryptochrome proteinsinteract. The N-termini of Cryptchrome protein interacts with C-termini and cytoplasmictails of Melanopsin protein. The in vivo interaction is supported by preliminaryflourescent microscopy technique.
İki boyutlu kümelemeye dayalı analizle göğüs kanseri ifade davranışındaki işlevsel ve zamansal ilişkilerin açığa çıkarılması
The emerging microarray technology has useful applications in cancer research.Reports detailing the expression profiles of various types of cancers have pointed to theutility of this approach in defining cancer classes and subclasses with distinct molecularconfigurations and clinical behaviour. Another crucial application of microarrays tocancer research is the detection and selection of diagnostic marker genes. Thesemolecular markers give valuable additional information about tumour diagnosis,prognosis and therapy development . In the case of breast cancer, estrogen receptor (ER)and progesterone receptor (PR) have been used as prognostic markers in clinicalmanagement of breast cancer patients. Patients with ER negative breast tumors have apoorer prognosis than patients with ER positive tumors. Expression profiling of themammary gland to identify tumor associated genes differentially expressed in breasttumors regarding the presence or absence of ER and PR lead to the identification ofgenes, the function of some of which is unknown. However most of these previousstudies uses clustering methods which focus on the global pattern seen in the expressionprofiles. On the other hand, in this study, we used biclustering, which captures the localexpression changes better to improve the understanding on the molecular mechanismswhich underlie the response to estrogen in breast cancer cells. In addition, functionalannotation enrichment, pathway information, transcriptional factor binding site analysiswere also utilized. The results show that estrogen responsive genes act in groups of genesor the so-called `modules? which follow an order in time. Furthermore the temporal orderis associated with a biologically very meaningful functional order, which is supported bythe transcription factor analysis. The pathway analysis indicates that estrogen isresponsible for eliciting mechanisms required for tumorigenesis. All these results showthat biclustering if very useful in the analysis of time series data and it is capable ofelucidating the underlying functional and temporal relationship between genes bettersince it catches local expression behaviours better than the global ones.Advisor: Yar. Doç. Dr. Özlem Keskin Date: 16/11/2006Co-advisor: Doç. Dr. Attila Gürsoy Date: 16/11/2006Director: Prof. Dr. Süleyman Özekici Date: 16/11/2006
Eşler arası epidemik içerik dağıtım protokol dizaynı ve performans değerlendirmesi
Peer-to-peer (P2P) cooperative systems are becoming extremely popular as they finddiverse applications. One major application area is the content distribution over large-scale networks. As the usage of the Internet grows up, the number of large contents suchas software packages and popular movie files, and also the user population retrievingthese contents increase exponentially.In this thesis, we propose and design a peer-to-peer system, SeCond, addressing thedistribution of large sized content to a large number of end systems in an efficient andeffective manner. In contrast to prior work, it employs an epidemic dissemination schemefor state propagation of available blocks and initiation of block transmissions. In order tosupport heterogeneity of peers, ease of deployment, scalability, and adaptivity to dynamicpeer arrivals/departures, and also to increase the utilization of the system resources, wepropose mechanisms such as adjusting protocol parameters according to the bandwidthusages dynamically. We describe our protocol SeCond and its discrete event simulationmodel. A comprehensive performance evaluation has been accomplished for a wide rangeof scenarios. A well known and widely used P2P content distribution system isBitTorrent which we also model and compare as a benchmark. Performance resultsinclude scalability analysis for different arrival/departure patterns, flash-crowd scenario,overhead analysis, and fairness ratio. The major metrics we study include the average filedownload time, load on the primary seed, uplink/downlink utilization, communicationoverhead, and the fairness ratio. SeCond peers download the file faster compared toBitTorrent peers for most of the scenarios and the protocol is as fair as BitTorrentalthough it has no explicit strategy addressing free-riding. We show that SeCond is ascalable and adaptive protocol which takes the heterogeneity of the peers into account.We also illustrate the applicability of an analytical fluid model to the behavior ofSeCond.
Gen düzenlemesi ağlarının yapısı ve dinamiği
The structure and dynamics of a typical biological system are complex dueto strong and inhomogeneous interactions between its constituents. The investigation of such systems with classical mathematical tools, such as differential equations for their dynamics, is not always suitable. The graph theoretical models may serve as a rough but powerful tool in such cases.In this thesis, I first consider the network modeling for the representation of the biological systems. Both the topological and dynamical investigation tools are developed and applied to the various model networks. In particular, the attractor features' scaling with system size and distributions are explored for model networks. Moreover, the theoretical robustness expressions are discussed and computational studies are done for confirmation.The main biological research in this thesis is to investigate the transcriptional regulation of gene expression with synchronously and deterministically updated Boolean network models. I explore the attractor structure and the robustness of the known interaction network of the yeast, Saccharomyces Cerevisiae and compare with the model networks. Furthermore, I discuss a recent model claiming a possible root to the topology of the yeast's gene regulation network and investigate this model dynamically.The thesis also included another study which investigates a relation between folding kinetics with a new network representation, namely, the incompatibility network of a protein's native structure. I showed that the conventional topological aspects of these networks are not statistically correlated with the phi-values, for the limited data that is available.
İklim görselleştirmesinde sebep-sonuç ilişkisinin dokunma hissi kullanılarak aktarılması
We investigate the potential role of haptics in augmenting the visualization of climate data. In existing approaches to climate visualization, different dimensions of climate data such as temperature, humidity, wind, precipitation, and cloud water are typically represented using different visual markers and dimensions such as color, size, intensity, and orientation. Since the number of dimensions in climate data is large and climate data needs to be represented in connection with the topography, purely visual representations typically overwhelm users. Rather than overloading the visual channel, we investigate an alternative approach in which some of the climate information is displayed through the haptic channel in order to alleviate the perceptual and cognitive load of the user. In this approach, haptic feedback is further used to provide guidance while exploring climate data in order to enable natural and intuitive learning of cause and effect relationships between climate variables. As the user explores the climate data interactively under the guidance of wind forces displayed by a haptic device, we believe that she/he can understand better the occurrence of events such as cloud and rain formation and the effect of climate variables on these events. We designed a set of experiments to demonstrate the effectiveness of this multimodal approach. Our experiments with 22 human subjects show that haptic feedback significantly improves the understanding of climate data and the cause and effect relations between climate variables as well as the interpretation of the variations in climate due to changes in terrain.
Protein dönme açılarını tahmin etmek için bilgi tabanlı yöntem
The three dimensional structure of a protein can be identified in terms of its torsion angles. These torsion angles can be considered as the degrees of freedom of a protein. In this study, a method grouping these torsion angles in different rotational isomeric states and estimating their probabilities is developed. Specifically, the probabilities of the various torsion angle states in Ramachandran maps is proposed and the accuracy of the method is examined using a knowledge based approach. Statistical independence and dependence of the states of different residues along the peptide chain are analyzed. The Flory isolated pair hypothesis, near neighbor correlations, context effects and long-range correlations are discussed. In the knowledge based approach, two different protein libraries i) coil library ii) full libarary are constructed and information from both these libraries is used. Results showed that amino acids have propensities for some rotational isomeric states that favor the choice of the native state torsion angles and they are context dependent, preferring different torsion states determined by the amino acid sequence of the protein. Context dependency is also related to chameleon sequences and the effect of chameleon sequences is also integrated into the method.
Protein-protein arayüzeylerinin nitelendirilmesi ve analizi
The diverse range of cellular functions is performed by the limited number of protein folds existing in nature. One may similarly expect that the number of protein-protein interface architectures would also be restricted. In this study, the recently derived dataset of protein-protein interfaces is analyzed and compared with older datasets to address questions like (i) how many different protein-protein interaction types are expected to exist in nature for necessary biological diversity; (ii) what fraction of interactions is already known toward elucidation of the organization of the cell; and (iii) whether the increase in the number of interface architectures and consequently the functional coverage and interaction map of the PDB are reaching a plateau. The results show that number of protein interfaces increases at a much faster rate compared to the number of folds and is not yet to level off. Functional coverage is also found to steadily increase. As an estimation of this study, the total number of different interfaces will be around 8000 and it will take almost 30 years to discover at the current rate of experiment. Also, despite the diversity of interface architectures, some are more favorable and frequently used, and of particular interest, those are the ones which are also preferred in single chains. Another significant result is that some species, especially eukaryotes, prefer intra chain domain-domain interactions; others, less complex organisms, prefer inter chain interaction. This adaptation may be the result of the crowded traffic in the eukaryotic cells. This thesis presents the multidirectional analysis and applications of the protein ? protein interfaces. We believe in that the dataset of protein ? protein interfaces is a rich source for researchers dealing with protein ? protein interactions, protein recognition mechanisms, drug design etc.
