Biology subjects

Bang, J.

Publications and source records attributed to Bang, J..

4 recordsLinked to original sources

bioRxiv2026

A Spin-Glass Metabolic Hamiltonian optimized by Quantum Annealing Reveals Thermodynamic Phases of Cancer Metabolism

Understanding why specific metabolic states become stable in cancer has remained a fundamental challenge, as current pathway-centric frameworks lack a unifying physical principle governing global metabolic organization. We introduce the Metabolic Spin-Glass (MSG) model, which represents cellular metabolism using a thermodynamically informed effective Hamiltonian that integrates reference reaction free energies, cofactor-mediated network couplings, and patient-specific transcriptomic fields within a frustrated many-body optimization framework. The Hamiltonian is formulated as a binary optimization problem and solved using hybrid quantum annealing. Embedding gastric cancer transcriptomes (n = 497) reveals that malignant phenotypes occupy distinct low-energy configurations within the effective metabolic landscape rather than representing isolated pathway perturbations. A thermodynamic order parameter stratifies patients into prognostically distinct subtypes independently of transcriptomic classification, suggesting clinically applicable non-redundant biomarkers. This work establishes a thermodynamically informed spin-glass energy-landscape framework for patient-specific characterization and stratification of cancer metabolic organization.

biophysics↗

bioRxiv2023

A novel human fetal lung-derived alveolar organoid model reveals mechanisms of surfactant protein C maturation relevant to interstitial lung disease

Alveolar type 2 (AT2) cells maintain lung health by acting as stem cells and producing pulmonary surfactant1-3. AT2 dysfunction underlies many lung diseases including interstitial lung disease (ILD), in which some inherited forms result from mislocalisation of surfactant protein C (SFTPC) variants4,5. Disease modelling and dissection of mechanisms remains challenging due to complexities in deriving and maintaining AT2 cells ex vivo. Here, we describe the development of expandable adult AT2-like organoids derived from human fetal lung which are phenotypically stable, can differentiate into AT1-like cells and are genetically manipulable. We use these organoids to test key effectors of SFTPC maturation identified in a forward genetic screen including the E3 ligase ITCH, demonstrating that their depletion phenocopies the pathological SFTPC redistribution seen for the SFTPC-I73T variant. In summary, we demonstrate the development of a novel alveolar organoid model and use it to identify effectors of SFTPC maturation necessary for AT2 health.

cell biology↗

bioRxiv2021

ABCnet : Self-Attention based Atom, Bond Message Passing Network for Predicting Drug-Target Interaction

Drug-target interaction (DTI) is a methodology for predicting the binding affinity between a compound and a target protein, and a key technology in the derivation of candidate substances in drug discovery. As DTI experiments have progressed for a long time, a substantial volume of chemical, biomedical, and pharmaceutical data have accumulated. This accumulation of data has occurred contemporaneously with the advent of the field of big data, and data-based machine learning methods could significantly reduce the time and cost of drug development. In particular, the deep learning method shows potential when applied to the fields of vision and speech recognition, and studies to apply deep learning to various other fields have emerged. Research applying deep learning is underway in drug development, and among various deep learning models, a graph-based model that can effectively learn molecular structures has received more attention as the SOTA in experimental results were achieved. Our study focused on molecular structure information among graph-based models in message passing neural networks. In this paper, we propose a self-attention-based bond and atom message passing neural network which predicts DTI by extracting molecular features through a graph model using an attention mechanism. Model validation experiments were performed after defining binding affinity as a regression and classification problem: binary classification to predict the presence or absence of binding to the drug-target, and regression to predict binding affinity to the drug-target. Classification was performed with BindingDB, and regression was performed with the DAVIS dataset. In the classification problem, ABCnet showed higher performance than MPNN, as it does in the existing study, and in regression, the potential of ABCnet was checked compared to that of SOTA. According to experiments, for Binary classification ABCnet have an average performance improvement of 1% for higher performance on DTI task than other MPNN and for regresssion ABCnet have CI with an average 0.01 to 0.02 performance degradation compared to SOTA. https://www.overleaf.com/project/618a05533676801d8f68ccf6

bioinformatics↗

bioRxiv2021

A nuclear cAMP microdomain suppresses tumor growth by Hippo pathway inactivation

cAMP signaling pathways are critical for both oncogenesis and tumor suppression. cAMP signaling is localized to multiple spatially distinct microdomains, but the role of cAMP microdomains in cancer cell biology is poorly understood. We developed a tunable genetic system that allows us to activate cAMP signaling in specific microdomains. We uncovered a previously unappreciated nuclear cAMP microdomain that functionally activates a tumor suppressive pathway in a broad range of cancers by inhibiting YAP, a key effector protein of the Hippo pathway, inside the nucleus. We show that nuclear cAMP induces a LATS-dependent pathway leading to phosphorylation of nuclear YAP solely at serine 397, export of YAP from the nucleus, without YAP protein degradation. Thus, nuclear cAMP inhibition of nuclear YAP is distinct from other known mechanisms of Hippo regulation. Pharmacologic targeting of specific cAMP microdomains remains an untapped therapeutic approach for cancer, and since Hippo pathway deregulation can lead to oncogenesis and chemotherapeutic resistance, drugs directed at the nuclear cAMP microdomain may provide new avenues for the treatment of cancer.

cancer biology↗