Biology subjects

Sengupta, D.

Publications and source records attributed to Sengupta, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Scalable Computational Phenomics of Nuclear Morphology

Interpretable computational pathology is constrained by a trade-off between scalable learned representations and biologically explicit phenotypes. Here we present NucXplore, a nuclear phenomics framework that converts each hematoxylin-and-eosin (H&E)-stained nucleus into 129 explicitly defined features spanning morphology, chromatin, intensity, texture, color, and spatial context. A Rust-based implementation accelerates extraction 17.6-fold over the Python reference implementation. Across three histopathology cohorts, NucXplore outperformed classical descriptors and larger deep-learning embeddings while retaining feature attribution. Applied to human skin, hierarchical multiple-instance models resolved eight cellular compartments and decoded age and sun-exposure states, revealing a largely shared aging program with age and cell-specific exposure-dependent recalibration. Conditional flow matching and neural ODE integration transformed cross-sectional phenotypes into model-implied trajectories, uncovering a midlife velocity minimum, late-life re-acceleration, and cell-specific changes in trajectory magnitude and direction. Source-free domain adaptation enabled preliminary transfer of age-associated predictions to an independent hospital cohort. NucXplore thus unifies interpretable representation, high-performance computing, and dynamical modeling for cellular phenotyping from routine histology.

pathology↗

bioRxiv2026

Evidence-constrained mechanistic synthesis for drug discovery

Biomedical evidence can become mechanistically informative before it becomes sufficiently commensurate to calibrate one system model. We developed evidence-constrained mechanistic synthesis (ECMS), a pre-calibration framework that converts only the mathematical information supported by each finding into constraints on a possibility space of executable mechanistic worlds. We reconstructed historical ECMS systems for alopecia areata (AA) and chronic spontaneous urticaria (CSU) using knowledge available by 31 December 2023, froze molecule/regimen inputs, observation semantics and finite ensembles before outcome reveal, and evaluated later studies at prespecified information-property resolution rather than as calibrated clinical predictions. Among 41 registered atomic properties, ECMS committed to 34 executable propositions: 29 were empirically concordant, one was clearly contradicted, and four could not be fully assessed because intermediate reporting or intervention isolation was insufficient; seven additional properties were prespecified abstentions and were not counted as empirical successes. An evidence-lineage audit further separated nine direct historical analogues from 16 near transfers, four mechanistic transfers and four cross-stream syntheses. Baricitinib withdrawal in AA was a cross-stream synthesis: no pre-cutoff baricitinib-withdrawal result was used, yet the frozen system reproduced early persistence followed by progressive loss. Selective MRGPRX2 antagonism in CSU transferred from pre-cutoff route-separation evidence, whereas patient-level clinical efficacy was withheld. A later baricitinib trajectory contradicted the frozen week-36-to-52 monotonicity property, preserving falsifiability. These results show that heterogeneous pre-calibration evidence can be made executable and externally challenged without being converted into unsupported coefficients,posterior biological probabilities or patient-response models.

systems biology↗

bioRxiv2026

A multitask single-cell analysis framework with knowledge graph as a prior

Single-cell RNA sequencing has reshaped cancer biology by revealing the cellular and molecular diversity of the tumor microenvironment, yet most analysis pipelines still treat clustering, annotation, pathway scoring, metabolic inference, and cell-cell communication as separate tasks. Here, we present scKNIFE (Knowledge graph-based NMF for Inference of Functional Entities), a unified graph-regularized non-negative matrix factorization framework that jointly reconstructs single-cell expression profiles and infers activities of diverse biological entities, including pathways, cell-type markers, metabolic reactions and tasks, ligand-receptor interactions, and cell-state programs. By embedding a heterogeneous knowledge graph directly into the factorization objective, scKNIFE propagates information between biologically related entities through Laplacian regularization while sparsity constraints preserve interpretability. The integrated prior graph spans 47,274 nodes and 506,620 edges, assembled from complementary resources including Gene Ontology, Reactome, Hallmark gene sets, Human-GEM, CellChat, PanglaoDB, Cytopus, and Tabula Muris-derived annotations. Across multiple cancer-focused single-cell datasets, scKNIFE yields competitive to leading performance for clustering and cell-type annotation, while also recovering biologically coherent metabolic programs that agree with dedicated metabolism-focused methods and capture therapy-response-associated states. In addition, the framework supports downstream inference of cell-type-specific biological activities from a single latent representation. Together, these results establish scKNIFE as a modular and extensible framework for end-to-end biological interpretation of single-cell cancer transcriptomics