Doğal halde olmayan peptitlerin konformasyonlari: Kabataslak model gösterimi
The native structure of proteins is stabilized by both local and non-local interactions. The available phase space is highly reduced in size due to the local interactions, but non-local interactions determine the final, physiologically active native structure. Although it is wellknown that these two types of interactions are the key factors in determining the tertiary structure, their relative contributions is open for debate. This study will lay the groundwork for the investigation of relative contributions of these interactions. The contribution of both local and non-local neighbors to the Ramachandran map of the residue in question is examined by using statistical weight matrices (U) constructed according to the Markov assumption. An efficient matrix multiplication scheme based on rotational isomeric states model is introduced for studying realistic conformations of homotripeptides of all-alanine, tryptophan, valine, and tyrosine and AXA tripeptides, where X represents alanine, valine, tryptophan, and tyrosine in the unfolded state. This scheme is based on U?s obtained from mono and dipeptide molecular dynamics simulations. By using these matrices one can obtain the Ramachandran map of the central residue of longer sequences, such as tripeptides. Comparison of explicit tripeptide simulations with the Markov model shows that the Markov assumption fails to capture interactions specific to the tripeptide. Here, a systematic correction is proposed for efficient calculation of realistic protein conformations. Preliminary results suggest that the Markov assumption can be improved significantly by adding the contributions from hydrogen bonds, which are only present in the tripeptide sequences. Such a coarse-grained model, Modified Markov model, will help elucidate the protein folding problem and improve secondary structure prediction algorithms.
Güvenilir içerik dağıtımı için yeni bir ara bellek yönetim modelinin tasarım ve analizi
For supporting reliability in distributed content dissemination services, message loss recovery mechanism achieved via efficient buffer management is an indispensable component. The available approaches for buffer management concentrate on several aspects of the problem such as flow control, reducing the memory usage, providing message stability and the replacement of buffer items. In this thesis study, we consider buffer management problem in support of large-scale bio-inspired peer-to-peer data dissemination services. Bio-inspired epidemic protocols have considerable benefits as they are robust against network failures, scalable and provide probabilistic reliability guarantees. Coupled with an efficient buffering mechanism, system wide buffer usage can be optimized while providing reliability and scalability in such protocols. We propose a novel algorithm, Stepwise Fair-share Buffering, that is shown to provide uniform load distribution in comparison to earlier approaches and reduces the overall buffer usage where every peer has the partial view of the system. A major aim of our approach is to be able to choose bufferers uniformly throughout the system so that the load of buffering will be well balanced among participating peers and the efficiency of content dissemination will be improved as a result. This also reduces the memory usage since only a small subset of the peers is chosen as bufferers for each message. Furthermore, it is applicable to large-scale scenarios, provides reliable delivery and is adaptable to dynamic join and leaves to the system. It adjusts the buffer size to achieve message stability with a high probability. Performance evaluation of the buffering model and extensive comparisons with earlier approaches are performed. The evaluations include scalability, reliability, adaptivity to failures and uniformity analysis. We also derive analytical results for reliability of dissemination as a function of buffer levels. These results are based on a Markov chain analysis and are evaluated numerically. Comparison with simulations shows that they provide a good lower bound for reliability. For high level of reliability values, the bounds are very close to the simulation results.
Yapıya-dayalı ilaç dizayn yöntemi ile prostat kanserine tedavisine yönelik ilaç geliştirilmesi
Structure-Based Drug Design is a powerful method for designing inhibitors with high specificity. This method can be used for diseases where a single biochemical or structural function can be targeted for treatment. Structure-Based Drug Design has been successful in developing drug for several diseases involving hypertension, influenza, cancer and AIDS.In this thesis, the objective is to design novel inhibitors for the treatment of prostate cancer using structure-based drug design approach. Prostate Cancer is the most common malignancy among males. There are several treatment procedures for prostate cancer. However, many cases are left untreated due to severe side effects and resulting decrease in quality of life. Nonetheless, inhibition of a single enzyme, CYP17, offers a specific treatment possibility with very low risk of side effects. Molecular dynamics methods are used to validate the model structure of CYP17. Computational tools for molecular docking are employed for discovering and developing novel agents inhibiting CYP17. Experimental approaches are followed for testing biological activity and toxicity of these molecules.
Protein ikincil yapılarını optimal olarak katlayan amino asit çiftleri arasındaki potensiyellerin hesaplanması
We present a method to calculate the pair potentials for folding of protein secondary structures. In the first part of the method necessary training data are generated to compute the potentials. For this purpose a Go-type model and dynamic optimization is used to compute the optimal folding trajectories. A coarse-grained model for a helix and a beta sheet, each consisting of 12 residues has been constructed by representing each amino acid as a bead. The dynamic optimization gives the total optimal force acting on each residue (bead) to fold the protein from an initial configuration to its native state. Next, forces between pairs of residues are derived from this data. This is done by first projecting the optimal residue forces onto the pair-wise directions between residues and expressing these mean forces as (nonlinear) functions of pair-wise distances. We show how to compute the forces between pairs from the mean forces. We next incorporate the derived pair forces into the dynamic model. Thus, for new initial conditions folding is achieved in a predictive way by simulating this model without any need for optimization. We further show that the folding pathways obtained by such ?simple? simulation are similar to folding pathways which can be obtained by the rigorous dynamic optimization. To measure similarity between folds we use MPCA (Multi-way Principal Component Analysis). In addition, mean forces between pairs are presented and analyze
Doğrusal olmayan viskoelestik nesnelerin deneysel veriye dayalı parçacık tabanlı modellenmesi
Simulation-based training using Virtual Reality techniques is a promisingalternative to traditional training in minimally invasive surgery. Surgical simulators let thetrainee touch, feel, and manipulate virtual tissues and organs through the same surgical toolhandles used in actual minimally invasive surgery while viewing images of tool-tissueinteractions on a monitor as in real laparoscopic procedures. Developing realistic organforcemodels for simulating soft-tissue behavior is an integral part of a surgical simulator.The particle system approach provides a better solution than mesh-based methods tothe topological changes encountered in simulation of surgical cutting and tearing. Inaddition, they are computationally less expensive and easier to implement than the meshbasedmethods. However, the material coefficients of each individual mesh element shouldbe calculated and fine tuned to integrate the realistic tissue properties into particle models,which is not trivial.This thesis presents an end-to-end solution to realistic particle-based simulation ofnonlinear viscoelastic tissue behavior based on the experimental data collected by a roboticindenter. First, the strain-dependent nonlinear elastic response and time-dependentviscoelastic response of a tissue-like silicon phantom is measured via static loading andstress relaxation experiments performed by a robotic indenter. The collected experimentaldata is used to construct a lumped model of the tissue phantom represented by a nonlinearviscoelastic Maxwell Solid. Then, a 3-dimensional particle-based network is developed tomimic the behavior of the lumped Maxwell model. The material coefficients of theindividual Maxwell elements connecting the particles are estimated through a set of noveloptimization algorithms.
Yapısal bilgiyi kullanarak insan protein-protein etkileşim ağının ve kanser proteinlerinin analizi
Protein-protein interaction networks reveal that some proteins are highly connectedto others (acting as hub proteins), whereas some others have a few interactions. Thesame or overlapping binding sites should be repeatedly used in hub proteins (singleinterface hub proteins) making them promiscuous. Alternatively, multi-interface hubproteins make use of several distinct binding sites to bind to different partners.Understanding the interactions with respect to their physical and chemical propertiesrequires the atomistic details of the proteins, namely the three-dimensional structures.Then again, cancer-related proteins are more likely to act as hubs in interactionnetworks. In this thesis, we investigate ?what features of cancer-related proteininterfaces make them act as hubs? and ?how it is possible for them to bind to manydifferent proteins with varying affinity?. We provide a detailed analysis of humanprotein-protein interaction network including cancer-related interactions. First weanalyze the global behavior of cancer-related proteins, second we hold a structuralperspective to elucidate how these proteins interact and figure out which interactionscan occur simultaneously and which ones exclude each other. The results reveal thatcancer-related proteins tend to interact with their partners through distinct interfaces,thus corresponding mostly to multi-interface hubs (56% of cancer-related proteins aremulti-interface) and constituting the nodes with higher essentiality in the network (76%of them are essential). In addition, they have smaller, more planar, more charged andless hydrophobic binding sites compared to non-cancer ones which may indicate lowaffinity and high specificity of the cancer-related interactions. These findings might beimportant in obtaining new targets in cancer as well as finding the details of specificbinding regions of putative drug candidates in cancer.
