Biology subjects

Momen-Roknabadi, A.

Publications and source records attributed to Momen-Roknabadi, A..

4 recordsLinked to original sources

bioRxiv2026

A massively parallel synthetic gene atlas for learning compact cis-regulatory grammar across cellular contexts

Virtual-cell models increasingly learn from large perturbation atlases, but their view of cis regulation remains limited to endogenous genes embedded in broad native regulatory contexts. Here we introduce Therna Biosciences' Chronos platform and its first public dataset release, comprising two complementary modules of a massively parallel synthetic gene atlas: Penta-47x27K for 5' UTRs/internal-promoter elements and Tria-47x28K for 3' UTR stability elements. Together, these datasets measure ~60,000 compact cis-regulatory elements across approximately 50 cell lines in one pooled experiment. The choice of cell lines here, provided to us courtesy of Tahoe Tx, was rooted in our aim to make this dataset maximally useful for the virtual-cell modeling community. At Therna, we routinely apply Chronos -- part of our RNA-Logix(TM) platform -- beyond cancer cell-line pools, to systems such as primary cells and organoids, via LNP-formulated mRNA delivery. Chronos captures both transcriptional and post-transcriptional gene expression control. Episomal DNA delivery measures DNA-normalized mRNA output, while direct RNA delivery with longitudinal sampling reliably quantifies RNA decay. The RNA-delivery arm uses chemically modified synthetic mRNA incorporating N1-methylpseudouridine, a modification widely used in mRNA therapeutics. To resolve high-complexity libraries of up to 30,000 elements, Chronos pushes the sensitivity limits of single-cell RNA sequencing to quantify individual RNA molecules within single cells. By massively expanding gene regulatory networks with synthetic genes whose variable regulatory code is short and defined, Chronos provides an auxiliary cis-regulatory lens for virtual-cell modeling and enables context-specific cis-trans regulatory interactions to be learned directly.

systems biology↗

bioRxiv2025

Adaptation by stochastic tuning of gene expression in mammalian cells

Cells often face environmental challenges for which they lack pre-programmed regulatory responses. We have previously shown that yeast cells can, nevertheless, adapt to such unfamiliar conditions by a phenomenon we have termed stochastic tuning. In this process, cells use gene expression noise to randomly change the expression of individual genes and actively reinforce those changes that improve the overall health of the cell. Here, we provide experimental evidence for stochastic tuning in human cells adapting to the lethal chemotherapeutic agent methotrexate. As in yeast, the tuning process is not driven by mutations and is reversible upon removal of methotrexate challenge. We show that stochastic tuning is a conserved eukaryotic mechanism of cellular adaptation, and potentially a key factor in the phenomenon of cancer cell plasticity underlying chemotherapy resistance.

systems biology↗

bioRxiv2025

A multi-modal cell-free RNA language model for liquid biopsy applications

Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multi-modal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pre-trained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise, and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics.

bioinformatics↗

bioRxiv2020

An inducible CRISPR-interference library for genetic interrogation of Saccharomyces cerevisiae biology

Genome-scale CRISPR interference (CRISPRi) is widely utilized to study cellular processes in a variety of organisms. Despite its dominance as a model eukaryote, a genome-wide CRISPRi library, optimized for targeting the Saccharomyces cerevisiae genome, has not been presented to date. We have generated a comprehensive, inducible CRISPRi library, based on spacer design rules optimized for yeast. We have validated this library for genome-wide interrogation of gene function across a variety of applications, including accurate discovery of haploinsufficient genes and identification of enzymatic and regulatory genes involved in adenine and arginine biosynthesis. The comprehensive nature of the library also revealed parameters for optimal transcriptional repression, including upstream distance, nucleosomal occupancy, and strand bias. CRISPRi screens, using this library can identify genes and pathways with high precision and low false discovery rate across a variety of experimental conditions, enabling rapid and reliable genome-wide assessment of gene function and genetic interactions in S.cerevisiae.

systems biology↗