Modular Workflow Research Articles

Modular design workflow for 3D printable bioresorbable patient-specific bone scaffolds: extended features and clinical validation.

A previously in-house developed patient-specific scaffold design workflow was extended with new features to overcome several limitations and to broaden its adaptability to diverse bone defects, thereby enhancing its fit for routine clinical use. It was applied to three clinical cases for further validation. A virtual surgical resection tool was developed to remove regions of the bone defect models. The minor cavity fill module enabled the generation of scaffold designs with smooth external surfaces and the segmental defect fill module allowed a versatile method to fill a segmental defect cavity. The boundary representation method based surgical approach module in the original workflow was redeveloped to use functional representation, eliminating previously seen resolution dependant artefacts. Lastly, a method to overlay the scaffold designs on computed tomography images of the defect for design verification by the surgeon was introduced. The extended workflow was applied to two ongoing clinical case studies of a complex bilateral femoral defect and a humerus defect, and also to a case of a large volume craniomaxillofacial defect. It was able to successfully generate scaffolds without any obstructions to their surgical insertion which was verified by digital examination as well as using physical 3D printed models. All produced surface meshes were free from 3D printing mesh errors. The scaffolds designed for the ongoing cases were 3D printed and successfully surgically implanted, providing confidence in the extended modular workflow's ability to be applied to a broad range of diverse clinical cases.

The SmARTR pipeline: A modular workflow for the cinematic rendering of 3D scientific imaging data

The SmARTR pipeline: A modular workflow for the cinematic rendering of 3D scientific imaging data

A programmable platform for probing cell migration and proliferation.

The advent of advanced robotic platforms and workflow automation tools has revolutionized the landscape of biological research, offering unprecedented levels of precision, reproducibility, and versatility in experimental design. In this work, we present an automated and modular workflow for exploring cell behavior in two-dimensional culture systems. By integrating the BioAssemblyBot® (BAB) robotic platform and the BioApps™ workflow automater with live-cell fluorescence microscopy, our workflow facilitates execution and analysis of in vitro migration and proliferation assays. Robotic assistance and automation allow for the precise and reproducible creation of highly customizable cell-free zones (CFZs), or wounds, in cell monolayers and "hands-free," schedulable integration with real-time monitoring systems for cellular dynamics. CFZs are designed as computer-aided design models and recreated in confluent cell layers by the BAB 3D-Bioprinting tool. The dynamics of migration and proliferation are evaluated in individual cells using live-cell fluorescence microscopy and an in-house pipeline for image processing and single-cell tracking. Our robotics-assisted approach outperforms manual scratch assays with enhanced reproducibility, adaptability, and precision. The incorporation of automation further facilitates increased flexibility in wound geometry and allows for many experimental conditions to be analyzed in parallel. Unlike traditional cell migration assays, our workflow offers an adjustable platform that can be tailored to a wide range of applications with high-throughput capability. The key features of this system, including its scalability, versatility, and the ability to maintain a high degree of experimental control, position it as a valuable tool for researchers across various disciplines.

Modular Point-of-Need Tropane Alkaloid Detection at Regulatory Levels: Combining Solid-Liquid Extraction from Buckwheat with a Paper-Immobilized Liquid-Phase Microextraction and Immuno-Detection in Interconnectable 3D-Printed Devices.

Contamination with tropane alkaloids in cereals is expected to increase globally. However, current identification tools (e.g., liquid chromatography-mass spectrometry) for tropane alkaloids are time-consuming and expensive. Furthermore, their miniaturized alternatives lack sensitivity and robustness. Therefore, there is a pressing need for inexpensive and effective screening methods. Here, an on-site applicable modular workflow for tropane alkaloid detection in buckwheat is presented. The modular workflow combines paper microfluidics and interconnectable 3D-printed sample preparation tools and was evaluated for different tropane alkaloids, including atropine and scopolamine. Furthermore, integration with an indirect competitive lateral flow immunoassay (icLFIA) for atropine detection at relevant levels was demonstrated. In the modular workflow, to minimize matrix coextraction, tropane alkaloids were extracted from the milled buckwheat cereals by a mixture of alkaline aqueous and immiscible organic solvents (extraction recoveries: 66-79%). The tropane alkaloids were subsequently concentrated with a newly developed paper-immobilized liquid-phase microextraction (PI-LPME, extraction recoveries: 34-60%, concentration factor to immobilized solution in paper: 60-108×). After the PI-LPME, with an integrated 3D-printed setup, the tropane alkaloids were directly eluted (elution recoveries: 83-93%) and detected with the icLFIA. Digital read-out of the icLFIA, by employing a hand-held reader, enabled semiquantification of atropine (IC50 = 0.56 ng mL-1 in standard solutions). The modular workflow was validated by analyzing 24 blank and spiked buckwheat cereal samples with 5 and 10 μg kg-1 atropine. A cutoff value was established with an estimated false negative rate of 1% and estimated false positive rate of 0.68%. Therefore, the modular workflow can aid in fast, inexpensive, and on-site atropine detection by nonexperts, and when integrated with a scopolamine-specific icLFIA expanded toward scopolamine detection. Moreover, the developed sample extraction and concentration method (PI-LPME) is suitable for the analysis of many other compounds with pH-dependent polarity.

AutoBioTech─A Versatile Biofoundry for Automated Strain Engineering.

The inevitable transition from petrochemical production processes to renewable alternatives has sparked the emergence of biofoundries in recent years. Manual engineering of microbes will not be sufficient to meet the ever-increasing demand for novel producer strains. Here we describe the AutoBioTech platform, a fully automated laboratory system with 14 devices to perform operations for strain construction without human interaction. Using modular workflows, this platform enables automated transformations of Escherichia coli with plasmids assembled via modular cloning. A CRISPR/Cas9 toolbox compatible with existing modular cloning frameworks allows automated and flexible genome editing of E.coli. In addition, novel workflows have been established for the fully automated transformation of the Gram-positive model organism Corynebacterium glutamicum by conjugation and electroporation, with the latter proving to be the more robust technique. Overall, the AutoBioTech platform excels at versatility due to the modularity of workflows and seamless transitions between modules. This will accelerate strain engineering of Gram-negative and Gram-positive bacteria.

Multiparameter quantitative analyses of diagnostic cells in brain tissues from tuberous sclerosis complex.

The advent of high-dimensional imaging offers new opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example case is found in tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine learning pipeline (BAlloon IDENtifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom 36-antibody panel and imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC marker proteins and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. Here, we present a modular workflow encompassing BAIDEN, a custom antibody panel, a control sample microarray, and analysis pipelines-both open-source and in-house-and apply this workflow to understand the abundance, structure, and signaling activity of BCs as an example case of how high-dimensional imaging can be applied within human tissues.

A workflow management system for reproducible and interoperable high-throughput self-driving experiments

To foster self-driving experimentation and address the reproducibility crisis in bioprocess development in a collaborative environment, a modular Workflow Management System (WMS) is required. In this work, a WMS based on Directed Acyclic Graphs that offers a modular and flexible design for plug-and-play integration of computational tools is presented. A case study is used to demonstrate that the implementation of a computational WMS in robotic experimental facilities promotes the application of Findable, Accessible, Interoperable and Re-usable principles, allowing researchers to readily share protocols, models, methods and data. As a proof of concept, we integrated three different computational workflows for online re-design of feeding rates in 24 parallel E. coli fed-batch cultivations producing elastin-like proteins. This approach provides a solid foundation for increasing scientific data generation in robotic experimental facilities, fostering open collaboration, and addressing the challenges of reproducibility in research.

Good Statistical Monitoring: A Flexible Open-Source Tool to Detect Risks in Clinical Trials

BackgroundRisk-based quality management is a regulatory-recommended approach to manage risk in a clinical trial. A key element of this strategy is to conduct risk-based monitoring to detect potential risks to critical data and processes earlier. However, there are limited publicly available tools to perform the analytics required for this purpose. Good Statistical Monitoring is a new open-source solution developed to help address this need.MethodsA team of statisticians, data scientists, clinicians, data managers, clinical operations, regulatory, and quality compliance staff collaborated to design Good Statistical Monitoring, an R package, to flexibly and efficiently implement end-to-end analyses of key risks. The package currently supports the mapping of clinical trial data from a variety of formats, evaluation of 12 key risk indicators, interactive visualization of analysis results, and creation of standardized reports.ResultsThe Good Statistical Monitoring package is freely available on GitHub and empowers clinical study teams to proactively monitor key risks. It employs a modular workflow to perform risk assessments that can be customized by replacing any workflow component with a study-specific alternative. Results can be exported to other clinical systems or can be viewed as an interactive report to facilitate follow-up risk mitigation. Rigorous testing and qualification are performed as part of each release to ensure package quality.ConclusionsGood Statistical Monitoring is an open-source solution designed to enable clinical study teams to implement statistical monitoring of critical risks, as part of a comprehensive risk-based quality management strategy.

