Jeff Saucerman

More snow hike pics, Percival's Island, #Lynchburg

Winter wonderland 🥾❄️

Here's the comparison of time-evolving phase portraits of cardiac differentiation in WT (2 saddlenode bifurcations: mono->bi->new monostable) vs. Brm-KO (reversal of 1 saddlenode bifurcation: mono->bi->old bistable)

Still, I'm very excited by the potential for more generalized Waddington/energy landscapes such as yours for revealing a wide range of transition types.

In contrast, as @benoitbruneau.bsky.social noted the original Waddington diagram only shows pitchfork bifurcations (2 stable states split off from 1)

Great questions, agreed! There are a number of possible transition types. I think this demo by my daughter of how Brahma KO breaks the second saddle node bifurcation was surprisingly our most intuitive.

Yes this is very cool! Our Brahma model was more focused and hypothesis driven, but would be cool to see if this data driven approach can find the saddle node bifurcations automatically.

I think "should" is very important during review. "Suffering happens in the gap we hold between the expectations we have (of preliminary data) and reality."
— Poppy Jamie

Would love to see a survey on what percent of an NIH grant should already be completed as preliminary data.

Sure thanks will DM!

Announcing the 4th Netflux and Network Biology Virtual Conference, Jan 7! Free registration and abstract submission (Dec 12). Live demo of the new user-friendly Netflux webapp, student talks, keynote by Suzanne Gaudet (Novartis): bit.ly/4i1eQD6

Sauce lab dressed for Halloween as favorite proteins! 🎃👻🕷️. I was a serotonin receptor bound to LexaPro for Taylors recent paper.

More Riprap #hiking

Rain fog and leaves turning along Riprap. One of my favorite trails in Shenandoah NP. #hiking

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

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/

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...

I would say that tracks but...

More pics

Spooky fall day in Shenandoah NP

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

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

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...

Thanks! The Point, Rose River Loop, Big Meadows

More hiking pics

Nearing sunset, Shenandoah NP #cville

@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.

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

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

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