Bridging Quantitative Science with Biological Research: Jumpstarting Computational Systems Biology Research at PVAMU

Project Overview:

The Historically Black Colleges and Universities Research Infrastructure for Science and Engineering (HBCU-RISE) activity within the Centers of Research Excellence in Science and Technology (CREST) program supports the development of research capabilities at HBCUs that offer doctoral degrees in science and engineering disciplines. HBCU-RISE projects have a direct connection to the long-term plans of the host department(s) and the institutional mission, and plans for expanding institutional research capacity as well as increasing the production of doctoral students in science and engineering. With support from the National Science Foundation, Prairie View A&M University (PVAMU) aims to provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. The project aims to 1) jumpstart computational biology research to stimulate students’ interest and enhance the PhD program in Electrical Engineering, 2) improve student enrollment and retention, and 3) attract more minority students to pursue graduate study, especially doctoral degrees. This project is aligned with the mission of the institution and the goals of the Electrical and Computer Engineering (ECE) Department. The proposed activities will support the ECE department in building a strong research program in computational biology, thus achieving the goals of enhancing the PhD program in the ECE department and broadening participation in computational biology at PVAMU. The proposed project will greatly improve African American involvement in cutting edge research that is extremely valuable to the nation.

The aim of this project is to study and analyze the dynamic evolution of drug/cell interactions using biomedical big data, including both public domain data and dynamic time series data from systematic drug perturbations experiments. Innovative image processing, machine learning, dynamic modeling and control techniques are proposed to help understand the genetic regulation of cancer cells and the mechanism of action of molecularly targeted agents on gene regulation. Specifically, combining the information from robust image feature extraction using advanced image processing techniques (Thrust 1) with candidate drug targets and the identification of drug treatments identified using a novel network-based computational tool, Evaluation of Differential DependencY (EDDY; Thrust 2). Dynamic modeling and analysis of drug response in critical biological pathways will be carried out in Thrust 3. Equipped with the knowledge extracted from biomedical big data obtained in Thrust 2 and a predictive preclinical model that reveal how biological regulatory networks react when perturbed from time series data in Thrust 3, novel therapeutic interventions will be designed in Thrust 4 using advanced control theory. Findings from this study will provide innovative solutions to more effective and efficient drug development by bridging quantitative research with biomedical science. This project will be conducted in collaboration with the TEES-AgriLife Center for Bioinformatics and Genomics Systems Engineering (CBGSE) at Texas A&M University and the Translational Genomic Research Institute (TGen). The knowledge gained from this project will be disseminated broadly to a community of scientists and engineers.

