Bioinformatics and Systems Biology in Regeneration

A. Keith Dunker, PhD
James Glazier, PhD
Snehasis Mukhopadhyay, PhD
Mathew Palakal, PhD
Mu Wang, PhD
Frank A. Witzmann, PhD

A. Keith Dunker, PhD

Positions

• Director, Center for Computational Biology and Bioinformatics
• Professor, Biochemistry and Molecular Biology
• Professor and Director, Bioinformatics Program, Indiana University School of Informatics

Address
Center for Computational Biology and Bioinformatics
Indiana University School of Medicine
Medical Information Sciences Building
410 West 10th Street
Indianapolis, IN 46202-2111

Phone: (317) 278-9220
Fax: (317) 278-9217
Email: kedunker@iupui.edu

Research Interests

Dr. Dunker started research in computational biology and bioinformatics in the mid-1980s and began using bioinformatics to study intrinsically disordered proteins in the mid-1990s, where he and his collaborators were the first to consider these proteins as a distinct class with important biological functions. His bioinformatics research goals over the next several years include the improvement of intrinsic disorder predictions, especially with respect to identifying different types of disorder (flavors) and then to understanding the relationships between the different types of disorder and protein function, i.e., to understand flavor-function relationships. In addition, he wants to combine bioinformatics prediction with laboratory experimentation to develop new approaches for understanding protein-protein signaling interactions that involve intrinsically disordered proteins and to develop proteomics methodologies specifically geared to uncovering the unfoldome (e.g. the set of intrinsically unfolded proteins in a given organism).

Recent Publications

• Sickmeier, M., Hamilton, J.A., LeGall, T., Vacic, V., Cortese, M.S., Tantos, A., Szabo, B., Uversky, V.N., Tompa, P., Chen, J., Obradovic, Z., and Dunker, A.K. DisProt: The database of disordered proteins. Nucleic Acids Res. 35(Database issue): D786-93. (2007)
• Le Gall, T., Romero, P., Cortese, M.S., Uversky, V.N., and Dunker, A.K., Intrinsic disorder in the Protein Data Bank. J. Biomol. Struct. Dyn. 24: 303-428 (2007).
• Uversky, V. N., Dunker, A.K. Understanding protein non-folding. To fold or not to fold: Some current concepts of protein chemistry. (Zbilut, J., Scheibel, T., eds.) Nova Science Publishers, Inc. Hauppauge, NY. (In Press) (2007)
• Kuznetsova I.M., Turoverov K.K., Dunker A.K., Uversky V.N. Analysis of folded, partially folded and misfolded proteins with fluorescent dyes. In Protein Structures: Methods in Protein Structure Analysis (Uversky V.N., Permyakov E.A., Eds.) Nova Science Publishers, Inc., Hauppauge, NY. (In press) (2007)
• Uversky V.N., Radivojac P., Iakoucheva L.M., Obradovic Z., Dunker A.K. Prediction of intrinsic disorder and its use in functional proteomics. In: Methods in Molecular Biology: Gene Function Analysis (editor: Michael F. Ochs), Humana Press, Totowa, NJ. (In press) (2007)
• Midic, U. Dunker, K. and Obradovic, Z. Exploring alternative knowledge representations for protein secondary-structure prediction. Int’l Journal of Data Mining and Bioinformatics. (In press) (2007)

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James Glazier, PhD

Position

• Professor of Physics
• Director, Biocomplexity Institute
• Adjunct Professor of Informatics and Biology
• Vice Chair of the Division of Biological Physics of the American Physical Society

Address
Department of Physics
Swain Hall West 159
727 E 3rd St.
Indiana University, Bloomington
Bloomington, IN 47405

Phone: (812) 855-3735
Fax: (812) 855-5533
E-mail: glazier@indiana.edu

Research Interests

Research in my laboratory has focuses on embryonic development and the development of experimental and computational methodology for its study. We develop computer simulations to model morphogenesis (pattern-formation), including simulations of limb and vascular development, gastrulation, somitogenesis (segmentation) and tumor growth and we conduct experiments to track cell movements during these developmental phenomena using confocal microscopy. We have also developed (and continue to develop) an open-source modeling environment CompuCell3D, that makes writing even very sophisticated simulations fairly straightforward (it is available for download from https://simtk.org/home/compucell3d). We also develop biosensors to quantify protein, lipid and carbohydrate concentrations for scientific and medical applications. In addition, I organize conferences promoting interdisciplinary approaches to major biomedical issues.

