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Computational Biology Mississippi Computational Biology Consortium (MCBC) Building Capability and Collaborative Networks in Computational Biology Research Leaders: Raphael Isokpehi Jackson State University Dawn Wilkins University of Mississippi Frank Moore University of Southern Mississippi Joe Zhang Susan Bridges Mississippi State University Shane Burgess Current NSF EPSCoR GranGrant t (2006‐2009): Interrelated Research Focus Areas in Computational Sciences Computational Biology MSU, JSU, USM Education & Outreach Computational Chemistry Computational Modeling of Biological Systems JSU, UM, USM UMMC, JSU, MSU Current NSF EPSCoR Grant (2006‐2009): Aim • EtEstablishing national prominence in computational sciences research by bildibuilding on the State's exitiisting strengths in high performance computing. Current NSF EPSCoR Grant (2006‐2009): S pec ifi c GlGoals ‐‐ 1 • (1) i ncrease the Sta te ' s research capacity by – (a) recruit ~12 outstanding faculty with competitive stttart‐up packages (~$100K/year ), – (b) support and mentor new and existing faculty in interdisciplinary computational sciences research, and (c) enhance the computational sciences infrastructure with new equipment and support staff Current NSF EPSCoR Grant (2006‐2009): Specific Goals – 2 to 5 • (2) expand the collaboration among MRC institutions and outside llbaborator ies; • (3) increase opportunities for women and underrepresen te d groups in th e seltdlected researc h areas; • (4) increase the number of participating graduate students and their interface with K‐12 students and teachers; and • (5) foster state economic development through new intellectual pppropert y and its commercialization. Motivation for Investment in Computational Biology • Biosc i ence thtechno logy will b e major didriver of the economy in the 21st century • Modern bioscience requires – High throughput bioscience technologies – High performance distributed computation • Mississippi has embryonic and unique niche at the intersection of biology and high p erformance computation Goals of Computational Bio logy • Grow fledgling programs in computational biology by hiring new faculty into tenure track positions • Build a national prominence in computational biology through increased interaction and collaboration among scientists • Attract and train the best and brightest undergraduate, graduate, and post doctoral students in the state • Increase opportunities for underrepresented groups • Expand the STEM pppipeline by outreach to teachers EPSCoR Strategies • Invest in human capital: Hire (5) new tenure‐ track faculty in computational biology • Jump start research: Establish a seed grant ppgrogram to facilitate generation of preliminary data for grant proposals • Build a state‐wide network: Develop practices that facilitate communication and collaboration • Train undergraduates and teachers: Work with education component Status: InvestmentInv estmen t in Human Capital • Dr. Robert Diehl, Biological Sciences, USM – Ph .D. from University of Illinois – NSF Postdoctoral fellowship at USM • Dr. Raphael Isokpehi, Biological Sciences, Jackson State University – Ph.D. from University of Lagos, Nigeria – Post Doc at South African National Bioinformatics Institute • Dr. Bindu Nanduri, Veterinaryy Medicine, MSU – Ph.D. from the University of Arkansas Little Rock – Post Doc at University of Medicine and Dentistry, NYC and Mississippi State • Dr. Andy Perkins , Computer Science and Engr., MSU – Ph.D. from the University of Tennessee • USM to hire faculty member this spring in Biological Sciences Additional Investment in Human Capital New Hires New to Comp Biology • USM • USM – Jonathan Sun – Nan Wang, CS – Preetam Ghosh, CS • JSU – Shahid Karim, Biologgcical Sciences – Hari Cohly (Biology) • JSU – Tzusheng Pei (CS) – H. Anwar Ahmad, Biology – Natarajan Meghanathan (CS) • Ole Miss – Sungbum Hong (CS) – Mohammed Ali (CS) – Yixin Chen, CS – Wellington K. Ayensu (Biology) • MSU – Changhe Yuan, CSE • MSU – Song Zhang, CSE – TJ JkJankun ‐KllKelley (CSE) – Fiona McCarthy, Vet Med – Ed Swan (CSE) – Yogi Dandass (CSE) – Mahalingam Ramkumar (CSE) Status: Jump Start Research • Seed grant funding of $75,000 per year – $25,000 to each university – Each university determined how to award funds – Criteria • Sound science • Multidisciplinary combining computation and biology • Potential to lead to competitive funding • Year 1 funds: Multidisciplinary research • Year 2 funds: Must include faculty from at least two MS campuses • Year 3 funds: Must include faculty from at least two MS campuses 2006‐2007 Seed Grants • Dynamic spatio-temporal modeling of plant invasion PIs: G. Ervin (Biology) and S. Oppenheimer (Math), MSU • Environmental stress-mediated regulation of cellular metal ion and water transport: From sequence