天美传媒

Dr. Walter has spent his 28-year academic career at a primarily undergraduate campus that has just recently been designated an 鈥淓merging Research Institution.鈥� He has served in the Department of Biology (9 years) and then moved to the Department of Chemistry & Biochemistry to assist in development of a Biochemistry undergraduate program. Dr. Walter developed partnership grant proposals aimed towards providing scholarships for student groups that are underrepresented in the sciences (URM). In Fall 2013, he was awarded a Bridges to Biomedicine (B2B) grant wherein Texas State University is partnering with two Alamo Community College campuses to establish a program focused on increasing success of URM students in the biomedical sciences upon transfer to the baccalaureate institution. The B2B program addresses the most important obstacles to upper-division degree completion experienced by students showing an early commitment to a biomedical career. Additionally, Dr. Walter serves as Co-PI for the South Texas Doctoral Bridge Program (STDBP).  The STDBP is aimed at student matriculation from the MS degree into highly competitive doctoral programs. The STDBP is established between the Univ. of Texas Health Science Center at San Antonio (UTHSCSA, medical school) and Texas State University. The STDBP is designed to provide a combination of mentoring and student development activities as well as enhance didactics and research training during a thesis-based M.S. degree in Biochemistry.

Dr. Raphael Gottardo is a computational biologist who specializes in applying rapidly evolving ideas in data science to solving problems in cancer and related diseases. As scientific director of the Translational Data Science Integrated Research Center, he is at the center of the busy intersection of biology, data science and technology at Fred Hutchinson Cancer Research Center.

His goal is to expand data-driven innovations for patients by cultivating a cross-disciplinary environment in which doctors and laboratory scientists work seamlessly with their colleagues in biostatistics and computational sciences to take advantage of the flood of information made possible by advanced technologies. 

The aim is to bring scientific discoveries from research labs to the bedside sooner using data-driven approaches. To do so, bench scientists and clinical researchers from many corners of the Hutch work collaboratively with experts in data science.

Much of his work is focused on profiling the cellular components of the human immune system 鈥� using data science to understand how to make immunotherapies work better for patients. 

鈥淚t鈥檚 when you get into the details that it really becomes interesting,鈥� he said. 鈥淭he immune system is very complex, and it turns out we don鈥檛 know a whole lot about it yet. Looking at these single-cell technologies generating massive amounts of data has brought me to really cool statistical and computational challenges.鈥�

Dr. Gottardo鈥檚 own research involves the development of computational tools for vaccine and immunology studies, including high-throughput experiments that may use flow cytometry or high-speed genome sequencing. His current studies include:
鈥�	Statistical and computational analysis of flow cytometry data
鈥�	Development of statistical and computational methods for single-cell genomics
鈥�	Immune responses to malaria and HIV infection and immunization within the Human Immunology Project Consortium (HIPC)
鈥�	Development of the HIPC database and research portal (www.immunespace.org)
鈥�	Contribution to the Bioconductor project, an open computing resource for genomics
鈥�	Leadership for the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery of the Bill and Melinda Gates Foundation
鈥�	Leadership for the Vaccine Statistical Support (VSS) Global Health Vaccine Accelerating Platform (GH-VAP) of the Bill and Melinda Gates Foundation

Dr. Gottardo is the J. Orin Edson Foundation Endowed Chair at Fred Hutch and a member of the Vaccine and Infectious Disease and Public Health Sciences Divisions. He, along with other Fred Hutch researchers, is co-leading a collaboration with the Allen Institute for Immunology to chart the human immune system by harnessing big data and emerging technologies.

An affiliate professor of statistics at the University of Washington, he teaches courses in stochastic modeling, bioinformatics and statistical computing and supervises biostatistics and statistics doctoral students on statistical-methods research for high-dimensional omics data analysis

Efrem Lim is an expert in viruses, biomedicine, microbiology and molecular biology.

He is a virologist and an assistant professor in the School of Life Sciences, and the Biodesign Center for Fundamental and Applied Microbiomics.

Lim's research focuses on viromes, microbiomes and the SARS-CoV-2 viral strain.

Professor Lim created the "Lim Lab" at ASU which integrates molecular virology and bioinformatics approaches in clinical cohorts.

Dr. Melanie Sutton, professor, teaches bioinformatics, health information systems, medical informatics, medical terminology, and computer and geographic information systems applications in public health.

Sutton applies her computer science training into the broader multi-disciplinary field of informatics by applying algorithms and tools from her object recognition research in new domains.

She has written and co-written many peer-reviewed articles and book chapters on various aspects of computer vision, robotics, digital mammography, and online instruction and assessment.