Afet sonrasında ihtiyaç duyulacak yardım malzemesi için stoklama kararları
Natural disasters are unexpected crisis events causing devastating human and financial losses. These events trigger a critical need for effective preparedness, mitigation, response and recovery operations to reduce the impact of the disasters.Humanitarian relief agencies participate in massive relief efforts to provide life-supporting resources, such as food, water, sanitation, emergency care, shelter and other essential non-food items; to distribute supplies and to coordinate international aid after a disaster. The success of such large scale and time-critical operations require effective pre-disaster planning.In this thesis, we consider stocking decisions for humanitarian relief agencies that provide emergency relief items to people affected by natural disasters. We present a mathematical model to determine the optimum stocking quantity for one type of relief commodity. The proposed model is an extension of the well-studied newsvendor model that incorporates the disaster risk. The probability that a disaster takes place within the lifetime of the stocked commodity and the demand distribution for this commodity under such a disaster are taken into consideration in the model. We extend our model to determine the optimum stocking quantities for two agencies that stock the same commodity at different locations prone to differing disaster risk, and that work in full cooperation, just like the case of the Turkish Red Crescent and the International Federation of Red Cross and Red Crescent agencies. An equilibrium solution to the model that can be calculated numerically is derived. We investigate the characteristics of the solution under various parameter settings and identify cases where cooperation is beneficial to one or both of the agencies. We analyze the case for Istanbul to demonstrate the use of this modeling approach. The probability of a major earthquake occurrence in Istanbul and the potential demand for relief commodities throughout the city are estimated and used as inputs of the model. A numerical analysis gives insights to the potential benefits arising from cooperation of an agency in Istanbul with an outside agency. Our analysis with respect to realistic scenarios and parameter estimations provides useful guidelines for the relief agencies in Istanbul.
JMJD2A enziminin metilasyon spesifisitesinin karşılaştırmalı moleküler dinamik çalışması
Specific patterns of post-translational modifications of histones act as a molecular ?code? recognized and used by non-histone proteins to regulate specific chromatin functions. K9 methylation on Histone 3 (H3) tail, mainly trimethylation, induces formation of constitutive heterochromatin via a well-known pathway, which employs heterochromatin formation protein (HP1) and DNA methyl transferase (DNMT). Jumonji domain containing 2A (JMJD2A) is a histone demethylase that specifically removes K9 and K36 trimethyl marks on H3 tail. This enzyme does not function on monomethyl marks and has almost 20-fold reduced activity on dimethyl forms compared to trimethyl forms.In order to gain insight into how JMJD2A discriminates between its substrates, we performed molecular dynamics simulations of mono-, di- and trimethylated histone tails in complex with JMJD2A catalytic domain and analyzed positional fluctuations, located the hydrogen bonds and calculated some critical distances. We revealed the importance of water molecules and the oxygen-enclosed environment in appropriate orientation of methylammonium head in the active site. We also calculated binding free energy and energy contribution of each residue. We found out that recognition is mostly driven by van der Waals and Coulombic interactions in enzyme-substrate interface. We also revealed the role of Arg8 on the H3 tail in binding and stabilizing the necessary conformation of substrate peptide.
Metillenmiş histon peptitlerinin JMJD2A enziminin bitişik tudor domenleri tarafından moleküler düzeyde tanınmaları
In this thesis, we report a detailed molecular dynamics simulation and MM-PBSA/GBSA approach (MM: Molecular Mechanics; PB: Poisson Boltzmann; GB: Generalized Born; SA: Surface Area) analysis, unraveling the recognition of the methylated histone tails H3K4me3, H4K20me3, H4K20me2 and H3K9me3 by JMJD2A-tudor. In this respect, 25 ns fully unrestrained molecular dynamics simulations were conducted for each of the bound and free structures. We investigated the important hydrogen bonds and coulombic interactions between the tudor domains and the peptide molecules; hence unveiled critical residues occupied in stabilizing the complexes. Normal mode and molecular mechanics calculations were performed to obtain the entropic determinants of the binding affinities. Suggested by the resulting binding free energies obtained via GB and PB approaches, we found that H4K20me3 peptide has the highest affinity to JMJD2A-tudor in GB calculations whereas H4K20me2 peptide has the highest affinity to JMJD2A-tudor in PB calculations. Furthermore, we discerned that H3K9me3 peptide has the lowest affinity to JMJD2A-tudor in both of the model calculations. We also revealed that while H4K20me2 peptide adopting the same binding mode with H4K20me3 peptide, H3K9me3 peptide adopts the same binding mode with H3K4me3 peptide. Decomposition of the enthalpic and the entropic contributions to the binding free energies indicated that the recognition of the histone peptides is mainly driven by favourable van der Waals interactions in both GB and PB models. Based on GB calculations pairwise and per residue decomposition of the binding free energies with backbone and sidechain contributions as well as their energetic constituents were also carried out to identify the hotspots of the structures. Thus, the van der Waals and the electrostatic interactions which are prominent for the recognition of the peptides were clarified.
Histon kuyruğunda bulunan lizin amino asitlerinin demetilasyonuna yönelik kuantum mekanik (KK) ve melez kuantum mekanik/moleküler mekanik (MM) yöntemleri kullanarak reaksiyon mekanizması analizi
Chromatins, the basic structural units of the genetic material, consist of DNA and histone proteins. Eukaryotic DNA is wrapped around the histone proteins and they form `bead-like? structures. Histone proteins control many crucial cell regulatory processes, e.g. gene transcription, gene silencing, DNA replication and repair and etc., via post-translational modifications. Among the post-translational modifications, methylation was recently shown to be reversible by the discovery of Lysine-Specific Demethylase (LSD1) enzyme. As many previous studies have shown the relation of some cancer types and other diseases with the abnormalities in the balance of methylation/demethylation, drug molecule design based on the information gained from reaction mechanism studies becomes very crucial for the fight against these diseases.In this thesis, a chemically-reliable reaction mechanism is proposed for the demethylation of histone tail lysine residues and the reaction path analysis of this mechanism is carried out. Specifically, demethylation of H3 tail fourth lysine residue, i.e. H3K4, is analyzed. Potential and free energy profiles as well as structural properties are calculated using available Quantum Mechanical (QM), i.e. PM3, B3LYP and MP2, Molecular Mechanical (MM), i.e. UFF, as well as the hybrid QM/MM methods, i.e. ONIOM, which are implemented in Gaussian09 software package.As the result of the calculations, it is proved that the proposed chemical mechanism is actually simulating the real-life process and suitable for the demethylation of mono- and dimethylated lysines found on the histone tails. This comment is based on the calculated reaction rates, which are in high agreement with the experimental observations. Besides, some important observations made on the chemical mechanism, which enhances the understanding of how the demethylation process occurs at the molecular level, are explained and discussed in the thesis in fine details.These results offer an understanding for the details of the reaction mechanism, which will form a fundamental knowledge basis for further studies involving inhibitor molecules. By comparing the standard (i.e. in the absence of any inhibitor molecule) energy profiles and thermodynamic properties with the ones obtained in the presence of the inhibitor candidate, or directly comparing these profiles for different inhibitor candidates, one can have an understanding of the efficiency of the drug candidate for regulating the de/methylation balance. Knowing the key points in the reaction mechanism, one can design novel inhibitor molecules that are inspired by the reaction mechanism.
Proteinlerde bulunan ligand bağlanma yerlerinin ağ yapı modeli kullanarak tespit edilmesi
Biomolecular interactions play key roles in biological activity. Investigation of those interactions, including protein-ligand interactions, is crucial for understanding the way that nature designed its biological machinery. Ligand binding particularly requires, recognition of the ligand by the protein, which in turn arranges the three dimensional structure of the protein, mostly directed by the energetic interactions involved. Based on these requirements, ligand-binding has been considered as a local process. Yet, it has been recently shown that ligand binding depends not on the local structure, but rather on an interaction pathway, that takes part in rearrangement of the protein into the most favorable conformation upon binding.The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem.Comparison with known structures shows that the predictive capability of the method is significant.
