Chimeric antigen receptors (CARs) and synthetic Notch (synNotch) are engineered cell-surface that sense a target respond by activating T cell receptor signaling or customized gene program, respectively. Here, to expand the targeting capabilities of these receptors, we develop "universal" systems for which specificity can be directed post-translationally via covalent attachment co-administered antibody bearing benzylguanine (BG) motif. A SNAPtag self-labeling enzyme is genetically fused...

Computational modeling is important for understanding biological systems, however models of signaling networks are often constructed manually, limiting their size and complexity. Machine reading can extract molecular interactions cellular described in the scientific literature collected databases to provide extensive knowledge that could be incorporated automatically into executable if reliable methods assembly, extension evaluation were available. Here, we evaluated utility breadth-first...

Abstract Chimeric antigen receptors (CARs) and synthetic Notch (synNotch) are engineered cell-surface that sense a target respond by activating T cell receptor signaling or customized gene program, respectively. To expand the targeting capabilities of these receptors, we have developed switchable adaptor systems for which specificity can be directed post-translationally via covalent attachment co-administered antibody. Instead directly an antigen, our contain SNAPtag self-labeling enzyme,...

Many clinical and consumer electrocardiogram (ECG) devices collect fewer electrodes than the standard twelve-lead ECG either report less information or employ algorithms to reconstruct a full signal. We assessed optimal electrode selection number that minimizes redundant collection while maximizing reconstruction accuracy. employed validated deep learning model signals from 250 different patients in PTB database. Different numbers combinations of were removed before measure effect inclusion...

Abstract Background The current gold standard of coronary artery disease (CAD) diagnosis is invasive angiography, during which fractional flow reserve (FFR) measurement may be performed to confirm the clinical significance a stenosis. yield routine and indiscriminate FFR in identifying hemodynamically significant stenoses low. To combat this, we have developed an artificial intelligence model - ECGio – designed deployed at point care determine through analysis resting digital 12-lead...

Abstract Published research articles are rich sources of data when the knowledge is incorporated into models. Complex biological systems benefit from computational modeling’s ability to elucidate dynamics, explain and address hypotheses. Modeling pancreatic cancer could guide treatment this devastating disease that has a known mutational profile disrupting signaling pathways but no reliable therapies. The approach described here utilize discrete modeling major pathways, metabolism tumor...

Abstract The process through which macrophages differentiate into either an M1 or M2 state is thought to be well elucidated. Literature suggests that only a handful of receptors and their ligands play role in deciding macrophage fate. These share many signaling pathways work together create phenotype. While these phenotype generalizations are broad, the states have been used as placeholders describe drastically different metabolic phenotypes. To determine if this complex could controlled by...

The large amount of knowledge contained in the scientific literature can be mined using natural language processing and utilized to automatically construct models complex networks order obtain a greater understanding systems. In this paper, we describe Dynamic System Explanation (DySE) framework, which configures hybrid executes simulations over time, relying on granular computing approach range different element update functions. A standardized tabular format assembles collected into for...

Abstract Background : Due to the complexity and redundancy of biological systems, computational models are difficult laborious create update. Therefore, machine reading automated model assembly great interest systems biologists. Here, we describe FIDDLE (Finding Interactions using Diagram Driven modeL Extension), tool that built with goal automatically assemble or extend knowledge extracted from published literature. The two main methods developed as part called Breadth First Addition (BFA)...

There is significant interest in using existing repositories of biological entities, relationships, and models to automate model assembly extension. Current methods aggregate human-curated information into executable, simulatable models, but these do not resemble human curated recapitulate experimental results. Here, we outline the process automated extension, while demonstrating it on both synthetic signaling networks. We begin with an iterative, greedy, combinatoric approach demonstrate...

Introduction: ECG construction with deep learning can help standardize ECGs and remove noise artifacts, transforming potentially unusable into clinically useful ones. However, levels, often unpredictable in clinical settings, may impact model performance. Hypothesis: This study explores the relationship between levels performance constructing noise-free ECGs. We hypothesize that beyond a certain threshold, model's output ceases to be usable. Aims: Our aims determine effects on of models...

With the tremendous increase in amount of biological literature, developing automated methods for extracting big data from papers, building models and explaining mechanisms becomes a necessity. We describe here our approach to translating machine reading outputs, obtained by bio- logical signaling discrete cellular networks. use out- puts three different engines, their features, using examples cancer literature. also outline several issues that still arise when assembling network...

There are multiple modalities used to diagnose abnormalities of the heart consisting various invasive and noninvasive tests. Patients may undergo tests, progressing more methods at expense patient risk cost pair. HEARTio, through machine learning algorithmic processing our proprietary software, hopes improve accuracy electrocardiography: a century old technology most commonly performed cardiac test. It is attacks, rhythm problems operates as gateway testing for patients undergoing...

Abstract Chimeric antigen receptors (CARs) are artificial T cell that re-target patients’ cells to specifically bind and kill tumor cells. Adoptive therapy with CAR targeting CD19 has revolutionized treatment of refractory B acute lymphoblastic leukemia, there is great interest in generating treating other cancers by additional antigens. Another promising class engineered synthetic Notch (synNotch) can sense an on a neighboring turn expression any transgene(s) interest. To expand the...

The automated assembly and extension of dynamic network models using information extracted from literature are challenging due to the amount inconsistency in published literature. Recently, efforts have been made automatically efficiently assemble into models. In this review, we summarize basic concept, performance, advantages, limitations five methods. Each method was tested for its ability reconstruct a model T-cell differentiation as compared against number predefined system properties.

Abstract Background Current standard electrocardiogram analysis algorithms cannot predict the presence and extent of coronary artery disease (CAD), especially in stable patients. Objectives This study assessed ability a novel artificial intelligence algorithm (ECGio) to presence, location, severity lesions an unselected patient population. Methods A cohort 1659 outpatients were randomly divided into training (86%) validation (14%) subsets, maintaining population characteristics. ECGio was...