cancer biology↗

bioRxiv2026

Deterministic retrieval recovers biomedical associations lost by language models

Biomedical research increasingly relies on expert-curated databases to connect diseases, genes, variants, phenotypes, pathways and therapeutics. Incomplete or irreproducible retrieval can distort interpretation, mechanistic inference, variant assessment or therapeutic prioritization despite an unchanged evidence base. Agentic natural-language-to-SQL (NL2SQL) systems and Model Context Protocol-enabled agents can autonomously retrieve evidence from biomedical databases, including Open Targets and the Highly Confident Drug-Target Database, in response to natural-language questions. However, they may truncate large result sets, miss records when terminology differs from database vocabulary and return different outputs across runs. We systematically quantify these failures and introduce BioChirp, a goal-directed autonomous system for reliable retrieval from structured biomedical databases, built on interpretation-execution separation. A coordinated language-model layer interprets intent and resolves database-field assignments, after which entity resolution maps user terms to database terminology. A deterministic Steiner-tree planner and executor then retrieve matching records without further language-model involvement. Across curated biomedical databases spanning more than one million associations, BioChirp retrieved thousands of records for exhaustive queries, whereas baseline agents returned at most a few hundred or failed. Across 70 queries, five runs and three BioChirp database backends, BioChirp achieved a median cross-run Jaccard similarity of 1.0. Across 910 database-question evaluations derived from expert-written BioASQ questions and spanning ten databases, BioChirp retrieved at least one database record in 85.4% of cases and produced a fully correct answer in 51.1% of cases, compared with 44.6% and 17.4% for agentic NL2SQL. BioChirp is publicly available at https://biochirp.iiitd.edu.in.

bioinformatics↗

bioRxiv2026

Viral rewiring of APC/C-CDC20 drives Aurora B hyperubiquitination, mitotic regression and polyploidy

Cytokinesis requires elaborate processes in the midbody, including the timely inactivation of Aurora B kinase (AurB) and membrane fission to separate the daughter cells. Here, we show that human adenovirus (AdV) perturbs midbody function, prevents daughter separation and induces cytoplasmic regression of both cancer cells and primary diploid human airway basal cells resulting in polyploidy. Infected cells undergo cleavage furrow regression after midbody formation driven by the viral protein E4orf4 independently of E4orf4 interaction with protein phosphatase 2A. The co-activator of the anaphase-promoting complex/cyclosome (APC/C), CDC20 is upregulated in AdV infection and required for regression. E4orf4 directly interacts with CDC20 and redirects the E3-ubiquitin ligase APC/C-CDC20 towards AurB, promoting hyperubiquitination and premature extraction of AurB from the midbody. Loss of midbody-associated AurB coincides with midbody collapse and furrow regression. Our findings reveal a previously unrecognized APC/C-CDC20-dependent pathway controlling late cytokinesis through AurB extraction from the midbody.

cell biology↗

bioRxiv2025

Phase separation as a concentrating mechanism of protein/peptide hormones for the secretory granule storage

Protein/peptide hormones in the regulated secretory pathway are stored within secretory granules (SGs) as densely packed aggregates for extended periods of time until their stimulated release. These aggregated dense cores are reported as functional amyloids for several pituitary hormones. However, the molecular events initiating hormone aggregation and SG biogenesis remain poorly understood. Using a diverse set of protein/peptide hormones with different sizes and structures, we demonstrated that all these hormones undergo phase separation in trans-Golgi network (TGN)-relevant conditions (including low pH, the presence of glycosaminoglycans, etc.), which rapidly transitioned into solid-like structures and eventually form amyloid fibrils. Further, the cellular model expressing two protein hormones (GH-EGFP and PRL-EGFP) showed the highly dynamic, liquid-like condensate formation at the TGN volume, which was transported via microtubule to the cellular periphery/tips and formed a ready pool of mature SGs containing amyloid-rich hormone aggregates. Importantly, both in vitro and cellular studies confirmed that solid-like amyloid aggregates are reversible and can release bioactive monomeric hormones, recapitulating regulated exocytosis. Together, our findings suggest hormone phase separation at the TGN as a common molecular principle for concentrating cargo, driving rapid functional aggregation, and ensuring efficient storage within SGs.

