Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators.

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.

5ß-reduced neuroactive steroids as modulators of growth and viability of postnatal neurons and glia.

Endogenous neurosteroids (NS) and their synthetic analogs, neuroactive steroids (NAS), are potentially useful drug-like compounds affecting the pathophysiology of miscellaneous central nervous system disorders (e.g. Alzheimer´s disease, epilepsy, depression, etc.). Additionally, NS have been shown to promote neuron viability and neurite outgrowth upon injury. The molecular, structural and physicochemical basis of the NS effect on neurons is so far not fully understood, and the development of new, biologically relevant assays is essential for their comparative analysis and for assessment of their mechanism of action. Here, we report the development of a novel, plate-based, high-content in vitro assay for screening of NS and newly synthesized, 5ß-reduced NAS for the promotion of postnatal neuron survival and neurite growth using fluorescent, postnatal mixed cortical neuron cultures isolated from thy1-YFP transgenic mice. The screen allows a detailed time course analysis of different parameters, such as the number of neurons or neurite lengths of 7-day, in vitro neuron cultures. Using the screen, we identify a new NAS, compound 42, that promotes the survival and growth of postnatal neurons significantly better than several endogenous NS (dehydroepiandrosterone, progesterone, and allopregnanolone). Interestingly, we demonstrate that compound 42 also promotes the proliferation of glia (in particular oligodendrocytes) and that the glial function is critical for its neuron growth support. Computational analysis of the biological data and calculated physicochemical properties of tested NS and NAS demonstrated that their biological activity is proportional to their lipophilicity. Together, the screen proves useful for the selection of neuron-active NAS and the comparative evaluation of their biologically relevant structural and physicochemical features.

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models.

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

COBREXA.jl: constraint-based reconstruction and exascale analysis.

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.

GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets.

BACKGROUND: The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. RESULTS: We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. CONCLUSIONS: GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.

ShinySOM: graphical SOM-based analysis of single-cell cytometry data.

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.

Interoperable chemical structure search service.

MOTIVATION: The existing connections between large databases of chemicals, proteins, metabolites and assays offer valuable resources for research in fields ranging from drug design to metabolomics. Transparent search across multiple databases provides a way to efficiently utilize these resources. To simplify such searches, many databases have adopted semantic technologies that allow interoperable querying of the datasets using SPARQL query language. However, the interoperable interfaces of the chemical databases still lack the functionality of structure-driven chemical search, which is a fundamental method of data discovery in the chemical search space. RESULTS: We present a SPARQL service that augments existing semantic services by making interoperable substructure and similarity searches in small-molecule databases possible. The service thus offers new possibilities for querying interoperable databases, and simplifies writing of heterogeneous queries that include chemical-structure search terms. AVAILABILITY: The service is freely available and accessible using a standard SPARQL endpoint interface. The service documentation and user-oriented demonstration interfaces that allow quick explorative querying of datasets are available at https://idsm.elixir-czech.cz .

Generalized EmbedSOM on quadtree-structured self-organizing maps.

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-based embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.

Widespread evolutionary crosstalk among protein domains in the context of multi-domain proteins.

Domains are distinct units within proteins that typically can fold independently into recognizable three-dimensional structures to facilitate their functions. The structural and functional independence of protein domains is reflected by their apparent modularity in the context of multi-domain proteins. In this work, we examined the coupling of evolution of domain sequences co-occurring within multi-domain proteins to see if it proceeds independently, or in a coordinated manner. We used continuous information theory measures to assess the extent of correlated mutations among domains in multi-domain proteins from organisms across the tree of life. In all multi-domain architectures we examined, domains co-occurring within protein sequences had to some degree undergone concerted evolution. This finding challenges the notion of complete modularity and independence of protein domains, providing new perspective on the evolution of protein sequence and function.

Sachem: a chemical cartridge for high-performance substructure search.

BACKGROUND: Structure search is one of the valuable capabilities of small-molecule databases. Fingerprint-based screening methods are usually employed to enhance the search performance by reducing the number of calls to the verification procedure. In substructure search, fingerprints are designed to capture important structural aspects of the molecule to aid the decision about whether the molecule contains a given substructure. Currently available cartridges typically provide acceptable search performance for processing user queries, but do not scale satisfactorily with dataset size. RESULTS: We present Sachem, a new open-source chemical cartridge that implements two substructure search methods: The first is a performance-oriented reimplementation of substructure indexing based on the OrChem fingerprint, and the second is a novel method that employs newly designed fingerprints stored in inverted indices. We assessed the performance of both methods on small, medium, and large datasets containing 1, 10, and 94 million compounds, respectively. Comparison of Sachem with other freely available cartridges revealed improvements in overall performance, scaling potential and screen-out efficiency. CONCLUSIONS: The Sachem cartridge allows efficient substructure searches in databases of all sizes. The sublinear performance scaling of the second method and the ability to efficiently query large amounts of pre-extracted information may together open the door to new applications for substructure searches.