Protein-protein etkileşimlerinin çoklu ölçekte analizi ve tahmini
Proteins act coherently in the cells and their roles span functions as diverse as being molecular machines and signaling. The mechanism behind this excellent synchronization is still uncovered. However, considerable effort has been centered on identifying of binding partners and binding regions, because the vast majority of the chores in the living cell involve protein?protein interactions. Proteins interact through their interfaces which contain hot spots, the residues contributing more to the binding energy. Hot spots are important for drug targeting and interaction specificity. In addition, structural modeling of protein interactions and incorporating them into the protein interaction networks are prerequisites for understanding cell function. Hence, the focus of this dissertation is directed to the question ?how do the proteins interact?? rather than the question ?which proteins interact?? at the top level. Towards this aim, firstly, this dissertation focuses on the prediction of hot spots in protein interfaces and their organization. Here, an efficient hot spot prediction model is developed and implemented that reaches an accuracy of 70% on the experimental data. A web server, namely HotPoint, is constructed based on this model. In another aspect, a novel graph-based method based on minimum cut trees developed to determine the organization of hot spots which reveal the cooperative relation between them. Nature presents a limited number of distinct binding site motifs and structurally different protein pairs can use the same binding architectures. Based on this origin, secondly, a multi-scale combinatorial strategy is illustrated to model protein complexes at proteome-level. This work shows how available structural information can help in modeling a pathway by using structural similarity. Here, the sample pathway is the tumor suppressor protein p53 pathway. Finally, the multi-partner proteins dataset is extracted from Protein Databank. Integration of time notion into protein interaction networks is demonstrated on two hub proteins, p53 and Mdm2 using both predictions and available structural data.
Proteinin içsel normal modları ve farklı protein konformasyonları arasındaki ilişki
Proteins are indispensible components of cellular functions. Although proteinstructure is determined as a static picture for most of the proteins, the dynamics or thetime-dependent behavior of proteins act as main contributors to protein function. Inaddition to ligand induced structural motions proteins also bear intrinsic motions arisingfrom thermal energy they contain. These ligand-independent motions possess functionalimportance according to experimental evidences for a large number of proteins and alink between these motions and functional motions are established both in terms ofstructure and timescale. These intrinsic fluctuations are revealed by low-frequencyindividual modes of proteins which are determined using a simplified version of normalmode analysis termed as Anisotropic Network Model (ANM). In this study, we applymodal analysis to eleven proteins including enzymes, antibodies and signal proteins.We investigate a kinetic relation between modal analysis and protein motions. For thispurpose we employ eigenvalues of Hessian matrix which carry information about thevibrational frequencies of these modes. In ANM studies these eigenvalues are used todetermine the low and high frequency modes of protein. Our findings imply acorrespondence between eigenvalues and kinetic/thermodynamic properties of proteinmotions. These intrinsic motions establish a dynamic equilibrium between distinctconformers of the protein and as we have proposed eigenvalues of Hessian matrixcorrelate well both with the timescales of these motions and thermodynamics of thesemotions.
Protein bağlanması ve proteinlerde mod bağlanması
In order to understand the the change in thermodynamic properties upon binding and determine the binding sides, two hexa-peptides and their bound complex structures were analyzed. In order to extract the thermodynamic properties and determine the binding side, a harmonic model was applied.The harmonic formulation is extended to large ? uctuations of residues in order to account for effects of anharmonicity. The ? uctuation probability function is constructed for this purpose as a tensorial Hermite series expansion with higher order moments of ? uctuations as coef ? cients.Mode coupling and anharmonicity in a native fluctuating protein is investigated in modal space. Molecular dynamics trajectories of Crambin are generated and used to evaluate the terms of the polynomials and to obtain the modal energies. Slowest modes have energies that are below that of the harmonic energy, kT/2 per mode, and a few fast modes have energies significantly larger than the harmonic which is a result of coupling. Detailed analysis of the lowest order two mode coupling terms is presented.It is was shown that mode coupling and anharmonicity are important for modeling the multidimensional energy landscape of Crambin. The effect of them on the fluctuational entropy is on the order of a few percent.The fluctuations and unbinding free energy profiles of two very similar proteins, HLA-B51 and HLA-B52, were investigated. HLA-B51 is related to the Behçet?s disease whereas HLA-B52 is not. Change in the dynamics of 1 helix were analyzed. Unbinding from HLA-B52 resulted in greater free energy differences than for HLA-B51.
Peptid dizayn stratejileri
Short peptide segments have gained importance as drug candidates. There exist three main problems for peptide design: determining the appropriate sequence with the desired function; properly docking peptide on the protein surface; and the unbound state of the peptide that is to be used as a drug. The `unbound state? means peptide chains in the denatured state at physiological conditions. The details of the potentials for peptide docking simulations and the statistical features of peptides are defined in the literature.As a solution to the peptide sequence determination problem, several experimental and in silico techniques exist to screen peptides. There is no general computational tool to determine peptide sequences. On the other hand, peptide motifs are crucial for selective and specific binding. There have been successful attempts to discover biological motifs by different research groups. To our knowledge, the efforts in the literature are based on the alignment of evolutionarily conserved motifs from proteins. The evolutionary peptide motif search algorithms/servers/software are available. However, there is no general methodology to discover a binding peptide motif for any protein target. We aim to predict peptide sequences and peptide binding motifs for any given protein using no prior information. Here, four different algorithms are developed for peptide design. The implementation of genetic algorithm, Markov model and hidden Markov model with Viterbi decoding leads to prediction of peptides for different protein targets. The algorithms are successful to determine peptide sequences with good theoretical binding affinities. The peptide motifs for two case-studies are also offered. A web-server, VitAL, is constructed based on Viterbi decoding.The statistical thermodynamics features of the unbound peptide as a small thermodynamics system in a thermal reservoir is lacking in the literature. A novel statistical thermodynamics approach is applied to the free peptide segments in order to classify them according to their conformational energies and entropies and heat capacities. The conformational partition function, Helmholtz free energy, energy, entropy and heat capacity are obtained. The model is applied to randomly produced peptides and to known peptide inhibitors. Peptides with low energy, low entropy and low heat capacity are determined to be essential for a peptide to be a good candidate inhibitor.
Düzenleyici gen topluluklarının saklı Markov modelleri kullanılarak zaman serisi mikrodizi ekspresyon profillerinden belirlenmensi
Time series microarrays capture multiple gene expression levels at discrete time points varying from minutes to days of a continuous cellular process. Analysis of high through put data requires automated and computer aided solutions. We propose a hidden Markov model (HMM) based approach to identify regulatory relations between the periodic genes from the cell cycle time-series microarrays. We train and test our models by using distinct types of biological data present literature. In our study we use Pramila time series dataset. Training gene pairs include transcriptional regulation and protein level regulation. After identification of gene to gene regulatory relationships, we form a network of gene regulation relationships: Gene Regulatory Neighborhood Networks (GRNN). We explore potential use of sub networks (communities) in GRNN by comparing gene clusters found by popular clustering algorithms such as K-means clustering. Our results indicate we manage to identify denser and more specific enrichment in community structure based clusters than the clusters acquired with K-means.
Peptit bazlı nanomalzemelerin moleküler dinamik simulasyonlar aracılığı ile yapısal ve termodinamik özelliklerinin analizi
Peptides are oligomers with aminoacids as building blocks. Discovery of naturallyoccuring functional peptides has led to a dramatic increase in research for both under-standing of natural peptides as well as design of novel synthetic ones. As individualmolecules, peptides serve a variety of purposes: such as drugs, antigens, ligands andantibiotics. They also act as building blocks for self-assembled nanofibers, nanotubes,micelles, and monolayers. Designing novel peptide-based materials with desired prop-erties and functions can only be possible by understanding the link between sequence,structure and organization of these molecules.This thesis work is composed of four main sections. In the first study, we analyzedstructure and thermodynamics of small amphiphilic peptides that spontaneously formmonolayers at the air/water interface. We accurately calculated the free energy oftransferring peptides from bulk water to the air/water interface, and analyzed itscomposition. Next, in the light of information gathered from small amphiphilic pep-tides we analyzed folding of a carefully designed 24-residue amphiphilic peptide atthe air/water interface. We calculated free energy of adsorption, and decomposedit into enthalpic and entropic contributions. We determined key elements requiredto adapt the targeted ß-hairpin conformation and for adsorption at the interface viain-silico mutations. In addition, we also determined organization of ß-hairpins withinsurface monolayers, which will help improve design strategies for manufacturing such2-D structures. In the third study, we tried to understand self-assembly of tri-blockpeptides into nanofibers in bulk water. By substituting aliphatic residues with aro-matic ones located in the central block of these peptides, we analyzed the stabilityand strength of these fibers. We also investigated possible nucleation mechanisms forfiber formation, which can be used to design stable and functional peptide nanofibers.Investigation of material properties and bulk behavior of peptides requires moreefficient computational techniques. In this regard, finally, we investigated the ?trans-ferability? of a recently developed solvent-free coarse-grained (CG) peptide model [1]that was shown to capture quantitatively structural and thermodynamic propertiesof a hydrophobic di-phenlyalanine peptide (FF). By mimicking the CG mapping usedin the FF CG model and transferring bonded and nonbonded interaction potentialsto other hydrophobic di-peptides, namely valine-phenyalanine (VF) and isoleucine-phenylalanine (IF), we tested the generality of this CG model. We devised a generalprotocol to transfer the original CG model to other hydrophobic di-peptides, whichare in the form of XF, where X represents an arbitrary amino acid. Hydrophobic di-peptides are the smallest molecules forming self-assembled hierarchical structures inaqueous solution. Therefore, development of a transferable CG model for simulationof such systems will help elucidate the driving forces important in the self-assemblyof peptide-based materials and peptide aggregation.
