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**
Meeting ID: 821 6367 6866
Hosted by: Utkarsh Sharma, PhD, Gibson Lab

Research Links:
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.

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Harris H. Wang, PhD Columbia University-“Spatial metagenomics, culturomics, and engineering of human and environmental microbiomes”

Harris H. Wang, PhD Columbia University-“Spatial metagenomics, culturomics, and engineering of human and environmental microbiomes”

BWH Computational Pathology Special Seminar

Harris H. Wang, PhD
Associate Professor of Systems Biology
Interim Chair, Department of Systems Biology, Columbia University

Microbes that colonize the gastrointestinal tract play important roles in host metabolism, immunity, and homeostasis. Microbes that live in soil are responsible for a variety of key decomposition and remediation activities in the biosphere. Better tools to study and alter these microbiomes are essential for unlocking their vast potential to improve human health and combat climate change. This talk will describe our recent efforts to develop next-generation tools to study and modify microbial communities. Specifically, I will discuss new advances in spatial metagenomics, AI-enabled culturomics, and precision microbiome editing to program different microbiomes with new traits. These capabilities provide a foundation to accelerate the development of microbiome-based products and therapies.

Research Links:

Harris Wang is an Associate Professor of Systems Biology and serves as the Interim Chair of the Department of Systems Biology at Columbia University, Vagelos College of Physicians and Surgeons. He is also jointly appointed the Department of Pathology and Cell Biology. Dr. Wang received his B.S. degrees in Mathematics and Physics from MIT and his Ph.D. in Biophysics from Harvard University. His research group mainly develops enabling genomic technologies to characterize the mammalian gut microbiome and to engineer these microbes with the capacity to monitor and improve human health. Dr. Wang is an Investigator of the Burroughs Wellcome Fund and the recipient of numerous awards, including the Vilcek Prize for Creative Promise in Biomedical Science, Blavatnik National Award Finalist, NSF CAREER, Sloan Research Fellowship, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

Date: Friday October 27, 2023
Time:  2:00-3:00pm ET
In Person: Wolf Conference Center, Hale BTM 02006B, 60 Fenwood Rd Boston 02115
Meeting ID: 891 9982 9402

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

Gerber Lab awarded $3.1 Million Five Year NIH-NIGMS R35 Grant “Probabilistic deep learning models and integrated biological experiments for analyzing dynamic and heterogeneous microbiomes”

This work will leverage deep learning technologies to advance the microbiome field beyond finding associations in data, to accurately predicting the effects of perturbations on microbiota, elucidating mechanisms through which the microbiota affects the host, and improving bacteriotherapies to enable their success in the clinic. New deep learning models will be developed that address specific challenges for the microbiome, including noisy/small datasets, highly heterogenous human microbiomes, the need for direct interpretability of model outputs, complex multi-modal datasets, and constraints imposed by biological principles. Computational models and biological experiments will be directly coupled through reinforcing cycles of predicting, testing predictions with new experiments, and improving models. An important objective will also be to make computational tools widely available to the research community, through release of quality open-source software.



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:

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.


Yongju Lee, PhD, Genentech – “Contextual representation of pathology, immune repertoire by transformer and graph neural network, and transcriptomic contextual embedding via single-cell foundation model”

Square-framed headshot of Yongju Lee, PhD, in red collared shirt and glasses.The graph neural network (GNN) and transformer model are two renowned neural network architectures for obtaining contextual embeddings from biomedical data. However, each model has a trade-off in terms of the required dataset for training and representation power of the model. As examples, I will discuss the TEA-graph which employs GNN to define the contextual pathological features related to cancer patients’ survival, and GRIP, which utilizes a combination of GNN and transformer to define the set of immune receptors linked to patients’ survival.

Furthermore, an interesting and complex biomedical data rich in contextual information is genomics. Similar to how vision and language research leverages a transformer-based foundation model – the model trained with datasets ranging from millions to billions through self-supervised learning, showing powerful performance for a wide range of downstream applications – nowadays, we can train a large model using ~50M single-cell RNA-seq datasets. Some initial efforts have already shown promising results in understanding genetic mechanisms through perturbation prediction and in silico perturbations. With the contextual gene embedding obtained from the model, we can even transfer gene embedding for analyzing bulk RNA-seq datasets. Aligned with these efforts, I would like to share the recent progress to obtain meaningful contextual gene embedding utilizing the transformer architecture and discuss opportunities for multi-modal training to link transcriptomics with images or text.

Research links

Yongju Lee is a Postdoctoral Fellow at Genentech Research and Early Development, under the mentorship of Aviv Regev since spring 2023. He recently earned his Ph.D. from the Department of Electrical and Computer Engineering at Seoul National University, advised by Sunghoon Kwon. His research focuses on tailoring deep learning models for various biomedical data modalities and accelerating scientific and medical discovery by interpreting the deep learning model outcomes. He has developed methods for pathology image, immune repertoire, and spatial omics data. His ongoing research involves establishing a single-cell foundation model and expanding its capabilities to include biomedical images and text data.

All Welcome! Note this event will take place on Zoom:

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Gerber Lab at ICML Workshop on Computational Biology 2023

Gerber Lab at ICML Workshop on Computational Biology 2023

The ICML Workshop on Computational Biology (WCB) highlights how ML approaches can be tailored to making both translational and basic scientific discoveries with biological data, such as genetic sequences, cellular features or protein structures and imaging datasets, among others. It aims to bring together interdisciplinary ML researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community.

The Gerber Lab had the following two papers accepted:

Gerber GK, Bhattarai SK, Du M, Glickman MS, Bucci V. Discovery of Host-Microbiome Interactions Using Multi-Modal, Sparse, Time-Aware, Bayesian Network-Structured Neural Topic Models. International Conference on Machine Learning Workshop on Computational Biology, 2023.

Uppal G, Urtecho G, Richardson M, Moody T, Wang HH, Gerber GK. MC-SPACE: Microbial communities from spatially associated counts engine. International Conference on Machine Learning Workshop on Computational Biology, 2023.