Recent Publications

• "A cell-centered approach to developmental biology," Roeland H. M. Merks and James Glazier, Physica A 352, 113-130 (2005)
• "A Framework for Three-Dimensional Simulation of Morphogenesis," Trevor M. Cickovski, Chengbang Huang, Rajiv Chaturvedi, Tilmann Glimm, H. George E. Hentschel, Mark S. Alber, James A. Glazier, Stuart A. Newman, and Jesus A. Izaguirre, IEEE/ACM Transactions on Computational Biology and Bioinformatics 2, 273-288 (2005).
• "Analysis of tissue flow patterns during primitive streak formation in the chick embryo," Cheng Cui, Xuesong Yang, ManLi Chuai, James A. Glazier, Corneljis J. Weijer, Developmental Biology 284, 37-47 (2005).
• "Cell elongation is key to in silico replication of in vitro vasculogenesis and subsequent remodeling," Roeland M. H. Merks, Sergey V. Brodsky, Michael S. Goligorksy, Stuart A. Newman and James A. Glazier, Developmental Biology 289, 44-54 (2006).
• "Adhesion between cells, diffusion of growth factors, and elasticity of the AER produce the paddle shape of the chick limb," Nikodem J. Poplawski, Maciej Swat, J. Scott Gens and James A. Glazier, Physica A 373, 521-532 (2007).

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Snehasis Mukhopadhyay, PhD

Position

• Associate Professor

Address
Department of Computer & Information Science
School of Science
Indiana University-Purdue University Indianapolis
723 W. Michigan St. SL 280J
Indianapolis, IN 46202

Phone: (317) 274-9732
Fax: (317) 274-9742
E-mail: smukhopa@iupui.edu

Research Interests

Understanding how human brain or other intelligent biological systems function has always been one of the holy grails of human endeavor. Recent advances in learning and adaptive systems make this goal less remote and unattainable than before. New computing paradigms such as artificial neural networks, reinforcement-based machine learning and multi-agent systems incorporate the ability to solve complex problems and make decisions in a dynamic environment in an adaptive fashion. At the same time, such intelligent systems have found wide-spread applications in many practical areas such as control of man-made engineering systems, management of information in digital libraries, and ?filtering and analysis of biological information. Our intelligent systems research group in the Computer and Information Science department at IUPUI has been active in furthering the progress of this field both in terms of the basic research as well as applications in important scientific and engineering problems. The basic research directions include learning and adaptation in multi-agent intelligent systems and decentralized adaptive control in distributed systems. Methods have been developed to achieve optimal or near-optimal performance, while minimizing communication overhead and dealing with uncertainties in a stable fashion. Application areas under investigation include filtering, delivery, and analysis of information in biological and other domains such as digital libraries. The primary objective of information filtering is to identify, in a large information collection, items that are relevant according to some criteria. Following this, the identified relevant information can be further analyzed with the objective of extracting high-level knowledge such as object-object associations. We have successfully developed learning approaches based on neural networks and reinforcement learning to both theses problems of filtering and analysis of data.

