Publications without Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
Browse
16 results
Search Results
Publication Metadata only Physics-informed and data-driven modeling of an industrial wastewater treatment plant with actual validation(PERGAMON-ELSEVIER SCIENCE LTD, 2024) Esenboga, Elif Ecem; Cosgun, Ahmet; Kusoglu, Gizem; Department of Chemical and Biological Engineering; Köksal, Ece Serenat; Asrav, Tuse; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Graduate School of Sciences and Engineering; College of EngineeringData-driven modeling is essential in chemical engineering, especially in complex systems like wastewater treatment plants. Recurrent neural networks are effective for modeling parameters in wastewater treatment process such as dissolved oxygen concentration and chemical oxygen demand due to their nonlinear adaptability. However, traditional models face challenges such as the requirement for larger datasets and more frequent sampling, noisy measurements, and overfitting. To address this, physics-informed neural networks integrate physical knowledge for improved performance. In our study, we apply both approaches to a wastewater treatment plant, enhancing prediction performance. Our results demonstrate that physics-informed models perform successfully in offline and online validation, especially when standard methods fail. They maintain effectiveness without frequent updates. Yet, integrating physics-informed knowledge can introduce noise when standard methods suffice. This result points out the need for careful consideration of model choice in different scenarios.Publication Metadata only Equipment selection for coupling a microgrid with a power-to-gas system in the context of optimal design and operation(Elsevier Ltd, 2024) Akülker, Handan; Department of Chemical and Biological Engineering; Aydın, Erdal; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); College of EngineeringThis study proposes a one-layer deterministic Mixed-Integer Nonlinear Programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.Publication Metadata only Modeling structural protein interaction networks for betweenness analysis(Springer-Verlag Berlin, 2014) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Demircioğlu, Deniz; Keskin, Özlem; Gürsoy, Attila; Researcher; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Computer Engineering; N/A; College of Engineering; College of Engineering; N/A; 26605; 8745Protein-protein interactions are usually represented as interaction networks (graphs), where the proteins are represented as nodes and the connections between the interacting proteins are shown as edges. Proteins or interactions with high betweenness are considered as key connector members of the network. The interactions of a protein are dictated by its structure. In this study, we propose a new protein interaction network model taking structures of proteins into consideration. With this model, it is possible to reveal simultaneous and mutually exclusive interactions of a protein. Effect of mutually exclusive interactions on information flow in a network is studied with weighted edge betweenness analysis and it is observed that a total of 68% of bottlenecks found in p53 pathway network differed from bottlenecks found via regular edge betweenness analysis. The new network model favored the proteins which have regulatory roles and take part in cell cycle and molecular functions like protein binding, transcription factor binding, and kinase activity.Publication Metadata only Analysis of single amino acid variations in singlet hot spots of protein-protein interfaces(Oxford Univ Press, 2018) N/A; N/A; Department of Computer Engineering; Department of Chemical and Biological Engineering; Özdemir, E. Sıla; Gürsoy, Attila; Keskin, Özlem; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Koç University Research Center for Translational Medicine (KUTTAM) / Koç Üniversitesi Translasyonel Tıp Araştırma Merkezi (KUTTAM); Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 8745; 26605Motivation: Single amino acid variations (SAVs) in protein-protein interaction (PPI) sites play critical roles in diseases. PPI sites (interfaces) have a small subset of residues called hot spots that contribute significantly to the binding energy, and they may form clusters called hot regions. Singlet hot spots are the single amino acid hot spots outside of the hot regions. The distribution of SAVs on the interface residues may be related to their disease association. Results: We performed statistical and structural analyses of SAVs with literature curated experimental thermodynamics data, and demonstrated that SAVs which destabilize PPIs are more likely to be found in singlet hot spots rather than hot regions and energetically less important interface residues. In contrast, non-hot spot residues are significantly enriched in neutral SAVs, which do not affect PPI stability. Surprisingly, we observed that singlet hot spots tend to be enriched in disease-causing SAVs, while benign SAVs significantly occur in non-hot spot residues. Our work demonstrates that SAVs in singlet hot spot residues have significant effect on protein stability and function.Publication Metadata only Plant-wide optimization and control of an industrial diesel hydro-processing plant(Pergamon-Elsevier Science Ltd, 2016) Mutlu, Mustafa; Dikbaş, Mine; İş, Gamze; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Aydın, Erdal; Arkun, Yaman; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; 311745; 108526Diesel hydro-processing (DHP) is an important refinery process which removes the undesired sulfur from the oil feedstock followed by hydro-cracking and fractionation to obtain diesel with desired properties. The DHP plant operates with varying feed-stocks. Also, changing market conditions have significant effects on the diesel product specifications. In