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Mississippi Computational Biology Mississippi Computational Biology Consortium (MCBC)Consortium (MCBC)
Building Capability and Collaborative Building Capability and Collaborative Networks in Computational BiologyNetworks in Computational Biology
Research Leaders:Raphael Isokpehi Jackson State University
Dawn Wilkins University of Mississippi
Frank Moore University of Southern MississippiJoe Zhang
Susan Bridges Mississippi State UniversityShane Burgess
Current NSF Current NSF EPSCoREPSCoR Grant (2006Grant (2006‐‐2009): 2009): Interrelated Research Focus Areas inInterrelated Research Focus Areas inInterrelated Research Focus Areas inInterrelated Research Focus Areas in
Computational Sciences
MSU, JSU, USM
Computational Biology
Education&
Outreach
Computational Modeling of Biological Systems
UMMC JSU MSU
Computational ChemistryJSU, UM, USM
UMMC, JSU, MSU
Current NSF EPSCoR Grant (2006Current NSF EPSCoR Grant (2006‐‐2009):2009):AimAimAimAim
E t bli hi ti l i i• Establishing national prominence in computational sciences research by b ildi th St t ' i ti t th ibuilding on the State's existing strengths in high performance computing.
Current NSF EPSCoR Grant (2006Current NSF EPSCoR Grant (2006‐‐2009):2009):S ifi G lS ifi G l 11Specific Goals Specific Goals ‐‐ 11
(1) i th St t ' h it b• (1) increase the State's research capacity by – (a) recruit ~12 outstanding faculty with competitive
t t k (~$100K/ )start‐up packages (~$100K/year),
– (b) support and mentor new and existing faculty in interdisciplinary computational sciences researchinterdisciplinary computational sciences research, and
(c) enhance the computational sciences– (c) enhance the computational sciences infrastructure with new equipment and support staff
Current NSF EPSCoR Grant (2006Current NSF EPSCoR Grant (2006‐‐2009):2009):Specific GoalsSpecific Goals –– 2 to 52 to 5Specific Goals Specific Goals 2 to 5 2 to 5
• (2) expand the collaboration among MRC institutions d id l b iand outside laboratories;
• (3) increase opportunities for women and d t d i th l t d hunderrepresented groups in the selected research
areas;
• (4) increase the number of participating graduate• (4) increase the number of participating graduate students and their interface with K‐12 students and teachers; andteachers; and
• (5) foster state economic development through new intellectual property and its commercialization. p p y
Motivation for Investment in Motivation for Investment in Computational BiologyComputational BiologyComputational BiologyComputational Biology
Bi i t h l ill b j d i f• Bioscience technology will be major driver 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 pp y qthe intersection of biology and high performance computationp p
Goals ofGoals ofl ll lComputational BiologyComputational Biology
• Grow fledgling programs in computational biology by hiring new faculty into tenure track positions
• Build a national prominence in computational biology throughBuild a national prominence in computational biology through increased interaction and collaboration among scientists
• Attract and train the best and brightest undergraduate• Attract and train the best and brightest undergraduate, graduate, and post doctoral students in the state
f d d• Increase opportunities for underrepresented groups
• Expand the STEM pipeline by outreach to teachersp p p y
EPSCoREPSCoR StrategiesStrategies
• Invest in human capital: Hire (5) new tenure track faculty in• Invest in human capital: Hire (5) new tenure‐track faculty in computational biology
• Jump start research: Establish a seed grant program to facilitate p g p ggeneration of preliminary data for grant proposals
• Build a state‐wide network: Develop practices that facilitate communication and collaborationcommunication and collaboration
• Train undergraduates and teachers: Work with education componentp
Status: Investment in Human CapitalInvestment in Human Capital
