Understanding and re-designing cell division machinery
We study the machines and mechanisms that help eukaryotic cells achieve accurate genome inheritance. Using these insights alongside de novo protein design, we reimagine key mechanisms and reverse engineer new machines for synthetic biology. Our research stands at the interface of cell biology, synthetic biology, biophysics, and biochemistry — from single molecules to living cells.
Three interconnected frontiers
AI-aided de novo protein design for engineering new cytoskeletal proteins
We harness generative AI tools — RFdiffusion, ProteinMPNN, AlphaFold — to design novel cytoskeletal proteins from scratch. Our goal is to build synthetic cytoskeletal systems with tunable mechanical and dynamic properties.
Read morePerturbation and adaptation of the mitotic checkpoint in cancer biology
We build detailed mathematical models of the mitotic checkpoint's signaling cascade using quantitative in vivo measurements — relying on CRISPR-engineered cell lines and live-cell fluorescence microscopy.
Read moreReverse engineering kinetochores using de novo-designed proteins
Building on decades of kinetochore research, we pioneer efforts to reverse engineer this machine using de novo-designed proteins — constructing simplified kinetochore-like machines for synthetic biology.
Read moreWhat's happening
Welcome, Riley Zheng and Mia Levin!
We are excited to welcome Riley Zheng and Mia Levin to the lab as undergraduate research assistants. We look forward to working with you!
Anish Virdi recognized as Outstanding Undergraduate Student
Anish Virdi has been recognized as the Outstanding Undergraduate Student by the Biophysics class of 2026. Congratulations, Anish, on this well-deserved honor!
Soubhagyalaxmi Jema receives Barbour Scholarship
Soubhagyalaxmi has been awarded the U-M Rackham Graduate School Barbour Scholarship, which supports academic excellence among women from Asia and the Middle East pursuing advanced degrees at U-M.
Jema's paper published in Current Biology
A critical new finding on spindle assembly checkpoint signaling! Congratulations Soubhagyalaxmi on this important contribution.
View on PubMedRoy et al. paper published in eLife
New work on MELT motifs in Spc105 that balance the strength and responsiveness of the spindle assembly checkpoint.
View on PubMedUS patent issued for eSAC technology
U.S. Patent No. 15/355,824 — "Activating Mitotic Checkpoint Control Mechanisms." Describes the eSAC method for controlling the duration of cell division using designed protein fragments.
Lab members
We are a collaborative, interdisciplinary team of scientists passionate about understanding cell division.
Ajit Joglekar
Professor · PI
Lina Pena
Graduate Student · Biophysics
Yiwei (Ryann) Li
Graduate Student · CDB
Soubhagyalaxmi Jema
Graduate Student · CDB
Anish Virdi
Undergraduate Student
Jennifer Guan
Undergraduate Student
Sydney Lee
Undergraduate Student
Riley Zheng
Undergraduate Student
Mia Levin
Undergraduate Student
Dubuke Ma
Technician
How to Train Custom Cell Segmentation Models Using Cell-APP. ↗
Training accurate cell segmentation models typically requires large annotated datasets and deep computational expertise—resources inaccessible to most cell biology labs. This protocol provides step-by-step instructions for using Cell-APP to train custom deep-learning segmentation models from user-generated microscopy images, requiring no programming background. The workflow covers image acquisition guidelines, annotation strategies, model training, and validation, with troubleshooting guidance for common failure modes. The protocol is designed to make state-of-the-art cell segmentation broadly accessible to biologists working with diverse cell types and imaging modalities.
PMID 41769259Cell-APP: A generalizable method for cell annotation and cell-segmentation model training. ↗
Accurate segmentation of cells in microscopy images is a fundamental bottleneck in quantitative cell biology, yet building custom deep-learning models typically demands significant computational resources and expertise. Cell-APP is a generalizable pipeline that combines automated segmentation with a user-friendly annotation interface, enabling researchers to generate training datasets and fine-tune models for their specific cell type and imaging conditions. The method was validated across multiple imaging modalities and cell types, substantially reducing the manual effort required for large-scale microscopy analysis. Applied to chromosome segregation imaging, Cell-APP enables systematic, high-throughput analysis of kinetochore and spindle dynamics.
PMID 39896521The structural flexibility of MAD1 facilitates the assembly of the Mitotic Checkpoint Complex. ↗
The spindle assembly checkpoint (SAC) relies on rapid assembly of the Mitotic Checkpoint Complex (MCC) at unattached kinetochores to halt cell division until all chromosomes are correctly bi-oriented. This study reveals that the middle domain of MAD1—long thought to be a simple coiled-coil spacer—undergoes significant conformational flexibility that is critical for MCC formation. Using FRET measurements and biochemical reconstitution, the authors show that MAD1 flexibility allows it to simultaneously engage both MAD2 and the BUBR1–BUB3 heterodimer in a multivalent fashion. This flexible architecture explains how a single MAD1 dimer can efficiently nucleate MCC assembly even when kinetochore-bound MAD1 levels are limiting.
PMID 36934097Signaling protein abundance modulates the strength of the spindle assembly checkpoint. ↗
The spindle assembly checkpoint must be calibrated to robustly delay mitosis when chromosomes are unattached, yet allow timely progression once all kinetochores achieve correct attachments. This study demonstrates that the absolute abundance of SAC proteins—particularly Bub1 and BubR1—modulates checkpoint strength in a graded manner across individual cells. By quantifying protein levels and correlating them with checkpoint duration, the authors show that natural cell-to-cell variation in protein copy number produces corresponding variation in checkpoint robustness. A mathematical model further shows that protein abundance tunes the system near a threshold, making the SAC sensitive to stoichiometric changes rather than simply switching between on and off states.
PMID 37738972For the complete list, visit the Publications page.