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DTSTART;TZID=Australia/Sydney:20251013T130000
DTEND;TZID=Australia/Sydney:20251013T140000
DTSTAMP:20260515T063734
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SUMMARY:Using genomics-informed agent-based models to understand neoplastic phenotype transitions in the human PDAC microenvironment
DESCRIPTION:Statistical Bioinformatics SeminarDr Jeanette Johnson\, University of Maryland\n\n\n\nThis was an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\n\n\nWe present a novel integration of data-driven single-cell analysis with mechanistic mathematical modeling to predict the impact of cancer-associated fibroblasts (CAF) on pancreatic ductal adenocarcinoma (PDAC) invasion. Bioinformatic analyses of high-throughput single-cell and spatial molecular assays give us access to human tumor gene expression at the time of measurement\, enabling direct characterization of the human tumor microenvironment (TME)\, complementing our understanding of cell behavior gained from preclinical models. Notably\, applying our Bayesian non-negative matrix factorization algorithm CoGAPS to single-cell RNA-seq data from PDAC allowed us to associate a gene program of concurrent epithelial-to-mesenchymal transition (EMT) and inflammation in epithelial cells with the presence of CAFs\, validated through organoid co-culture experiments. We hypothesize that this gene program represents a mechanism by which CAFs promote invasive behavior in neoplastic cells over time. Testing this hypothesis requires inference of temporal changes\, which is not possible with single-timepoint data collection in genomics data. In contrast to data-driven bioinformatics\, mechanistic mathematical modeling can capture processes that happen over time and make predictions about a system. Agent-based models (ABMs) are one form of such mechanistic mathematical models that are well suited for temporal modeling of cellular phenotypes. We developed new software for ABMs that abstract cells into software agents with individual states and their own rules of behavior from single-cell and spatial molecular assays for temporal inference. We apply this technique to understand how CAFs in the PDAC microenvironment support the series of phenotype transitions comprising tumor progression and invasion\, by encoding the CAF-induced ability of tumor cells to migrate and a cessation of proliferation in tumor cells\, induced by CAF-secreted\, non-diffusable substrate as inferred from our genomics analysis.  While this provides a virtual framework to test the impact of varying CAF density on tumor cell invasion\, it initially lacked integration with the spatial architecture of human tumors. We then apply the ABM to human PDAC spatial transcriptomics data to forecast tumor behavior over time in a spatially-informed setting\, varying the hypothesized mechanisms. This revealed that a substrate-driven mechanism results in a  broad mesenchymal-like zone surrounding epithelial-like outgrowths\, while a cell contact-mediated mechanism results in a single-cell-wide boundary surrounding more significant outgrowth. When we compared these to real tumor compositions\, this favors the explanation that secreted CAF substrate drives EMT in PDAC neoplastic cells\, which we then validate in organoids with CAF conditioned media. Our work demonstrates how integrating ABMs with genomic and spatial data enables testing of mechanistic hypotheses about tumor-stroma interactions and provides deeper insights into PDAC microenvironments\, potentially informing strategies to intercept disease progression. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Jeanette Johnson\n\n\n\nI am currently a postdoctoral fellow at the University of Maryland School of Medicine\, after completing my PhD in Immunology at Johns Hopkins this past May. My genomics obsession started as an undergraduate student at the University of British Columbia in computer science and immunology when I got to write processing scripts for some single-cell PBMC data. I will be presenting some of my PhD work which I did in Dr. Elana Fertig’s lab\, looking at ways to build agent-based computational models systematically from genomics data\, which I developed in the context of the pancreatic tumor microenvironment. Living in Baltimore\, Maryland with my two cats and partner\, my favorite hobby is convincing people to go eat seafood with me. I also love to do my nails and am actively searching for the maximally dramatic set that still lets me type and pipette.