Yapısal bilgiyi kullanarak PER-ARNT-SIM (PAS) bölgeleri içeren biyolojik saat proteinlerinin etkileşimlerinin analizi
Per-ARNT- Sim (PAS) domains are modular protein units those are critical for regulation of clock-controlled gene expression. The mammalian BMAL1 and CLOCK are the transcription factors that contain two basic helix-loop-helix domains and bind E-box elements (CACGTG) of clock regulated genes including the Period and Cryptochrome and activate their transcription. Then the PERIOD (PER) and CRYPTOCHROME (CRY) proteins form ternary complexes with casein kinase I? (CKI?) in the cytoplasm and translocate into the nucleus, where they act as a negative regulator of BMAL1/CLOCK-driven transcription. To understand the nature of interaction between PER2-CLOCK-BMAL1 complex, we performed structure based analysis on protein-protein interactions (PPI) those formed via PAS domains of clock proteins. This complex was analyzed by using structural data and efficient structural comparison algorithms to predict potential interactions. Since there are no available atomic structures for our proteins of interest, homology models are used. In our model, BMAL1 and CLOCK interacts with each other through their PAS B domains and the PER2 interacts with this dimer through the PAS B domain of the CLOCK. On BMAL1/CLOCK interface, we found 12 hotspots, 7 residues on BMAL1 (347,349,362,405,427,429,441), 5 residues on CLOCK (317, 338, 350, 352,376). On PER2/CLOCK interface, 8 hotspots are found, 3 residues on PER2 (414, 429,431) and 5 residues on CLOCK (332,354,356,333,361). Here we show how, using structural data and efficient comparison algorithms can explain forming the clock complexes at the molecular level. This study is not only important to understand clock mechanism at structural level but also will allow us to develop drugs against clock-regulated diseases, like Jet-Lag and some forms of depression.
Gaz ayırımları için mof membranların ve polimer/MOF karışık yataklı membranların moleküler modellenmesi
Polymer membranes have been commonly used for gas separation applications due to their ease of fabrication and low cost. However, polymer membranes have a trade-off between gas selectivity and permeability. For the past two decades, there has been a growing interest in developing mixed matrix membranes (MMMs) by combining non-polymeric materials with the polymers to overcome this permeability/selectivity trade-off. Metal organic frameworks (MOFs) which are a new class of nanoporous materials present greater promise for being used as non-polymeric materials to fabricate MMMs due to their unique properties such as well-defined pores and large surface areas. Recently, combining MOFs with the polymers, MOF-based MMMs have been synthesized, and the high gas separation performance of MMMs has been reported. However, choosing the appropriate MOFs as filler particles in MMM applications is very difficult due to the very large number of existing MOF materials. Therefore, theoretical models play a critical role in predicting polymer/MOF combinations prior to experimental efforts. In this thesis, the methodologies for selecting MOFs as filler particles in polymers was examined using atomistic and continuum modeling. The validity of several theoretical permeation models was tested by comparing the predictions of these models with the available experimental data for CO2/CH4 and H2/CH4 separations. Combining detailed atomistic simulations with the theoretical permeation models, the performances of new-MOF based MMMs were estimated. The results found in this thesis demonstrated that selecting the appropriate MOF/polymer combinations can result in membranes with high CO2 and H2 selectivities and permeabilities relative to those of pure polymer membranes. The methodologies that were described in this thesis for screening MOFs in an efficient and easy way will provide conceptual hints for the assessment of the best MOF/polymer pairs to obtain high performance MMMs for CO2/CH4 and H2/CH4 separations.
Kanserde biyolojik olarak önemli hedefler için küçük molekül inhibitörler saptamak
In this thesis, we investigated plant secondary metabolites as potential drugs for cancer and identified plant based small molecule inhibitors for biologically important targets by screening a plant based library that we constructed. We carried out two projects and developed new methods for this purpose.In the first part, we aimed to inhibit base excision repair (BER) pathway that is the major system for correcting many types of oxidative DNA damages and mono functional base modifications caused by endogenous and exogenous agents including chemotherapeutics. Whereas DNA repair pathways mediate resistance to DNA damage and protect the cells from its killing effects, and maintain genomic stability, inhibition of DNA repair pathways is required for the toxicity of several anticancer drugs and ionizing radiation. Because the cytotoxic effects of most chemotherapeutic agents and radiation are related to their ability to induce DNA damage. The DNA repairing system of cancer cells is therefore disadvantageous to cancer treatment. We identified plant based small molecule inhibitors for two critical BER proteins, mitochondrial DNA polymerase ? and nuclear DNA polymerase ß as potential therapeutic targets to sensitize tumors. We found an inhibitor for known active site of polymerase ß with more significant interactions than found in previous studies. We also identified three promising inhibitors for polymerase ? targeting a specific interface of polymerase ? for the first time in literature.In the second part, we investigated plant secondary metabolite specificities on three biologically important domains, PDZ, BROMO and SPRY, which have high drug target potency for a large variety of diseases including cancers. Interest in molecular target-class specificity information is getting increased due to the advantages into drug design. However, studies about information on molecular targets are lacking systematic methods to identify the specificities. We developed a novel method that will enable to analyze and identify the domain-class specificity more quantitatively and for this purpose we used the Gibbs Distribution of binding energies in order to differentiate different ligands. We identified three specific plant secondary metabolite classes, Triterpenoids, Stilbenoids and Stereoidal alkaloids for PDZ, BROMO and SPRY domains, respectively.
İnsan-robot işbirliğinde kuvvete bağlı rol paylaşımı ve niyet tespiti
This dissertation aims to present a perspective to build more natural shared control systems for physical human-robot cooperation. As the tasks become more complex and more dynamic, many shared control schemes fail to meet the expectation of an effortless interaction that resembles human-human sensory communication. Since such systems are mainly built to improve task performance, the richness of sensory communication is of secondary concern. We suggest that effective cooperation can be achieved when the human?s and the robot?s roles within the task are dynamically updated during the execution of the task. These roles define states for the system, in which the robot?s control leads or follows the human?s actions. In such a system, a state transition can occur at certain times if the robot can determine the user?s intention for gaining/relinquishing control. Specifically, with these state transitions we assign certain roles to the human and the robot. We believe that only by employing the robot with tools to change its behavior during collaboration, we can improve the collaboration experience. We explore how human-robot cooperation in virtual and physical worlds can be improved using a force-based role-exchange mechanism. Our findings indicate that the proposed role exchange framework is beneficial in a sense that it can improve task performance and the efficiency of the partners during the task, and decrease the energy requirement of the human. Moreover, the results imply that the subjective acceptability of the proposed model is attained only when role exchanges are performed in a smooth and transparent fashion. Finally, we illustrate that adding extra sensory cues on top of a role exchange scheme is useful for improving the sense of interaction during the task, as well as making the system more comfortable and easier to use, and the task more enjoyable.
