Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists” that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.

In complex systems, the network of interactions we observe between system's components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.The development of tools for the analysis of real-world complex networks has significantly advanced our understanding of complex systems in fields as diverse as molecular and cell biology [1], neuroscience [2], biomedicine [3,4], ecology [5,6], economics [7], and sociology [8]. One of the main successes of the network approach has been to unravel the relationship between the modular organization of interactions within a complex system [9], and the function and temporal evolution of the system [10][11][12][13]. As a result, a large body of research has been devoted to the detection of the modular structure (or community structure) of complex networks, that is, to the division of the nodes of the network into densely connected subgroups [14].Stochastic block models (SBMs) [15][16][17] are a class of probabilistic generative network models that provide a more general description of the (mesoscopic) group structure of real-world networks than modular models. In SBMs, nodes are assumed to belong to groups and connect to each other with probabilities that depend only on their group memberships. The simple mathematical form of SBMs has enabled not only the identification of generalized community structures in networks [17][18][19][20][21][22][23][24][25][26], but also to make network inference a predictive tool to detect missing and spurious links in empirical network data [27], to predict human decisions [28,29] and the appearance of conflict in work teams [30], and for the identification of unknown interactions between drugs [31].While these approaches have pushed forward our understanding of complex network structure, a limitation is that they rely on the premise that there is a single mechanism that describes the connectivity of the network, even though we know that real-world networks are often the result of processes occurring on different "layers" (for example, social networks comprise relationships that arise in the familiar...

In addition to being a master regulator of cell cycle progression, E2F1 regulates other associated biological processes, including growth and malignancy. Here, we uncover a regulatory network linking E2F1 to lysosomal trafficking and mTORC1 signaling that involves v-ATPase regulation. By immunofluorescence and time-lapse microscopy we found that E2F1 induces the movement of lysosomes to the cell periphery, and that this process is essential for E2F1-induced mTORC1 activation and repression of autophagy. Gain- and loss-of-function experiments reveal that E2F1 regulates v-ATPase activity and inhibition of v-ATPase activity repressed E2F1-induced lysosomal trafficking and mTORC1 activation. Immunoprecipitation experiments demonstrate that E2F1 induces the recruitment of v-ATPase to lysosomal RagB GTPase, suggesting that E2F1 regulates v-ATPase activity by enhancing the association of V0 and V1 v-ATPase complex. Analysis of v-ATPase subunit expression identified B subunit of V0 complex, ATP6V0B, as a transcriptional target of E2F1. Importantly, ATP6V0B ectopic-expression increased v-ATPase and mTORC1 activity, consistent with ATP6V0B being responsible for mediating the effects of E2F1 on both responses. Our findings on lysosomal trafficking, mTORC1 activation and autophagy suppression suggest that pharmacological intervention at the level of v-ATPase may be an efficacious avenue for the treatment of metastatic processes in tumors overexpressing E2F1.

The objective assessment of the prestige of an academic institution is a difficult and hotly debated task. In the last few years, different types of university rankings have been proposed to quantify it, yet the debate on what rankings are exactly measuring is enduring.To address the issue we have measured a quantitative and reliable proxy of the academic reputation of a given institution and compared our findings with well-established impact indicators and academic rankings. Specifically, we study citation patterns among universities in five different Web of Science Subject Categories and use the PageRank algorithm on the five resulting citation networks. The rationale behind our work is that scientific citations are driven by the reputation of the reference so that the PageRank algorithm is expected to yield a rank which reflects the reputation of an academic institution in a specific field. Given the volume of the data analysed, our findings are statistically sound and less prone to bias, than, for instance, ad-hoc surveys often employed by ranking bodies in order to attain similar outcomes. The approach proposed in our paper may contribute to enhance ranking methodologies, by reconciling the qualitative evaluation of academic prestige with its quantitative measurements via publication impact.

BackgroundThe energetics of cerebral activity critically relies on the functional and metabolic interactions between neurons and astrocytes. Important open questions include the relation between neuronal versus astrocytic energy demand, glucose uptake and intercellular lactate transfer, as well as their dependence on the level of activity.ResultsWe have developed a large-scale, constraint-based network model of the metabolic partnership between astrocytes and glutamatergic neurons that allows for a quantitative appraisal of the extent to which stoichiometry alone drives the energetics of the system. We find that the velocity of the glutamate-glutamine cycle (Vcyc) explains part of the uncoupling between glucose and oxygen utilization at increasing Vcyc levels. Thus, we are able to characterize different activation states in terms of the tissue oxygen-glucose index (OGI). Calculations show that glucose is taken up and metabolized according to cellular energy requirements, and that partitioning of the sugar between different cell types is not significantly affected by Vcyc. Furthermore, both the direction and magnitude of the lactate shuttle between neurons and astrocytes turn out to depend on the relative cell glucose uptake while being roughly independent of Vcyc.ConclusionsThese findings suggest that, in absence of ad hoc activity-related constraints on neuronal and astrocytic metabolism, the glutamate-glutamine cycle does not control the relative energy demand of neurons and astrocytes, and hence their glucose uptake and lactate exchange.