Biology subjects

Sabuwala, B.

Publications and source records attributed to Sabuwala, B..

5 recordsLinked to original sources

bioRxiv2026

Cell-level integration of gene expression and B cell receptor data captures complementary B cell information

Single-cell B cell analyses commonly define relationships among cells using gene expression (GEX), although paired B cell receptor (BCR) sequences provide complementary information about receptor features, differentiation, and clonal relationships. We evaluated multiple approaches for integrating paired GEX and BCR data into cell-level representations and compared their ability to capture complementary aspects of B cell biology. We compared BCR representations based on language-model embeddings and engineered sequence features across multiple integration strategies. Spaces dominated by BCR language-model embeddings captured broad sequence and clonal information but could distort cell type relationships, as assessed using independent cell surface protein measurements. In contrast, concatenating GEX with selected BCR features (isotype, somatic hypermutation frequency, and CDRH3 length) incorporated receptor information while retaining transcriptional and surface-protein-associated structure. The resulting space reduced discordance between GEX-based cell type annotations and BCR characteristics and revealed B-cell substructure supported by both transcriptional and receptor-derived features. Thus, integrating GEX and BCR data captures complementary biological information and provides a richer foundation for single-cell B cell analysis.

bioinformatics↗

bioRxiv2026

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.

immunology↗

bioRxiv2022

Coupled mutual inhibition and mutual activation motifs as tools for cell-fate control

Multistability is central to biological systems as it plays a key role in adaptation, evolvability, and differentiation. Multistability can be achieved by integrating positive feedback loops into the regulation. The simplest of such feedback loops are a) a mutual inhibition (MI) loop, b) a mutual activation (MA) loop, and, c) self-activation. While it is established that all three motifs can give rise to bistability, the characteristic differences in the bistability exhibited by each of these motifs is relatively less understood. Here, we use ODE-based simulations across a large ensemble of parameter sets and initial conditions to study the characteristics of the bistability of these motifs and their limits in the parameter space. We also examine the behavior of these motifs under mutual degradation and self-activation. Finally, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling. Through our analysis, we found that MI-based architectures offer robustness in maintaining distinct multistability and allow for coordination among multiple genes. This coordination paves way for understanding the naturally occurring gene regulatory network and may also help in the better design of robust and controllable synthetic networks.

systems biology↗

bioRxiv2019

Integrative analysis and machine learning based characterization of single circulating tumor cells

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic systems for label-free enrichment of CTCs.

genomics↗

bioRxiv2019

Identifying inhibitors of epithelial-mesenchymal plasticity using a network topology based approach

Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-Mesenchymal Plasticity (EMP) - an important arm of phenotypic plasticity - through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way towards an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis - by reducing the number of positive feedback loops.

systems biology↗