Düşük çözünürlüklü bir DNA modeli ve süpersarmallanma dinamikleri
DNA polymers are the primary containers of information in all living organisms. In addition to its fundamental role as a biomolecule, DNA has technological applications as a building block for nanostructures. Even though molecular structure of DNA was established as a double helix in 1953, very little about its dynamical properties were discovered during the following decades. With the advent of single molecule experiments, probing dynamical processes in single DNA molecules has been possible in the last decades. Although recent experiments provide unprecedented insight, they have limited resolution in both time and length domains. Molecular simulations, on the other hand, provide complete knowledge of the system but are limited by computational cost. Coarse grained models aim to decrease the cost of molecular simulations while preserving most of the resolution. In this work, we built a coarse grained model to represent both single and double stranded DNA strands with high enough performance to simulate biologically relevant time and length scales. Supercoiling is a mechanism where a twist storing polymer such as DNA expresses torsional stress through conformational changes. Supercoiling is of interest from a biological point of view as it is readily observed in genome packing schemes and presumably affects gene expression patterns. A recent single molecule experiment provided indirect observations on the supercoil dynamics of overwound DNA. In this experiment, supercoiled regions along the length of DNA demonstrated fast hopping behaviour in addition to diffusive motion. In this work, we reproduced this experimental scenario in silico using a coarse grained model. We simulated up to 1 kilobase long overwound DNA strands under tension and observed time evolution of the conformation of the molecule. We produced supercoiled conformations associated with local density peaks observed in the experiment and time evolution of curvature along the length. We varied the degree of overwinding (?) and the amount of tension and observed a buckling transition at different threshold ? values. We reproduced the experimentally observed hopping behaviour in addition to diffusive motion near the threshold ? values.
Peptitlerde çevresel etkilerle yapısal geçişler
Proteins are fascinating molecular machines with their ability to fold into unique 3-dimensional structures identified as their native states. Despite their surprisingly robust folding capability, proteins and peptides exhibit a strong tendency to form ordered aggregates if the environmental conditions are correctly tuned. The amphiphilic nature of peptides plays an important role in enabling aggregation in aqueous environment or at interfaces and surfaces or by allowing peptides to penetrate through or aggregate in membranes. In many cases the aggregation or the interaction of a peptide with a hydrophobic/hydrophilic interface triggers a conformational change in the molecule, which is usually coupled to the partitioning of the hydrophobic/hydrophilic residues of the peptide. Well known examples of the interplay of conformational change and aggregation or partitioning at interfaces are the misfolding of proteins upon amyloid aggregation, or more generally the induction of higher beta-sheet content by aggregation or by the presence of an interface. In order to better understand and ultimately control structure formation in peptide aggregates and peptide-based materials, knowledge of the relevant interactions, driving forces, pathways and assembly mechanisms is essential. In this thesis we utilize molecular dynamics simulations to provide microscopic structural and thermodynamic insight into the interplay of folding, aggregation and partitioning in peptide based systems. In order to illustrate environment driven conformational change, the first model system we have focused on is phenylalanine dipeptide (FF). With its only two aminoacid long sequence, this molecule forms one-dimensional nanotubes, in which the molecules adopt a cis-like conformation unlike their preferred state in water. Here, by analyzing molecular dynamics simulations of FF in bulk water and cyclohexane/water interface, we demonstrate how the hydrophobic/hydrophilic interface triggers the trans-to-cis conformational change. Moreover, we demonstrate that even a molecular interface can lead to a similar conformational change, and discuss the similarities and differences between macroscopic and molecular interfaces. Next, in order to overcome the time and length scale barriers in observing aggregation of peptides in molecular simulations, we develop a coarse-grained (CG) model capable of representing the conformational behavior of FF. Our CG model is unique in its ability to capture the correct representation of the target molecule in two different environments. We show that correct representation of a structural change, such as a trans-to-cis conformational switch, relies on thermodynamic driving forces. Hence, a solvation free energy based tuning is required to capture the correct partitioning behavior. In the second study we switch to the LK peptide which is a designed synthetic molecule. We demonstrate how the interplay of hydrogen bonding, hydrophobic interactions, and electrostatics leads to an intrinsically disordered peptide. When isolated in bulk water it lacks a well defined secondary structure and only in the presence of a macroscopic or molecular interface its targeted $\alpha$-helical secondary structure can be realized. In the case of LK the presence of an interface leads to a population shift in the conformational phase space of the molecule. We also calculate the potential of mean force as a function of aggregate size and demonstate that in agreement with experimental findings tetramers of LK are the stable form in solution. Our findings highlight the challenges associated with the coupled nature of aggregation, folding and partitioning for peptides. We show that molecular dynamics simulations provide atomistic resolution analysis of the driving forces for such phenomena, perfectly complementing experimental techniques.
Proteomdaki protein tümleşiklerini modellemek
Most (if not all) proteins function when associated in multimolecular assemblies. An important aim of structural biology is to attain the structures of protein assemblies at the atomic scale. Experimentally, structures are increasingly available but many are still missing or incomplete. Some experimental methods provide high resolution data for small proteins and some provide low resolution data for large proteins. Computational approaches can help bridge this resolution gap. They are needed to determine structural data of multi-molecular protein assemblies at atomic scale. Existing computational methods have made substantial progress toward this aim; however, current approaches are still limited. Some involve manual adjustment of experimental data; some are automated docking methods, which are computationally expensive and not applicable to large-scale proteome studies; still others exploit the symmetry of the complexes, thus they are not applicable to non-symmetrical complexes. Our study aims to take steps toward overcoming these limitations. We have developed a strategy to construct protein assemblies computationally based on binary interactions predicted by a motif-based protein interaction prediction tool, PRISM (PRotein Interactions by Structural Matching). PRISM predicts pair-wise interactions; here we take a step toward multimolecular assemblies, which reflects the more prevalent cellular scenarios. This method is able to construct homo-/hetero-complexes and symmetric/asymmetric complexes without a limitation on the number of components, considers conformational changes and is applicable to large-scale studies. We modeled a benchmark set of various protein assemblies starting from the unbound forms and obtained successful predictions (0.5 - 5.6 Å). Moreover, we exploit different conformations of the proteins available in the Protein Data Bank (PDB) to consider protein flexibility in modeling protein assemblies. We could increase the success in prediction of binary interactions from 27% to 67% and obtained higher accuracy in modeling protein assemblies by exploiting alternative conformations. Furthermore, we modified our method to exploit electron microscopy (EM) density maps to eliminate improper structures. Filtering structures through EM data prevents assembly construction based on wrong structures and saves computational time. We successfully modeled protein assemblies using EM data, most of them with RMSD less than 5 Å and correlation in density maps are close to or higher than 0.8. Comparing our results with other methods' showed higher accuracy in our interface predictions. We present the methods, illustrate their results, and highlight the current limitations.
Mavi ışığın tek hücreli organizmaların transkriptom profiline etkisi
The ability of light perception is crucial for the survival of most organisms that enables them to adjust their physiology and metabolism to the changing environmental conditions. Light, in contrast, is also a threat to any living organism owing to the deleterious effects it can have on nucleic acids, lipids and proteins. Therefore, the capacity to sense and respond to light is widespread among prokaryotes and eukaryotes to survive and adapt themselves as a result of selective pressure of the solar irradiation. This dissertation concentrates on the analysis of light regulated pathways in unicellular organisms, non-phototrophic prokaryote Vibrio cholerae and phototrophic red alga Cyanidioschyzon merolae, via transcriptome profiling. C. merolae is one of the most primitive of photosynthetic eukaryotes. Since it is an extremophile, it is conceivable to study the effect of light on this organism to see how it adapts itself to different environmental conditions and to establish an evolutionary conserved global light response between algae and land plants. Therefore, we decided to investigate the direct effect of red and blue lights at the transcriptional level as well as to verify known blue light receptor genes and the involved transduction pathways using next generation RNA-seq approach. Our results indicated that transcriptional regulations of 35 % of the total genes, including the genes encodes 46 % of transcription factors, were regulated by blue and red lights in C. merolae. Unexpectedly, although there are yet no identified red light photoreceptors, 22 % of the total genes (1116) were regulated by the red light; 521 genes were solely red-light responsive. Transcriptional modulation due to light exposure does not arise from photo-oxidative stress. Blue light dependent regulation of three cryptochromes (CmPHR2, CmPHR3 and CmPHR7) implies a potential role in light-dependent transcriptional regulation in C. merolae. In spite of absences of any red light photoreceptors, a great impact of the red light on the biological processes may suggest the importance of retrograde signaling in this organism. Secondly, we investigated the effect of blue light in non-phototrophic bacteria V. cholerae by using genetics and transcriptome profiling. Genome-wide analysis revealed that the transcription of 6.3% of the genes was regulated by blue light in V. cholerae. To understand signaling mechanisms, we generated several knockout cell lines and subjected to genome-wide analysis under blue light condition. Studies with a double mutant confirm an anti-sigma factor (ChrR) and a novel putative metalloregulatory-like protein (MerR) are responsible for the genome-wide regulation to blue light response in V. cholerae. We further showed that blue light enhances ROS production, possibly generated through the oxidative phosphorylation pathway. These results demonstrate that V. cholerae responds to blue light with a novel mechanism to produce an appropriate response against photo-oxidative stress. This response regulates the transcription of genes involved in cellular protection, DNA repair, and carbon metabolism. Outside its host, V. cholerae can survive for extended periods in natural aquatic environments. Therefore, the regulation of light response for V. cholerae is a critical cellular process for its survival. Collectively, all these data reveal that light is important not only for phototrophs but also non-phototrophs to regulate their metabolism and related physiological pathways through elegant and complex signaling cascades.