Recent Publications

• N. Jayadevaprakash, S. Mukhopadhyay, M. Palakal. Generating Association Graphs of Non-coocurring Text Objects using Transitive Methods. . pp.141 - 145, ACM Symposium on Applied Computing (ACM SAC), Santa Fe, New Mexico, USA, March 13 - 17, 2005.
• S. Mukhopadhyay, S. Peng, R. Raje, J. Mostafa, M. Palakal. Distributed Multi-Agent Information Filtering: A Comparative Study. Journal of the American Society for Information Science and Technology. Vol.56, No.8, pp.834 - 842, 2005.
• M. Palakal, S. Mukhopadhyay, M. Stephens. Identification of Biological Relationships from Text Documents. Book Chapter in "Medical Informatics: Advances in Knowledge Management and Data Mining in Biomedicine, Ed. H. Chen. Kluwer Publishers, pp.449 - 489, 2005.
• V. Narayanasamy, S. Mukhopadhyay, M. Palakal, D. Potter. TransMiner: Mining Transitive Associations among Biological Objects from Text. Journal of Biomedical Science. Karger, Vol.11, No.6, pp.864 - 873, 2004.
• S. Mukhopadhyay, S. Peng, R. Raje, M. Palakal, J. Mostafa. Multi-Agent Information Classification Using Dynamic Acquaintance Lists. Journal of the American Society for Information Science and Technology (JASIST). pp.966 - 975, 2003.

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Mathew Palakal, PhD

Position

• Associate Dean, Research & Graduate Programs, School of Informatics
• Director, Informatics Research Institute
• Professor of Computer Science

Address
School of Informatics
Indiana University-Purdue University Indianapolis
535 W. Michigan St. IT 475
Indianapolis, IN 46202

Phone: (317) 278-7689
Fax: (317) 278-4140
Email: mpalakal@cs.iupui.edu

Research Interests

The biomedical literature databases continue to grow rapidly with vital information that is important for conducting sound biomedical research. BioMap is an attempt to create a scalable knowledgebase of biological relationships from vast amount of literature data. The development of BioMap system addresses several innovative research issues related to knowledge discovery from literature documents and real-time, interactive access of this knowledge. Specific problems that are being investigated are: identification of multiple biological objects and discovering their direct, transitive and directional relationships and generating pathways of new hypothesis using the hypergraph based on graph algorithms. Protein-protein, gene-protein, disease-drug interactions are examples of biological associations that are automatically discovered from large number of literature documents. These associations are further validated using computational techniques. BioMap can discover interactions in specific biomedical problem domains such as inflammatory diseases, regenerative biology, and cancer.

Recent Publications

• Naidu, P., Palakal, M., and Hartanto, S: On-the-fly data integration models for biological databases, 2007 ACM Symposium on Applied Computing, Korea, in press, 2007.
• Palakal M, Bright J, Sebastian T, Hartanto S: A Comparative Study of Cells in Inflammation, EAE and MS using BioMedical Literature Data Mining. Journal of Biomedical science (in press), 2006.
• Palakal M, Mukhopadhyay S,Stephens M: Identification of Biological Relationships from Text Documents. Book Chapter in "Medical Informatics: Advances in Knowledge Management and Data Mining in Biomedicine, Ed. H. Chen. Kluwer Publishers, 449, 2005.
• Jayadevaprakash, N., Mukhopadhyay, S., and Palakal, M: An Approach to Generating Association Graphs of Non-Co-Occurring Text Objects using Transitive Methods, 20th ACM Symposium on Applied Computing, 2005.
• Kumar, K., Palakal, M., Mukhopadhyay, S., BioMap: Toward the Development of a Knowledge Base of Biomedical Literature, 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus, 2004.
• Palakal M, Stephens M, Mukhpodhyay S, Raje R, Rhodes S: Identification of Biological Relationships from text documents using efficient computational Methods. Journal of Bioinformatics and Computational Biology, 2003,1, (2) , 1.

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Mu Wang, PhD

Positions

• Assistant Professor of Biochemistry and Molecular Biology
• Adjunct Assistant Professor of Informatics
• Director, Protein Analysis Research Center
• Indiana Centers for Applied Protein Sciences (INCAPS)

Address
Department of Biochemistry and Molecular Biology
Indiana University School of Medicine
Indiana University-Purdue University Indianapolis
Biotechnology Research and Training Center
1345 W. 16th St, Room 312
Indianapolis, IN 46202

Phone: (317) 278-0296; (317) 274-1446 (INCAPS)
Fax: (317) 278-9739
Email: muwang@iupui.edu

