Lightnews — Scholar-powered news

Jeff Saucerman

@jsauce7.bsky.social

3.5K followers 1.6K following 280 posts

Professor of Biomedical Engineering and Cardiovascular Medicine at UVA. Systems biology to discover drugs for heart disease. Hiking with my dog in Shenandoah NP, around Charlottesville. https://engineering.virginia.edu/faculty/jeffrey-saucerman

engineering.virginia.edu

Posts Media Videos Starter Packs

Pinned

Jeff Saucerman @jsauce7.bsky.social · Apr 7

Terrific paper by post-doc @aclark.bsky.social in @plos.org
Comp Biol. This tutorial enables students and biologists to simulate biological networks with our tool Netflux, no programming required! #sysbio #cardiosky 🧪

1 8 31

Jeff Saucerman @jsauce7.bsky.social · 6d

These show that neuregulin-1 induces cardiomyocyte Feret elongation via sustained PI3K signaling, while transient p38 signaling increases cell size but not shape. This study leverages experimental-computational methods to dissect how signaling dynamics distinctly regulate cell size vs. shape. 3/3

Jeff Saucerman @jsauce7.bsky.social · 6d

We first characterized the proteomic, gene expression, and phenotypic responses of cardiomyocytes (including our new cell shape metric Feret elongation) to diverse ligands. We integrated this data with data-driven and mechanistic computational models, validated with new perturbation experiments. 2/

1 2

Jeff Saucerman @jsauce7.bsky.social · 6d

How do signaling pathways differentially control cell size vs. shape? In a new preprint led by Alice Luanpaisanon, we iterate between experimental and computational methods to discover such pathways. 1/ www.biorxiv.org/content/10.1...

1 2 6

Jeff Saucerman @jsauce7.bsky.social · 9d

I would say that tracks but...

Jeff Saucerman @jsauce7.bsky.social · 9d

More pics

Jeff Saucerman @jsauce7.bsky.social · 9d

Spooky fall day in Shenandoah NP

1 3

Jeff Saucerman @jsauce7.bsky.social · 22d

Finally, check out the eye candy! Awesome multi-channel live-cell videos analyzed with machine learning segmentation and tracking. Shows the complex fates that cells commit to. 3/3

Jeff Saucerman @jsauce7.bsky.social · 22d

Inhibition of caspase-3 changes a cell's bias from apoptosis to hypertrophy. Initial cell and nuclear morphological features also bias a cell's decisions. These results demonstrate that the single-cell dynamics of cardiomyocyte growth and death are highly heterogeneous and dynamic. 2/3

1 5

Jeff Saucerman @jsauce7.bsky.social · 22d

Paradoxically, heart failure involves both cell death and growth. How does a cell decide? In a paper at JMCC Plus led by Bryan Chun and Lavie Ngo, we find that rather than dying following hypertrophy, stressed cardiomyocytes commit early to either grow or die.
www.sciencedirect.com/science/arti...

1 1 7

Jeff Saucerman @jsauce7.bsky.social · 26d

Thanks! The Point, Rose River Loop, Big Meadows

1 1

Jeff Saucerman @jsauce7.bsky.social · 26d

More hiking pics

1 2

Jeff Saucerman @jsauce7.bsky.social · 26d

Nearing sunset, Shenandoah NP #cville

1 7

Jeff Saucerman @jsauce7.bsky.social · Sep 5

@j-muncie-vasic.bsky.social and @benoitbruneau.bsky.social show that MEF2C is not a one trick pony- it distinctly regulates different regions of heart tube formation. Glad @aclark.bsky.social and I could contribute gene regulatory networks that predict mechanisms.

Jon Muncie-Vasic @j-muncie-vasic.bsky.social · Sep 3

Excited to share that our manuscript “MEF2C controls segment-specific gene regulatory networks that direct heart tube morphogenesis” is now online @genesdev.bsky.social !
genesdev.cshlp.org/content/earl...
(1/n)

MEF2C controls segment-specific gene regulatory networks that direct heart tube morphogenesis

A biweekly scientific journal publishing high-quality research in molecular biology and genetics, cancer biology, biochemistry, and related fields

genesdev.cshlp.org

1 4

Jeff Saucerman @jsauce7.bsky.social · Sep 1

Nice to walk out of my backyard onto the #Crozet Connector Trail. #cville

1 5

Jeff Saucerman @jsauce7.bsky.social · Aug 26

Further, pharmacologic and postnatal genetic inhibition of DYRK1A enhanced cardiomyocyte cycling and cardiac functional recovery after MI. Overall, our findings reveal network mechanisms by which a small molecule inhibitor drives cardiomyocyte cycling and post-MI functional recovery. 3/3

Jeff Saucerman @jsauce7.bsky.social · Aug 26

We used network modeling to predict mechanisms by which DYRK1A inhibition induces cardiomyocyte cell cycling. We validated these predictions using a recently described small molecular inhibitor of DYRK1A by imaging and RNA sequencing of cultured cardiomyocytes. 2/3

1 1

Jeff Saucerman @jsauce7.bsky.social · Aug 26

Happy to share our latest preprint, "Network Modeling Predicts How DYRK1A Inhibition Promotes Cardiomyocyte Cycling after Ischemic/Reperfusion Injury", in collaboration with Matthew Wolf's lab, led by Bryce Murillo and Alex Young! www.biorxiv.org/content/10.1...

1 8

Jeff Saucerman @jsauce7.bsky.social · Aug 25

Cooling off at Sugar Hollow #cville

1 3

Jeff Saucerman @jsauce7.bsky.social · Aug 17

More pics

Jeff Saucerman @jsauce7.bsky.social · Aug 17

Sunset showers at Shenandoah NP

1 8

Jeff Saucerman @jsauce7.bsky.social · Jul 30

Thank you!

Jeff Saucerman @jsauce7.bsky.social · Jul 29

Our pipeline provides a proof-of-concept and benchmarking framework for LLM-generated models of signaling networks. But their accuracies do not yet approach that of literature-curated networks. We appreciate your feedback on the preprint! Jeevan and Ben 👏. 3/3 www.biorxiv.org/content/10.1...

Benchmarking of signaling networks generated by large language models

Computational models of signaling networks provide frameworks for predicting how molecular cues guide cell decisions. But they are typically limited by manual curation from incomplete literature. Here...

www.biorxiv.org

1 12

Jeff Saucerman @jsauce7.bsky.social · Jul 29

We find that LLM-generated networks reconstruct 24-58% of the structure of literature-curated networks. When challenged to predict perturbation responses from independent literature, LLM-generated networks validate at 5-26% compared to 77-95% validation accuracy with literature-curated models. 2/

1 2 10

Jeff Saucerman @jsauce7.bsky.social · Jul 29

Do large language models understand how cells work? To find out, we built a pipeline that generates logic-based models of signaling networks using general purpose LLMs GPT, Gemini, or Claude. We benchmarked LLM-generated networks against 3 large-scale literature-curated and validated networks. 1/

1 6 27