Β2-adrenergic proteininin allosteri mekanizmalarının ve üçüncü hücre içi ilmiğinin G-protein kenetli proteinler kapsamında incelemesi
G-protein coupled receptors are encoded over 700 genes in human genome. They share a common seven transmembrane domain and differing intracellular and extracellular loops. As transmembrane domains, they function as gate-keepers of intercommunication of cells. Thus, their functioning mechanisms are one of most unique ongoing research area. In this study after the introduction of GPCR families, human β2-adrenergic receptor and endogenous ligand epinephrine are investigated. First of all, usually omitted part of β2-Ar intracellular loop 3 and its unbinding energy from inactive to the active state is estimated. Secondly, intracommunications between residues are investigated via correlation matrices. In allostery analysis, two trajectories of β2-Ar embedded in lipid membrane are analyzed. One of the trajectories is gathered from 1 μs molecular dynamics simulation and second trajectory is gathered from 500 ns molecular dynamics simulation where the ligand binding pocket is restrained to 8 Å in order to mimic ligand binding. Specifically for β2Ar, experimental measurements show that the distance range of 8Å - 10Å between Asp113 and Ser207 is sufficient for receptor activation. In 1 μs simulation, it is observed that ICL1 (residues 64 to 66) is in communication with intracellular end of TM6 (Cys265, Lys267). Thr66 of ICL1 is important in protein stabilization in lipid membrane and functions through Tyr123 (TM3) and Ile154 (TM4). Trp99 of ECL1 is correlated to ECL2 (Cys191) and membrane region of TM5 (Ile 214). Phe101 of ECL1 is correlated extra cellular end of TM6 and intracellular end of TM5. In 500 ns simulation, Met98 of ECL1 is correlated to intracellular end of TM1 (Arg53), ICL1 (Phe61), intracellular end of TM2 (Thr68) and cytoplasmic tail (Leu339). Gly102 of ECL1 is correlated to membrane region of TM2 (Glu82), intracellular end of TM3 (Arg131), ICL2 (Ser143) and intracellular end of TM5 (Phe223). Ile135 of TM3 at intracellular region is correlated to extracellular part of TM2 (Ala92), membrane region of TM4 (Val160, Leu163) and membrane region of TM5 (Ala202). These finding imply that ICL1 may be responsible for ligand recognition signals whereas ECL1 is responsible for protein stabilization and recognition of intracellular effectors. In estimation of required energy for unbinding of intracellular loop 3 is found as 1629 kJ/mol and confirmed with G-protein coupling energy to β2-Ar with the work of -1455 kJ/mol. Finally, the endogenous ligand epinephrine of β-adrenoceptors is studied in order to estimate critical differences between β1, β2, β3 ligand binding pockets for epinephrine and their selectivity.
Yapay öğrenme ile uzam-zamansal modelleme
An ideal machine learning algorithm for spatiotemporal modeling should be able (i) to integrate both temporal and spatial data from different sources, (ii) to discover patterns and, (iii) to make inference, without human intervention. Gaussian processes provide a Bayesian framework for analyzing spatiotemporal data, which were used widely to estimate values across space and time, yet their computational and storage complexity have been a limiting factor when it comes to application. In this thesis, we proposed computational frameworks that integrate Gaussian processes into spatiotemporal modeling scenarios with a particular focus on scalable inference by exploiting the structure of the covariance matrix generated by matrix multiplication of spatial and temporal covariance matrices. We also aimed to increase the interpretability using kernel methods, which have deep connections with spatial statistics. With the combination of multiple kernel learning and structured Gaussian processes, we increased both accuracy and expressiveness of the inference. We showed the power of these methods on real-world regression data sets. Our proposed methods were applied to a spatiotemporal data set of a vector-borne disease using official patient records in Turkey. We showed our proposed machine learning algorithms were better than their counterparts in terms of accuracy. In addition, our developed methods are also more interpretable, which means that they are able to answer questions drawn from the domain of public health and give insight to policy makers for quick response planning and resource allocation.
Doğrusal olmayan disipatif denklemlerin çözümlerinin global davranışı
In this thesis, we investigate the global behavior of solutions of initial-boundary value problems (IBVP) for nonlinear dissipative equations. We particularly focus on two problems in hydrodynamics: IBVP for Burgers' original model of turbulence original Burgers' equations, and IBVP for Burgers' equation with nonlocal nonlinearity. Motivated by the studies in finite-dimensional asymptotic behavior of dissipative equations, we prove the stabilization of these equations by using finitely many controllers, such as finitely many Fourier modes, finitely many volume elements and finitely many nodal values. We also prove that the asymptotic behavior of solutions of original Burgers' equations can be completely determined by finite number of determining modes. Additionally, we show the existence, uniqueness and stability of the solutions of the inverse source problem for both equations. We show that, under proper assumptions, the solutions of the inverse source problem tends to a particular stationary state solution of the direct problem, and the unknown source term tends to zero as time goes to infinity. Finally, we perform numerical experiments to verify the validity of our theoretical findings on the finite-parameter feedback control problems for original Burgers' equations.
Gerçek zamanlı yatan hasta antibiyotik tüketimi izleme aracı
Inappropriate antibiotic use poses significant health threats on both local and global scale. Therefore, for the aim of improving antibiotic usage, antibiotic management programs are being implemented by health institutions worldwide. The main objectives of these programs are to ensure that prescribing complies with certain criteria, to monitor the trends of antibiotic consumption and to make corrective interventions when necessary. In order to achieve these objectives, several types of medical information must be evaluated simultaneously. However, software used for the evaluation of this information in healthcare facilities is often insufficient to meet the needs such as processing large data sets and managing relationships between various data groups. STEWARD, a web application to be utilized in antibiotic consumption monitoring, is introduced in this thesis. The application has an easy-to-use interface and allows users to perform in-depth investigations without the need for technical knowledge. Moreover, it helps users evaluate antibiotic use accuracy by combining information stored in several data sets. Since the application is developed using open source technologies, it is free to use, and at the same time, advanced in terms of technical capabilities. When synchronized with central data management systems of health centers, it allows for real-time antibiotic consumption monitoring, which is particularly valuable at patient level antibiotic stewardship. The proposed application is field tested using the data of İstanbul American Hospital. This way, the capabilities of the application have been affirmed and the tool is shown to be able to operate integrally with existing healthcare systems. And finally, antibiotic consumption trends in İstanbul American Hospital are examined and observations are presented.
