Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

Neutrophils exhibit remarkable phenotypic and functional diversity across tissues and diseases^1,2, yet the lack of understanding of how this immune compartment is globally organized challenges translation to the clinic. Here we performed single-cell transcriptional profiling of neutrophils spanning 47 anatomical, physiological and pathological scenarios to generate an integrated map of the global neutrophil compartment in mice, which we refer to as NeuMap. NeuMap integrates and expands existing models^3,4 to generate fundamental new insights; it reveals that neutrophils organize in a finite number of functional hubs that distribute sequentially during maturation to then branch out into interferon-responsive and immunosuppressive states, as well as a functionally silent state that dominates in the healthy circulation. Computational modelling and timestamp analyses identify prototypical trajectories that connect these hubs, and reveal that the dynamics and preferred paths vary during health, inflammation and cancer. We show that TGFβ, IFNβ and GM-CSF push neutrophils along the different trajectories, and projection of chromatin accessibility sites onto NeuMap reveals that the transcription factor JUNB controls angiogenic and immunosuppressive states and promotes tissue revascularization. The architecture of NeuMap appears to be conserved across sex, environmental and genetic backgrounds, as well as in humans. Finally, we show that NeuMap enables inference of the pathophysiological state of the host by profiling blood neutrophils. Our study delineates the global architecture of the neutrophil compartment and establishes a framework for exploration and exploitation of neutrophil biology.

This paper presents some results devoted to providing network analysis functionalities for vulnerability assessment in public transportation networks with respect to disruptive events and/or targeted attacks to stations. The results have been obtained on two public transportation networks: the bus network in Florence, Italy, and the transportation network in the Attika region, Greece. The analysis implements a topological approach, based on graph theory, using a multi-graph to model public transportation networks and analyse vulnerabilities with respect to the removal of one or more of their components. Both directed attacks and cascading failures are considered. While the first type of disruptive events is related to a static analysis, where nodes are removed according to a rank related to some centrality measures, the second type is related to a dynamic analysis, where a failure cascade is simulated making unavailable the node with the highest betweenness value. Vulnerability measures are computed as loss of connectivity and efficiency, with respect to both the two different types of disruptive events considered. This study allows to evidence potential vulnerabilities of the urban networks, that must be considered to support the planning process into the creation of resilient structures.

Recommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search.

Background: Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. Methods: We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. Results: We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. Conclusions: Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.