Protocol for potential energy-based bifurcation analysis, parameter searching, and phase diagram analysis of noncanonical bistable switches

We have explored the design principles of noncanonical bistable switches using high-throughput bifurcation analysis of positive feedback loops under dual signaling. Here, we present a protocol to carry out bifurcation analysis using pseudo-potential energy of the dynamical system. We also describe steps to perform automated parameter searching for canonical and noncanonical switches and multi-parameter phase diagram analysis of these switches. For complete details on the use and execution of this protocol, please refer to Das et al.1.

Protocol for evaluating mechanistic pathways associated with HIV acquisition via nested Least Absolute Shrinkage and Selective Operator analysis

Statistical analysis to evaluate mechanistic pathways can be limited by non-causal associations as well as co-linearity of high-dimensional data. Here, we present a protocol evaluating statistical associations between multiple exposure variables (sociodemographic and behavioral), immune biomarkers, and HIV acquisition. We describe steps for study setup, combining Least Absolute Shrinkage and Selective Operator with the standard regression approach, and building nested models. This approach can determine to what extent associations between risks for exposure contributes to HIV acquisition with or without associated changes in immune activation. For complete details on the use and execution of this protocol, please refer to Bender Ignacio et al.1.

Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping: A pipeline for whole-brain structural and activity analyses

Here, we present Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping, a user-friendly pipeline for the registration and analysis of whole-brain images in larval zebrafish. We describe steps for pre-processing, registration, quantification, and visualization of whole-brain phenotypes in zebrafish mutants of genes associated with neurodevelopmental and neuropsychiatric disorders. By utilizing BioImage Suite Web, an open-source software package originally developed for processing human brain imaging data, we provide a highly accessible whole-brain mapping protocol developed for users with general computational proficiency. For complete details on the use and execution of this protocol, please refer to Weinschutz Mendes et al. (2023).1.

Using DeepContact with Amira graphical user interface

DeepContact is a deep learning software for high-throughput quantification of membrane contact site (MCS) in 2D electron microscopy images. This protocol will guide users through incorporating available DeepContact models in Amira's artificial intelligence module, thereby allowing invoking of DeepContact functions in organelle segmentation and quantifying of MCS with a user-friendly graphical user interface of Amira software. For complete details on the use and execution of this protocol, please refer to Liu et al. (2022).1.

Simulating the conformational dynamics of the ATPase complex on proteasome using its free-energy landscape

The AAA+ ATPase complex on proteasome powers its functions through a series of intricate conformational transitions. Here, we describe a procedure to simulate the conformational dynamics of the proteasomal ATPase complex. We first empirically determined the free-energy landscape (FEL) of proteasome and then simulated proteasome's conformational changes as stochastic transitions on its FEL. We compared the FEL-predicted proteasomal behaviors with experimental measurements and analyzed the map of the ATPase's global dynamics to gain mechanistic insights into proteasomal degradation. For complete details on the use and execution of this protocol, please refer to Fang et al. (2022).1.

Protocol to analyze fundus images for multidimensional quality grading and real-time guidance using deep learning techniques

Data quality issues have been acknowledged as one of the greatest obstacles in medical artificial intelligence research. Here, we present DeepFundus, which employs deep learning techniques to perform multidimensional classification of fundus image quality and provide real-time guidance for on-site image acquisition. We describe steps for data preparation, model training, model inference, model evaluation, and the visualization of results using heatmaps. This protocol can be implemented in Python using either the suggested dataset or a customized dataset. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

Computational protocol to perform a spatiotemporal reconstruction of an epidemic

Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. For complete details on the use and execution of this protocol, please refer to Galli et al. (2023).1.

Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S

Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations. For complete details on the use and execution of this protocol, please refer to Yan et al (2023).1.

Protocol to implement a computational pipeline for biomedical discovery based on a biomedical knowledge graph

Biomedical knowledge graphs (BKGs) provide a new paradigm for managing abundant biomedical knowledge efficiently. Today's artificial intelligence techniques enable mining BKGs to discover new knowledge. Here, we present a protocol for implementing a computational pipeline for biomedical knowledge discovery (BKD) based on a BKG. We describe steps of the pipeline including data processing, implementing BKD based on knowledge graph embeddings, and prediction result interpretation. We detail how our pipeline can be used for drug repurposing hypothesis generation for Parkinson's disease. For complete details on the use and execution of this protocol, please refer to Su et al.1.

Protocol for quantifying the germination properties of individual bacterial endospores using PySpore

PySpore is a Python program that tracks the germination of individual bacterial endospores. Here, we present a protocol for segmenting spores and quantifying the germination properties of individual bacterial endospores using PySpore. We describe steps for using GUI-based tools to optimize image processing, annotating data, setting gates, and joining datasets for downstream analyses. We then describe procedures for plotting functionality tools without the user needing to modify the underlying code. For complete details on the use and execution of this protocol, please refer to Ribis et al. (2023).1.