• Dr. Robert Diehl, Biological Sciences, USM– Ph D from University of Illinois– 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, Veterinary Medicine, MSUr. indu Nanduri, Veterinary 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• 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 CapitalAdditional Investment in Human CapitalAdditional Investment in Human CapitalAdditional Investment in Human Capital
New Hires• USM
New to Comp Biology• USM• USM
– Nan Wang, CS
– Preetam Ghosh, CS
– Shahid Karim, Biological Sciences
– Jonathan Sun
• JSU– Hari Cohly (Biology)S a d a , o og ca Sc e ces
• JSU– H. Anwar Ahmad, Biology
• Ole Miss
– Tzusheng Pei (CS)
– Natarajan Meghanathan (CS)
– Sungbum Hong (CS)
– Yixin Chen, CS
• MSU– Changhe Yuan, CSE
– Mohammed Ali (CS)
– Wellington K. Ayensu (Biology)
• MSUTJ J k K ll (CSE)– Song Zhang, CSE
– Fiona McCarthy, Vet Med
– TJ Jankun‐Kelley (CSE)
– Ed Swan (CSE)
– Yogi Dandass (CSE)
– Mahalingam Ramkumar (CSE)Mahalingam Ramkumar (CSE)
Status: Jump Start ResearchStatus: Jump Start Researchpp
• Seed grant funding of $75,000 per year– $25,000 to each university
– Each university determined how to award funds
– Criteria– Criteria• Sound science
• Multidisciplinary combining computation and biology
• Potential to lead to competitive funding• 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
20062006‐‐2007 Seed Grants2007 Seed Grants
• Dynamic spatio-temporal modeling of plant invasionPIs: G. Ervin (Biology) and S. Oppenheimer (Math), MSU
• Environmental stress-mediated regulation of gcellular metal ion and water transport: From sequence to text miningPIs: R. Isokpehi (Biology), H. Cohly (Biology), T. Pei (CS) d B Wil (Bi l ) JSU(CS), and B. Wilson (Biology), JSU
• Parallel multi-class support vector machine for solving large scale classification problems insolving large-scale classification problems in computational biology and bioinformaticsPIs: Y. Deng (Biology), R. Diehl (Biology), and J. Zhang (CS) USM(CS), USM
Outcomes from 2006Outcomes from 2006‐‐2007 Seed 2007 Seed GrantsGrantsGrantsGrants
• 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)
I k hi C hl P i d Wil (JSU)• Isokpehi, Cohly, Pei, and Wilson (JSU):– 4 poster presentations– 2 journal articles in preparation– Award of NIH Research Funding as component of RCMI Center for EnvironmentalAward of NIH Research Funding as component of RCMI Center for Environmental
Health
• Deng, Diehl, Zhang (USM):– 1 journal article accepted1 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
20072007‐‐2008 Seed Grants2008 Seed Grants
• Systems analysis of Streptococcus pneumoniae TIGR4 i i i i iliTIGR4 response to iron restriction using tiling 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
hl ( i l ) S d j l llPIs: H. Cohly (Biology) JSU and Co‐PI R. Rajnarayanan, Tougaloo College
• Inferring gene regulatory networks from time‐series microarray dataseries 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 2007Outcomes from 2007 2008 Seed Grants2008 Seed GrantsOutcomes from 2007Outcomes from 2007--2008 Seed Grants2008 Seed Grants• Nanduri and Swiatlo (MSU and UMMC):
– 1 published manuscript (Proteomics 2008 8(10): 2104‐14)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 ResearchHealth 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)– 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 g g y
20082008‐‐2009 Seed Grants2009 Seed Grants
• Applying network analysis to study novel antifungal compounds and host response to bacterial infectionPI D Bi d N d i (MSU) i ll b ti ith D A tPIs: Dr. Bindu Nanduri (MSU) in collaboration with Dr. AmeetaAgarwal, National Center for Natural Products Research, School of Pharmacy, University of Mississippi
• Bioinformatics tools categorizerDr. Natarajan Meghanathan (Department of Computer Science, Jackson State University), and Dr. Raphael D. Isokpehi (DepartmentJackson State University), and Dr. Raphael D. Isokpehi (Department of Biology, Jackson State University)
• Developing intelligent algorithms to mine biological data from p g g g gweather radar archives. Dr. Robb Diehl (USM Department of Biological Sciences) and Dr. Joe Zhang (USM School of Computing).
