MOTIVATION: Sensitivity analysis is a useful tool to identify key parameters in metabolic models. It is typically only applied to the growth rate, disregarding the sensitivity of other solution variables to parameters. Further, sensitivity analysis of elementary flux modes could provide low-dimensional insights into optimal solutions, but they are not defined when a model is subject to inhomogeneous flux constraints, such as the frequently used ATP maintenance reaction. RESULTS: We introduce optimal flux modes (OFMs), an analogue to elementary flux modes (EFMs), but specifically applied to optimal solutions of constraint-based models. Further, we prove that implicit differentiation can always be used to efficiently calculate the sensitivities of both whole-model solutions and OFM-based solutions to model parameters. This allows for fine-grained sensitivity analysis of the optimal solution, and investigation of how these parameters exert control on the optimal composition of OFMs. This novel framework is implemented in DifferentiableMetabolism.jl, a software package designed to efficiently differentiate solutions of constraint-based models. To demonstrate scalability, we differentiate solutions of 342 yeast models; additionally we show that sensitivities of specific subsystems can guide metabolic engineering. Applying our scheme to an Escherichia coli model, we find that OFM sensitivities predict the effect of knockout experiments on waste product accumulation. Sensitivity analysis of OFMs also provides key insights into metabolic changes resulting from parameter perturbations. AVAILABILITY AND IMPLEMENTATION: Software introduced here is available as open-source Julia packages DifferentiableMetabolism.jl (https://github.com/stelmo/DifferentiableMetabolism.jl) and ElementaryFluxModes.jl (https://github.com/HettieC/ElementaryFluxModes.jl), which both work on all major operating systems and computer architectures. Code to reproduce all results is available from https://github.com/HettieC/DifferentiableOFMPaper, and as an archive from https://doi.org/10.5281/zenodo.15183208.

SUMMARY: Constraint-based metabolic models offer a scalable framework to investigate biological systems using optimality principles. Construction and simulation of detailed models that utilize multiple kinds of constraint systems pose a significant coding overhead, complicating implementation of new types of analyses. We present an improved version of the constraint-based metabolic modeling package COBREXA, which utilizes a hierarchical model construction framework that decouples the implemented analysis algorithms into independent, yet re-combinable, building blocks. By removing the need to re-implement modeling components, assembly of complex metabolic models is simplified, which we demonstrate on use-cases of resource-balanced models, and enzyme-constrained flux balance models of interacting bacterial communities. Notably, these models show improved predictive capabilities in both monoculture and community settings. In perspective, the re-usable model-building components in COBREXA 2 provide a sustainable way to handle increasingly complex models in constraint-based modeling. AVAILABILITY AND IMPLEMENTATION: COBREXA 2 is available from https://github.com/COBREXA/COBREXA.jl, and from Julia package repositories. COBREXA 2 works on all major operating systems and computer architectures. Documentation is available at https://cobrexa.github.io/COBREXA.jl/.

BACKGROUND: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. RESULTS: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. CONCLUSION: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

SUMMARY: COBREXA.jl is a Julia package for scalable, high-performance constraint-based reconstruction and analysis of very large-scale biological models. Its primary purpose is to facilitate the integration of modern high performance computing environments with the processing and analysis of large-scale metabolic models of challenging complexity. We report the architecture of the package, and demonstrate how the design promotes analysis scalability on several use-cases with multi-organism community models. AVAILABILITY AND IMPLEMENTATION: https://doi.org/10.17881/ZKCR-BT30. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

K-means is one of the most frequently used algorithms for unsupervised clustering data analysis. Individual steps of the k-means algorithm include nearest neighbor finding, efficient distance computation, and cluster-wise reduction, which may be generalized to many other purposes in data analysis, visualization, and machine learning. Efficiency of the available implementations of k-means computation steps therefore directly affect many other applications. In this work, we examine the performance limits in the context of modern massively parallel GPU accelerators. Despite the existence of many published papers on this topic, we have found that crucial performance aspects of the GPU implementations remain unaddressed, including the optimizations for memory bandwidth, cache limits, and workload dispatching on problem instances of varying cluster count, dataset size, and dimensionality. We present a detailed analysis of individual computation steps and propose several optimizations that improve the overall performance on contemporary GPU architectures. Our open-source prototype exhibits significant speedup over the current state-of-the-art implementations in virtually all practical scenarios.

In the last decade, the Video Browser Showdown (VBS) became a comparative platform for various interactive video search tools competing in selected video retrieval tasks. However, the participation of new teams with an own, novel tool is prohibitively time-demanding because of the large number and complexity of components required for constructing a video search system from scratch. To partially alleviate this difficulty, we provide an open-source version of the lightweight known-item search system SOMHunter that competed successfully at VBS 2020. The system combines several features for text-based search initialization and browsing of large result sets; in particular a variant of W2VV++ model for text search, temporal queries for targeting sequences of frames, several types of displays including the eponymous self-organizing map view, and a feedback-based approach for maintaining the relevance scores inspired by PICHunter. The minimalistic, easily extensible implementation of SOMHunter should serve as a solid basis for constructing new search systems, thus facilitating easier exploration of new video retrieval ideas.

SUMMARY: ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for the batch processing of large sample volumes. AVAILABILITY AND IMPLEMENTATION: ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.