October 23 Monday
IMM Guest Seminar: Prof. Smita Krishnaswamy, from Yale school of Medicine, will lecture on "Manifold-Learning Frameworks for Extracting Structure from High-throughput Single-Cell Datasets", Monday Oct 23rd, 2017
Prof. Smita KrishnaswamyAssistant Professor of Genetics and of Computer Science, Yale school of Medicine
10:00 , Wolfson Building for Biological Research
Recent advances in single-cell technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression and epigenetics for thousands of single cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges in terms of scale, complexity, noise and measurement artifact which require advanced mathematical and algorithmic tools to extract underlying biological signals. In this talk, I cover one of most promising techniques to tackle these problems: manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to naturally process and the data, visualize the data and understand progressions as well as to find phenotypic diversity as well and infer patterns in it. I will cover two alternative approaches to manifold learning, diffusion-based and deep learning-based and show results in several projects including:1) MAGIC (Markov Affinity-based Graph Imputation of Cells): an algorithm for denoising and transcript recover of single cells applied to single-cell RNA sequencing data from the epithelial-to-mesenchymal transition in breast cancer, 2) PHATE (Potential of Heat-diffusion Affinity-based Transition Embedding): a visualization technique that offers an alternative to tSNE in that it emphasizes progressions and branching structures rather than cluster separations shown on several datasets including a newly generated embryoid body differentiation dataset, and 3) SAUCIE (Sparse AutoEncoders for Clustering Imputation and Embedding): a novel auto encoder architecture that performs denoising, batch normalization, clustering and visualization simultaneously for massive single-cell data sets from multi-patient cohorts shown on mass cytometry data from Dengue patients.
October 24 Tuesday
Identification of a unique cell cycle regulator in Streptococcus pneumoniae by en masse GFP localization, Tn-seq and CRISPRi phenotyping
Prof. Jan-Willem VeeningUniversity of Lausanne
11:00 - 12:00 , Max and Lillian Candiotty Building
October 25 Wednesday
Triple-stage mass spectrometry unravels the heterogeneity of endogenous protein complexes
Dr. Gili Ben-Nissan
12:00 - 13:00 , Gerhard M.J. Schmidt Lecture Hall
October 30 Monday
The clinical implications of leukemia evolution
Cancer Research Club
Dr. Liran Shlush
14:00 - 15:00 , Max and Lillian Candiotty Building
Acute myeloid leukemia (AML) is a devastating disease with less than 10% of elderly patients survive five years. While AML originates from stem cells which evolve over many years it presents acutely due to the expansion of more committed progenitors. Over the recent years we were able to identify the origins of AML relapse, and also to study AML years before it is diagnosed. We now can predict AML 6 years before diagnosis. Future studies will soon provide evidence whether early treatment will be beneficial.
November 13 Monday
Special Guest Seminar
Prof. Elizabeth MartinezDepartment of Pharmacology, Hamon Center for Therapeutic Oncology, Research, UT Southwestern Medical Center, Dallas, USA
14:00 - 15:00 , Max and Lillian Candiotty Building
November 20 Monday
Chemistry Colloquium
Dr. Liat Ben DavidGeneral Director, Davidson Institute of Science Education
11:00 - 12:15 , Gerhard M.J. Schmidt Lecture Hall
November 20 Monday
Cancer Research Club Seminar
Prof. Cyrille CohenBar-Ilan University
14:00 - 15:00 , Max and Lillian Candiotty Building
November 30 Thursday
Integrating genetic and epigenetic mechanisms of MAP kinase pathway targeted therapy resistance toward rational combination therapies
Cancer Research Club
Prof. Keith T. FlahertyMassachusetts General Hospital Cancer Center Harvard Medical School, USA
14:00 - 15:00 , Max and Lillian Candiotty Building
Efforts to describe mechanisms of de novo and adaptive resistance to BRAF and MEK inhibitors in melanoma have provided evidence of a convergent resistance phenotype defined by neural crest markers. Cells with this phenotype have been described as slowly cycling and invasive in comparison to isogenic cells with expressing melanocyte differentiation markers. Additionally, these neural crest-like cells utilize receptor tyrosine signaling to drive survival pathways and oxidative phosphorylation as their primary metabolic feature. These insights have provided new leads for therapeutic intervention to target these resistant cells. In parallel work, tumors that are not responsive to immune checkpoint antibodies have been found to have many of the same features: most notably loss of melanocyte lineage antigens and expression of neural crest markers. These data suggest that similar next-generation therapeutic strategies aimed at overcoming therapeutic resistance may be useful in combination with both MAPK pathway and immune checkpoint inhibitors.
December 04 Monday
Surfaces spanning composition and structure space: From corrosion to enantioselectivity
Chemistry colloquium
Prof. Andrew J. GellmanDepartment of Chemical Engineering, Carnegie Mellon University
11:00 - 12:15 , Gerhard M.J. Schmidt Lecture Hall
December 11 Monday
Annual Pearlman lecture
"Activity-Based Sensing to Decipher Transition Metal Signaling in the Brain and Beyond"
Prof. Christopher (Chris) ChangDepartment of Chemistry, UC Berkeley
11:00 - 12:15 , Gerhard M.J. Schmidt Lecture Hall