URL:https://spds.sydney.edu.au/event/using-genomics-informed-agent-based-models-to-understand-neoplastic-phenotype-transitions-in-the-human-pdac-microenvironment/
LOCATION:NSW
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/02/Complex-systems-1-edited-scaled.jpeg
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BEGIN:VEVENT
DTSTART;TZID=Australia/Sydney:20251020T130000
DTEND;TZID=Australia/Sydney:20251020T140000
DTSTAMP:20260515T063734
CREATED:20251013T013941Z
LAST-MODIFIED:20251013T013957Z
UID:4222-1760965200-1760968800@spds.sydney.edu.au
SUMMARY:High-resolution Characterization of Age-specific Changes in HPV-negative HNSCC through Building a scRNA-Sequencing Atlas
DESCRIPTION:Statistical Bioinformatics SeminarLina Kroehling\, Boston University\n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nAge is strongly associated with both the incidence and mortality risk of head and neck cancer. While cancer and aging share many of their defining “hallmarks”\, including chronic inflammation\, increased genomic instability\, and increased senescence\, more research is needed to elucidate the specific mechanisms contributing to cancer aggressiveness in older patients. \n\n\n\nTo this end\, we have assembled a hiqh-quality human single-cell RNA-sequencing HNSCC atlas profiling more than 290\,000 cells across more than 70 patients\, with ages ranging between 18 and 90\, which provides a unique resource to investigate age-associated changes in the disease’s heterogeneity.  \n\n\n\nTo create the atlas\, we integrated seven publicly available single-cell RNAseq datasets from 73 HPV-negative patients. Cells were clustered\, classified\, and characterized by gene set enrichment analysis\, both in the epithelial cell compartment and in the tumor microenvironment (TME). Differential expression and cell type proportion analyses were performed to identify genes and cell type compositional changes associated with age. Cell cell communication analysis was performed to identify interacting cell types and modeled to identify specific ligands and receptors changing with age. \n\n\n\nWe identified distinct age-related changes in cell type composition\, including vascular endothelial cells increasing with age\, and several tumor clusters with distinct functions also changing with age. Further analyses are ongoing\, and we plan to functionally validate the hypotheses generated\, specifically the presence of differentially abundant cell populations\, and age-specific ligand-receptor signaling events that contribute to tumor growth. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLina Kroehling\n\n\n\nLina Kroehling is a bioinformatics PhD candidate in the Monti Lab at Boston University where she utilizes multi-omics data to study Head and Neck Cancer. She has a B.S. and M.S. in biochemistry from Clark University in Worcester MA\, and worked as a bioinformatician in academic immunology labs before starting her PhD. 
URL:https://spds.sydney.edu.au/event/high-resolution-characterization-of-age-specific-changes-in-hpv-negative-hnscc-through-building-a-scrna-sequencing-atlas/
LOCATION:NSW
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/02/Complex-systems-1-edited-scaled.jpeg
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DTSTART;TZID=Australia/Sydney:20251027T130000
DTEND;TZID=Australia/Sydney:20251027T140000
DTSTAMP:20260515T063734
CREATED:20251020T004350Z
LAST-MODIFIED:20251027T033711Z
UID:4394-1761570000-1761573600@spds.sydney.edu.au
SUMMARY:Harnessing the human microbiome for therapeutic purposes
DESCRIPTION:Statistical Bioinformatics SeminarDr Shanlin Ke\, The Ohio State University\n\n\n\nThis is an online event held via Zoom: https://uni-sydney.zoom.us/j/85114748391 \n\n\n\n\n\n\n\n\n\nThe human microbiome plays a critical role in health and disease and represents a promising source for novel therapeutic strategies. In this talk\, I will first provide an overview of how microbiomes are currently leveraged for therapeutic purposes. I will then introduce a computational approach we have developed to uncover complex host–microbiota interactions. Using our recent PTSD–microbiome study as an example\, I will demonstrate how this approach can reveal host–microbiota interactions and be validated through animal models. Next\, I will present our AI-based framework for discovering antimicrobial peptides (AMPs) from the urinary microbiome\, highlighting its potential to combat antimicrobial resistance. I will conclude by discussing an idea: rejuvenating the human gut microbiome to preserve the “healthy” microbiome for future therapeutic use. \n\n\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Shanlin Ke\n\n\n\nDr Shanlin Ke joined The Ohio State University as an Assistant Professor in May 2025 in the Division of Gastroenterology\, Hepatology\, and Nutrition within the Department of Internal Medicine. Dr Ke is a biologist with expertise in bioinformatics\, microbiome\, machine learning\, multi-omics\, and wet-lab techniques. He received his PhD in Animal Genetics from Jiangxi Agricultural University in 2020. In 2018\, he joined Dr Yang-Yu Liu’s lab at the Channing Division of Network Medicine\, Harvard Medical School as a visiting student. He continued his microbiome research as a postdoctoral fellow in Dr Liu’s lab beginning in 2021\, where he developed and applied computational approaches to study host–microbiota interactions in chronic conditions (e.g.\, inflammatory bowel disease and post-traumatic stress disorder) and infectious diseases (e.g.\, Clostridioides difficile infection and urinary tract infection). In parallel\, he conducted in vitro and in vivo experiments to validate computational findings and investigate the mechanisms underlying host-microbiota interactions in human diseases. \n\n\n\nAt OSU\, Dr Ke’s lab focuses on developing innovative methodologies and leveraging bioinformatics tools\, metagenomic sequencing\, machine learning\, and wet-lab techniques to investigate the role of the human microbiome in pancreatic diseases and to develop microbiome-based therapeutics.
URL:https://spds.sydney.edu.au/event/harnessing-the-human-microbiome-for-therapeutic-purposes/
LOCATION:NSW
ATTACH;FMTTYPE=image/jpeg:https://spds.sydney.edu.au/wp-content/uploads/2025/02/Complex-systems-1-edited-scaled.jpeg
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