RESEARCH WORKSHOP NOVEMBER 14, 2007 @
Participant NumberGraduate Student 16Undergraduate Student 5U i it F lt 16
, @Mississippi JSU E‐Center
University Faculty 1637
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
h itherapies.
Approach: High Density Tiling arrays
Analysis pipeline
Results
• Identified 50 sRNAs identified (four encode novel genes)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
19C3
sRNA-target interaction in virulencePhylogram of sRNAs
Gene Regulatory Network Gene Regulatory Network Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)
• Inferring Gene Regulatory Network of Yeast Cell‐Cycle using Dynamic Bayesian Networky– 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.established gene regulatory relationships.
• DREAM3 In‐Silico‐Network Challenge – Reverse engineering of gene networks from the in silico generated
steady state and time series gene expression datasets.– The USM CBBL team ranked the second place among 40 teams from
10 countries.• Reconstruction of Gene Regulatory Networks from Time Series Fish Ovary
Microarray Data using Dynamic Bayesian NetworkMicroarray Data using Dynamic Bayesian Network– The fish ovary gene regulatory networks with 319 genes have been
reconstructed from real biological time series expression data using dynamic Bayesian network.
Gene Regulatory Network Gene Regulatory Network Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)Reconstruction in CBBL (USM)
• Inferring Gene Regulatory Networks using Expectation‐Maximization Algorithms and Kalman Filterg– We used the expectation‐maximization algorithms and kalman filter to
solve the equations based on state space model to infer gene regulatory networks. The approach is evaluated using a benchmark synthetic dataset which is generated from Escherichia coli.synthetic dataset which is generated from Escherichia coli.
• Inference of Gene Regulatory Networks Using the Predictive Minimum Description Length Principle and Conditional Mutual Information.– We proposed an inference algorithm which implements mutual
i f ti (MI) diti l t l i f ti (CMI) d di tiinformation (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from microarray data.
• NSF EPSCoR related publications and activities in 2008– One journal paper, three conference papers, six oral presentations and
two posters– Dr. Joe Zhang is the Program Committee Chair of the IJCBS’09
conference and was the Vice Chair of the BioComp’08 conferenceconference and was the Vice Chair of the BioComp 08 conference
Building classification algorithms to identify biological targets in weather radar data
Unclassified radar data shows precipitation as shades of pink / purple. Targets more likely to g ybe biological are in shades of green.
Classified data are scored as either biological (blue) or non-biological(brown).
• Weather radars are proven biological instruments with applications that range from aiding in infectious disease tracking to monitoring the pace of climate change.
• Use of these data has been limited by the enormous size of the radar data archive and interspersed nature of biological and non‐biological echoes.
l d ll b b ld d d l f• Biologists and computer scientists are collaborating to build data mining and classification algorithms that can efficiently and accurately subset the data archive into useful biological data sets.