NSF RISE Program

Program Start Date: September 1, 2017
Program End Date: Feb 28, 2023 (Estimated)
Supported by NSF
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
  • Students of this project participated in The National Consortium for Data Science annual career panel, Analyzing Data Science Careers on October 19, 2022. During the free virtual event, data science professionals from Google, IQVIA, Bain & Co, and UNC-Chapel Hill provided insight into what it takes to capture the attention of top recruiters in data science and data analytics.
  • The PIs participated in the 29th HBCU Faculty Development Network Annual Conference in Houston, Texas, on Oct 20-22, 2022
  • A hands-on Python tutorial was offered during the Fall 2019 semester. Many faculty, staff, and students participated the training and very positive feedback has been received. The PIs plan to offer more tutorials in the coming semesters.
  • A new course, ELEG 6603 “Modern Artificial Intelligence” has been created and taught during the Spring 2020 semester. The research results obtained in this project have been incorporated in the course materials to keep students informed of the state-of-the-art technologies. We have received very positive feedback from the students.
  • Several students working on this project have participated the Texas A&M System 16th Annual Pathways Student Research Symposium held on November 7-8, 2019 on the TAMIU campus in Laredo, Texas and presented their research results.
  • Co-PI (Obiomon) and PI (Qian) supervised five undergraduate students participating the FAA Challenge of Future Smart Airport, and submitted project proposal titled “Context Aware Location based Services for Future Smart Airports”.
  • A Machine Intelligence Biology Lab has been established by the PIs and a new recruited faculty, Dr. Dong in the Department of Electrical and Computer Engineering at PVAMU. The main task of this lab will focus on computational biology, which is to use existing tools or develop novel methods to speed up the procedure of genetic data analysis or enhance the analysis performance.
  • The PI (Qian) supervised a senior design project titled “Intelligent Sensing and Alarm System for Bicycle Accidents Reduction” during the Fall 2019 and Spring 2020 semesters. The undergraduate senior design group demonstrated their project during the Senior Design Showcase in Spring 2020.
  • The PIs have worked with Dr. Jianping Hua from Texas A&M University Genomic Signal Processing Lab on biomedical image processing.
  • The PIs participated the training of “TruSeq Stranded Total RNA Sample Preparation”, Provided by Illumina Inc. in March 2019.
  • The co-PI (Li) serves as review Editor in “Bioinformatics and Computational Biology”, Frontiers Community.
  • The co-PI (Li) serves as TPC member of the Fourth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2019).
  • The co-PI (Li) was awarded a grant titled “Deep Learning for Biomedical Informatics ” by the Office of Research, Innovation, and Sponsored Programs (ORISP) at PVAMU for the 2019-2020 academic year.
  • The co-PI (Li) participated the “Connections in Smart Health Workshop” of Smart and Connected Health Principal Investigator Meeting in Arlington, VA, on September 24-26, 2018.
  • The co-PI (Kim) has been awarded a Translational Team Science Award (DoD-USAMRMC-CDMRP-TTSA), titled “Development of Classifiers for Novel Bladder Cancer Subtypes,” in collaboration with Dr. Woonyoung Choi at Johns Hopkins Medicine.
  • The co-PI (Kim) presented a poster at qBio 2019 (July 31 – August 3) https://qbio2019.ucsf.edu, titled “Tumor cell phenotype and heterogeneity differences in IDH1 mutant vs wild-type gliomas”.
  • August 2, 2018 – The PIs met with the research team of the International Goat Research Center to discuss collaborations on August 2, 2018.
  • Ms. Shanta Chowdhury supervised by Dr. X. Li, Co-PI of the NSF-RISE project, received her MS degree in May 2018, and continue to pursue her PhD degree at PVAMU
  • Dr. Prachee Chaturvedi, Project Lead from Monsanto Company was invited to give a talk titled “How Big Data and Analytics is Changing Agriculture?” at PVAMU on May 15, 2018
  • Dr. X. Li, Co-PI of the NSF-RISE project, serve as TPC of The Fifth International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC 2018).
  • The PIs and graduate research assistants participated the Illumina NextSeq 550 Training by Imani Bethel, Field Applications Scientist, illumina, Inc., on April 5-6 (Thursday-Friday), 2018.
  • Ms. Shanta Chowdhury received NSF Travel Award to IEEE ICIBM 2018 to present her paper titled “A Multitask bi-directional RNN Model for Named Entity Recognition on Electronic Medical Records”, in Los Angeles, CA, 2018.
  • One of the Research Assistants, Ms. Shanta Chowdhury received “2018 Outstanding MS Student of the Year” in the Roy G. Perry College of Engineering at Prairie View A&M University in 2018.
  • Dr. S. Kim, Co-PI of the NSF-RISE project, delivered a talk titled “GPU-accelerated differential dependency network analysis” at The 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2018) in Cambridge, UK, on Mar 21-23, 2018.
  • Dr. Qian, PI of the NSF-RISE project, visited Tuskegee University to discuss collaborations and recruit students on 3/25/2018-3/27/2018.
  • Dr. X. Li, Co-PI of the NSF-RISE project, created and taught a new course, ELEG 6913-P21 “Modeling in Computational Systems Biology” in Spring 2018 semester.
Supported by NSF
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation
  • Nwosu, Lucy and Li, Xiangfang and Qian, Lijun and Kim, Seungchan and Dong, Xishuang “Calibrated bagging deep learning for image semantic segmentation: A case study on COVID-19 chest X-ray image” PLOS ONE , v.17 , 2022 https://doi.org/10.1371/journal.pone.0276250 Citation Details
  • Bamgbose, Samuel Oludare and Li, Xiangfang and Qian, Lijun “Neural network-based non-linear adaptive controller design for a class of bilinear system” Cognitive Computation and Systems , v.2 , 2020 10.1049/ccs.2019.0015 Citation Details
  • Berens, Michael E. and Sood, Anup and Barnholtz-Sloan, Jill S. and Graf, John F. and Cho, Sanghee and Kim, Seungchan and Kiefer, Jeffrey and Byron, Sara A. and Halperin, Rebecca F. and Nasser, Sara and Adkins, Jonathan and Cuyugan, Lori and Devine, Karen “Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas” PLOS ONE , v.14 , 2019 10.1371/journal.pone.0219724 Citation Details
  • Dong, Xishuang and Chowdhury, Shanta and Qian, Lijun and Li, Xiangfang and Guan, Yi and Yang, Jinfeng and Yu, Qiubin and Galstyan, Aram “Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN” PLOS ONE , v.14 , 2019 10.1371/journal.pone.0216046 Citation Details
  • Chowdhury, Shanta and Dong, Xishuang and Qian, Lijun and Li, Xiangfang and Guan, Yi and Yang, Jinfeng and Yu, Qiubin “A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records” BMC Bioinformatics , v.19 , 2018 10.1186/s12859-018-2467-9 Citation Details
  • Bamgbose, Samuel Oludare and Li, Xiangfang and Qian, Lijun “Closed loop control of blood glucose level with neural network predictor for diabetic patients” 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom) , 2017 10.1109/HealthCom.2017.8210817 Citation Details
  • Oduola, Wasiu Opeyemi and Li, Xiangfang L. and Duan, Chang and Qian, Lijun and Dougherty, Edward R. “Sequential Therapeutic Response Modeling for Tumor Treatment Using Computational Hybrid Control Systems Approach” IEEE Transactions on Biomedical Engineering , v.65 , 2018 10.1109/TBME.2017.2723957 Citation Details
  • Oduola, Wasiu Opeyemi and Li, Xiangfang “Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System” Cancer Informatics , v.17 , 2018 10.1177/1176935118790262 Citation Details
  • Nwosu, Lucy and Li, Xiangfang and Qian, Lijun and Kim, Seungchan and Dong, Xishuang “Semi-supervised Learning for COVID-19 Image Classification via ResNet” EAI Endorsed Transactions on Bioengineering and Bioinformatics , v.1 , 2021 https://doi.org/10.4108/eai.25-8-2021.170754 Citation Details
  • Dong, Xishuang and Chowdhury, Shanta and Victor, Uboho and Li, Xiangfang and Qian, Lijun “Semi-supervised Deep Learning for Cell Type Identification from Single-Cell Transcriptomic Data” IEEE/ACM Transactions on Computational Biology and Bioinformatics , 2022 https://doi.org/10.1109/TCBB.2022.3173587 Citation Details
  • Bamgbose, Samuel Oludare and Qian, Xiangfang Li “Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles” Advances in Science, Technology and Engineering Systems Journal , v.4 , 2019 10.25046/aj040121 Citation Details

