ABC Seminar: Brian Hie, PhD – Stanford University – “Sequence modeling from molecular to genome scale with Evo”

Title: Sequence modeling from molecular to genome scale with Evo
Speaker:  Brian Hie, PhD
Affiliation: Stanford University, Chemical Engineering and Data Science
Position:  Assistant Professor
Host: Jiening Zhu, PhD – Gerber Lab

Date: Monday November 18, 2024
Time: 4:00-5:00PM ET
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Abstract: The genome is a sequence that encodes the DNA, RNA, and proteins orchestrating an organism’s function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report the first scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA co-design with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genome scales of complexity, advancing our understanding and control of biology.

Brian Hie is an Assistant Professor of Chemical Engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, and an Innovation Investigator at Arc Institute, where his group conducts research at the intersection of biology and machine learning.

Click here to be added to our mail list.

ABC Hybrid Seminar: Bokai Zhu, PhD – Ragon Institute – “Integration of spatial-omics and single-cell data across modalities with weakly linked features”

Bokai Zhu
Speaker:  Bokai Zhu, PhD
Affiliation: Ragon Institute of MGH, Harvard and MIT, Broad Institute, MIT
Position:  Postdoctoral Research Fellow
Host: Muhammad Shaban, PhD – Mahmood Lab

Date: Monday September 23, 2024
Time: 1:00-2:00PM ET
In person: Duncan Reid Conference Room (directions below)
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866

Abstract: Advancements in single-cell and spatial-omics technologies have created a need for integrating datasets across modalities with limited and weakly correlated features, such as those between spatial proteomics and transcriptomics. Existing tools, usually designed for strongly linked data, often fail in these scenarios. Recently, we have developed a series of methods (Mario and MaxFuse), that improves integration by refining weak correlations between modalities through an iterative smoothing and co-embedding process, and achieves single-cell level matching across these weakly linked modalities, enabling in-depth understanding of tissue micro-environments.

Research Links: https://bokaizhu.github.io/

Bokai Zhu is currently a postdoctoral researcher supervised by Prof. Alex Shalek at the Ragon Institute at MGH, MIT, and Harvard. Prior to that, Dr. Zhu obtained his PhD in Microbiology and Immunology from Stanford University, under the supervision of Prof. Garry Nolan. He received a bachelor’s degree in Biology from Cornell University and Zhejiang University. Dr. Zhu’s doctoral research focused on: 1) Experimental assay development for multiplex imaging platforms; 2) Computational algorithm development for single-cell multi-omic integrations; 3)Application of the above tools in various biological systems.

Click here to be added to our mail list.

Directions: Duncan Reid Conference Room is located in ASB-1 2nd Floor
When entering the hospital from the main entrance, 75 Francis Street
– Follow the lobby around to the right and take large staircase in front of you to the 2nd floor, then take a left at top of stairs
– When on the main Pike, take the next right at corridor (across from children’s hospital bridge), and a right past the (K) elevators, door straight ahead(Rm #A1020)

December ABC Seminar: Miriam Adler, PhD – Hebrew University

December ABC Seminar: Miriam Adler, PhD – Hebrew University

Fibrosis is a pathology of excessive scarring which causes morbidity and mortality worldwide. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. In this talk, I will introduce a mathematical model we recently developed for a cell circuit between myofibroblasts and macrophages – the two cell types that produce and remodel the scar. The mathematical framework predicts two types of fibrosis – hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. Moreover, we use the model to predict that the autocrine signal for myofibroblast division is a potential therapeutic target to reduce fibrosis. Finally, I will discuss how we use myocardial infarction (MI), a widely studied in-vivo injury model for cardiac fibrosis, to test these theoretical concepts and intervention strategies experimentally.

Speaker: Miriam Adler, PhD
Speaker Affiliation: Alexander Silberman Institute of Life Sciences and the Faculty of Medicine, Hebrew University of Jerusalem
Speaker Position: Senior Lecturer

Date: Monday  December 18, 2023
Time:  10:00am-11:00am ET **New Time**
Zoom: https://partners.zoom.us/j/82163676866
Meeting ID: 821 6367 6866
Hosted by: Utkarsh Sharma, PhD, Gibson Lab