Integrative Analysis of Mammalian and Chicken Aquaporins q pfor Function Beyond Water Transport
Raphael D. Isokpehi1, Hari H.P. Cohly1, Cynthia D. Jeffries1,
Tolulola O. Oyeleye1, Rajendram V Rajnarayanan2y y , j j y
1Center for Bioinformatics & Computational BiologyDepartment of BiologyJackson State UniversityJackson, Mississippi
2Department of ChemistryDepartment of ChemistryTougaloo College
Jackson, Mississippi
AQP11
AQP12AQP12
Visualization of comparison of suggested body site expression of UniGene data for human (h), mouse (m), rat (r), and chicken (c) aquaporins
RESULTS EVOLUTION OF LOCALIZATION MOTIFRESULTS: EVOLUTION OF LOCALIZATION MOTIF Bos taurus MGRQKELVNRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Microcebus murinus MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Equus caballus MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 E inace s e opae s MGRQKELVTRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50Erinaceus europaeus MGRQKELVTRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50Oryctolagus cuniculus MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Canis familiaris MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Myotis lucifugus MGRQKELVNRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Homo sapiens MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Pan troglodytes MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50Pan troglodytes MGRQKELVSRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50Macaca mulatta MGRQKELMSRCGEMLHIRHRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Mus musculus MGRQKELMNRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Rattus norvegicus MGRQKELMNRCGEMLHIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Monodelphis domestica MGRQKELVSRCGDLLRIRYRLLRQALAECLGTLILVMFGCGSVAQVVLSR 50 Ornithorhynchus anatinus MGRQKELLSRCGEMLRIRYKLLRQALAECLGTLILVMFGCGSVAQVILSR 50Ornithorhynchus anatinus MGRQKELLSRCGEMLRIRYKLLRQALAECLGTLILVMFGCGSVAQVILSR 50Gallus gallus MGRQKDVLATIEEHLRIRNKLVRQALAECLGTLILVLFGCGSVAQIVLSR 50 Xenopus tropicalis MGRQKDFINKCNQLLRLRNKLLRQALSECLGTLILVMFGCGSVAQVVLSK 50 Takifugu rubripes MGRQKVYLEKLSHFFQIRNLLIRQGLAECLGTLILVMFGCGAVAQLVLSK 50 Tetraodon nigroviridis MGRQKVYLEKLSHFFQIRNLLIRQGLAECLGTLVLVMFGCGAVAQLVLSK 50 Gasterosteus aculeatus MGRHKFYLDKLSRFFQIRNLLLRQALAECLGTLILVMFGCGSVAQLVLSG 50Gasterosteus aculeatus MGRHKFYLDKLSRFFQIRNLLLRQALAECLGTLILVMFGCGSVAQLVLSG 50Oryzias latipes MSRQKIILDKLARTFQIRNKLLRQSLAECLGTLILVMFGCGACAQHVLSE 50 Danio rerio 1 MGWQKSVLDKLAQTFQIRNKLLRQGLAECLGTLILVMFGCGSLAQLKLSE 50 Danio rerio 2 MGRQKVILEKMARIFQIRNMLMRQALAECLGTLILVMFGCGALAQHILSG 50 *. :* : :::* *:**.*:******:**:****: ** **
The platypus (Ornithorhynchus anatinus) a beaked mammal whose The platypus (Ornithorhynchus anatinus), a beaked mammal whose females lay eggs, had an YKLL motif aligned to the NKLV motif of the
chicken sequence.
RESULTS EVOLUTION OF LOCALIZATION MOTIFRESULTS: EVOLUTION OF LOCALIZATION MOTIF
The platypus (Ornithorhynchus anatinus) a beaked mammal whoseThe platypus (Ornithorhynchus anatinus), a beaked mammal whose females lay eggs, had an YKLL motif aligned to the NKLV motif of the
chicken sequence.
Center for Bioinformatics & Computational Biology Tools
Developer: Tolulola Oyeleye
Center for Bioinformatics & Computational Biology Tools
Developer: Matthew Anyanwu
Developer: Mehdi Pirooznia
Developer: Mehdi Pirooznia
Status: Build a StateStatus: Build a State‐‐Wide NetworkWide Network• Mississippi Computational Biology Consortium (MCBC)
– Steering Committee Chair rotates each year– Steering Committee monthly meetings to evaluate progress and map plansg y g p g p p
• MCBC Website (http://mcbc.usm.edu/)• Meet twice per year with student oral and poster presentations
• MCBIOS (MidSouth Computational Biology Society)– Establishing a Mississippi chapter– Dawn Wilkins of UM was president of MCBIOS in 2008‐2009
D (USM) B id (MSU) d I k hi (JSU) l d MCBIOS B d f– Deng (USM), Bridges (MSU), and Isokpehi (JSU) elected to MCBIOS Board of Directors
– UM, JSU, MSU, & USM presentations at MCBIOS 2006—2009– Mississippi students have won awards for best poster/presentation at all MCBIOS