Computing Facility

      • 4 x NVIDIA DGX-1 cluster equipped with
        • 2x Intel Xeon E5-2698 v3 (16 core, Haswell-EP)
        • 8x NVIDIA Tesla P100 (3584 CUDA Cores)
        • 512GB DDR4-2133 (LRDIMM)
        • 128GB HBM2 (8x 16GB)
        • 4x Samsung PM863 1.92TB SSDs
        • 4x Samsung PM863 1.92TB SSDs
        • 4x Samsung PM863 1.92TB SSDs
      • 4 x Workstations

Next Generation Sequencing Facility

The Next Gen Sequencing Center offers Illumina sequencing by synthesis. It is equipped to isolate, quantitate, evaluate quality of nucleic acid preparations and prepare libraries for sequencing using a workflow that minimizes contamination. The Illumina sequencer is covered under an annually renewed PROD Care NSQ 550 Comprehensive service contract through Illumina. It guarantees an average 3 business day on-site response time by a field engineer, full coverage on parts and labor, and an annual preventative maintenance visit. The center also offers microarray processing and analysis. We will train, support, and provide fee-for-service work, access to ancillary equipment for sample preparation and bioinformatics support to Center investigators and the wider scientific community.

    • Illumina NextSeq 550 . Features include: reads up to 150 bp long; low cost/base; fast turn-around times; mid- or high-output modes (150M or 400M clusters/sample, respectively).
    • Agilent 2100 Bioanalyzer to analyze the quality of protein, DNA and RNA.
    • Nanodrop and Qubit 4 to determine the concentration of nucleic acids.
    • Real Time PCR for superior accuracy of quantification