HapTable: an interactive tabletop providing online haptic feedback for surface gestures
Bu tez, insan-bilgisayar etkileşiminin daha doğal hâle getirebilecek geniş ve çok dokunuşlu bir etkileşim yüzeyi sunmayı hedeflemektedir. Son on yıl içinde dokunmatik arayüzler, klavye ve fare gibi dolaylı etkileşim araçlarının yerini almaya başlamış ve kişilerin günlük etkileşimlerinde yaygınca kullandığı bir teknoloji olmuştur. Fakat, fiziksel etkileşim araçlarından uzaklaşarak sadece dokunmatik araçlarla gerçekleştirdiğimiz insan-bilgisayar etkileşimleri, bu araçlarda dokunsal geri bildirim olmaması sebebiyle kişilerin görevi tamamlama başarılarının ciddi derecede düşmesine sebep olmuştur. Kişiler, fiziksel etkileşim araçlarını kullanırken, yaptıkları el hareketlerine ve etkileşim aracının o anki duruma göre sürekli olarak dokunsal geri bildirim alırlar ve gelecek hareketlerini bu bildireme göre ayarlarlar. Bu nedenle biz, dokunmatik ekranlara eklenilebilecek dokunsal geri bildirimlerle kişilerin görevi tamamlama başarılarının artabileceğini düşünüyoruz. Bunun yanı sıra şu anda, dokunmatik ekranlar sadece görsel kanaldan bilgi verebilmektedir. Bu sebeple eklenecek dokunsal sistem, kullanıcıya ek bilgi sunabilmek ve böylece görsel bilgi yüklenmesini hafifletmek için kullanılabilir. Bu tezde, dokunsal yöntemler ile günümüzdeki dokunmatik ekranları nasıl geliştirebileceğimizi araştırdık. Bu sebeple, HapTable adını verdiğimiz geniş ve birden fazla duyuya hitap eden dokunmatik bir masa geliştirdik. HapTable, kullanıcıların doğal el hareketlerini kullanarak sanal içerik ile etkileşim haline girmesini sağlamakta, ve bu etkileşim sırasında uygun görsel ve dokunsal geri bildirimleri kullanıcıya sunmaktadır. HapTable, piezo elektrik motorlar ve elektrostatik-bazlı titreşim yöntemlerini birleştirerek hem normal yönde, hem de teğetsel yönde dokunsal geribildirim sağlamaktadır. Bu tez, ilk yöntemi kullanarak yüzeyde, rüzgârın yönü gibi ek bilgi sunmayı araştırmıştır. Araştırmalarımızın sonucunda, kişilerin hem iki parmak arasında, hem de ellerinin altında bir akıntının yönünü başarıyla tanıdığını bulduk. İkinci dokunsal teknolojiyi ise görsel bir düğmeye dokunsal geribildirim eklemek için kullandık. Nicel sonuçlarda düğmenin çevrilmesindeki görev başarısı artmasa da, nitel sonuçlarda kullanıcıların, dokunsal bildirimi olan düğmeleri, dokunsal bildirimi olmayana göre daha fazla tercih ettiklerini gözlemledik.
Zaman serisi tahmini için otomatik çok modelli yaklaşım
In this thesis, we present a multi-model approach for time series forecasting. Our framework is automated, easy to use and spans around short-medium terms forecast horizons. We focus on multiple basic and advance time series forecasting models. Our basic models include simple moving average, simple exponential smoothing, Holt's and winter's model. We optimize the parameters used in these models, and for advance models we extend Box-Jenkins methodology to automated Auto- Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) models. The ARIMA and SARIMA models are complex models and need expert judgement and iterative procedures to select a best fitting model.We substitute expert judgement with statistical tests and iterative procedures with automated enumeration technique. The best fitting model is selected based on the results of a number of statistical and error estimation tests. We tested our system on M-competition data set provided by International Institute of Forecasters. The dataset is comprised of multiple time series from social and economic backgrounds. From our experimental work, we conclude that our SARIMA model outperforms all other models with an average MAPE of <1% for 1-period ahead and approximately 9% for 6 periods-ahead forecast horizons.
HotRegion v2.0: Protein-protein etkileşim arayüzlerindeki sıcak bölgeleri tahmin etmek için yeni bir yöntem
Proteins interact with each other through their interface to fulfil essential functions in the cell. The study of protein interactions will have a profound effect on understanding various biological pathways. Binding free energies are not uniformly distributed among the residues found in protein-protein interfaces (PPI). Hot regions are tightly packed residue clusters in PPIs that account for the majority of the binding free energy of proteins, and hence, are crucial for the stability of complexes. Providing specificity to binding sites, these regions are of great importance for drug discovery in pharmaceutical research. Experimental discovery of hot regions is time-consuming and requires high effort. Hence, there is a need for computational methods. The existing hot region prediction algorithms perform clustering on computationally predicted hot spots and ignore the potential existence of non-hot spot residues in hot regions. However, experimental studies have demonstrated that non-hot spot residues can also be found in hot regions. In this thesis, using unsupervised learning approaches, we propose a novel method to predict hot regions which may contain both hot spot and non-hot spot residues. We combine affinity propagation (AP) and density-based spatial clustering of applications with noise (DBSCAN) to cluster interface residues and develop a web-based tool to predict and visualize hot regions. Furthermore, we have developed a database that contains hot region information for more than 600.000 protein complexes. In our web server, users can query data from our database or submit a new run to obtain hot regions of a complex that is not found in the database. Our tool demonstrates a 3D structural visualization of the complex with colored hot regions and highlighted hot spot residues. Structural features of interface residues, i.e., accessible surface area values and knowledge-based pair potentials, are also indicated. In order to evaluate our method, we have compared our results with the experimental studies. The precision of our algorithm is 0.68 and the accuracy is 0.62. Additionally, we have conducted a case study to test the significance of our predicted regions for clinical studies. We have investigated the complex of human programmed death-1 (PD-1) and its ligand PD-L1. Our algorithm correctly identified the residues which are known to be significant for PD-L1-antibodies and small-inhibitors. Lastly, we have compared our algorithm with our previous hot region prediction method. The results have shown that our tool outperforms the previous version of HotRegion and may be a leveraging step to novel pharmaceutical studies.
Kripto para fiyat analizi için makine öğrenme yaklaşımları
Bu çalışma, finansal zaman serilerinin kısa vadeli tahmini amacıyla çeşitli veri yapıları ve model konfigürasyonlarının kapsamlı bir değerlendirmesini sunmakta; teknik göstergelerle zenginleştirilmiş Long Short-Term Memory (LSTM) tabanlı bir derin öğrenme modeli önermektedir. Model, saatlik ve dakikalık çözünürlükte yapılandırılmış veri setleri kullanılarak, fiyat-hacim bilgileri ile teknik analiz göstergelerinin ayrı girişler olarak işlendiği farklı senaryolar altında test edilmiştir. Ayrıca LSTM, GRU ve BiLSTM mimarileri; MSE, MAE, Huber ve büyük hataları daha fazla cezalandıracak şekilde tasarlanmış özel bir ağırlıklı kayıp fonksiyonu ile değerlendirilmiştir. Deneysel sonuçlar, teknik göstergelerle desteklenen LSTM modelinin hem fiyat hareketlerinin yönünü hem de büyüklüğünü doğru şekilde tahmin etmede yüksek performans sergilediğini göstermektedir. Bu çalışma, derin öğrenme tabanlı finansal tahmin bağlamında farklı veri türleri ve modelleme stratejilerini birlikte ele alan bütüncül çerçevesiyle literatüre anlamlı bir katkı sunmaktadır.
Türdeş olmayan uzay zaman modeli için gökkuşağı kütleçekim kuramı çerçevesinde enerji momentum gösterimleri
Bu tezde, genel görelilikte uzun yıllardır tartışmalı olan enerji–momentumun yerelleştirilmesi problemi, klasik enerji-momentum kompleksleri (Einstein, Bergmann–Thomson, Landau–Lifshitz, Papapetrou, Tolman, Weinberg ve Møller) kullanılarak incelenmiş ve bu tanımlar Gökkuşağı Kütleçekim Kuramı (Rainbow Gravity) çerçevesine genelleştirilmiştir. Çalışmada türdeş olmayan bir uzay-zaman modeli olarak Van Stockum'un dönen silindirik toz çözümü ele alınmış; model kartezyen koordinatlara dönüştürülerek enerji yoğunluğu hesaplamaları gerçekleştirilmiştir. Gökkuşağı kütleçekimi, parçacık enerjisine bağlı iki fonksiyon üzerinden metrik geometrisini değiştirdiğinden, test parçacıklarının yüksek enerjilerde uzay-zamanı farklı şekilde "algılaması" beklenmektedir. Yapılan hesaplamalar, tüm enerji-momentum gösterimlerinin klasik limitte (f1=f2=1) birbiriyle tutarlı sonuçlar verdiğini ve genel görelilikle uyumlu enerji dağılımları sunduğunu göstermiştir. Ancak enerjiye bağlı düzeltmeler eklendiğinde, özellikle Einstein, Bergmann–Thomson ve Møller gösterimlerinde enerji yoğunluğu üzerinde belirgin değişimler ortaya çıkmıştır. Modifiye Dağınım ve Üstel Model gibi literatürde yaygın kullanılan çeşitli Gökkuşağı fonksiyonları için enerji yoğunluklarının grafiksel davranışları incelenmiş; bazı modellerde düzeltmeler baskın hale gelirken, özellikle Landau–Lifshitz ve Weinberg gösterimlerinde Gökkuşağı etkilerinin zayıf kaldığı görülmüştür. Møller gösterimi ise koordinat bağımsız yapısı sayesinde modele en genel yorumu sağlamış ve yüksek enerjili test parçacıklarında enerji yoğunluğunun düzenli şekilde azaldığını göstermiştir.