Jacob C. Kimmel

jacob@jck.bio

San Francisco, California

Education

Ph.D. — Dept. Biochemistry & Biophysics, UC San Francisco, 2015 - 2018
Funding: NSF Fellowship, PhRMA Fellowship, NIH T32, UCSF Discovery

Recent Experience

NewLimit, South San Francisco, CA, 2022 - Present
Co-founder & President

• Building a therapeutics firm to develop epigenetic reprogramming medicines for aging

Calico Life Sciences, South San Francisco, CA, 2020 - 2022
Principal Investigator, R&D, 2021-2022
Computational Fellow, Computing, 2020-2021

• Led a laboratory combining computational and experimental methods to repurpose developmental programs for therapeutic applications.
• Developed machine learning and functional genomics methods to measure and reprogram cell age & identity

Calico Life Sciences, South San Francisco, CA, 2018 - 2020.
Data Scientist, Computing

• Discovered that cell identity rather than tissue environment defines the trajectory of cellular aging & a lineage decision where old muscle stem cells fail to form mature muscle
• Contributed to small molecular and antibody therapeutics programs

University of California San Francisco, San Francisco, CA, 2015 - 2018
PhD Candidate
Principal Investigators: Wallace Marshall, Andrew Brack
Thesis: Inferring stem cell state from cell behavior

• Developed statistical and machine learning methods to quantify cellular behavior in timelapse microscopy images
• Discovered state transitions in myogenesis that are impaired with age

IBM Research, Cell Engineering Group, San Jose, CA, 2017 Fall
Deep Learning Research Intern
Principal Investigator: Simone Bianco

• Developed convolutional neural networks to process timelapse imaging data for biological sensors
• Implemented a natural evolution strategies (NES) optimization framework to improve cellular tracking for biological sensors

Selected Publications

1. Sengine L, Kummerlowe CS, Reynolds DL, Bernstein NJ, Kimmel JC. In silico design of epigenetic reprogramming payloads. International Conference on Machine Learning (ICML), Generative Biology. 2025. url: https://openreview.net/forum?id=kPQ6NKVAiT.
2. Roux A, Zhang C, Paw J, Zavala-Solorio J, Vijay T, Kolumam G, Kenyon C, Kimmel JC. 2022. Cell Systems. doi: https://doi.org/10.1016/j.cels.2022.05.002. PDF
3. Kimmel JC, Kelley DR. scNym: Semi-supervised adversarial neural networks for single cell classification. 2021. Genome Research. doi: https://doi.org/10.1101/gr.268581.120. Awared Top Paper at ICML Computational Biology, 2020.
4. Kimmel JC, Yi N, Roy M, Hendrickson DG, Kelley DR. Differentiation reveals the plasticity of age-related change in murine muscle progenitors. 2021. Cell Reports. https://doi.org/10.1016/j.celrep.2021.109046.
5. Kimmel JC, Hwang AB, Marshall WF, Brack AS. Aging induces aberrant state transition kinetics in murine muscle stem cells. 2020. Development. https://doi.org/10.1242/dev.183855. Chosen as a Research Highlight by Development: Muscling in on Stem Cell Aging.
6. Kimmel JC. Disentangling latent representations of single cell RNA-seq experiments. 2020. bioRxiv. https://doi.org/10.1101/2020.03.04.972166.
7. Kimmel JC, Penland L, Rubinstein ND, Hendrickson DH, Kelley DR, Rosenthal AZ. A murine aging cell atlas reveals cell identity and tissue-specific trajectories of aging. 2019. Genome Research. doi: 10.1101/gr.253880.119. Featured on the cover of Genome Research.
8. Kimmel JC, Brack AS, Marshall WF. Deep convolutional and recurrent neural networks for cell motility discrimination and prediction. 2019. IEEE Transactions on Computational Biology and Bioinformatics. doi: 10.1109/TCBB.2019.2919307. Preprint featured in Company of Biologists: the Node.
9. Kimmel JC, Chang AY, Brack AS, Marshall WF. Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance. 2018. PLoS Computational Biology 14(1): e1005927. https://doi.org/10.1371/journal.pcbi.1005927. Featured as an Editor’s Pick in PLoS Editor’s Collections: Cell Biology.

Service

Peer Reviewer

• Bioinformatics
• Cell Reports
• Cell Systems
• eLife
• IEEE Journal of Biomedical and Health Informatics
• Nature Medicine
• Nature Methods
• PLoS Computational Biology
• Proceedings of the National Academy of Sciences

Open Source Software

• Maintainer: scnym, velodyn, scmmd, heteromotility, lanternfish, pytorch_modelsize
• Contributor: scvi-tools, statsmodels, gseapy