the presence of such a dynamic environment, the DHP plant has to run in the most profitable and safe way and satisfy the product requirements. In this study, we propose a hierarchical, cascaded model predictive control structure to be used for real-time optimization of an industrial DHP plant. (C) 2016 Elsevier Ltd. All rights reserved.Publication Metadata only Topological, functional, and structural analyses of protein-protein Interaction networks of breast cancer lung and brain metastases(Ieee, 2017) N/A; N/A; Department of Computer Engineering; N/A; Department of Chemical and Biological Engineering; Halakou, Farideh; Gürsoy, Attila; Kılıç, Emel Şen; Keskin, Özlem; PhD Student; Faculty Member; Master Student; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 8745; N/A; 26605Breast cancer is the second most common cause of death among women. However, it is not deadly if the cancerous cells remain in the breast. The life threat starts when cancerous cells travel to other parts of body like lung, liver, bone and brain. So, most breast cancer deaths derive from metastasis to other organs. In this study, we introduce novel proteins and cellular pathways that play important roles in brain and lung metastases of breast cancer using Protein-Protein Interaction (PPI) networks. Our topological analysis identified genes such as RPL5, MMP2 and DPP4 which are already known to be associated with lung or brain metastasis. Additionally, we found four and nine novel candidate genes that are specific to lung and brain metastases, respectively. The functional enrichment analysis showed that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing were more involved in brain metastasis. By enriching the traditional PPI network with protein structural data, we show the effects of mutations on specific protein-protein interactions. By using the different conformations of protein CXCL12, we show the effect of H25R mutation on CXCL12 dimerization.Publication Metadata only Computers and chemical engineering virtual special issue in honor of professor george stephanopoulos foreword(Elsevier, 2022) Bakshi, Bhavik R.; Realff, Matthew; Morari, Manfred; Department of Chemical and Biological Engineering; Arkun, Yaman; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; 108526Publication Metadata only Topological, functional, and structural analyses of protein-protein interaction networks of breast cancer lung and brain metastases(Institute of Electrical and Electronics Engineers (IEEE), 2017) N/A; N/A; Department of Computer Engineering; N/A; Department of Chemical and Biological Engineering; Halakou, Farideh; Gürsoy, Attila; Kılıç, Emel Şen; Keskin, Özlem; PhD Student; Faculty Member; Master Student; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 8745; N/A; 26605Breast cancer is the second most common cause of death among women. However, it is not deadly if the cancerous cells remain in the breast. The life threat starts when cancerous cells travel to other parts of body like lung, liver, bone and brain. So, most breast cancer deaths derive from metastasis to other organs. In this study, we introduce novel proteins and cellular pathways that play important roles in brain and lung metastases of breast cancer using Protein-Protein Interaction (PPI) networks. Our topological analysis identified genes such as RPL5, MMP2 and DPP4 which are already known to be associated with lung or brain metastasis. Additionally, we found four and nine novel candidate genes that are specific to lung and brain metastases, respectively. The functional enrichment analysis showed that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing were more involved in brain metastasis. By enriching the traditional PPI network with protein structural data, we show the effects of mutations on specific protein-protein interactions. By using the different conformations of protein CXCL12, we show the effect of H25R mutation on CXCL12 dimerization.Publication Metadata only Observation of the correlations between pair wise interaction and functional organization of the proteins, in the protein interaction network of saccaromyces cerevisiae(World Acad Sci, Eng & Tech-Waset, 2006) Haliloğlu, Türkan; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Keskin, Özlem; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; 245513; 26605Understanding the cell's large-scale organization is an interesting task in computational biology. Thus, protein-protein interactions can reveal important organization and function of the cell. Here, we investigated the correspondence between protein interactions and function for the yeast. We obtained the correlations among the set of proteins. Then these correlations are clustered using both the hierarchical and biclustering methods. The detailed analyses of proteins in each cluster were carried out by making use of their functional annotations. As a result, we found that some functional classes appear together in almost all biclusters. On the other hand, in hierarchical clustering, the dominancy of one functional class is observed. In brief, from interaction data to function, some correlated results are noticed about the relationship between interaction and function which might give clues about the organization of the proteins.Publication Metadata only Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy(Oxford Univ Press, 2009) N/A; Department of Chemical and Biological Engineering; Department of Computer Engineering; Department of Chemical and Biological Engineering; Tunçbağ, Nurcan; Gürsoy, Attila; Keskin, Özlem; Faculty Member; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; College of Engineering; 245513; 8745; 26605Motivation: Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces. Results: We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction. Conclusion: Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions.