Biology subjects

Lubbock, A. L. R.

Publications and source records attributed to Lubbock, A. L. R..

7 recordsLinked to original sources

bioRxiv2021

Microbench: Automated metadata management for systems biology benchmarking and reproducibility in Python

MotivationComputational systems biology analyses typically make use of multiple software and their dependencies, which often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility. ResultsMicrobench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version, and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes. AvailabilityInstall from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench. Contactc.lopez@vanderbilt.edu

systems biology↗

bioRxiv2021

Programmatic modeling for biological systems

Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models are typically simulated, calibrated, and analyzed either within a single application, or using import and export from various tools. Here, we describe a programmatic modeling paradigm, in which modeling is augmented with best practices from software engineering. We focus on Python - a popular, user-friendly programming language with a large scientific package ecosystem. Models themselves can be encoded as programs, adding benefits such as modularity, testing, and automated documentation generators while still being exportable to SBML. Automated version control and testing ensures models and their modules have expected properties and behavior. Programmatic modeling is a key technology to enable collaborative model development and enhance dissemination, transparency, and reproducibility. HighlightsO_LIProgrammatic modeling combines computational modeling with software engineering best practices. C_LIO_LIAn executable model enables users to leverage all available resources from the language. C_LIO_LICommunity benefits include improved collaboration, reusability, and reproducibility. C_LIO_LIPython has multiple modeling frameworks with a broad, active scientific ecosystem. C_LI

systems biology↗

bioRxiv2020

ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7x speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

bioinformatics↗

bioRxiv2020

A computational framework to explore cellular response mechanisms from multi-omics datasets

Recent technological advances have made it feasible to collect multi-condition transcriptome and proteome time-courses of cellular response to perturbation. The increasing size and complexity of these datasets impedes mechanism of action discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we introduce MAGINE, a software framework to explore complex time-course multi-omics datasets and build mechanistic hypotheses of dynamic cellular response. MAGINE combines data management, enrichment, and network analysis and visualization within an interactive, Jupyter notebook-based environment to enable human-in-the-loop inquiry of complex datasets. We demonstrate how measurements from HL-60 cellular response to bendamustine treatment can be used to build a mechanistic, multi-resolution description of cellular commitment to fate. We present a systems-level description of signal execution from cellular DNA-damage response, to cell cycle arrest, and eventual commitment to apoptosis, mediated by over 2000 biochemical species. We further show that MAGINE can reveal unexpected, non-canonical effects of bendamustine treatment, including disruption of cellular pathways relevant to HIV infection response. MAGINE is available from https://github.com/lolab-vu/magine.

systems biology↗

bioRxiv2020

Accelerated Simulations of Chemical Reaction Systems using the Stochastic Simulation Algorithm on GPUs

1Stochasticity due to fluctuations in chemical reactions can play important roles in cellular network-driven processes. Although the Stochastic Simulation Algorithm (SSA, aka Gillespie Algorithm) has long been accepted as a suitable method to solve the time-dependent chemical master equation, its computational cost is prohibitive for large scale complex networks such as those found in cellular processes. Here we present GPU-SSA, an implementation of the SSA formalism utilizing Graphics Processing Units for use in Python using the PySB modeling framework. We show that the GPU implementation of SSA can achieve significant speedup compared to parallel CPU or single-core CPU implementations. We further include supplementary didactic material to demonstrate how to incorporate GPU-SSA workflows for interested readers.

systems biology↗

bioRxiv2019

Visualization and analysis of high-throughput in vitro dose-response datasets with Thunor

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose-response curves, and derived dose-response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization, and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs, and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net.

pharmacology and toxicology↗

bioRxiv2018

Functionally Coherent Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling and Oncogenic Notch

BackgroundCell identity is governed by gene expression, regulated by Transcription Factor (TF) binding at cis-regulatory modules. Decoding the relationship TF binding patterns and the regulation of cognate target genes is nontrivial, remaining a fundamental limitation in understanding cell decision-making mechanisms. Identification of TF physical binding that is biologically neutral is a current challenge. We studied cell identity in the context of Epithelial to Mesenchymal Transition (EMT), a cell programme fundamental for normal embryonic development that contributes to tumour progression and fibrosis.\n\nResultsWe developed the NetNC software for discovery of functionally coherent TF targets. NetNC was applied to analyse gene regulation by the EMT TFs Snail, Twist in early embryogenesis and also to modENCODE HOT regions. Predicted neutral binding accounted for 50% to [≥]80% of candidate target genes assigned from significant binding peaks. Novel gene functions and network modules were identified, including regulation of chromatin organisation and crosstalk with notch signalling. Orthologues of predicted TF targets discriminated breast cancer molecular subtypes and NetNC analysis predicted new gene functions; for example, evidencing networks that reshape Waddingtons landscape during EMT-like phenotype switching. Predicted invasion roles for SNX29, ATG3, UNK and IRX4 were validated using a tractable cell model.\n\nConclusionsWe found extensive neutral TF binding across the nine datasets examined and showed that NetNC performs well in identifying functionally coherent targets. HOT regions had comparatively high functional coherence. Our results illuminate conserved molecular networks that regulate epithelial remodelling in development and disease, with potential implications for precision medicine.

systems biology↗