A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics

Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration. We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components. We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi‐automated, contain different components than those presented here, or have parallel components for comparative studies. We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.

Shaping the Smart Search Possible: An Inside Story of ETD Metadata

Metadata is very important in scientific literature. Metadata in electronic dissertations and dissertations (ETDs) requires a modular workflow based on a flexible architecture. Metadata methods must be simplified by adapting procedures for different document formats and structures. The structure of the dissertation document, including Fig.s, tables, footnotes, etc., requires greater attention to the identification and organization of metadata in the ETD for rich textual content. Reliable extraction of appropriate metadata elements, such as title, author, abstract, keywords, etc., is essential for future citation capabilities. Such mechanisms are crucial for evaluating processes and adjusting ETD metadata to facilitate architecture expansion. Most modern ETDs rely on the organization of quality text documents, ranging from raw PDF documents or semi-structured XML documents to uniform standard TEI (Text Encoding Initiative) documents. The present study discusses metadata creation and harvesting, various extraction methods and digital library search, metadata for MARC records in ETD, and access to ETD metadata in the context of the current scenario. It also highlights the tags adopted for the creation of the MARC records in JNU Central Library ETD System for simple-level learning

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Paving the road towards automated homogeneous catalyst design.

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

DeePhys: A machine learning–assisted platform for electrophysiological phenotyping of human neuronal networks

Reproducible functional assays to study invitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of invitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.

Fully synthetic platform to rapidly generate tetravalent bispecific nanobody–based immunoglobulins

Nanobodies bind a target antigen with a kinetic profile similar to a conventional antibody, but exist as a single heavy chain domain that can be readily multimerized to engage antigen via multiple interactions. Presently, most nanobodies are produced by immunizing camelids; however, platforms for animal-free production are growing in popularity. Here, we describe the development of a fully synthetic nanobody library based on an engineered human VH3-23 variable gene and a multispecific antibody-like format designed for biparatopic target engagement. To validate our library, we selected nanobodies against the SARS-CoV-2 receptor-binding domain and employed an on-yeast epitope binning strategy to rapidly map the specificities of the selected nanobodies. We then generated antibody-like molecules by replacing the VH and VL domains of a conventional antibody with two different nanobodies, designed as a molecular clamp to engage the receptor-binding domain biparatopically. The resulting bispecific tetra-nanobody immunoglobulins neutralized diverse SARS-CoV-2 variants with potencies similar to antibodies isolated from convalescent donors. Subsequent biochemical analyses confirmed the accuracy of the on-yeast epitope binning and structures of both individual nanobodies, and a tetra-nanobody immunoglobulin revealed that the intended mode of interaction had been achieved. This overall workflow is applicable to nearly any protein target and provides a blueprint for a modular workflow for the development of multispecific molecules.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments.

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

BootRanges: flexible generation of null sets of genomic ranges for hypothesis testing.

Enrichment analysis is a widely utilized technique in genomic analysis that aims to determine if there is a statistically significant association between two sets of genomic features. To conduct this type of hypothesis testing, an appropriate null model is typically required. However, the null distribution that is commonly used can be overly simplistic and may result in inaccurate conclusions. bootRanges provides fast functions for generation of block bootstrapped genomic ranges representing the null hypothesis in enrichment analysis. As part of a modular workflow, bootRanges offers greater flexibility for computing various test statistics leveraging other Bioconductor packages. We show that shuffling or permutation schemes may result in overly narrow test statistic null distributions and over-estimation of statistical significance, while creating new range sets with a block bootstrap preserves local genomic correlation structure and generates more reliable null distributions. It can also be used in more complex analyses, such as accessing correlations between cis-regulatory elements (CREs) and genes across cell types or providing optimized thresholds, e.g. log fold change (logFC) from differential analysis. bootRanges is freely available in the R/Bioconductor package nullranges hosted at https://bioconductor.org/packages/nullranges.

Abstract 6564: Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis

Abstract Breast cancer is the most common form of cancer, and afflicted over 2.2 million people in 2020. The sheer volume of patients who suffer from breast cancer warrants continued research and discovery efforts to improve treatment options. Triple negative breast cancer (TNBC) affects 10-15% of breast cancer patients. Unlike other forms of breast cancer, TNBC does not have estrogen or progesterone receptors and makes little to none of the HER2 protein. Due to the lack of these biomarkers that are typical treatment targets for other kinds of breast cancers, the hormone therapies and drugs that target other breast cancers are often ineffective against TNBC. This leaves chemotherapy and radiation therapy as the main treatment options. Although chemotherapy and radiation have treatment benefits, the recurrence rate after treatment is around 40%. Furthermore, these treatment options are very detrimental to the body, resulting in a weaker patient and a necessary recovery time between treatments. In this study, we analyzed publicly available RNA-sequencing (RNA-seq) data to identify the upregulated and downregulated transcriptional mechanism(s) that play a role in TNBC compared to healthy breast tissue. The analysis of the RNA-seq data was completed with the Automated Reproducible Modular Workflow for Preprocessing and Differential Analysis of RNA-seq Data (ARMOR), which trims, maps, and quantifies the mRNA sequencing reads to the human transcriptome for each read. The ARMOR program identified more than 12,000 differentially expressed genes between TNBC and healthy breast tissue samples. We then applied an artificial intelligence-based classification method, a random forest algorithm, to identify new biomarkers that best differentiate TNBC cells from healthy cells. These specific transcriptional biomarkers for TNBC could potentially be used as diagnostic biomarkers or as therapeutic targets. We anticipate that this data may expand the treatment options for TNBC. Citation Format: Jenna B. Poulsen, Mauri E. Dobbs, Naomi Rapier-Sharman, Brett E. Pickett. Biomarker discovery in triple negative breast cancer using RNA-sequencing analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6564.

A pipeline for testing drug mechanism of action and combination therapies: From microarray data to simulations via Linear-In-Flux-Expressions: Testing four-drug combinations for tuberculosis treatment

Computational methods are becoming commonly used in many areas of medical research. Recently, the modeling of biological mechanisms associated with disease pathophysiology have benefited from approaches such as Quantitative Systems Pharmacology (briefly QSP) and Physiologically Based Pharmacokinetics (briefly PBPK). These methodologies show the potential to enhance, if not substitute animal models. The main reasons for this success are the high accuracy and low cost.Solid mathematical foundations of such methods, such as compartmental systems and flux balance analysis, provide a good base on which to build computational tools. However, there are many choices to be made in model design, that will have a large impact on how these methods perform as we scale up the network or perturb the system to uncover the mechanisms of action of new compounds or therapy combinations.A computational pipeline is presented here that starts with available-omic data and utilizes advanced mathematical simulations to inform the modeling of a biochemical system. Specific attention is devoted to creating a modular workflow, including the mathematical rigorous tools to represent complex chemical reactions, and modeling drug action in terms of its impact on multiple pathways. An application to optimizing combination therapy for tuberculosis shows the potential of the approach.

Using "Galaxy-rCASC": A Public Galaxy Instance for Single-Cell RNA-Seq Data Analysis.

rCASC is a modular workflow providing an integrated environment for single-cell RNA-seq (scRNA-Seq) data analysis exploiting Docker containers to achieve functional and computational reproducibility. It was initially developed as an R package usable also through a Java GUI. However, the Java frontend cannot be employed when running rCASC on a remote server, a typical setup due to the significant computational resources commonly needed to analyze scRNA-Seq data.To allow the use of rCASC through a graphical user interface on the client side and to harness the many advantages provided by the Galaxy platform, we have made rCASC available as a Galaxy set of tools, also providing a dedicated public instance of Galaxy named "Galaxy-rCASC." To integrate rCASC into Galaxy, all its functions, originally implemented as a set of Docker containers to maximize reproducibility, have been extensively reworked to become independent from the R package functions that launch them in the original implementation. Furthermore, suitable Galaxy wrappers have been developed for most functions of rCASC. We provide a detailed reference document to the use of Galaxy-rCASC with insights and explanations on the platform functionalities, parameters, and output while guiding the reader through the typical rCASC analysis workflow of a scRNA-Seq dataset.

Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline

Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis.