Abstract

Cells do not live in isolation, and they send and receive messages from their environment and with neighboring cells or themselves, which is critical in cell’s growth and normal operation. For example, cell-cell communication allows specialization of groups of cells to form specific tissues such as muscle, blood, and brain cells, via intercellar coordination. Disruption in cell-cell communication often leads to disease such as cancer.

Recent advancement of single cell RNA sequencing (scRNAseq) is enabling scientists to study cellular and molecular heterogeneity of tissues and how such heterogeneities contributes to cell’s fate. In addition, there has been an increased interested in using scRNAseq data to study cell-cell communication by analyzing the expression of ligands and corresponding receptors. Many computational tools and resources have been developed to support such analysis. More recently, spatial transcriptomic (ST) technology has been developed to measure genome-wide gene expression while preserving the spatial information of cells. Spatial information available from ST can help us decipher cell-cell communication in spatial contexts; however, none of the computational tools currently available takes this into account in analyzing ST data for cell-cell communication. In this talk, I will introduce a computational approach that incorporates spatial distributions of cells in identifying active cell-cell communications and where cell-cell communications are most active. Spatially resolved cell-cell communications can discover hotpots of cell signaling, directly overlaid on tissue, enabling biologists to associate cell’s morphological features with cell signaling.

Speaker Bio

Dr. Seungchan Kim is a Chief Scientist and Executive Professor at the Department of Electrical and Computer Engineering and the Director of the CRI Center for Computational Systems Biology at the Prairie View A&M University (PVAMU). Prior to this appointment, he was the Head of Biocomputing Unit and an Associate Professor at Integrated Cancer Genomics Division of Translational Genomics Research Institute (TGen). He was one of the founding faculty members of TGen, founded in 2002, by Dr. Trent, then-Scientific Director of the National Human Genome Research Institute at the National Institutes of Health, leading computational systems biology research at the institute. He was also an Assistant Professor in the School of Computing, Informatics, Decision Systems Engineering (CIDSE) at the Arizona State University from 2004 till 2011. Dr. Kim received B.S. and M.S. degrees in Agriculture Engineering from the Seoul National University, and Ph.D. in Electrical Engineering from the Texas A&M University. He also got his post-doctoral training at the Cancer Genetics Branch of National Human Genome Research Institute.

Dr. Kim’s research interests include: 1) mathematical modeling of genetic regulatory networks, 2) development of computational methods to analyze multitude of high throughput multi-omics data to identify disease biomarkers, and 3) computational models to diagnose patients or predict patient outcomes, for example, disease subtypes or drug response. His studies have had a large influence on the development of computational tools to study underlying mechanisms for cancer development and better understand the molecular mechanisms behind cancer biology and biological systems.

Note: The talk was disrupted due to unforeseen technical issue. The partial recording of the talk is avaialble via the link above. Dr. Martin will come back with more update in the Spring 2023.

Abstract

In collaboration with Dr. Seungchan Kim and Sandia National Laboratory’s Bioresource & Environmental Security Team, our research team seeks to identify key microbial and environmental factors for upregulating methanotrophy in typically methanogenic systems in agriculture and waste management, including landfills, waste-water, and rice cultivation, among others. To this end, we employ topological metabolic analysis to synthesize and reconstruct methane pathways in methanotrophic and methanogenic microorganisms. The overarching goal is to provide guidance on how to maintain stable consortia of microorganisms that convert greenhouse gases into usable biomass. In this talk, we will investigate the interplay between anaerobic ammonium oxidative and denitrifying anaerobic methane oxidative (anammox-DAMO) biochemical pathways, using topological metabolic analysis (TMA), in hopes of optimizing consortia of ammonia-producing methanogenic and methanotrophic bacteria. Specifically, we focus on the development of a TMA framework that enables reconstruction of anammox-DAMO pathways and subsequent optimization of these oxidative processes.

Speaker Bio

Dr. Lealon L. Martin is a faculty member in the Chemical Engineering Department at Prairie View A&M University. Dr. Martin also serves as Associate Dean (interim) of the Roy G. Perry College of Engineering. He received his B.S. degree in Chemical Engineering from Tuskegee University, and a Ph.D. in Chemical Engineering at UCLA in the area of Process Systems Engineering. Prior to joining Prairie View, Lealon served on the faculty at Rensselaer Polytechnic Institute in Troy, NY - with joint appointments in the Iserman Department of Chemical and Biological Engineering and the Department of Decision Science and Engineering Systems – and on the faculty at the University of Texas at Austin in the McKetta Department of Chemical Engineering.

Dr. Martin’s primary research interest lies in the synthesis and reconstruction of metabolic networks using state space optimization-based approaches. Past projects include: probing metabolic networks in Chinese hamster ovary cells to identify energetic limitations in the translational and post-translational processes of monoclonal antibody production, investigating the potential of Eichhornia crassipes (water hyacinth) as an industrial-scale source of biomass, and exploring Pseudomonas putida for microbial fuel cell applications.

Dr. Martin’s current research in the area of systems biology deals with probing metabolic pathways found Brassicae-family microgreens to find ways to increase glucosinolate compound production rates. Glucosinolates (and isothiocyanates) found in microgreens have nutritional and potentially medicinal benefits that far exceed those in their mature counterparts. Thus, the research on cellular metabolic processes in microgreens is to better understand limits on glucosinolate production and the growth conditions that influence those limits. To this end we pose the several fundamental questions: (1) What metabolic pathway networks govern glucosinolate production in microgreens? (2) What are the limits on glucosinolate production over all metabolic pathway networks? and (3) What conditions influence network-controlled limits on glucosinolate production?

As an aside, Dr. Martin is also a licensed attorney and a member of the State Bar of Texas.

Dr. Tesfamichael Kebrom led the team of multidisciplinary scientists from Prairie View A&M University and Texas A&M University for $500,000 research grant from the 1890 Capacity Building Grant Program of the National Institute of Food and Agriculture (NIFA), United Stated Department of Agriculture (USDA).

Project Title: Molecular and Genetic Analysis of Axillary Bud Dormancy and Outgrowth in Sorghum and Maize to Identify Shoot Branching Genes [Sponsor’s site]

Principal Investigator: Tesfamichael Kebrom, Ph.D.

Project Summary: The funding award is to identify candidate genes that control shoot branching in plants. Shoot branching develop from buds and the number of branches and their position determine the final plant shoot architecture, a major factor that determines resource use efficiency and yield of agronomic and horticultural crops grown for food, feed, and biofuel feedstocks. Most crop species activate many buds to grow into branches. The branches compete for light and nutrients. Some of the branches die before reaching maturity and producing fruits or grains. Resource used by such unproductive branches is lost. In addition, the unproductive branches compete with the productive branches for light, water, and nutrients. The competition weakens the growth of the productive branches and reduce their yield. Farmers remove branches manually, known as pruning, to improve the resource use efficiency and yield of fruit crops. The labor cost for pruning could exceed 20% of the total cost of production, and thus reduces farm profitability. Most of the agronomic and horticultural crops that are grown for food, feed, and biofuel feedstocks cannot be pruned. The long-term goal of Dr. Kebrom’s research is advancing fundamental knowledge of the physiological and molecular basis and identify genes that control shoot branching. The objective of the funded project is to identify candidate shoot branching genes using molecular and genetic approaches. The genes will be useful to develop new crop types with the ideal plant shoot architecture that maximizes resource use and yield of agronomic and horticultural crops. These crop types will not need pruning, thus will enhance profitable crop production systems in US and ensure global food security. In particular, small-scale farmers with limited resources will benefit from growing crops that do not require pruning.

kebrom

Dr. Kebrom is a Research Scientist with a joint appointment with the Cooperative Agricultural Research Center in the College of Agriculture and Human Sciences, and the CIR Center for Computational Systems Biology in the College of Engineering. He received Ph.D. In Molecular & Environmental Plant Science from Texas A&M University, and postdoctoral training from the Boyce Thompson Institute for Plant Science Research at Cornell University and the Commonwealth Scientific and Industrial Research Organization (CSIRO, a national lab) in Australia.

A team of researchers including Dr. Seungchan Kim (a computational Systems Biologist and Director of the CIR Center for Computational Systems Biology in the College of Engineering), Dr. Peter Ampim (Agronomist in The College of Agriculture and Human Sciences), and Dr. William Rooney (Regents Professor of Plant Breeding and Genetics at TAMU) will collaborate in this project to generate a large RNA sequencing (RNA-seq) and quantitative trait loci (QTL) data from model sorghum and maize plants that are suitable for identifying shoot branching genes.

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance and decrease prediction uncertainties simultaneously.

Speaker Bio

Lucy Nwosu (Presenter) received B.S. degree in electronic engineering, a M.S. degree in computer engineering, and is a Ph.D. student, pursuing multidisciplinary research in biomedical data analysis and machine learning, in CRI Center for Computational Systems Biology at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU), supervised by Dr. Xishuang Dong. She is also a Graduate Research Assistant with the center, performing her research in biomedical image processing, deep learning, and computational systems biology.

Xishuang Dong is a member of CRI Center for Computational Systems Biology and Assistant Professor at Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). He received B.S. degree in computer science and technique at Harbin University of Science and Technology, M.S. degree in computer software and theory at Harbin Engineering University, and Ph.D. in computer application at Harbin Institute of Technology. His research interests include: (1) machine learning based computational systems biology; (2) biomedical information processing; (3) deep learning for big data analysis; (4) natural language processing.

Abstract

Many years and billions spent for research has not yet produced an effective answer to cancer. Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. Moreover, the transcriptome topology has random dynamics due to the stochastic fluctuations of the microenvironment and external stimuli. The unrepeatable heterogeneous transcriptomic organization among humans makes senseless the meta-analysis of large populations and the quest for universal biomarkers and “fit-for-all” treatments. We present a bioinformatics procedure to identify for each patient his/her unique triplet of cancer Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation from high throughput gene expression profiles. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, variability and coordination with each other gene, a four order of magnitude increase of the information provided by the transcriptomic experiments. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. So far, the method was applied to microarray data generated in the IacobasLab by profiling surgically removed prostates, kidneys and thyroids from people with metastatic cancers, and standard cell cultures of human cancers of prostate, thyroid, lung and blood. The applications verified that each cancer case is unique and predicted the consequences of the GMRs’ manipulation. The experiments were performed in the academic laboratories of former trainees from: Australia, Brazil, Georgia, Israel, New Jersey, New York, Romania and Texas.

Speaker Bio

Dr. Dumitru A. Iacobas, Research Professor and Director of the CCSB Personalized Genomics Laboratory since 2018, is an expert of both experimental and computational genomics. Trained as a biophysicist (PhD of the University of Bucharest, Romania), he was on faculty positions at medical schools from Romania (1981-2001) and NY (Albert Einstein College of Medicine-Neuroscience 2001-2013, New York Medical College-Pathology 2013-2017). At NYMC he founded and directed the Systems Biology Core and at Einstein he was the Associate Director of the Neurogenomics and Biometry Core Facilities. Of the 323 Iacobas’ publications (3 patents, 37 books/editions, 21 book chapters, 105 articles, 27 conference proceedings, 86 genomic dbases, 2 bioprotocols, 7 bioprojects, 7 nucleotides, 24 proteins etc.) 74 are as single author, 157 as first author and 32 as last (senior) author. The cancer-related publications include 3 book chapters and 10 papers in: Biological Theory, Cancers, Cancer and Oncology Research, Cells, Current Issues in Molecular Biology, Genes, J Cancer Immunology, Oncotarget, and World J Clinical Oncology. Moreover, he is the Guest Editor of the MDPI Special Issue “Molecules at Play in Cancer” https://www.mdpi.com/journal/cimb/special_issues/molecules_cancer (12 articles so far).