She was co-principal investigator of two projects funded by Florida's Great Northwest to develop a software engineering graduate program, and health sciences and technology training retreats for high school guidance counselors and academy directors. She was also principal investigator for a grant that developed and assessed protocols for the efficient utilization of large-scale digital mammography databases.

During her tenure as co-director and academic advisor of the certificate in medical informatics program at UWF, she chaired a self-study committee that led to the accreditation of UWF's online master of public health program, just one three accredited online programs in the U.S.

Before coming to UWF in 1996, she was a software engineer for Harris Corporation in the space systems division.

She received a bachelor's degree in computer engineering, master's in computer science, and doctorate in computer science and engineering with a focus on computer vision and robotics, all from the University of South Florida.

Dr. Steiling is a Pulmonary/Critical Care Physician-Scientist with a longstanding interest in improving the ability to effectively detect, treat, and cure smoking-induced lung diseases such as lung cancer and chronic obstructive pulmonary disease (COPD). She completed her fellowship training in Pulmonary and Critical Care Medicine at Boston University Medical Center, and concurrently completed a Master of Science in Bioinformatics through the Boston University College of Engineering.

Dr. Steiling鈥檚 research centers on improving the diagnosis, treatment and prevention of lung cancer and COPD. Using the airway field of injury hypothesis, which posits that cigarette smoking induces molecular changes throughout the respiratory tract, she has studied alterations in the airway transcriptome that reflect the presence, susceptibility, and progression of smoking-induced lung diseases. She has used whole-genome expression profiling of the bronchial airway epithelium to describe the relationship between upper and lower airway gene expression, and leveraged this information to develop clinically-relevant biomarkers of lung cancer, COPD, and other diseases that affect the lung.

In addition to her translational research, Dr. Steiling has led the implementation of two important clinical programs focused on improving the early detection of lung cancer in at-risk individuals. She founded the Boston Medical Center Lung Nodule Clinic, a sub-specialty referral resource that supports the evidence-based evaluation of incidental pulmonary nodules detected by diagnostic and screening CT scans. She also led the implementation of a multi-disciplinary Lung Cancer Screening Program at Boston Medical Center. Her team was recognized with a 2016 Clinical Quality Improvement Award from the Boston University Medical Center Evans Foundation. She currently co-chairs the Boston Medical Center Lung Cancer Screening Steering Committee. Dr. Steiling sees patients in the Lung Nodule Clinic, multi-disciplinary Thoracic Oncology Clinic and attends in the Medical Intensive Care Unit at Boston Medical Center.

 explores molecular diversity and how molecular structure determines biological function in plants, animals, fungi, and microbes of significance to agriculture. He studies the origin, structure, and evolution of genomes, proteomes, RNomes, and functionomes for applications including bioengineering, biomedicine, and systems biology.
More information: 
Caetano-Anollés' atelier of evolutionary bioinformatics and plant bioinformation focuses on creative ways to mine, visualize and integrate data from structural and functional genomic research. His group is particularly interested in the evolution of macromolecular structure and networks in biology, the reconstruction of evolutionary history, the incorporation of evolutionary considerations in genomic research, the study of levels and patterns of genome-wide mutation, and processes that are linked to co-evolutionary phenomena (such as plant pathogenesis and symbiosis). In particular, his research has been productive in two specific areas, the evolution of the structure of macromolecules and the molecular basis of biodiversity.
Affiliations: 
Caetano-Anollés is a professor of bioinformatics in the  in the  (ACES) and health innovation professor in the  at the . He is also a faculty affiliate in the .

Prof. Wang received his Ph.D. in Applied Mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), and won the Special Prize of President Scholarship and Excellent Ph.D. thesis Award of CAS. Between 2011 and 2015, he was a Postdoctoral Research Scientist at Columbia University. In 2015, he was named as the Precision Medicine Fellow and promoted to Associate Research Scientist. He established the Wang Genomics Laboratory @HKUST in 2016, focusing on the application of data science in biology and medicine. He has made substantial contributions to (1) characterization, modeling, and prediction of cancer evolution from genomics (Nat Genet 2016; Nat Genet 2017; Nat Commun 2021); (2) discovery, elucidation, and clinical application of MGMT fusion (Nat Genet 2016; Nat Commun 2020) and METex14 in adult gliomas (Nat Genet 2018; Cell 2018); (3) discovery of MAP3K3-I441M in CCM (AJHG 2021) and elucidation of EndMT in bAVM (Circ Res 2021); (4) reconstruction of RNA Exosome-regulated non-coding transcriptomes (Nature 2014; Cell 2015). He won the Excellent Young Scientist Award of NSFC (2019), the School of Engineering Young Investigator Research Award (2019), the School of Science Research Award (2021), and the Zhong Nanshan Youth Science and Technology Innovation Award (2021).


 


Research Question


 


Recent advances in next-generation sequencing are revolutionizing numerous areas in life science and medicine. Prof. Wang's research is focused on discovering and elucidating functional genomic alterations in complex human diseases, such as intracranial cancers and vascular malformations, by developing and/or applying computational methods based on multi-omics integration, statistics, and machine learning, aiming to bridge the gaps among data, bench, and bedside. More specifically, Prof. Wang's team has been mainly working on the following two scientific questions.


 


Question 1: How does clonal evolution drive cancer progression that leads to malignant transformation and therapeutic resistance?


 


Clonal evolution of cancer is a major challenge leading to treatment failure, but the molecular mechanisms of how cancer cells evolve and gain the capability of surviving intensive chemo- and/or radio- therapies remain elusive. Therefore, it is critically important to characterize the spatial and temporal dynamics of cancer cells and thereby mathematically modelling this process via big data integration. We have been working on diffuse gliomas, the most common and aggressive forms of primary tumors in adult brain whose treatment outcome is still very poor. Current therapies inevitably lead to tumor recurrence and the recurrent gliomas commonly become treatment resistance and incurable. Analyzing longitudinal and single-cell multi-omics data on this disease, our team aims to address the following questions: a) why cancer cells always display complex patterns of intratumoral heterogeneity; b) what is the temporal order of multiple somatic mutations detected in various cancer clones; c) how to predict the evolutionary path and clinical response of cancer cells under a certain therapy based on the sign seen earlier; and d) what are the key factors in tumor and its microenvironment that shape cancer evolution and determine cancer cell response under clinical intervention. In the process of addressing these questions, we will be able to unravel the mysteries of cancer evolution and it might provide a theoretical foundation for designing new means of treatment or diagnostics for better precision cancer medicine via targeting cancer dynamics.


 


Question 2: What is the role of genetic interaction between germline variants and somatic mutations in initializing and regulating the development of cancer and other genetic disorders?


 


Somatic genomic and epigenomic mutations are regarded as the direct drivers of cancer initialization and evolution, whereas de novo and inherited germline alterations could predispose the cancer risk and regulate population-specific disease incidence and treatment response. However, the underlying genetic interactions between germline variants and somatic mutations remain unclear, and the biological and medical implications of these interactions have not been extensively explored. New technologies of genomic sequencing allow low-cost profiling of somatic and germline mutations in not only case-unaffected-parental trios but also disease lesions at a high resolution, providing a unique opportunity to systematically investigate disease-relevant genomes by uncovering the joint contribution of the germline variants and somatic mutations in the process of disease development. Understanding whether and how the germline risk alleles interact with somatic mutations in terms of pathway activation and/or cellular interaction will help us to better understand disease etiology for the purpose of developing novel methods for genome-guided disease risk evaluation and personalized clinical intervention.


 


Representative Publications


 




Biaobin Jiang, Quanhua Mu, Fufang Qiu, Xuefeng Li, Weiqi Xu, Jun Yu, Weilun Fu, Yong Cao, Jiguang Wang#. Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors. Nature Communications 12, Article number: 6692, 2021.


 


Hao Li*, Yoonhee Nam*, Ran Huo*, Weilun Fu*, Biaobin Jiang, Qiuxia Zhou, Dong Song, Yingxi Yang, Yuming Jiao, Jiancong Weng, Zihan Yan, Lin Di, Jie Li, Jie Wang, Hongyuan Xu, Shuo Wang, JiZong Zhao, Zilong Wen, Jiguang Wang#, Yong Cao#. De Novo Germline and Somatic Variants Convergently Promote Endothelial-to-Mesenchymal Transition in Simplex Brain Arteriovenous Malformation. Circulation Research, 129(9), 825–839, 2021.


 


Jiancong Weng*, Yingxi Yang*†, Dong Song*†, Ran Huo*, Hao Li, Yoonhee Nam†, Yiyun Chen†, Qiuxia Zhou, Yuming Jiao, Weilun Fu, Zihan Yan, Jie Wang, Hongyuan Xu, Lin Di, Jie Li, Shuo Wang, Jizong Zhao, Jiguang Wang#, Yong Cao#. Somatic MAP3K3 Mutation Defines a Subclass of Cerebral Cavernous Malformation. American Journal of Human Genetics 108(5):942-950, 2021.


 


Barbara Oldrini*, Nuria Vaquero-Siguero*, Quanhua Mu*†, Paula Kroon, Ying Zhang, Marcos Galán-Ganga, Zhaoshi Bao‡, Zheng Wang, Hanjie Liu, Jason Sa, Junfei Zhao, Hoon Kim, Sandra Rodriguez-Perales, Do-Hyun Nam, Roel Verhaak, Raul Rabadan§, Tao Jiang#, Jiguang Wang#, and Massimo Squatrito#. MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas. Nature Communications, 11(1):3883, 2020.