biochemistry↗

bioRxiv2025

Chemical Dice Integrator (CDI): A Scalable Framework for Multimodal Molecular Representation Learning

The machine learning landscape for molecular property prediction is fragmented, with numerous Featurizers each capturing a narrow, specialized view of chemical structure. This heterogeneity forces a suboptimal choice of representation a priori, limiting model generalizability. We introduce the Chemical Dice Integrator (CDI), a hierarchical framework that unifies six orthogonal molecular representations, physicochemical (Mordred), topological (GROVER), visual (ImageMol), biological (Signaturizer), quantum-mechanical (MOPAC), and linguistic (ChemBERTa), into a single, coherent embedding. The framework consists of CDI-Basic, a two-tiered autoencoder that fuses these modalities, and CDI-Generalised, a Mamba State-Space Model (SSM) that learns a direct, efficient map from SMILES strings to the unified embedding space. Extensive benchmarking across 23 classification (171 tasks) and 10 regression datasets demonstrates that CDI embeddings consistently achieve superior predictive performance compared to individual Featurizers and standard feature aggregation methods. The CDI-Generalised model achieves this performance with exceptional computational efficiency, outperforming deep learning Featurizers in terms of speed and resource overhead. Furthermore, we demonstrate that the CDI embedding is chemically intuitive, allowing for the sensitive distinction of nuanced structural variants, such as chiral enantiomers and kekulized SMILES forms. By bridging multimodal chemical intelligence with scalable, sequence-based inference, CDI offers a strong foundation for molecular machine learning.

bioinformatics↗

bioRxiv2025

Screening of a chemogenetic library unveils dual-stage antimalarials with low cytotoxicity

The global effort to eradicate malaria requires the development of drugs with novel mechanisms of action and differentiated pharmacological profiles. Both liver and blood phases of Plasmodium infection are essential for disease progression and transmission, making them primary targets for therapeutic intervention. In this study, we established a dual-luciferase reporter assay to simultaneously assess Plasmodium berghei liver-stage development and host cell viability. Screening a library of bioactive small molecules with annotated human targets revealed several inhibitors with potent liver-stage activity and minimal cytotoxicity. Further profiling against P. falciparum asexual blood stages identified compounds with dual-stage activity, underscoring their potential utility for development as either prophylactic or therapeutic agents. Knowledge of their human targets will facilitate investigation into host- or parasite- directed antimalarial mechanisms, thereby accelerating both biological insight and rational drug development. Our findings constitute a valuable resource for research on malaria biology and the discovery of next-generation antimalarials.

microbiology↗

bioRxiv2025

Mechanism-aware inference of response to targeted cancer therapies

Targeted therapies like small-molecule inhibitors often work by blocking proteins that cancer cells rely on for survival. Omics based modeling of drug sensitivity alone lack mechanistic grounding. We propose FORGE (Factorization Of Response and Gene Essentiality) a simple yet powerful joint matrix factorization framework that co-models drug response and target gene essentiality, enabling the stratification of promising treatment groups for targeted therapy consideration. FORGE also provides Benefit Score -- a predictive score that estimates treatment efficacy from basal gene expression profiles. We validated the predictive performance of FORGE across multiple targeted therapies, including Erlotinib (EGFR inhibitor) and Daporinad (NAMPT inhibitor). Our meta-analysis of large scale in-vitro studies underscores FORGEs ability to identify common determinants of drug vulnerabilities and target gene essentiality. Such convergences were not observed when treatment vulnerabilities and gene essentialities were modeled independently. We also demonstrated the universality of Erlotinib Benefit Scores by transferring transformations learned from high-throughput drug response studies across other published datasets, including the TAHOE-100M single-cell perturbation atlas and patient-derived xenograft studies. FORGE successfully identified key regulators within the molecular pathways targeted by these therapies, reinforcing its potential for mechanistically grounded treatment stratification.