Protocol for estimating the impact of climate change on economic growth and inequality under climate policies

The impact of climate change on economic inequality has attracted increasing attention from both government and academia. Here, we present a protocol for estimating both the impact of climate change on economic growth and economic growth inequality under multiple climate policies. We describe steps for constructing an uncertainty analysis framework, collecting and pre-processing data, and estimating the climate-economic response. We then detail procedures of predicting climate policy impact and calculating inter-country economic growth inequality. For complete details on the use and execution of this protocol, please refer to Tang et al. (2023).1.

Protocol for fabricating pseudocapacitor electrodes using ultra-short laser pulses for in situ nanostructure generation

Ultra-short laser pulses for in situ nanostructure generation (ULPING) enable the production of high-performance capacitive electrodes for pseudocapacitors, opening avenues for optimal electrode design. Here, we present a protocol for fabricating pseudocapacitor electrodes using ULPING. We describe steps for electrode fabrication, coin cell assembly, material characterization, and electrochemical analysis. Additionally, we present strategies for generating data and constructing machine learning algorithms to predict the electrochemical properties of the fabricated electrodes. For complete details on the use and execution of this protocol, please refer to Khosravinia et al. (2023).1.

Mapping neuronal ensembles and pattern-completion neurons through graphical models

Neuronal ensembles are coordinated groups of neurons that serve as functional building blocks of neural circuits. Here, we present PatMap, a computational toolbox for identifying pattern-completion neurons, key trigger cells capable of reactivating entire neuronal ensembles. We describe a protocol for modeling neural circuits as probabilistic graphical models, linking behavior with specific neuronal ensembles, and identifying their pattern-completion neurons. By linking the cellular and circuit level, PatMap provides a springboard for targeted manipulation and control of neural circuits. For complete details on the use and execution of this protocol, please refer to Carrillo-Reid et al. (2021).1.

Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network

Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.

Protocol for a distributed smart building solution using semi-physical simulation

Honeycomb is a distributed smart building system that is robust, flexible, and portable. Here, we present a protocol that uses semi-physical simulation to construct a Honeycomb prototype. We describe steps for software and hardware preparation, as well as the implementation of a video-based occupancy detection algorithm. Besides, we provide examples and scenarios of distributed applications, including node failure and recovery. We further provide guidance on data visualization and analysis to facilitate the design of distributed applications for smart buildings. For complete details on the use and execution of this protocol, please refer to Xing et al.1.

Examining asymmetric pairwise pre-reaction and transition states in enzymatic catalysis by molecular dynamics simulation and quantum mechanics/molecular mechanics calculation

Here, we present a protocol to examine asymmetric pairwise pre-reaction and transition states in enzymatic catalysis. We describe steps to set up the calculated systems, run umbrella sampling molecular dynamics simulation, and conduct quantum mechanics/molecular mechanics calculations. We also provide analytical scripts to yield potential of mean force of pre-reaction states and reaction barriers. This protocol can generate quantum-mechanistic data for constructing pre-reaction state/transition state machine learning models. For complete details on the use and execution of this protocol, please refer to Luo et al. (2022).¹.

Protocol for predicting drug-resistant protein mutations to an ERK2 inhibitor using RESISTOR

Prospective predictions of drug-resistant protein mutants could improve the design of therapeutics less prone to resistance. Here, we describe RESISTOR, an algorithm that uses structure- and sequence-based criteria to predict resistance mutations. We demonstrate the process of using RESISTOR to predict ERK2 mutants likely to arise in melanoma ablating the efficacy of the ERK1/2 inhibitor SCH779284. RESISTOR is included in the free and open-source computational protein design software OSPREY. For complete details on the use and execution of this protocol, please refer to Guerin et al..1.

Deep texture representation analysis for histopathological images

Deep texture representations (DTRs) produced from a bilinear convolutional neural network allow objective quantification of tumor histopathology images effectively. They can be used for various analyses, including visualization of morphological correlation between histology images, content-based image retrieval (CBIR), and supervised learning. This protocol describes the simplified workflow to analyze DTRs from data preparation, visualization of the histological profile, and CBIR analysis, to supervised learning model development to predict the profile from histological images. For complete details on the use and execution of this protocol, please refer to Komura et al. (2022).¹.

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics

Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capable of capturing optically encoded dynamic signals. Then, we detail the data processing and model training of a view-channel-depth (VCD) neural network, which enables instant 3D image reconstruction from a single 2D light-field snapshot. Finally, we describe VCD-LFM imaging of several model organisms and demonstrate image-based quantitative studies on neural activities and cardio-hemodynamics. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).1.

Protocol for using serial two-photon tomography to map cell types and cerebrovasculature at single-cell resolution in the whole adult mouse brain

Here, we present a protocol using serial two-photon tomography (STPT) to quantitatively map genetically defined cell types and cerebrovasculature at single-cell resolution across the entire adult mouse brain. We describe the preparation of brain tissue and sample embedding for cell type and vascular STPT imaging and image processing using MATLAB codes. We detail the computational analyses for cell signal detection, vascular tracing, and three-dimensional image registration to anatomical atlases, which can be implemented for brain-wide mapping of different cell types. For complete details on the use and execution of this protocol, please refer to Wu et al. (2022),1 Son et al. (2022),2 Newmaster et al. (2020),3 Kim et al. (2017),4 and Ragan et al. (2012).5.