imeetings– Mississippi hosted the annual meeting in 2009
• Moving forward with sharing of coursework via distance learning
MidSouthMidSouth Computational Biology and Computational Biology and Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)
• Faculty and graduate students represented the MCBC at the Fourth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society, Feb. 2, 2007, New Orleans, Louisiana
MidSouthMidSouth Computational Biology and Computational Biology and Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)Bioinformatics Society (MCBIOS)
• Faculty and graduate students represented the MCBC at the Fourth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society, Feb. 2008, Oklahoma City, OK
MCBIOS 2009MCBIOS 2009Indication of the Growth of our NetworksIndication of the Growth of our NetworksIndication of the Growth of our NetworksIndication of the Growth of our Networks• Four research universities in Mississippi hosted MCBIOS 2009 at
Mississippi State at the Hunter Henry Center
• 140 attendees from ten states
• 68 attendees from Mississippi• 68 attendees from Mississippi
• Dawn Wilkins and Susan Bridges were co‐chairs
• Raphael Isokpehi was chair of student activities– Employer panel
– Speed networking event
• Andy Perkins and Bindu Nanduri were poster co‐chairsAndy Perkins and Bindu Nanduri were poster co chairs– Record number (80) poster presentations
– ERDC provided poster and presentation awards
MCBIOS 2009MCBIOS 2009Speed Networking EventSpeed Networking Event
Computational Biology Computational Biology Education and OutreachEducation and Outreach
• Summer workshops for K‐12 teachersp
• Support for undergraduate research
• Development of competitive proposals to support computational biology education
Andy LindemanMSU Computer ScienceMSU, Computer ScienceJuniorPresentation at REU symposiumPoster accepted at IEEE InfoViz 2007
Christopher NevelsChristopher NevelsJSU, Biology
JuniorPresentation at Tougaloo College
Applied to Graduate School at JSUApplied to Graduate School at JSUPrize Winner at Mississppi Academy of Sciences Meeting
Joe BuzaMSU, BioengineeringFreshman
Some of our
undergraduatePresentation at REU symposium researchers
Poster Presentation by High School Student (Christina Bernard) of the Center for Bioinformatics & Computational Biology (JSU) at the 6th Annual Conference of MidSouth Computational Biology and
Bioinformatics Society. Student also presented at MSEF-Region II Science Fair and won Intel Excellence in Computer Science Award ($200)won Intel Excellence in Computer Science Award ($200)
Awards Proposals for Awards Proposals for Educational FundingEducational FundingEducational FundingEducational Funding
• JSU received funding for Bioinformatics in Biodefense Career Development Program ($500,000) from Homeland Security($ , ) y
– Majority of the funds to provide scholarship and fellowships – Support for 7 undergraduate and 2 graduate students
• USM received funding from the Education Department and the Department ofUSM received funding from the Education Department and the Department of Energy's Joint Genome Institute selected USM pilot collaborators for the Undergraduate Research Program in Microbial Genome Annotation
• MSU submitted GAANN proposal for funding of Graduate Fellowships (Department ofMSU submitted GAANN proposal for funding of Graduate Fellowships (Department of Education)
• MSU submitted Minority Graduate Fellowships in Agricultural Genomics and Bioinformatics at MSU (USDA National Needs Program) Not funded but resubmissionBioinformatics at MSU (USDA National Needs Program). Not funded but resubmission underway
• Undergraduate Research and Mentoring in the Biological Sciences—collaboration with MSU and 6 HBCUs including JSU (not funded has been resubmitted)MSU and 6 HBCUs including JSU (not funded has been resubmitted)
JACKSON STATE UNIVERSITYJACKSON STATE UNIVERSITYBIOINFORMATICS & COMPUTATIONAL BIOLOGY GROUPBIOINFORMATICS & COMPUTATIONAL BIOLOGY GROUP
Summer Internship for High School Students (June 2007)
Stedman A. is now Freshman at
High School Students (First Row L‐R): Bharat Agrawal, Melody Jones and Stedman Ashley.