Research Interests

1. Molecular Mechanisms of DNA Damage and Repair
Damage to DNA is an important etiologic pathway for a number of important processes such as cancer, birth defects and aging. Therefore understanding DNA repair pathways is very important. One of my research interests is to study DNA repair mechanisms in both normal and repair deficient mammalian cell lines. We have developed a method for monitoring the interactions between DNA repair proteins and damaged DNA using surface plasmon resonance (SPR) technology, and we are able to make DNA containing different types of damage produced by selected mutagenic and carcinogenic agents such as UV and cisplatin. By using DNA substrates containing site specific adducts, we have been able to determine the preference in DNA binding activity for DNA repair proteins on damaged DNA. Understanding the mechanism of DNA repair pathway will help development of gene therapy as well as chemotherapy of the DNA repair-deficient diseases such as xeroderma pigmentosum (XP). In addition, we are also interested in studying Fanconi anemia (FA), a disorder characterized by bone marrow failure and development of leukemia and other malignancies, in which there is a marked hypersensitivity to DNA interstrand cross linking agents. To date, eleven complementation groups (FA-A to FA-L) have been identified. However, the function of each complementing protein remains largely unknown. We are particularly interested in studying the interactions of FA proteins with other DNA repair proteins using both molecular biology and bioanalytic chemistry techniques. With recent advancement of proteomic technologies, mass spectrometry will be the main tool applied for identification of proteins that interact with FA proteins. The goal of our study is to test 1) whether these FA proteins form complex at the damaged site on DNA, either with each other or with other DNA repair proteins, and 2) what is the role of each protein in damage recognition and/or DNA repair processes.

2. Biomarkers for Cisplatin Resistance in Human Tumor Cells using Proteomics
Platinum-based chemotherapy is still the primary treatment for many types of cancer. Most patients with the disease are initially responsive to chemotherapeutic treatment. However, the majority of cancer patients eventually relapse and become refractory to additional treatment. This drug-resistance is a major impediment to the successful treatment of cancer. To date the mechanisms of drug-resistance are poorly understood. Previous studies have suggested that many proteins, such as BRCA1, BRCA2, MDR1, MRP1, MDM2, hMLH1, HSP27, and HSP70, are differentially expressed in drug-resistant tumor cells, such as ovarian tumor cells, by mRNA differential display analysis. However, global protein pattern changes in these tumor cells have not yet been demonstrated. With recent developments in electrophoresis, imaging, and mass spectrometric technologies, along with the explosion in genomic and protein bioinformatics, the complex status of protein expression, defined as “expression” proteomics, can be analyzed. It is possible to examine, at the molecular level, the perturbations of physiological processes, as well as mechanisms of disease, injury, and therapeutic intervention, by profiling the proteins comprising the organellar, cellular, or extracellular proteomes. Proteomics has provided a very important tool to revolutionize disease diagnosis, drug target discovery, and new therapeutic development.

Since there is no established protein expression profile between drug-sensitive and -resistant ovarian cancer cells, we will choose established ovarian cancer cell lines as the model system for our investigation. We will apply the cutting-edge proteomic technologies such as MudPIT and Label-free Protein Quantification to identify novel proteins associated with cisplatin-resistance in human ovarian cancer cells and thus define new therapeutic targets in ovarian cancer intervention. Once new targets are identified by proteomics, we will develop a strategy to modulate the expression of particular proteins, and then evaluate whether this strategy can lead to the reversal of cisplatin sensitivity of the ovarian cancer cells.

Along the way, we will develop a strategy for subcellular fractionation of lysates obtained from ovarian cancer cells in order to increase depth-of-field (protein dynamic range). Application of this strategy will enhance our ability to examine low abundance proteins in the whole cell lysates. Each fraction will be quantitatively analyzed by quantitative mass spectrometry. Our work will provide important information about biomarkers for drug-resistance in ovarian cancer cells. Identification of biomarker(s) for cisplatin-resistance in ovarian cancer cells and modulation of these biomarkers' expression levels will be clinically significant for finding potential therapeutic targets to develop antitumor drugs and control tumor growth.

