Chitrang Dani

Two papers from lab alumni in this issue of Current Biology! Very proud of the exciting science from @danichitrang @abhilash1690 Super proud!

@CurrentBiology This is also the first experimental evidence that annual change in daylength can be sufficient as a selective force for the evolution of self-sustained clocks over damped clocks.

@CurrentBiology We competed self-sustained, damped, and arrhythmic cyanobacteria strains under different photoperiods. The result? While damped clocks can hold their own in winter-like short days, they get out-competed by self-sustained clocks in equinox and summer conditions.

Our new paper is out in @CurrentBiology where we address the question of why self-sustenance, a defining feature of circadian clocks might have been selected, when environments on Earth are rarely constant! sciencedirect.com/science/articl… #circadian #cyanobacteria #evolution

Honored to serve on the organizing committee and present my research for this exceptional meeting!

Honored to present my research and serve on the organizing committee for my first Cyanobacteria Workshop & grateful to all participants, sponsors, and @VanderbiltU for an exceptional meeting!

Clock people: here's Gapdh in brown fat, every 2 hours for 2 days. It's rhythmic everywhere. Maybe not the best control.

The vast majority over discoveries are low hanging fruit when they are made. The key to scientific progress is progressively making more fruit low-hanging. One of the deep problems we have in science is that we reward the people who pick the fruit rather than the ones who lower the branches.

Five people have seen a color never before visible to the naked human eye, thanks to a new retinal stimulation technique called Oz. scim.ag/4inNeqa

Happening tomorrow!

Abstracts are invited in: 1) Advances in Light Harvesting and Structural Biology 2) Computational and Systems Biology 3) Ecology, Evolution, and Natural Products 4) Physiology, Metabolism, and Regulatory Networks 5) Synthetic Biology and Biotechnology Applications

Just three days left for the deadline for oral abstract submissions (and 2 weeks for poster abstracts) for the 15th Workshop on Cyanobacteria at @VanderbiltU - Nashville, TN, USA. Workshop dates: 4-7th June 2025. More information: web.cvent.com/event/3d0bd32b…. Don't miss out!

Final day of lectures, motivated students inspiring teachers and selfless volunteers

The best thing about this meeting is its focus on trainee presentations. More info: sites.google.com/view/riser2025

If you work on #circadian rhythms and are located in or around South-East USA, this meeting is for you: The 8th Rhythms in the SouthEastern Region (RISER) meeting is happening at Vanderbilt University, Nashville (TN) on May 17, 2025. Abstracts due in 25 days! Register soon!

For #circadian trainees: The 8th Rhythms in the SouthEastern Region (RISER) meeting will be happening at Vanderbilt University, Nashville (TN) on May 17, 2025. Abstracts due: March 14, 2025! More info: sites.google.com/view/riser2025 Register here: forms.gle/CKLkt4a3K9qP9E…

We are thrilled to invite you to the 2025 Annual VIRAL #Research #Symposium. Details are below. Registration is open now. redcap.link/lgcrd0af Abstract submission deadline: Oral Presentation (March 10), Poster Presentation (March 28). A generous travel award for all winners.

🪰 We're thrilled to kick off the year ✨with a new preprint from our lab: 'DANCE – An Open-Source Analysis Pipeline and Low-Cost Hardware to Quantify Aggression and Courtship in Drosophila.' Let’s dive in! 🧵 #DANCE #innovation #ScienceTwitter #AIML #ML biorxiv.org/content/10.110…

I continue to think this is one of the most important cartoons of recent years. @marketoonist

One of the remarkable things for me about NeurIPS this year was how quickly the entire AI for Biology community has gone all-in on biological foundation models. Virtual cell models will enable us to predict how cell states will change in response to chemical perturbations. Protein language models will enable us to identify better enzymes for degrading plastics, and so on. Everyone wants bigger data on more things to throw into bigger models. These models are going to be awesome, but real biology discoveries look somewhat different. Contrast these dreams of foundation models with the latest table of contents from Science or Nature: --“A long noncoding eRNA forms R-loops to shape emotional experience–induced behavioral adaptation” — The authors identified a lncRNA in mice that is expressed in response to neuronal activity that modulates the 3D structure of chromatin, thereby activating genes that are involved in neuronal plasticity. The authors further identified that this lncRNA is essential for certain forms of learning. --“Cancer cells impair monocyte-mediated T cell stimulation to evade immunity” — The authors identified that mouse melanoma cells secrete a lipid metabolite that prevents monocytes from activating CD8+ T cells. --“Postsynaptic competition between calcineurin and PKA regulates mammalian sleep–wake cycles” — By generating mouse knockout lines, the authors identified phosphatases and kinases that are critical for regulating the sleep-wake cycle, and showed that they act through regulation of proteins at excitatory postsynaptic sites. I struggle to imagine how any of these discoveries could fall out of a multimodal biology foundation model. This is not intended to be a straw man argument. Surely, a foundation model could potentially identify the lncRNA from the first paper, but I am not sure how such a foundation model would associate it with chromatin remodeling. A multimodal foundation model with enough data could also potentially identify metabolic changes associated with melanoma cells subjected to certain kinds of treatments, but I don’t see how that foundation model could identify the effect of those metabolites in preventing CD8+ T cell activation. Indeed, I do not think that any of the foundation models that are being developed today would be capable of generating rich new biological insights of the kind described in these papers. And yet, these are the kinds of insights that new therapies are made from. The issue, I think, is that machine learning models work extremely well on structured data, and so all the foundation models that are being built are highly structured. Take a protein sequence as input and produce a protein sequence as output. Take a cell state and a chemical perturbation as input and produce a new cell state as output. Biology, however, is poorly structured. The lncRNA insight is case in point: what structured representation can we use for the action of the lncRNA in modulating chromatin architecture? Protein models cannot represent it; DNA models cannot represent it; virtual cell models cannot represent it. Perhaps a model that incorporates RNA expression and 3D genome state could represent it, but then how would that model represent the lipid modulation of the monocytes? I worry that every discovery may need its own representation space. Indeed, the nature of biology is such that there likely is no representation, short of an atomic-resolution real-space model of the entire organism, that is sufficient to represent the diversity of biological phenomena that are relevant for disease. Except, of course, for natural language, which is evolved to represent all concepts that humans are capable of contemplating. Indeed, I think natural language has an essential role to play in representing biology, and is ultimately unavoidable, insofar as it is the only medium we know of that is sufficiently structured for machine learning and sufficiently flexible to represent the full diversity of biological concepts. At FutureHouse, we work on language agents, which is one way of combining language and biology, but this is not the only way. Models that combine natural language with protein, DNA, transcriptomics, and so on will also be extremely productive, provided the addition of the structured datatypes does not restrict their ability to represent unstructured concepts. However we do it, I think this essential role of natural language in representing biology is currently largely underappreciated. The history of biology is built on tools that we have found in nature to study biological phenomena. As all biologists know, trying to engineer things from scratch (almost) never works; what works is finding things in nature and repurposing them. It will be aesthetically pleasing if it turns out that our engineered representations are yet again insufficient for studying biology, and that natural language is simply another such tool that we have found in nature that must be applied instead.