cancer biology↗

bioRxiv2025

SynGlue: AI-Driven Designer for Clinically Actionable Multi-Target Therapeutics

The rational design of protein degraders, such as proteolysis-targeting chimeras (PROTACs), requires the simultaneous optimization of multiple molecular properties, a complex challenge that limits efficient discovery. Here, we introduce SynGlue, a generative artificial intelligence (AI) framework that addresses this challenge through two core modules: data-driven, leveraging large-scale protein-ligand intelligence, and structure-guided, for physics-aware molecular design. SynGlue harness MagnetDB, a curated database of 6.37 million experimental protein-ligand interactions, and couples it with deep learning models that quantitatively predict degradation potency (DC50), maximal degradation (Dmax), and guide ternary-complex-compatible linker design. Benchmarked against 6,935 compounds, SynGlue demonstrates superior performance in relevant pharmacology prediction. To validate SynGlue, we engineered degraders for BRD4 and GSPT1. Our data-driven design for BRD4 yielded compounds with novel warhead scaffolds (<50% warhead similarity with known PROTACs), which proved to be potent degraders in vitro (DC50 = 0.19 nM) and efficacious in vivo in mouse models. Independently, our structure-guided de novo design for GSPT1 produced ultrapotent degraders (DC50 {approx} 0.0011 M) that are also effective both in vitro and in vivo, uncovering a new oncogenic dependency. By unifying data-driven and physics-aware design, SynGlue establishes a generalizable AI framework for the rapid development of clinically relevant protein degraders, with principled extension to other multi-target modalities.

bioinformatics↗

bioRxiv2025

MutAIverse: An AI-Powered, Mechanism-backed Platform for Discovering Novel DNA Adducts and their precursor Genotoxins

Genotoxin exposure leads to DNA adduct formation, potentially causing mutations if unrepaired. Current DNA adductomics platforms or analytical workflows are limited by incomplete spectral libraries, reliance on experimentally validated adducts, limited cellular contexts, and inefficient computational methodologies. We introduce MutAIverse, an advanced AI-driven DNA adductomics analysis platform that overcomes these limitations by leveraging intracellular mechanistic modeling of genotoxin bioactivation to construct a comprehensive DNA adduct library and offers advanced spectral mapping. MutAIverse integrates experimentally validated and chemically valid putative DNA adducts, enabling interpretable retrograde tracking to parental genotoxins. Validation of MutAIverse against experimental MS/MS datasets demonstrated the detection of both known and novel adducts. Furthermore, application to in-house generated DNA adductomics data from tissue biopsies of smokeless tobacco-induced head and neck cancer patients revealed selective enrichment of both novel and known DNA adducts; a subset of them was validated using MS/MS analysis. Collectively, MutAIverse provides a robust, end-to-end, and interpretable platform for advanced DNA adductomics analysis.

bioinformatics↗

bioRxiv2025

H2AK119ub Safeguards Against Ectopic Transcription Factor Mediated Gene Activation in the Developing Forebrain

The PRC1 complex regulates developmental gene expression in mammals by ubiquitinating histones (H2AK119ub) and nucleating repressive chromatin interactions. Human genetic data and functional experimentation have demonstrated that H2AK119ub is required for cortical development, however the molecular mechanism for this remains unknown. Here, we show that mouse embryos expressing catalytically deficient RING1B exhibit intact early neurogenesis but display impaired neuroectodermal fate restriction. Using in vivo, primary, and in vitro models, we demonstrate that reduced H2AK119ub leads to ectopic activation of lineage-inappropriate transcriptional programmes, including mesodermal and endodermal gene expression during neural differentiation. This transcriptional deregulation is not solely attributable to H2AK119ub or H3K27me3 loss but instead reflects sensitisation of PRC1 target genes to inappropriate transcription factor (TF)-mediated activation. Synthetic induction of candidate TFs, including GATA6, SOX7, and SNAI1, phenocopies the fate-skewing effects of PRC1 catalytic dysfunction, confirming their causal role. Our results uncover a buffering role for PRC1-catalysed H2AK119ub in safeguarding neural progenitor identity by preventing inappropriate TF-driven transcription and provides a mechanistic framework for understanding the cellular heterogeneity and phenotypic variability observed in Polycomb-associated neurodevelopmental disorders.

molecular biology↗

bioRxiv2025

Non-canonical regulation of the plasma membrane copper transporter CTR1 through modulation of membrane mechanical properties

We describe a non-canonical, membrane receptor-like regulation of the human copper transporter CTR1 in response to copper stimuli. CTR1 is the sole high-affinity trimeric plasma-membrane copper-importing channel that self-regulates by undergoing endocytosis to limit copper uptake. We observed that preceding copper-induced endocytosis, CTR1 forms clusters on the plasma membrane, a phenomenon that is typically observed in membrane receptors. We deciphered the mechanism of CTR1 clustering and studied its ramifications on plasma membrane physical properties harboring the clusters that favors endocytosis. Membrane tension and fluctuation are fundamental regulators of pre- and post-endocytic events. Using coarse-grain MD simulation and coupled Interference Reflection Microscopy-Total Internal Reflection Fluorescence Microscopy we demonstrated that CTR1 clusters induce positive membrane curvature, increase in local membrane tension and decrease in local membrane fluctuation; alterations that favors formation of endocytic pits. Clustering is facilitated by copper-sequestering Methionine rich extracellular amino-terminus of CTR1. MD-simulations and IRM-TIRF imaging revealed that CTR1 clustering is facilitated by membrane cholesterol, depletion of which delays CTR1 endocytosis. CTR1 clustering promotes clathrin-coated pit formation that engages recruitment of adapter protein AP-2. To summarize, we report a hitherto unknown pre-endocytic receptor-like phenomenon of ligand-induced clustering of a metal channel, that in-turn regulates self-endocytosis by modulating membrane properties.

cell biology↗

bioRxiv2024

Cardiomyopathic mutations of lamin A perturb mutual interactions of lamin, nuclear membrane, and chromatin leading to LLPS

Lamins are nuclear intermediate filaments constituting the nuclear lamina which maintains the structural integrity of the nucleus and play a key role in the spatial organisation of the genome. Mutations in the lamin protein have been associated with diverse diseases collectively known as laminopathies. In this study, we focused on two lamin A mutants - E161K and K97E - associated with dilated cardiomyopathy (DCM). Through confocal imaging, we established that these mutations cause large scale disruption of the peripheral lamin and consequent heterochromatin organisation, along with the formation of lamin aggregates inside the nucleoplasm. Using coarse-grained polymer simulations, we uncovered the role of lamin-lamin, lamin-membrane and lamin-chromatin interactions in maintaining wild-type lamin and chromatin organisation and showed that disruptions in these interactions can reproduce the experimental observations in the lamin mutants. These predictions were verified using 3D-FISH experiments to quantify the reorganisation of chromosome territories in these mutants. Using advanced imaging methods, we characterised the dynamical properties of the lamin aggregates in the mutants to show for the first time a liquid-like state of the lamin aggregates through a liquid-liquid phase separation. The altered lamin and chromatin interactions in the mutants thus manifest as liquid-like aggregates in the nucleoplasm leading to disruption of the spatial organisation of the genome in these laminopathy-associated mutants.

biophysics↗

bioRxiv2024

RCoxNet: deep learning framework for enhanced cancer survival prediction integrating random walk with restart with mutation and clinical data

Cancer poses a significant global health challenge, characterized by a complex disease progression and disrupted growth regulation. A thorough understanding of cellular and molecular biological mechanisms is essential for developing novel treatments and improving the accuracy of patient survival predictions. While prior studies have leveraged gene expression and clinical data to forecast survival outcomes through current machine learning and deep learning approaches, gene mutation data--despite being a widely recognized metric--has rarely been incorporated due to its limited information, inadequate representation of gene relationships, and data sparsity, which negatively affects the robustness, effectiveness, and interpretability of current survival analysis approaches. To overcome the challenges of mutation data sparsity, we propose RCoxNet, a novel deep learning neural network framework that integrates the Random Walk with Restart (RWR) algorithm with a deep learning Cox Proportional Hazards model. By applying this framework to mutation data from cBioportal, our model achieved an average concordance index of 0.62 {+/-} 0.05 across four cancer types, outperforming existing deep neural network models. Additionally, we identified clinical features critical for differentiating between predicted high- and low-risk patients, with the relevance of these features being partially supported by previous studies.

bioinformatics↗

bioRxiv2024

Role of SCAL1 in Modulating Oxidative Stress, Cancer Stemness, Apoptotic Resistance in Tumorigenic Differentiation of Cigarette Smoke-Exposed BEAS-2B Cells

The smoke and cancer-associated lncRNA 1 (SCAL1) is an emergent biomarker in lung cancer. However, the precise role of SCAL1 as a mediator of tobacco smoke-induced lung carcinogenesis remains unclear. BEAS-2B cells were cultured and exposed to 20% cigarette smoke extract (CSE), followed by quantification of SCAL1. We evaluated the impact of SCAL1 on cell viability, ROS mitigation, cancer stemness, tumorigenic differentiation, cellular invasiveness, and apoptosis for different CSE incubation time points through SCAL1 expressional modulation using SCAL1-specific siRNAs and scrambled controls. We observed an upregulation of SCAL1 in cells exposed to CSE for 2, 4, and 6 hours, with the highest expression observed at 6 hours (p<0.001). Exposure of BEAS-2B cells to CSE showed the formation of focal adhesions and stress fibers resembling tunneling nanotubes. Intracellular ROS levels significantly increased upon CSE exposure compared to control cells (p<0.05). We found increased levels of anti-apoptotic and cancer stem (CSC) cell markers like BCL2, ALDH1A1, CD133, CD44, and TCTP and decreased levels of TP53 in CSE-exposed cells. Knockdown of SCAL1 using siRNA transfection reversed these effects at all time points. Additionally, we observed a significant decrease in the number of spheroid colonies in siSCAL1 (+) cells compared to siSCAL1 (-) cells (p<0.01) exposed to CSE. SCAL1 is pivotal in mediating cellular responses to cigarette smoke, leading to tumorigenic differentiation of BEAS-2B cells. Understanding the mechanisms could provide valuable insights into lung cancer pathogenesis and therapeutic approaches.

cancer biology↗

bioRxiv2024

Selective promotion of oligonucleotides in the course of evolution

Evolution shapes the structure and content of genomes, yet the contribution of local sequence composition to variant selection remains poorly understood. While traditional models emphasize protein function or cross-species conservation, we propose that intra-genomic patterns of oligonucleotide (k-mer) frequencies also reflect selective forces. To explore this, we developed kGain score, a metric that quantifies the frequency shift of a k-mer upon single-nucleotide substitution, using the surrounding genomic context as a baseline. We hypothesize that variants arising in high-kGain contexts are more likely to persist due to evolutionary favorability. We validated this hypothesis across multiple systems. In E. coli and S. cerevisiae long-term evolution experiments, we found that fixed, essential, and parallel mutations consistently show elevated kGain scores. This trend held in SARS-CoV-2 variants of concern and in an in-house antibiotic adaptation experiment, where a high-kGain fusA Y515N mutation conferred resistance and maintained fitness when overexpressed, demonstrating a causal link between kGain and adaptive potential. To enable cross-species generalization, we trained a transformer-based neural network regressor on LTEE-derived mutations to predict kGain from sequence alone. The model achieved high correlation in held-out in-domain data (Pearson r = 0.81) and accurately predicted kGain trends in out-of-domain data (Pearson r = 0.82), demonstrating that k-mer-based sequence constraints learned from one genome can be effectively transferred to others. Together, our results establish kGain as a biologically meaningful, scalable metric for probing within-genome sequence selection, offering a complementary lens to existing conservation-based frameworks for understanding evolutionary fitness and variant persistence.

evolutionary biology↗

bioRxiv2024

PRC2 Promotes Canalisation during Endodermal Differentiation

The genetic circuitry that encodes the developmental programme of mammals is regulated by transcription factors and chromatin modifiers. During early gestation, the three embryonic germ layers are established in a process termed gastrulation. The impact of deleterious mutations in chromatin modifiers such as the polycomb proteins manifests during gastrulation, leading to early developmental failure and lethality in mouse models. Embryonic stem cells have provided key insights into the molecular function of polycomb proteins, but it is impossible to fully appreciate the role of these epigenetic factors in development, or how development is perturbed due to their deficiency, in the steady-state. To address this, we have employed a tractable embryonic stem cell differentiation system to model primitive streak formation and early gastrulation. Using this approach, we find that loss of the repressive polycomb mark H3K27me3 is delayed relative to transcriptional activation, indicating a subordinate rather than instructive role in gene repression. Despite this, chemical inhibition of polycomb enhanced endodermal differentiation efficiency, but did so at the cost of lineage fidelity. These findings highlight the importance of the polycomb system in stabilising the developmental transcriptional response and, in so doing, in shoring up cellular specification.

molecular biology↗