I.  Invited Lectures:

      • Nov. 9, 2022, Speaker: Stephen Chan, MD/PhD/FAHA, Professor of Department of Medicine, Vitalant Chair of Vacular Medicine, Director, Vascular Medicine Institute, School of Medicine, University of Pittsburgh; Title: “Integrated multi-omic analyses identify genetic causes of endothelial inflammation in pulmonary hypertension”
      • Jan 26, 2022; Speaker: Byung-Jun Yoon, Ph.D., Associate Professor, Department of Electrical and Computer Engineering, College of Engineering, Texas A&M University; Title: “Machine Learning for Computational Network Biology”
      • Feb 9, 2022; Speaker: Seungchan Kim, Ph.D., Chief Scientist and Executive Professor, Director, CCSB, Electrical and Computer Engineering Department, PVAMU; Title: “Computational Network Biology Enabled by Large-Scale Transcriptomic Data”
      • Mar 9, 2022; Speaker: Anna Joy, Ph.D., Research Associate Professor, Director, Genomics Laboratory, CCSB, PVAMU; Title: “Biomarker development for personalized immunotherapy”
      • Apr 20, 2022; Speaker: Archana Ramesh, Ph.D., Principal Data Science Manager, Microsoft; Title: “Improving Windows Customer Experiences via Data Science & ML”
      • May 18, 2022; Speaker: Lucy Nwosu, Ph.D. student and Graduate Research Assistant, CCSB, PVAMU; Title: “Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image”
      • Title: Feeding the ten billion: from green revolution to gene revolution, by Tesfamichael Kebrom, Ph.D., Research Scientist, Prairie View A&M University, on Wed., Sept. 11, 2019
      • Title: Molecular insights into inflammatory breast cancer (IBC) aggressiveness and metastasis, by Bisrat G Debeb, DVM, Ph.D., Assistant Professor, M.D. Anderson Cancer Center, on Wed., Sept. 25, 2019
      • Title: Brain, sex, synapses and neurological diseases – a transcriptomic story, by Dumitru Iacobas, Ph.D., Research Professor, Prairie View A&M University, on Wed., Oct. 9, 2019
      • Title: Molecular Subtypes in Muscle Invasive Bladder Cancer, by Woonyoung Choi, Ph.D., Assistant Professor, Johns Hopkins University, on Wed., Oct. 23, 2019
      • Title: Mechanistic Protein Modeling for Rational Drug Discovery, by Yang Shen, Ph.D., Assistant Professor, Texas A&M University, on Thur., Nov. 7, 2019
      • Title: Discovery of Glioblastoma immune subtypes to improve immunotherapy, Anna Joy, Ph.D., Research Associate Professor, Prairie View A&M University, on Wed., Dec. 11, 2019
      • Title: Validation of the Gene Master Regulators Theory for Cancer Gene Therapy, by Dr. Iacobas, on Feb. 13, 2019
      • Title: Differential, tissue-specific effects of germline mutations on epithelial stem cell development, by Dr. Victoria Mgbemena, on Feb. 27, 2019
      • Title: Cancer Research – A Physicist’s View, by Dr. Kumar, on Mar. 27, 2019.
      • Title: Neural Networks Based Feature Selection for Genetic Data Analysis, by Dr. X. Dong, on April. 10, 2019.
      • Title: Gene Expression in Developing Goat Testes: Identification of a Caprine Spermatogenesis Transcriptome, by Dr. Lewis, on April. 24, 2019.
      • Title: Single Cell Genomics at the Center for Computational Systems Biology, by Dr. Anna Joy, on May 8, 2019.
      • Title: Radiation Dosimetry Measurements Onboard Air and Space Craft for Human Safety Programs and Cancer Epidemiology Studies, by Dr. Brad “Buddy” Gersey, on May 22, 2019.
      • ” The Gene Master Regulators Approach of the Personalized Cancer Gene Therapy ” by Dr. Dumitru Andrei Iacobas, Associate Professor of Pathology, Director of Systems Biology Core, New York Medical College, on August 24 (Thursday), 2017.
      • ” Predicting interacting peptides in protein complexes using Evolutionary and Structural Information ” by Bernard Fongang, Ph.D., Research Scientist, UTMB-Galveston, on March 2 (Friday), 2018.
      • ” Systematic computational network-based analysis to predict subnetworks/key genes associated with pathogenicity/fumonisin or defense response in maize-F. verticillioides interaction ” by Dr. Jimmy Man Kim, Postdoctoral Research Associate, Plant Pathology and Microbiology, Texas A&M University, on March 9 (Friday), 2018.
      • ” Deep Learning for Data Analysis” by Xishuang Dong, Ph.D., Postdoctoral Researcher, CREDIT Center, PVAMU, on March 19 (Monday), 2018.
      • ” Exascale Computing Project ” by Professor Barbara Chapman, Stony Brook University, on March 29 (Thursday), 2018.
      • ” Emerging Topics in Genome Sequencing and Analysis ” by Dr. Chun-Chi Chen, Postdoctoral Researcher, CBGSE, Texas A&M University, on April 3 (Tuesday), 2018.
      • ” Potential clinical applications for Glioblastoma subtypes derived from AKT pathway based clustering “, by Dr. Anna Joy, Research Assistant Professor at Barrow Neurological Institute, Phoenix, AZ, on September 14 (Thursday), 2017.

II. Training:

      • Lab Training of “TruSeq Stranded Total RNA Sample Preparation”, Provided by Illumina Inc. in March 2019.
      • ” Illumina NextSeq 550 Training ” by Imani Bethel, Field Applications Scientist, illumina, Inc., on April 5-6 (Thursday-Friday), 2018.
Supported by NSF
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation

Dr. Lijun Qian

Department of Electrical & Computer Engineering
Prairie View A&M University
(Texas A&M University System)
P.O. Box 2847
Prairie View, Texas 77446 (USA)
(T) (936) 261-9908
(F) (936) 261-9930
Email: liqian@pvamu.edu
Supported by NSF
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation