COCOA: A Framework for Fine-scale Mapping Cell-type-specific Chromatin Compartments with Epigenomic Information.

Chromatin compartmentalization and epigenomic modification are crucial in cell differentiation and diseases development. However, precise mapping of chromatin compartmental patterns requires Hi-C or Micro-C data at high sequencing depth. Exploring the systematic relationship between epigenomic modifications and compartmental patterns remains challenging.

HTAD: a human-in-the-loop framework for supervised chromatin domain detection.

Topologically associating domains (TADs) are essential units of genome architecture, influencing transcriptional regulation and diseases. Despite numerous methods proposed for TAD identification, it remains challenging due to complex background and nested TAD structures. We introduce HTAD, a human-in-the-loop TAD caller that combines machine learning with human supervision to achieve high accuracy.

A new clinical age of aging research.

Aging is a major risk factor for a variety of diseases, thus, translation of aging research into practical applications is driven by the unmet need for existing clinical therapeutic options. Basic and translational research efforts are converging at a critical stage, yielding insights into how fundamental aging mechanisms are used to identify promising geroprotectors or therapeutics. This review highlights several research areas from a clinical perspective, including senescent cell targeting, alleviation of inflammaging, and optimization of metabolism with endogenous metabolites or precursors.

Machine learning unveils RNA polymerase II binding as a predictor for SMAD2-dependent transcription dynamics in response to Actvin signalling.

The transforming growth factor-β (TGF-β) superfamily, including Nodal and Activin, plays a critical role in various cellular processes. Understanding the intricate regulation and gene expression dynamics of TGF-β signalling is of interest due to its diverse biological roles. A machine learning approach is used to predict gene expression patterns induced by Activin using features, such as histone modifications, RNA polymerase II binding, SMAD2-binding, and mRNA half-life.

PhyloVelo enhances transcriptomic velocity field mapping using monotonically expressed genes.

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field.

Optogenetic Control of TGF-β Signaling.

Cells employ signaling pathways to make decisions in response to changes in their immediate environment. The Transforming Growth Factor β (TGF-β) signaling pathway plays pivotal roles in regulating many cellular processes, including cell proliferation, differentiation, and migrations. In order to manipulate and explore the dynamic behavior of TGF-β signaling at high spatiotemporal resolution, we developed an optogenetic system (the optoTGFBRs system), in which light is used to control TGF-β signaling precisely in time and space.

Absolute Quantification of TGF-β Signaling Proteins Using Quantitative Western Blot.

Cell signaling governs the basic functions of cells by molecular interactions that involve of many proteins. The abundance of signaling proteins can directly influence cellular responses to external signal, contributing to cellular heterogeneity. Absolute quantification of proteins is important for modeling and understanding the complex signaling network.

Revisiting the Role of TGFβ Receptor Internalization for Smad Signaling: It is Not Required in Optogenetic TGFβ Signaling Systems.

Endocytosis is an important process by which many signaling receptors reach their intracellular effectors. Accumulating evidence suggests that internalized receptors play critical roles in triggering cellular signaling, including transforming growth factor β (TGFβ) signaling. Despite intensive studies on the TGFβ pathway over the last decades, the necessity of TGFβ receptor endocytosis for downstream TGFβ signaling responses is a subject of debate.

Programmable Extracellular Vesicles for Macromolecule Delivery and Genome Modifications.

Getting large macromolecules through the plasma membrane and endosomal barriers remains a major challenge. Here, we report a generalizable method of delivering proteins and ribonucleoproteins (RNPs) to cells in vitro and mouse liver tissue in vivo with engineered ectosomes. These ectosomes, referred to as "Gectosomes," are designed to co-encapsulate vesicular stomatitis virus G protein (VSV-G) with bioactive macromolecules via split GFP complementation.

Cell-type-specific role of CHK2 in mediating DNA damage-induced G2 cell cycle arrest.

Cancer is a life-threatening disease that affects one in three people. Although most cases are sporadic, cancer risk can be increased by genetic factors. It remains unknown why certain genes predispose for specific forms of cancer only, such as checkpoint protein 2 (CHK2), in which gene mutations convey up to twofold higher risk for breast cancer but do not increase lung cancer risk.

Molecular Engineering of the TGF-β Signaling Pathway.

Transforming growth factor beta (TGF-β) is an important growth factor that plays essential roles in regulating tissue development and homeostasis. Dysfunction of TGF-β signaling is a hallmark of many human diseases. Therefore, targeting TGF-β signaling presents broad therapeutic potential.

eDetect: A Fast Error Detection and Correction Tool for Live Cell Imaging Data Analysis.

Live cell imaging has been widely used to generate data for quantitative understanding of cellular dynamics. Various applications have been developed to perform automated imaging data analysis, which often requires tedious manual correction. It remains a challenge to develop an efficient curation method that can analyze massive imaging datasets with high accuracy.

Tissue-Specific Chk1 Activation Determines Apoptosis by Regulating the Balance of p53 and p21.

The DNA damage response (DDR) protects cells against genomic instability. Surprisingly, little is known about the differences in DDR across tissues, which may affect cancer evolutionary trajectories and chemotherapy response. Using mathematical modeling and quantitative experiments, we found that the DDR is regulated differently in human breast and lung primary cells.

Spatiotemporal Control of TGF-β Signaling with Light.

Cells employ signaling pathways to make decisions in response to changes in their immediate environment. Transforming growth factor beta (TGF-β) is an important growth factor that regulates many cellular functions in development and disease. Although the molecular mechanisms of TGF-β signaling have been well studied, our understanding of this pathway is limited by the lack of tools that allow the control of TGF-β signaling with high spatiotemporal resolution.

Measuring TGF-β Ligand Dynamics in Culture Medium.

TGF-β plays an important role in a myriad of cell activities including differentiation, proliferation, and growth arrest. These effects are influenced by the concentration of TGF-β in the surrounding milieu, which is interpreted by mammalian cells and subsequently translated into meaningful signals that guide their proliferation, survival, or death. To predict cellular responses to TGF-ß signaling based on molecular mechanisms, it is important to consider how cells respond to different ligand doses and how variations in ligand exposure impact Smad signaling dynamics and subsequent gene expression.

Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR).

Bayesian network and linear regression methods have been widely applied to reconstruct cellular regulatory networks. In this work, we propose a Bayesian model averaging for linear regression (BMALR) method to infer molecular interactions in biological systems. This method uses a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods.

Characterization of early autophagy signaling by quantitative phosphoproteomics.

Under conditions of nutrient shortage autophagy is the primary cellular mechanism ensuring availability of substrates for continuous biosynthesis. Subjecting cells to starvation or rapamycin efficiently induces autophagy by inhibiting the MTOR signaling pathway triggering increased autophagic flux. To elucidate the regulation of early signaling events upon autophagy induction, we applied quantitative phosphoproteomics characterizing the temporal phosphorylation dynamics after starvation and rapamycin treatment.

A tutorial on mathematical modeling of biological signaling pathways.

Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics.

Dynamics of TGF-β/Smad signaling.

The physiological responses to TGF-β stimulation are diverse and vary amongst different cell types and environmental conditions. Even though the principal molecular components of the canonical and the non-canonical TGF-β signaling pathways have been largely identified, the mechanism that underlies the well-established context dependent physiological responses remains a mystery. Understanding how the components of TGF-β signaling function as a system and how this system functions in the context of the global cellular regulatory network requires a more quantitative and systematic approach.

Time-dependent quantitative multicomponent control of the G₁-S network by the stress-activated protein kinase Hog1 upon osmostress.

Control of cell cycle progression by stress-activated protein kinases (SAPKs) is essential for cell adaptation to extracellular stimuli. Exposure of yeast to hyperosmotic stress activates the SAPK Hog1, which delays cell cycle progression through G₁ by direct phosphorylation of the cyclin-dependent kinase (CDK) inhibitor Sic1 and by inhibition of the transcription of the genes encoding the G₁ cyclins Cln1 and 2. Additional targets of Hog1 may also play a role in this response.

Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics.

Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation.

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

Unlabelled: Parameter estimation is crucial for the modeling and dynamic analysis of biological systems. However, implementing parameter estimation is time consuming and computationally demanding. Here, we introduced a parallel parameter estimation tool for Systems Biology Markup Language (SBML)-based models (SBML-PET-MPI).

A quantitative study of the Hog1 MAPK response to fluctuating osmotic stress in Saccharomyces cerevisiae.

Background: Yeast cells live in a highly fluctuating environment with respect to temperature, nutrients, and especially osmolarity. The Hog1 mitogen-activated protein kinase (MAPK) pathway is crucial for the adaption of yeast cells to external osmotic changes.

Methodology/principal Findings: To better understand the osmo-adaption mechanism in the budding yeast Saccharomyces cerevisiae, we have developed a mathematical model and quantitatively investigated the Hog1 response to osmotic stress.

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool.

Background: It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.