Drexel BIOMED Website :: BIOMED Faculty Details -- Ahmet Sacan

BIOMED Faculty Details: Ahmet Sacan, Ph.D.
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Ahmet Sacan, Ph.D. [Details] [Update My Profile]
Assistant Professor, School of Biomedical Engineering, Science & Health Systems
Office: Bossone 7-702 Email: as3344@drexel.edu
Phone: 215.895.1804 x Fax: . . x
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Research Keywords:
Bioinformatics, structural bioinformatics, databases, data mining, protein folding, protein structure alignment, protein-protein interactions, computational drug design, graphical user interfaces, gene expression analysis, image processing, computer vision, automated cell tracking, online education tools.

Personal News:

Biosketch:
Ahmet Sacan is currently an Assistant Professor at Drexel University, School of Biomedical Engineering, Science and Health Systems. Ahmet received his B.Sc. degrees in Computer Science and in Cellular and Molecular Biology from University of Michigan, Ann Arbor (2001). He received his Ph.D. from the Computer Engineering Department of the Middle East Technical University (Turkey, 2008). He spent several years as a visiting scholar and Post-Doctoral Researcher at the Bioinformatics Research Group of the Ohio State University (2006-2008).

His research interests lie broadly in the areas of Structural Bioinformatics, Imaging Informatics, and Bitmap Indices. He is specifically focused on similarity search, data mining, and analysis of biomolecular databases; protein folding and docking; automated detection, tracking, and modeling of live cells in digital microscopy imaging; and maintenance and indexing of data warehouses. Besides his research interests, he has worked as system administrator and software developer and has contributed in the development of a number of online learning technologies. A list of his software products, publications, and details of his accomplishments and qualifications can be found at http://sacan.biomed.drexel.edu/ahmet

RESEARCH PROJECTS
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====== Identification of Protein Functional Sites from Novel Surface Representations:

Protein Structure Initiatives are generating experimentally determined structures of proteins, and most of these target proteins have unknown functions. Annotation of these new protein structures requires careful comparison with the compendium of known functions. We are utilizing a novel representation of protein surfaces that map important biochemical features to 2D image representations that are computationally more practical to study. Comparison of these surface representations will aid in functional annotation and yield insights into functional evolution of protein families. The same surface representation will provide a means by which protein-protein interactions can be studied and predicted.

====== Gene Regulatory Network Reconstruction from Microarray Data:

Microarray experiments provide the expression levels of tens of thousands of genes and are nowadays routinely used to study cellular responses to stimuli. The ability to identify differentially expressed genes from microarray experiments has enabled discovery of genetic biomarkers for human diseases. In order to understand the biological mechanism of the identified genes, they are considered in the context of regulatory and metabolic networks. Our knowledge of these networks is limited to a small set of well-studied interactions. We are utilizing various genomic and expression data to reconstruct these networks. The integration of time-series microarray data, co-expression profiles obtained from different microarray datasets, sequence and evolutionary profiles of genes will improve the accuracy and coverage of the predicted interactions.

====== Micro-RNA Modulation of Gene Expression.

Micro-RNAs are a recently discovered class of small non-coding RNA molecules that degrade or inhibit translation of mRNAs. Aberrant Micro-RNA expression has been indicated in a variety of human diseases. Micro-RNA's regulate protein levels by destabilizing mRNA molecules and/or by their inhibiting translation. While several hundreds of conserved micro-RNAs have been identified, their effects on the gene expression levels are not well established. We are developing novel analysis methods for prediction of regulatory effects of micro-RNAs. Our case studies of microRNAs include their involvement in Chronic Regional Pain Syndrome and Spinal Chord Injury.

====== Integration of Biological Knowledgebases:
With the advent of high-throughput experimental methods in biology, and similar advances in information analysis methods has prompted many national and international efforts to catalogue the data generated from these high-throughput experiments. The heterogeneity of these data and differences in the data access methods have made an integrated utilization of the available data a major technical challenge. We are building novel data representation and communication methods to integrate the world of biological data. A graphical user interface utilizing this integrated data is being developed to enable its utilization for bioinformatics education and by non-programmers.

====== Learning to See: Segmentation and Tracking using Pre-annotated Images:
Many of the image processing and computer vision methods have been developed as general purpose methods whose application to domain-specific problems is non-trivial and labor intensive. We are building image segmentation and object tracking methods that can make use of a small set of pre-annotated images. A novel representation of existing information is utilized to train pattern classifiers that can segment new images or track new objects. The developed methods are being applied to wound images and live cell microscopy images.

====== Online Technologies for Programming Education:

Computer programming has become a required skill in engineering and is becoming so in many other data fields. In response to this change, computer programming is being taught more widely in secondary and post-secondary education. Online technologies to assist in self-centered learning and evaluation have not been sufficient to provide for the expanded interest in programming. We are building ProgramminBank as an online technology to assist programming education in engineering and other fields. ProgrammingBank will host a repository of questions that are closely tied to learning objectives and will provide real-time assessment of student work.

Education:
* PhD in Computer Engineering, Middle East Technical University, Ankara, Turkey (2001-2008)
* B.Sc. in Computer Science, University of Michigan (1997-2001)
* B.Sc. in Cell & Molecular Biology, University of Michigan (1997-2001)

Publications:
Peer Reviewed Journal Articles:
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* MicroRNA modulation in complex regional pain syndrome.
Irina Orlova, Guillermo Alexander, Rehman Qureshi, Ahmet Sacan, Alessandro Graziano, James E. Barrett, Robert Schwartzman, and Seena Ajit. Neurology. 2011 (to appear).

* Smolign: A Spatial Motifs Based Multiple Protein Structures Alignment Method.
Hong Sun, Ahmet Sacan, Hakan Ferhatosmanoglu, and Yusu Wang. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011.

* Comparing the Yeast Retrograde Response and NF-kB Stress Responses.
Visish Srinivasan, Andres Kriete, Ahmet Sacan, and S. Michal Jazwinski. Aging Cell, 2010.

* Sequence alignment reveals possible MAPK docking motifs on HIV proteins.
Perry Evans, Ahmet Sacan, Lyle Ungar, Aydin Tozeren. PLoS ONE, 5(1):e8942, 2010.

* MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs.
Ahmet Sacan, Nilgun Ferhatosmanoglu, Hakan Ferhatosmanoglu. BMC Bioinformatics, 10:304-309, 2009.

* Integrated Search and Alignment of Protein Structures.
Ahmet Sacan, I. Hakki Toroslu, and Hakan Ferhatosmanoglu. Bioinformatics, 24(24):2872-2879, 2008.

* CellTrack: An Open-Source Software for Cell Tracking and Motility Analysis.
Ahmet Sacan, Hakan Ferhatosmanoglu, and Huseyin Coskun. Bioinformatics, 24(14):1647-1649, 2008.

* LFM-Pro: A Tool for Detecting Significant Local Structural Sites in Proteins.
Ahmet Sacan, Ozgur Ozturk, Hakan Ferhatosmanoglu, and Yusu Wang. Bioinformatics, 23(6):709-716, 2007.

Patents:

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Phone 215.895.2215 | Fax 215.895.4983 | Email biomed@drexel.edu

Last Modified: 9/20/2011