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1. Microbench: automated metadata management for systems biology benchmarking and reproducibility in Python

   https://doi.org/10.1093/bioinformatics/btac580  

   Lubbock, Alexander L. R. ; Lopez, Carlos F. ; Kelso, ed., Janet ( August 2022 , Bioinformatics)

   Computational systems biology analyses typically make use of multiple software and their dependencies, which are 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.

   Microbench 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.

   Install from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.

   Supplementary data are available at Bioinformatics online.

    

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2. gmxapi: a high-level interface for advanced control and extension of molecular dynamics simulations

   https://doi.org/10.1093/bioinformatics/bty484  

   Irrgang, M. Eric ; Hays, Jennifer M. ; Kasson, Peter M. ; Valencia, ed., Alfonso ( June 2018 , Bioinformatics)

   Molecular dynamics simulations have found use in a wide variety of biomolecular applications, from protein folding kinetics to computational drug design to refinement of molecular structures. Two areas where users and developers frequently need to extend the built-in capabilities of most software packages are implementing custom interactions, for instance biases derived from experimental data, and running ensembles of simulations. We present a Python high-level interface for the popular simulation package GROMACS that i) allows custom potential functions without modifying the simulation package code, ii) maintains the optimized performance of GROMACS and iii) presents an abstract interface to building and executing computational graphs that allows transparent low-level optimization of data flow and task placement. Minimal dependencies make this integrated API for the GROMACS simulation engine simple, portable and maintainable. We demonstrate this API for experimentally-driven refinement of protein conformational ensembles.

   LGPLv2.1 source and instructions are available at https://github.com/kassonlab/gmxapi.

   Supplementary data are available at Bioinformatics online.

    

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3. BioModME for building and simulating dynamic computational models of complex biological systems

   https://doi.org/10.1093/bioadv/vbae023  

   Womack, Justin A. ; Shah, Viren ; Audi, Said H. ; Terhune, Scott S. ; Dash, Ranjan K. ; Lengauer, ed., Thomas ( February 2024 , Bioinformatics Advances)

   Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization.

   All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

    

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4. Vivarium: an interface and engine for integrative multiscale modeling in computational biology

   https://doi.org/10.1093/bioinformatics/btac049  

   Agmon, Eran ; Spangler, Ryan K. ; Skalnik, Christopher J. ; Poole, William ; Peirce, Shayn M. ; Morrison, Jerry H. ; Covert, Markus W. ; Valencia, ed., Alfonso ( February 2022 , Bioinformatics)

   This article introduces Vivarium—software born of the idea that it should be as easy as possible for computational biologists to define any imaginable mechanistic model, combine it with existing models and execute them together as an integrated multiscale model. Integrative multiscale modeling confronts the complexity of biology by combining heterogeneous datasets and diverse modeling strategies into unified representations. These integrated models are then run to simulate how the hypothesized mechanisms operate as a whole. But building such models has been a labor-intensive process that requires many contributors, and they are still primarily developed on a case-by-case basis with each project starting anew. New software tools that streamline the integrative modeling effort and facilitate collaboration are therefore essential for future computational biologists.

   Vivarium is a software tool for building integrative multiscale models. It provides an interface that makes individual models into modules that can be wired together in large composite models, parallelized across multiple CPUs and run with Vivarium’s discrete-event simulation engine. Vivarium’s utility is demonstrated by building composite models that combine several modeling frameworks: agent-based models, ordinary differential equations, stochastic reaction systems, constraint-based models, solid-body physics and spatial diffusion. This demonstrates just the beginning of what is possible—Vivarium will be able to support future efforts that integrate many more types of models and at many more biological scales.

   The specific models, simulation pipelines and notebooks developed for this article are all available at the vivarium-notebooks repository: https://github.com/vivarium-collective/vivarium-notebooks. Vivarium-core is available at https://github.com/vivarium-collective/vivarium-core, and has been released on Python Package Index. The Vivarium Collective (https://vivarium-collective.github.io) is a repository of freely available Vivarium processes and composites, including the processes used in Section 3. Supplementary Materials provide with an extensive methodology section, with several code listings that demonstrate the basic interfaces.

   Supplementary data are available at Bioinformatics online.

    

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5. GPU-I-TASSER: a GPU accelerated I-TASSER protein structure prediction tool

   https://doi.org/10.1093/bioinformatics/btab871  

   MacCarthy, Elijah A. ; Zhang, Chengxin ; Zhang, Yang ; KC, Dukka B. ; Cowen, ed., Lenore ( January 2022 , Bioinformatics)

   Accurate and efficient predictions of protein structures play an important role in understanding their functions. Iterative Threading Assembly Refinement (I-TASSER) is one of the most successful and widely used protein structure prediction methods in the recent community-wide CASP experiments. Yet, the computational efficiency of I-TASSER is one of the limiting factors that prevent its application for large-scale structure modeling.

   We present I-TASSER for Graphics Processing Units (GPU-I-TASSER), a GPU accelerated I-TASSER protein structure prediction tool for fast and accurate protein structure prediction. Our implementation is based on OpenACC parallelization of the replica-exchange Monte Carlo simulations to enhance the speed of I-TASSER by extending its capabilities to the GPU architecture. On a benchmark dataset of 71 protein structures, GPU-I-TASSER achieves on average a 10× speedup with comparable structure prediction accuracy compared to the CPU version of the I-TASSER.

   The complete source code for GPU-I-TASSER can be downloaded and used without restriction from https://zhanggroup.org/GPU-I-TASSER/.

   Supplementary data are available at Bioinformatics online.

    

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