1,720,990 research outputs found
Relationship between fitness and heterogeneity in exponentially growing microbial populations
Full text linkDespite major environmental and genetic differences, microbial metabolic
networks are known to generate consistent physiological outcomes across vastly
different organisms. This remarkable robustness suggests that, at least in
bacteria, metabolic activity may be guided by universal principles. The
constrained optimization of evolutionarily-motivated objective functions like
the growth rate has emerged as the key theoretical assumption for the study of
bacterial metabolism. While conceptually and practically useful in many
situations, the idea that certain functions are optimized is hard to validate
in data. Moreover, it is not always clear how optimality can be reconciled with
the high degree of single-cell variability observed in experiments within
microbial populations. To shed light on these issues, we develop an inverse
modeling framework that connects the fitness of a population of cells
(represented by the mean single-cell growth rate) to the underlying metabolic
variability through the Maximum-Entropy inference of the distribution of
metabolic phenotypes from data. While no clear objective function emerges, we
find that, as the medium gets richer, the fitness and inferred variability for
Escherichia coli populations follow and slowly approach the theoretically
optimal bound defined by minimal reduction of variability at given fitness.
These results suggest that bacterial metabolism may be crucially shaped by a
population-level trade-off between growth and heterogeneity.Comment: 12+30 pages (includes Supporting Text
Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle
Full text linkCancer cells utilize large amounts of ATP to sustain growth, relying primarily on non-oxidative,
fermentative pathways for its production. In many types of cancers this leads, even in the presence
of oxygen, to the secretion of carbon equivalents (usually in the form of lactate) in the cell’s
surroundings, a feature known as the Warburg effect. While the molecular basis of this phenomenon
are still to be elucidated, it is clear that the spilling of energy resources contributes to creating a
peculiar microenvironment for tumors, possibly characterized by a degree of toxicity. This suggests
that mechanisms for recycling the fermentation products (e.g. a lactate shuttle) may be active,
effectively inducing a mutually beneficial metabolic coupling between aberrant and non-aberrant
cells. Here we analyze this scenario through a large-scale in silico metabolic model of interacting
human cells. By going beyond the cell-autonomous description, we show that elementary physico-
chemical constraints indeed favor the establishment of such a coupling under very broad conditions.
The characterization we obtained by tuning the aberrant cell’s demand for ATP, amino-acids and
fatty acids and/or the imbalance in nutrient partitioning provides quantitative support to the idea
that synergistic multi-cell effects play a central role in cancer sustainmen
Statistical modeling of adaptive neural networks explains co-existence of avalanches and oscillations in resting human brain
No full textNeurons in the brain are wired into adaptive networks that exhibit collective dynamics as diverse as scale-specific oscillations and scale-free neuronal avalanches. Although existing models account for oscillations and avalanches separately, they typically do not explain both phenomena, are too complex to analyze analytically or intractable to infer from data rigorously. Here we propose a feedback-driven Ising-like class of neural networks that captures avalanches and oscillations simultaneously and quantitatively. In the simplest yet fully microscopic model version, we can analytically compute the phase diagram and make direct contact with human brain resting-state activity recordings via tractable inference of the model’s two essential parameters. The inferred model quantitatively captures the dynamics over a broad range of scales, from single sensor oscillations to collective behaviors of extreme events and neuronal avalanches. Importantly, the inferred parameters indicate that the co-existence of scale-specific (oscillations) and scale-free (avalanches) dynamics occurs close to a non-equilibrium critical point at the onset of self-sustained oscillations.This research was funded in whole, or in part, by the Austrian Science Fund (FWF) (grant no. PT1013M03318 to F.L. and no. P34015 to G.T.).The study was supported by the European Union Horizon 2020 research and innovation program under the Marie Sklodowska-Curie action (grant agreement No. 754411 to F.L.).Peer reviewe
Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding
Full text linkThe uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA), the second on linear programming (LP) and finally we employ the Lovazs ellipsoid method (LEM). Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Cross-feeding percolation phase transitions of intercellular metabolic networks
Full text linkIntercellular cross-talk is essential for the adaptation capabilities of populations of cells. While direct diffusion-driven cell-to-cell exchanges are difficult to map, current nanotechnology enables one to probe single-cell exchanges with the medium. We introduce a mathematical method to reconstruct the dynamic unfolding of intercellular exchange networks from these data, applying it to an experimental coculture system. The exchange network, initially dense, progressively fragments into small disconnected clusters. To explain these dynamics, we develop a maximum-entropy multicellular metabolic model with diffusion-driven exchanges. The model predicts a transition from a dense network to a sparse one as nutrient consumption shifts. We characterize this crossover both numerically, revealing a power-law decay in the cluster-size distribution, and analytically, by connecting to percolation theory. Comparison with data suggests that populations evolve toward the sparse phase by remaining near the crossover. These findings offer insights into the collective organization driving the adaptive dynamics of cell populations.L.C.F.L. acknowledges partial funding from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). A.D.M. is supported by the European REA, Marie Skłodowska-Curie Actions, grant agreement no. 101131463 (SIMBAD). D.D.M. acknowledges financial support from grants PIBA_2024_1_0016 (Basque Government) and Project PID2023-146408NB-I00 funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE.Peer reviewe
- …
