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SUMMARY:FSML Lecture Series - Nicholas Galioto: Discovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models
DESCRIPTION:Zoom link \nBio: Nick Galioto is a second-year postdoctoral research fellow in the Department of Computational Medicine and Bioinformatics at the University of Michigan (UM). He received his PhD at UM in aerospace engineering in 2023 under the advising of Alex Gorodetsky and remained in the lab for an additional year as a postdoc. In the Gorodetsky lab\, Nick researched how to use stochastic models of dynamical systems to improve system identification. Now\, Nick works in the Rajapakse lab researching how to create data-driven models of the dynamics of cell reprogramming. \n  \nDiscovery of Cellular Reprogramming Methodology Through Single-cell Foundation Models\n  \nAbstract: Cell reprogramming\, the transformation of a cell from one cell type to another through the introduction of exogenous transcription factors (TFs)\, is a rapidly developing research area that could lead to groundbreaking therapeutic technologies in areas such as tissue regeneration\, disease modeling\, and personalized medicine. However\, many challenges still exist that obstruct its practical viability. Discovering which TFs induce reprogramming requires a combinatorial search\, and testing a single candidate set of TFs experimentally can cost tens of thousands of dollars and take multiple months. Moreover\, even when an effective set of TFs is known\, cell conversion efficiency lies only around 5%. Faced with these challenges\, researchers have developed computational surrogate models to rapidly explore the TF search space at a fraction of the cost of wet lab experimentation. Unfortunately\, these models have seen limited success in practice due to the difficulty of capturing the complex gene-gene interactions within the cell\, most of which are still not well understood. With the recent high-profile rise of transformer-based foundation models for natural language\, researchers are now turning to the transformer to push past\, current performance limitations in a wide range of digital biology tasks\, including cell reprogramming. Of particular interest in these models is the attention mechanism\, which is potentially well-suited for capturing long-range gene-gene interactions at a higher fidelity than previously possible. In this talk\, I will describe how the transformer architecture has been adapted for cellular biology and analyze the utility of one such model\, Geneformer\, in identifying TFs for cell reprogramming. Specifically\, I will present the results of an in silico perturbation experiment for reprogramming fibroblast cells to hematopoietic stem cells and compare the outcomes to experimental results found in the literature. I will conclude the talk with a discussion of the drawbacks and limitations of the Geneformer model and provide an assessment of what will be needed in the future for digital biology to fully reap the benefits of large-scale foundation models.
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series-7/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:Engineering,FSML,Science
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DTSTART;TZID=America/Detroit:20241004T120000
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DTSTAMP:20241104T140255Z
CREATED:20241002T220654Z
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SUMMARY:FSML Lecture Series: Tokenization for Chemistry by Alex Wadell\, University of Michigan
DESCRIPTION:Alex Wadell is a PhD candidate at the University of Michigan Department of Mechanical Engineering. \nTokenization for Chemistry\nMolecular Foundation Models are emerging as a powerful tool for molecular design\, material science\, and cheminformatics. By leveraging the transformer architecture\, these models attempt to learn the language of chemistry and discover robust molecular embeddings. However\, current models are constrained by tokenizers that fail to capture the full breadth of chemical space or even the periodic table of elements. In his talk\, Alex will introduce smirk\, a new tokenizer for molecular foundation models that can represent the entirety of the OpenSMILES specification. We’ll also discuss performance metrics for tokenizers and the results of Alex’s systematic evaluation of thirteen chemistry-specific tokenizers using N-gram language models as a low-cost proxy for transformer models. \nIf you are unable to attend in person but are interested\, please feel free to join virtually. \nJoin Zoom Meeting\nhttps://umich.zoom.us/j/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1\nMeeting ID: 978 2352 7756\nPasscode: 2024
URL:https://micde.umich.edu/event/lecture-discussionsciml-lecture-series/
LOCATION:Walter E Lay Auto Lab – 2052
CATEGORIES:FSML,Micde,Sciml
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