Furkan Kurtoglu 🧬🦠🖥️🧫

Multiscale Systems Biologist • Bridging micro and macro levels • @MathCancer Lab and @IULuddy Alumni • @PhysiCell user • Open-Source

[Furkan Kurtoglu 🧬🦠🖥️🧫 @FKurtogluSysBio]

Hello World ! Starting today, I will share one great tool/software related systems biology/comp biology on each mondays.

[Nature Reviews Cancer @NatureRevCancer]

NEW content online! 3D models to capture and perturb tumour heterogeneity in vitro dlvr.​it/TWH3T1

[Nature Biotechnology @NatureBiotech]

As the @humancellatlas project enters its second decade, the question is no longer whether we can map human cells, it is what we will do with the map https://t.co/cRfYrHoRJz

[Nature Metabolism @NatMetabolism]

#Throwback

COMMENT | KS Lee, X Su & T Huan

Metabolites are not genes — avoiding the misuse of pathway analysis in metabolomics

https://t.co/YlaTvcsM4Q

[Waggoner Lab @LabWaggoner]

Review: Macrophage biology and immunoregulation in health and disease @DrSTANtheMAN @OhioStatePIIO
https://t.co/VDrWxguE8g

[antisense. @razoralign]

COMPASS: Systematic mapping of the prokaryotic noncoding sequence space with a genomic language model https://t.co/tJhNHC7kHH

[antisense. @razoralign]

DynaVelo: Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cells https://t.co/ngHmBHkOeV

[Nature Biomedical Engineering @natBME]

SpatialZoomer: multi-scale feature analysis of spatial transcriptomics go.​nature.​com/3UlkEQm

[Fabian Theis @fabian_theis]

🌐 AI for science just became science diplomacy.

On October 4, the US and 16 other countries, including Japan, South Korea, the UK, Germany and Singapore, endorsed the "Kyoto Vision for a Golden Age of Science" at the STS Forum in Kyoto. I only learned about it today, from a friend. In case you missed it too, here is the short version.

💡 The one-page text covers a lot of what I care about. More diverse funding, from long-term awards to fast grants and prizes. Room for new institutional models. Metascience and reproducibility. AI for science, including autonomous closed-loop discovery. Early investment in young talent.

Much of this I support. Closed-loop, AI-driven experimentation is at the core of what we do in the Theis lab, and funding systems everywhere could use more experimentation themselves.

The declaration builds on the White House report "Science: A New Golden Age" from July, which goes further, for example in rethinking the role of universities. Not trivial. How these ideas translate will differ between national systems. Germany's research ministry, for instance, calls the paper a non-binding basis for discussion and stresses that freedom of research and the independence of science remain non-negotiable.

🌍 For me, the test will be concrete: shared access to compute, data and automated labs, and collaboration across borders. Researchers should help shape what this means in practice.

How do you see it? Will it change how science is funded where you work?

🇩🇪 BMFTR statement: https://t.co/XacVzC1vKp
📄 Kyoto Vision: https://t.co/gq2JrSsE9y
📘 Science: A New Golden Age: https://t.co/ORzxbrXQRq
🇺🇸 White House release: https://t.co/dqMmZBy5FU

#AIforScience #ScienceDiplomacy #Metascience

[Mo Lotfollahi @mo_lotfollahi]

Excited to share ArchMap, now out in @NatureGenet 🧬

The Human Cell Atlas is building a reference map of every cell in the human body, much as the Human Genome Project gave us a reference for DNA. The real power of a reference is that every new dataset can be read against it, to see what's familiar and what's new.
ArchMap makes that possible for everyone. It's a free, no-code platform where any lab can map its single-cell data onto Human Cell Atlas references, all in the browser.

Built on scArches, ArchMap combines reference mapping, cell-type annotation, uncertainty estimates and collaborative exploration in a single workflow. What excites me most is seeing where new data departs from the reference. In a pulmonary fibrosis example, annotation uncertainty followed by gene-expression analysis points directly to disease-associated cell states.

Huge thanks to Chelsea Bright, Ronald Skorobogat, @MDLuecken , @fabian_theis

Try it: https://t.co/CzKqsHeiri
Paper: https://t.co/OfM9vtaLoK

#HumanCellAtlas #SingleCell

[Cell @CellCellPress]

In the latest issue! Ultrarapid deep 3D histology enables intraoperative mapping of glioma infiltration dlvr.​it/TVlcG1

[Prof. Nikolai Slavov @slavov_n]

Timescales in Cell Biology

[Cancer Cell @Cancer_Cell]

Tumor evolution shapes metastatic competence in colorectal cancer https://t.co/wAV2I4w6SE

[Ada Fang @AdaFang_]

Biology does not need models that just describe the world. It needs models that predict what happens when we intervene.

In @CellCellPress Focus on AI in Biology, we lay out how to build biomedical world models, from the data they need to how they should be trained and evaluated.

[JCI insight @JCI_insight]

This review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of #SpatialMultiomics methods in biomedical research: https://t.co/9yzH78Fvij

Multiomic Analysis Identifies T-cell Subsets and Mechanisms of Epithelial Interaction in Idiopathic Pulmonary Fibrosis by Haikuo Li @HaikuoLi and colleagues @sjtu1896 & @WashU.

[Nature Metabolism @NatMetabolism]

#Throwback

PERSPECTIVE | H Ren, Y Tang & D Zhang

The emerging role of protein l-lactylation in metabolic regulation and cell signalling

https://t.co/vGYVQxXpGL

[Tim Hwang @timhwang]

being simulated in silico feels wonderful

[Kevin A. Scott, PhD @Sculpturatus]

distinct cell states can be modeled by attractor spaces. every cell state resists external perturbations until it enters a new attractor space. the future of cell state-dependent drug discovery will rely on chaos theory, non-linear dynamics, and attractor spaces to define biology

https://t.co/SfVTNkmDde

[Dr. Luke in China @96Stats]

My university lab (shanghai jiao tong @sjtu1896 ) just made an “agentic” AI system for diagnosing rare diseases really well. It doesn’t just guess either, it shows traceable reasoning linked to evidence so you can verify.

This specific paper looks really cool too, it works like a team (multiple agents not just one chatbot). It takes messy clinical data and outputs a really nice list with reasons, exactly whats needed!

The west very rarely makes AI for rare diseases because it’s not profitable, so its nice to see China doing lots of work on it. This is the type of work that will help save lives (not chatgpt/claude chatbots..) and its where China is absolutely leading in

[Mingyao Li @DrMingyaoLi]

Excited to share our new paper in Nature Methods! 🚀
We present iSCALE, a “Google Map” for spatial transcriptomics, scaling cellular-resolution gene expression mapping to large-sized tissues beyond the reach of current platforms. 👉https://t.co/WBvR4Cyirr

[MorphoNet @MorphoTweet]

Having difficulties segmenting your 3D🔬 datasets ? You can try the CellposeSAM plugin🧑‍💻directly in MorphoNet in one click ! No python required 😌