Recent Publications

• Cocklin, R. R.; Zhang, Y.; O'Neill, K. D.; Chen, N. X.; Moe, S. M.; Bidasee, K. R.; Wang, M. (2003) Identity and localization of advanced glycation end products on human beta-2-microglobulin using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Biochem. 314, 322-325.
• Decker, E. D.; Zhang, Y.; Cocklin, R. R.; Witzmann, F. A.; Wang, M. (2003) Proteomic analysis of differential protein expression induced by ultraviolet light radiation in HeLa cells. Proteomics. 3, 2019-2027.
• Zhang, Y.; Cocklin, R. R.; Bidasee, K.R.; Wang, M. (2003) Rapid determination of advanced-glycation end products of proteins using MALDI-TOF mass spectrometry and PERL script peptide searching algorithm. J. Biomol. Tech. 14, 224-230.
• Tao, W.; Wang, M.; Voss, E. D.; Cocklin, R. R.; Smith, J. A.; Cooper, S. H.; Broxmeyer, H. E. (2004) Comparative proteomic analysis of human CD34+ stem/progenitor cells and mature CD15+ myeloid cells. Stem Cells. 22, 1003-1014.
• You, J-S.; Gelfanova, V.; Knierman, M. D.; Witzmann, F. A.; Wang, M.; Hale, J.E. (2005) The impact of blood contamination on the proteome of cerebrospinal fluid. Proteomics. 5, 290-296.

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Frank A. Witzmann, PhD

Position

• Professor of Cellular & Integrative Physiology

Address
Biotechnology Research & Training Center
Indiana University School of Medicine
1345 W. 16th Street, Room 308
Indianapolis, IN 46204

Phone: (317) 278-5741
Fax: (317) 278-9739
Email: fwitzman@iupui.edu

Research Interests

My research involves the application of proteomic techniques to the detection and analysis of protein expression in a variety of research paradigms. Specific projects include 1) differential protein expression by cells and tissues exposed to toxic agents in vivo or in vitro, 2) alcohol effects on protein expression in various brain cell types/regions 3) characterization of serum proteins associated with atherosclerosis, diabetic dyslipidemia, and alcoholism, and 4) improving gel-based proteomics by developing sample preparation and prefractionation strategies. In conducting these studies, we apply solution IEF, large format one- and two-dimensional gel electrophoretic separations and image analysis, MALDI-TOF MS, and tandem mass spectrometry (LC-MS/MS). Protein expression information obtained in this way can be used as indicators or “molecular biomarkers” of primary or secondary cellular effects, or used to better understand the molecular mechanisms that trigger altered or impaired physiological function.

Recent Publications

• Witzmann, F.A., N.A. Monteiro-Riviere, A.O. Inman, M.W. Kimpel, N.M. Pedrick, H.N. Ringham,, J.E. Riviere. (2005). Effect of JP-8 jet fuel exposure on protein expression in human keratinocyte cells in culture. Tox. Lett. 160, 8–21.
• Witzmann, F.A., M.R. Richardson. (2006). Two-dimensional gels for toxicological drug discovery applications. Expert Opinion on Drug Metabolism, Toxicology 2, 103-111.
• Witzmann, F.A., N.A. Monteiro-Riviere. (2006). Multi-walled carbon nanotube exposure alters protein expression in human keratinocytes. Nanomed: Nanotechnol. Biol. Med. 2: 158-168.
• Clack, J.W., M.L. Springmeyer, C.R. Clark,, F.A. Witzmann. (2006). Transducin subunit stoichiometry and cellular distribution in rod outer segments. Cell Biology Intern'l. 30, 829-835.
• Bell, R.L., M.W. Kimpel, Z.A. Rodd, W.N. Strother, F. Bai, C.L. Peper, R.D. Mayfield, L. Lumeng, D.W. Crabb, W.J. McBride, F.A. Witzmann. (2006). Protein expression in the nucleus accumbens and amygdala of inbred alcohol-preferring rats given either continuous or scheduled access to ethanol. Alcohol 40:3-17.

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