 

Hu H*, Mu Q*†, Bao Z*‡, Chen Y*†, Liu Y*, Chen J, Wang K, Wang Z, Nam Y†, Jiang B‡, Sa JK, Cho H-J, Her N-G, Zhang C, Zhao Z, Zhang Y, Zeng F, Wu F, Kang X, Liu Y, Qian Z, Wang Z, Huang R, Wang Q, Zhang W, Qiu X, Li W, Nam D-H, Fan X#, Wang J#, Jiang T#. Mutational landscape of secondary glioblastoma guides MET-targeted trial in brain tumor. Cell; 175 (6), 1665-1678, 2018.

 
Full list at .


 


Related News


 

 uses supercomputing and DNA sequencing to solve problems in plant, animal, and human genetics. His current research focuses on how crops are bred and on ways to treat and prevent plant, animal, and human diseases. He is particularly interested in the genetics of crop traits and the genetic and molecular interactions of soybeans with pathogens, pests, and other organisms.
More information: Hudson's research interests center on the use of high-performance computational techniques to pursue questions in genomic biology. His research program focuses on the genomic variants that control trait variation in plants, nonhuman animals and human populations, funded by grants from the NSF, DOE, and USDA as well as private companies, foundations, and commodity boards. He teaches award-winning classes at Illinois on the interface between biology and computing.
Affiliations: Hudson is a professor in the , part of the  (ACES) at U. of I. He is also co-director of the , science integration chair for the  (CABBI), and faculty affiliate at the .

Dr. Zhang joined UT Southwestern as an assistant professor in September, 2007. He currently serves as the director of BERD (Biostatistics, Epidemiology, and Research Design) for the UTSW CTSA program. He also serves on the NCI Central Institutional Review Board (Adult CIRB – Early Phase Emphasis). 
Dr. Zhang’s research interest in statistical methodology lies in two main areas: Bayesian hierarchical modeling and clinical trial design. He has published multiple papers on the application of Bayesian hierarchical models to disease mapping, joint modeling of longitudinal and survival outcomes, item-response theory for grant review, functional enrichment analysis to detect important pathways, and multi-level modeling to detect factors that impact cancer screening, etc. Another area of his research interest is design methodology for clinical trials to account for various pragmatic issues such as correlated outcomes (clustered and longitudinal), missing data, small sample sizes, historical control, random variability in cluster size, and cost constraints, etc. He has published multiple high quality papers in this area and in 2015 he co-authored a book titled “Sample Size Calculations on Clustered and Longitudinal Outcomes in Clinical Research” (Chapman & Hall, New York). Dr. Zhang has been successful in securing extramural grants as the PI to support his independent research program, examples include an NIH R03 grant to conduct secondary data analysis on VA HIV registry; an NSF grant to build risk prediction model based on electronic health record data; and a PCORI methodology development grant to address pragmatic design issues in stepped-wedge cluster randomized trials.

Dr. Lisa Waidner, an Assistant Professor, has a Ph.D. from the College of Marine Science at the University of Delaware. Before she joined UWF in 2016, Waidner had the unique opportunity to work in several small biotechnology companies in the capacity of genetic engineering, phylogenetics, and directed evolution to improve biofuel and bioenergy-producing microorganisms.  Her academic mentors were Richard Karpel (UMBC, M.S. program), David Kirchman (Delaware, Ph.D. program), Thomas Hanson (Delaware, post-doctoral position), and co-mentors Robin Morgan and Joan Burnside (Delaware, post-doctoral fellowship). 
Her findings have been published in the Journal of Shellfish Research, Applied and Environmental Microbiology, Virology, and Environmental Virology. Topics have included aspects of Marek’s disease, virioplankton populations, and crab populations near the mouth of the Delaware Bay. Waidner’s current research interests are in environmental microbiology, microbial ecology, and bioremediation in oceans, coastal waters, inland bays, and rivers. 
These studies include developing a better understanding of global elemental cycles, as well as ‘applied’ bioremediation research.  Her work uses model bacteria called the aerobic anoxygenic phototrophs (AAP), which are a diverse group of proteobacteria that may be involved in light-stimulated uptake of dissolved organic matter and of point-source pollution and legacy contaminants.  Cultured and uncultured AAP are used in molecular biological, microbiological, and ecological studies on this diverse group of freshwater, estuarine, and marine bacteria. Dr. Waidner has taught classes in Introduction to Bioinformatics and Environmental Genomics and is currently a UWF instructor for Genetics Lab.  She is now working with undergraduate students to characterize unique AAP bacteria from coastal and inland waters in and around the Pensacola Bay system.