to text mining PIs: R. Isokpehi (Biology), H. Cohly (Biology), T. Pei (CS), and B. Wilson (Biol ogy), JSU • Parallel multi-class support vector machine for solving large -scale classification problems in computational biology and bioinformatics PIs: Y. Deng (Biology), R. Diehl (Biology), and J. Zhang (CS) , USM Outcomes from 2006‐2007 Seed Grants • Ervin and Oppenheimer (MSU): – 2 poster presentations at national meetings – Journal article in progress – Proposal submitted and funded USDA National Research Initiative, $100,300 (2 years) • IkIsokpe hi, C ohlPhly, Pei, andWild Wilson (JSU): – 4 poster presentations – 2 journal articles in preparation – Award of NIH Research Funding as component of RCMI Center for Environmental Health • Deng, Diehl, Zhang (USM): – 1 journal article accepted – 4 poster presentations – Diehl has received funding from USGS and his preliminary research will contribute to his NSF Career Award application in the coming year 2007‐2008 Seed Grants • Systems analysis of Streptococcus pneumoniae TIGR4 response to iron restriiiction using tili ng DNA microarrays PIs: B. Nanduri (CVM) MSU collaborating with E. Swiatlo, University of Mississippi Medical Center, Division of Infectious Diseases • Text mining for cellular localization of mammalian aquaporins PIs: H. Cohly (i(Biology) JSU and Co‐PI R. Rajnarayanan, Tougaloo College • Inferring gene regulatory networks from time‐ series microarray data PIs: M. Pirooznia (Biology), Y. Deng (Biology), and C. Zhang (CS), USM in Collaboration with Dr. Ed Perkins, U.S. Army Corps of Engineers Engineer Research and Development Center, Vicksburg, MS. Outcomes from 2007 -2008 Seed Grants • Nanduri and Swiatlo (MSU and UMMC): – 1 published manuscript (Proteomics, 2008, 8(10): 2104‐14) – 2 manuscripts in preparation – Submitted one proposal to MFGN – Will submit a proposal to NIH in October 2008 • Cohly and Rajnarayanan (JSU, Tougaloo): – 1 Publication (Int. J. Environ. Res. Public Health 2008, 5(2), 115‐119) – 3 Abstracts at 5th International Symposium on Recent Advances in Environmental Health Research – Grant submitted to Department of Homeland Security on Visual Analytics – Grant submitted to NSF on Biological Responses to English as Second Language • Pirooznia, Deng, Zhang and Perkins (USM, ERDC): 1 publication accepted (Proceedings of the Fifth Annual MCBIOS Conference) – 2 manuscripts in preparation – Submitted one proposal to MFGN, one NIH submission – 3 poster and oral presentations – Zhang received funding from US Army ERDC in 2008 2008‐2009 Seed Grants • Applying network analysis to study novel antifungal compounds and host response to bacterial infection PIs: Dr. Bin du NdNanduri (MSU) in colla bora tion with Dr. Amee ta Agarwal, National Center for Natural Products Research, School of Pharmacy, University of Mississippi • Bioinformatics tools categorizer Dr. Natarajan Meghanathan (Department of Computer Science, Jackson State University), and Dr.Dr. Raphael D. Isokpehi (Department of Biology, Jackson State University) • Developpging intelli gent algorithms to mine biological data from weather radar archives. Dr. Robb Diehl (USM Department of Biological Sciences) and Dr. Joe Zhang (USM School of Computing). Participant Number RESEARCH WORKSHOP Graduate Student 16 NOVEMBER 14, 2007 @ Undergraduate Student 5 UiUnivers ity Faculty 16 Mississippi JSU E‐Center 37 Identification of novel non-coding small RNAs in S. pneumoniae TIGR4 genome Non- coding RNAs • Genetically encoded (intergenic regions) • Major regulators in adaptive response, translational quality control, acid resistance, homeostasis, regulating virulence Streptococcus pneumoniae TIGR4 • Gram positive pathogen that causes a number of infections in humans including acute sinusitis, otitis media, meningitis. • One of the top ten causes of mortality in the US in 2003. • Identification of genomic elements is crucial for understanding pathogen’s biology and developing therap ies. Analysis pipeline Approach: High Density Tiling arrays Results • Identified 50 sRNAs identified (four encode novel genes) • Length ranges from 74 - 480 nucleotides with two-third being shorter than 200 bp • A number of predicted sRNA targets are known virulence factors in pneumococcus C1 C2 Interaction network of sRNAs and their predicted target genes C3 19 sRNA-target interaction in virulence Phylogram of sRNAs Gene Regulatory Network Reconstruction in CBBL (USM) • Inferring Gene Regulatory Network of Yeast Cell‐Cycle using Dynamic Bayesian Network – We have applied a model of dynamic Bayesian networks to a benchmark dataset of yeast cell‐cycle, constructed gene regulatory networks, and compared the inferred networks with previously established gene regulatory relationships. • DREAM3 In‐Silico‐Network Challenge – Reverse engineering
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