Research Fellow at EMBB research group SCELSE, CEE NTU

Nanyang
Joined September 2016
my department is hiring!! more info: bsky.app/profile/catehartley… apply here by 11/15: apply.interfolio.com/195772
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Sakcham retweeted
Applications are now open for the 2027-28 mBio Early-Career Editorial Board! Grow as a peer reviewer through training & hands-on experience, learn about scholarly publishing, and be mentored by mBio Editors. Apply by December 11, 2026. Spaces are limited! journals.asm.org/journal/mbi…
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Looking for a faculty position combining research in fundamental cell and molecular biology with therapeutic discovery? Join our amazing faculty at the @weillinstitute at @Cornell University in Ithaca, New York. academicjobsonline.org/ajo/j… Apply by Nov. 15! Please share!
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The Jackson Laboratory is a wonderful, collaborative place to pursue basic & translational science. On the faculty job market? I strongly encourage you to consider joining us in Bar Harbor, Maine! Apply: go.jax.org/276n55 #JAXresearch #JAXstemcellsdev
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We’re hiring. The Department of Bioengineering is seeking faculty at the Assistant, Associate and Full Professor levels across bioengineering as part of a multi-year hiring initiative. Applications due Nov. 23, 2026. Learn more + apply: be.engineering.upenn.edu/abo… @PennEngineers
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🔥 Excited to share that #paper2agent is published in @Nature today! Papers have long been the primary format for communicating scientific knowledge, but they remain static. Putting that knowledge to work requires connecting findings to data, navigating supplementary materials, and adapting methods to new questions - effort repeated by each new reader. We introduce Paper2Agent, a multi-agent framework that automatically turns research papers into virtual authors. Paper2Agent turns a paper’s manuscript, code, data, and supplements into an MCP server that any AI agent can access, with automated testing and iterative refinement in an agentic loop. The paper then becomes a virtual author you can talk to: trace claims to evidence, analyze your own data, and collaborate with other papers’ virtual authors Agentifying a paper turns it from something people read into something people and AI agents can discover and build on - providing the context needed to interpret its findings and reuse its methods reliably. We tested how reliably these agents put papers to use. Across multiple benchmarks, agents created by Paper2Agent outperformed baselines such as Claude Code working directly with papers' PDFs and code repositories. Once papers become virtual authors, they can collaborate - much like human researchers do. In one case study, agents built from AlphaGenome and two large-scale genetic perturbation studies worked together to propose a new computational approach for integrating evidence across different perturbation datasets. By connecting predictions from one paper with experimental data from others, they helped pinpoint a likely causal gene for psoriasis. We hope Paper2Agent makes scientific knowledge easier to access, reuse, and build on - a first step toward a future where millions of paper agents proactively collaborate with human researchers and one another to advance discovery. 🤖Talk to the virtual author for Paper2Agent itself: paper2agent.ai 📎Paper: nature.com/articles/s41586-0… 💻Code: github.com/jmiao24/Paper2Age… VERY grateful to work with this incredible team: @james_y_zou, @jkpritch, Yaohui, and Joe!
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The Stony Brook University Biochemistry and Cell Biology Department has two open faculty searches. Details and links to the applications can be found here: stonybrook.edu/biochem/oppor…
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Biology already has powerful models for DNA, RNA, proteins and molecular interactions. The next bottleneck may be learning how to compose them. A preprint introduces Proto, a high-level programming language for generative biology. The analogy to software is tempting, but the biology is more interesting than that. In conventional programming, we compose deterministic instructions. Biological design is different: changing one sequence can alter folding, binding, expression, splicing and cellular context at the same time. There is rarely a clean molecular “module” that behaves like a Lego block. Proto moves the abstraction one level higher. A design campaign is expressed using four ideas: sequence → constraint → generator → optimizer The sequence is what we want to build. Constraints describe what it should do. Generative models propose candidates. Optimizers search for combinations that satisfy the objectives. Suddenly, designing an antibody, an intron or a synthetic promoter–repressor system can share the same computational grammar, even though the underlying biology is completely different. The most interesting demonstration is biological rather than computational. The researchers designed introns that splice differently across human cell types by combining generative models with predictors such as AlphaGenome and SpliceTransformer. Some successful sequences achieved the desired behavior through cryptic splice sites, a mechanistic solution that was not explicitly specified beforehand. That hints at something larger. We may be moving from asking individual models to design individual molecules toward specifying biological objectives, then letting multiple models negotiate the sequence space underneath them. The future programming language of biology may not describe every molecular instruction. It may describe the behavior we want biology to realize. biorxiv.org/content/10.64898…
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Microbial C1 assimilation pathways for chemical synthesis: from native metabolism to synthetic design sciencedirect.com/science/ar…
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Extracellular RNA (eRNA) in drinking water systems may be more than cellular debris—it could play important signalling and regulatory roles, while providing new insights into microbial persistence and antibiotic resistance. nature.com/articles/s41545-0…
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Reading a scientific paper takes hours. No PI, no student, can keep up. And keyword search finds words, not ideas. So we built a Research Process Graph: a new way to read the literature. rpg.connectome.tools 🌱 1/7
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Circular genome comparison is now live in #gbdraw! 🚀 The built-in LOSATN instantly runs pairwise alignments and generates homology rings for your sequences, 100% in your browser! Try it out: gbdraw.app #bioinformatics #genomics #microbiology
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