Marc R. Birtwistle

LFCT: A Benchmark Dataset for Low-Frame-Rate Cell Tracking in Long-Term Live-Cell Microscopy

The paper introduces a benchmark dataset for low-frame-rate cell tracking in long-term live-cell microscopy, containing annotated sequences from four human cell lines.

Serial Imaging of Tumour and microEnvironment (SITE) platform for live-cell ex vivo modelling of primary and metastatic cancer dynamics

The paper introduces the SITE platform for live-cell ex vivo modeling of cancer and TME interactions, revealing tissue-specific dynamics and ERK signaling patterns in breast cancer.

Mechanistic modeling of cell viability assays with in silico lineage tracing

The paper develops an algorithm to simulate and compare mechanistic models of cell viability assays with experimental data to understand drug response variability.

Genetically-Encoded Fluorescence Barcodes Allow for Single-Cell Analysis via Spectral Flow Cytometry

This paper introduces genetically-encoded fluorescence barcodes using spectral flow cytometry for single-cell analysis, demonstrating high classification accuracy for most barcodes.

Purifying circular RNA by ultrafiltration

This study demonstrates that ultrafiltration can purify circRNA with 86% purity and 50% yield, outperforming SE-HPLC.

A 96-Well Polyacrylamide Gel for Electrophoresis and Western Blotting

This paper introduces a 96-sample western blot format that improves efficiency and capacity while maintaining compatibility with standard protocols.

Network Analyses of Brain Tumor Patients' Multiomic Data Reveals Pharmacological Opportunities to Alter Cell State Transitions

The paper uses multiomic data to identify cell state transitions in GBM and proposes potential drug targets to alter these transitions.

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

The paper identifies cardiotoxic drug signatures using bulk transcriptomics and integrates them with genomic and single-cell data to predict toxicity mechanisms.

Increasing Signal Intensity of Fluorescent Oligo-Labeled Antibodies to Enable Combination Multiplexing

The paper improves fluorescent antibody labeling to enhance signal intensity for multiplex flow cytometry, showing internal labeling increases brightness and enables triple staining.

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

The paper predicts cancer cell responses to drug combinations using a temporal cell state network model based on single drug dose responses and cell cycle transitions.

Expanding large-scale mechanistic models with machine learned associations and big datasets

This paper combines machine learning with mechanistic models to improve cell-line-specific drug response predictions using large datasets.

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

The paper develops two solutions to communication bottlenecks in large-scale single-cell models, significantly speeding up simulations.

Network Inference from Perturbation Time Course Data

This paper introduces DL-MRA, a method to infer network structures from perturbation time course data, showing it can robustly identify small networks but has limitations for signaling networks.

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) for identification of context-specific networks and new regulatory mechanisms

MOBILE identifies context-specific molecular networks by integrating multi-omics data, revealing new regulatory mechanisms in immune and developmental pathways.

A Scalable, Open-Source Implementation of a Large-Scale Mechanistic Model for Single Cell Proliferation and Death Signaling

The paper develops an open-source, scalable mechanistic model for single-cell proliferation and death signaling, demonstrating its application to understand IFNγ's anti-proliferative effects.

Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

The paper uses single-cell imaging and machine learning to predict cell division from ERK and Akt activity dynamics in MCF10A cells, finding ERK alone predicts division with AUC=0.74.

Protein structure-based gene expression signatures

This paper introduces structural gene expression signatures (sGES) derived from protein structure features, showing their utility in identifying robust biological signatures across datasets and experimental platforms.

Wilms tumor 1 promotes memory flexibility

This study identifies WT1 as a transcriptional repressor that decreases memory strength and promotes memory flexibility by regulating synaptic plasticity and learning outcomes.

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

The paper quantifies gene-specific predictability of protein levels from mRNA data across human tissues and identifies factors influencing this predictability.

Origins of fractional control in regulated cell death

The paper shows that variable mitochondria abundance affects cell survival and fractional cell death response to TRAIL through Bax effective concentration variation.

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

The paper constructs a mechanistic model of pan-cancer driver pathways to predict stochastic proliferation and death in cancer cells.

Analysis of Copy Number Loss of the ErbB4 Receptor Tyrosine Kinase in Glioblastoma

The paper investigates copy number loss of the ERBB4 gene in glioblastoma, finding it is not a driver mutation or predisposing factor, despite initial hypotheses from cell line data.

A Comparison of mRNA Sequencing with Random Primed and 3’-Directed Libraries

This study compares 3-DGE and conventional mRNAseq, finding similar gene expression results with 3-DGE offering cost savings and scalability.