scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of biological processes. However, despite their high throughput, these measurements represent only a snapshot in time. But longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modeling to mechanistically describe molecular or cellular dynamics. In my talk, I will present two examples of how we are using time-resolved single-cell datasets to gain a better understanding of cellular signaling, immune responses, and tissue regeneration. Our multidisciplinary efforts are focused on developing methods for applying predictive models in biomedical contexts. For example, we envision that deconvolution of time-resolved bulk mRNA sequencing data could complement scRNA-seq resources, e.g. from the Human Cell Atlas, for ODE-based modeling to leverage large-scale single-cell data in clinical practice. Winner of the BioModels "Model of the Year" Competition 2023 See: https://www.ebi.ac.uk/biomodels/competition/model-of-the-year-2023 and https://www.ebi.ac.uk/biomodels/MODEL2103080001 https://www.cell.com/cell-reports/pdf/S2211-1247(21)00937-2.pdf https://www.biorxiv.org/content/biorxiv/early/2023/10/30/2023.10.27.561846.full.pdf https://academic.oup.com/bioinformatics/article/39/11/btad644/7325353 *Contents* 00:00 - Introduction 05:58 - scMod: Marrying machine learning and deterministic modelling of longitudinal single-cell data 40:03 - Questions and Discussion Moderator: James A. Glazier, PhD, Indiana University, Bloomington For a copy of the slides for this video visit: https://drive.google.com/file/d/13OvYidZI8_6uN4WV-ch5K8K1ck_8awVb/view?usp=sharing If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/