University of North Carolina, Chapel Hill
Back Row (L‐R): Dr Hari Cohly, Cynthia Jeffries (Graduate Student Mentor) and Dr. Raphael D. Isokpehi
Goal: Increase the number of participating graduate students and their interface with K-12 students and teachers;
Cynthia Jeffries, ORNL
Cynthia Jeffries is involved in annotation of microbial genomes
Juanquina Thomas, NBACC
Juanquina Thomas is involved in sequencing of isolates of biothreat pathogen
Jackson State University Bioinformatics in Biodefense ProgramBioinformatics in Biodefense Program
at DHS University Summit, Washington DC (March 20, 2008)
JACKSON STATE UNIVERSITYJACKSON STATE UNIVERSITYBIOINFORMATICS AWARENESS MONTHBIOINFORMATICS AWARENESS MONTH
WOMEN IN SCIENCE SEMINARWOMEN IN SCIENCE SEMINARSPEAKER: DR NINA FEFFERMAN, RUTGERS UNIVERSITY
Encouraging Women Scientists
Goal: Increase opportunities for women and underrepresented groups in the selected research areas;
VISUAL ANALYTICS & DISCRETE MATHEMATICS FOR HOMELAND SECURITY
JACKSON STATE UNIVERSITY IS PART OFTHE DHS COE FOR COMMAND CONTROL AND INTEROPERABILITY
National Prominence (Example)National Prominence (Example)( p )( p )
2008-2009 Pilot Project Award
Membership of Scientific Program Committee & Session Chair
BOARD MEMBERSHIP OF MIDSOUTH BIOINFORMATICS &
COMPUTATIONAL BIOLOGY SOCIETY (As of January 2009)
Dawn Wilkins [U Miss] (Current/5th Pres) 2010Stephen Winters-Hilt [U New Orleans] (3rd Pres) S B id [Mi St t ] 2010Susan Bridges [Miss State] 2010Ulisses Braga-Neto [Texas A&M] 2009 Doris Kupfer [FAA] {Oklahoma} 2011 James Fuscoe [FDA/NCTR] {Arkansas} 2009 Bill Slikker [FDA/NCTR] {Arkansas} (2nd Pres)Dan Berleant [UALR] {Arkansas} (Pres Elect/6th Pres) Raphael Isokpehi [Jackson State] {Miss} 2011 Stephen Winters-Hilt [U New Orleans] (3rd Pres) Alt Emailp [ ] ( )Johnathan Wren [ORMF] {Oklahoma} (Past/4th Pres) 200 Steve Jennings [UALR] {Arkansas} (1st Pres) Youping Deng [U Southern Miss] 2010
National Prominence (Example)National Prominence (Example)National Prominence (Example)National Prominence (Example)
International Prominence (Example)International Prominence (Example)( p )( p )
Avian Genomes Conference&
Gene Ontology WorkshopM 2008May 2008
Organizers:gSusan BridgesShane Burgess
Institute of Digital Biology, Mississippi State University
International Prominence (Example)International Prominence (Example)( p )( p )
International Joint Conferences on Bioinformatics, Systems l d ll ( ' )Biology and Intelligent Computing (IJCBS'09)
Shanghai, ChinaAugust 3‐6, 2009. g
http://www.isibm.org/IJCBS/index.html
Chaoyang (Joe) Zhang PhDChaoyang (Joe) Zhang, PhD IJCBS'09 Program Committee Chair
Associate Professor and Interim Director School of Computing p g
University of Southern Mississippi
International Prominence (Example)International Prominence (Example)( p )( p )
Technology and Informatics Luncheon&
Goal: Foster state economic development through new intellectual property and its commercialization.
& Speed Networking for Small Business and Higher Education
Friday, April 24, 2009 (11.00am – 3.00pm) at Mississippi E Center @ JSUat Mississippi E-Center @ JSU
1230 Raymond Road, Jackson MS 39204
Invited Luncheon Speakers:
Sterling NicholsOffice of Small Business and Disadvantaged Business Utilization
Department of Energy, Washington DC
Howard Bilofsky PhDHoward Bilofsky, PhD Vice-President of Integromics™ Inc.,
Senior Fellow, School of Engineering and Applied Science of the University of Pennsylvania
former Senior Executive at GlaxoSmithKlineformer Senior Executive at GlaxoSmithKline
The Speed Networking Event will provide a unique opportunity for participants to rapidly meet potential collaborators and business partners.
Further Information and Registration (by Friday April 17 2009):Further Information and Registration (by Friday April 17, 2009):Angelique C. Lee, Program Coordinator,
Center for Bioinformatics & Computational Biology, Jackson State University, Jackson, [email protected]
601-979-0328
New NSF EPSCoR ProposalNew NSF EPSCoR ProposalD i t Add B i t b i t t bli hi ti llDesign to Address Barriers to barriers to establishing nationally
competitive multidisciplinary research teams
• Recruitment of new faculty, development of younger faculty, and retention of experienced f ltfaculty
• Key research areas is comprised primarily of small and geographically dispersed groups making interand geographically dispersed groups, making inter‐institutional and interdisciplinary collaborations more difficult.more difficult.
• The need for added statewide connectivity, financial resources to upgrade and purchase research pg pequipment and limited financial resources at the state level.
New NSFNew NSF EPSCoREPSCoR ProposalProposalNew NSF New NSF EPSCoREPSCoR ProposalProposalModeling and Simulation of Complex Systems
• Status– Submitted in fall 2008
– Responded to issues raised by NSF in January 2009
• Three Focus Areas:– Multi‐scale simulation of Biological Systems (BioSim)
– Modeling of Biological Networks (CompBio)Modeling of Biological Networks (CompBio)
– Modeling and Simulation of Nanoscale Chemistry (CompChem)
• Includes funding for research, computational infrastructure, seed grant fundinggrant funding
• Integrated education, outreach, and workforce development effort across three focus areas.
GoalsGoals
• Expanded infrastructure
• ExpandedExpanded integration across focus areasfocus areas
Computational Biology Research Activities
Systems Biology Approaches: Experiment Bioinformatics and ComputationExperiment, Bioinformatics and Computation
System of Gene Regulation
System error source
Controls
Forward Model(Deterministic)
Inverse Model(State estimate andparameter learning)Observed Estimate of
Measuring devices
System state(desired but unknown)
Dynamic System(Stochastic)
parameter learning)Observed measurements system state
Update GRNsPrior
knowledge
Measurementerror source
Biological system and measurement GRN reconstruction model
Bayesian Learning and Optimization (BLO) Model
Joe Zhang et al. (University of Southern Mississippi)
Relevance of Proposed CompBio ResearchCompBio Research
Workshop Report Published in March 2009
Opportunities for kf lWorkforce Development
The Mississippi Workforce • Mississippi’s population is 2.9 million.pp p p• The population increases about 20,000 annually, 0.7 %. • The workforce is 1.3 million. • On average, 80,000 people are unemployed, about 6.2%.
Th i ti t d t b l 70 000 l f ki h t ti i ti• There is estimated to be nearly 70,000 people of working age who are not participating in the workforce. This percentage is one of the highest in the nation. • 73% of working age Mississippians have a high school education or higher. • 17% of working age Mississippians have bachelors degrees or higher.7% o wo g age ss ss pp a s ave bac e o s deg ees o g e .• 5.8% of working age Mississippians have graduate or professional degrees • 20.9% of working age Mississippians have some college but no degree. • 5.7% of working age Mississippians have an associate degree.
Mi i i i h th l t ti l 400 000 f ll ti k k b t• Mississippi has the lowest national wage – 400,000 full time workers make between $5.15 and $9.50 per hour • 180,000 workers are employed in manufacturing.
Source: Mississippi State Workforce Investment Board
SummarySummary• Computational biology capabilities in Mississippi have undergone exponential growth since the first EPSCoRg p gfunding in 2006.
• Human infrastructure at all four state research i iti i tl d duniversities is greatly expanded.
• We can accomplish more by collaborating than by competing.competing.
• Focus for the coming 5 years will be to:– Enhance research capability and integration of activities– Greater involvement of state HBCUs– Greater focus on workforce development