Research Links: https://adlermiri.wixsite.com/mysite
Google Scholar

Miri Adler completed a BSc in Physics at the Technion and obtained an MSc and a PhD in Physics at the Weizmann Institute with Prof. Uri Alon, studying design principles of biological circuits. In her postdoctoral research working jointly with Prof. Ruslan Medzhitov at Yale University and Prof. Aviv Regev at the Broad Institute of MIT and Harvard, Miri developed theoretical frameworks to uncover universal principles of the collective behavior of cells at the tissue level. Miri received a Fulbright scholarship, EMBO postdoctoral scholarship, Zuckerman STEM leadership program fellowship, and the Israel National Postdoctoral Award Program for Advancing Women in Science. Currently she is an associate research scientist at the Tananbaum Center for Theoretical and Analytical Human Biology at Yale University. As of December, she will be a senior lecturer at the Alexander Silberman Institute of Life Sciences and the Faculty of Medicine at the Hebrew University of Jerusalem.

Click here to be added to our mail list.

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

October ABC Seminar: Weiruo Zhang, PhD, Stanford University

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Weiruo Zhang, PhD, Stanford University-“Integrative spatial-omics analysis of cellular architecture mediating lymph node metastasis in head and neck cancer”

Spatial biology is a new frontier that has become accessible through advances in spatial profiling technologies, such as multiplexed in situ imaging spatial proteomics, which can provide single-cell resolution up to 60 markers. In this talk, I will introduce a computational analysis pipeline that performs integrative analysis of spatial proteomics and single-cell RNA sequencing to identify clinically-relevant cellular interactions. The pipeline features (1) CELESTA, an unsupervised machine learning method for cell type identification in multiplexed spatial proteomics data; (2) a geospatial statistical method to identify cell-cell colocalizations; and (3) an integrative coupling of spatial proteomics and single-cell RNA sequencing data that identified cell-cell crosstalk associated with lymph node metastasis in head and neck cancer which we have validated through mouse model studies.

 

Research link:
https://profiles.stanford.edu/weiruo-zhang

Dr. Zhang is currently a Research Engineer at the Department of Biomedical Data Science and the Center for Cancer Systems Biology, Stanford School of Medicine. Dr. Zhang received her M.S. and Ph.D. in Electrical Engineering, both from Stanford University, with a focus on bioinformatics and developing computational algorithms for metabolomics data analysis. Her current research at Stanford primarily focuses on developing and implementing computational methods to integrate and analyze single-cell and spatial multi-omics data, such as single-cell RNA sequencing, spatial proteomics and spatial transcriptomics. Her research aims to apply quantitative approaches that bridge multi-omics, imaging, machine learning, and artificial intelligence to decipher biology for cancer progression and guide treatment responses.

 

Tal Korem PhD, Columbia – “Contamination and genomic variability in microbiome data”

Microbiome studies hold tremendous potential along with substantial computational challenges. I will present two computational approaches for microbiome data analysis. First, I will present SCRuB, a new method for in silico removal of contamination from microbiome data. We show that modeling the taxonomic composition of contamination sources, rather than trying to infer whether specific taxa are categorically contaminant, allows for more robust decontamination and improves downstream phenotypic prediction. Second, I will present copangraph, a new graph-based approach for representing genomic variability within and across microbiomes. We demonstrate a hybrid co-assembly approach that yields high-quality representation of the microbiome.

Tal Korem’s research program focuses on the development of computational methods that identify and interpret host-microbiome interactions in various clinical setting. The ultimate goal of his research is to translate microbiome findings to clinical care, with microbiome-based therapeutics and microbiome-informed clinical practices. He has developed several approaches for microbiome data analysis, inferring microbial growth rates, structural variants, and microbiome-metabolite interactions; and has applied these methods in diverse clinical and biological investigations, most notably for personalization of dietary treatment and predicting preterm birth. He is a member of Columbia’s Program for Mathematical Genomics (PMG), an Assistant Professor in the Departments of Systems Biology and Obstetrics & Gynecology, and was previously a CIFAR-Azrieli global scholar by the Canadian Institute for Advanced Research.

All Welcome! Note this event will take place on Zoom: https://partners.zoom.us/j/84799112738

To be added to the ABC seminar email list, please email tarnoldmages@bwh.harvard.edu

Nov. 14, 2022: Martin Jankowiak, PhD, Broad Institute

“Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection,” https://partners.zoom.us/j/86162386947; We develop Bayesian Viral Allele Selection (BVAS), a method that leverages the millions of SARS-CoV-2 viral genomes that have been sequenced across the globe to identify mutations linked to increased viral fitness.