Biology subjects

Maritan, A.

Publications and source records attributed to Maritan, A..

At least 19 recordsLinked to original sources

bioRxiv2025

A minimal model of Norway spruce - bark beetle outbreak dynamics

Bark beetles pose a growing threat to coniferous forests worldwide, with outbreaks increasingly linked to environmental stress and host vulnerability. Most mechanistic models focus on equilibrium states, neglecting the dynamical nature of outbreaks triggered by environmental perturbations. Using Ips typographus as focal species, we present a minimal model capturing key processes such as threshold-driven mass attacks, with parameters adaptable to different environmental conditions. Under current conditions, the model predicts a stable endemic state that is reactive, meaning environmental shocks trigger isolated, transient outbreaks. Under altered conditions, reflecting potential climate change scenarios, the system undergoes a regime shift, producing recurrent outbreaks that arise spontaneously from insect-host feedbacks. Finally, we examine repeated environmental disturbances and forest management, showing that the effectiveness of outbreak mitigation depends on the interplay between management frequency and intensity. Overall, we provide a mechanistic framework for understanding outbreak dynamics and guiding management strategies under changing environmental conditions.

ecology↗

bioRxiv2025

Unraveling the temporal dependence of ecological interaction measures

Species interactions--ranging from direct predator-prey relationships to indirect effects mediated by the environment--are central to ecosystem balance and biodiversity. While empirical methods for measuring these interactions exist, their interpretability and limitations remain unclear. Here we examine the empirical matrix of pairwise interactions, a widely used tool, and analyze its temporal variability. We show that apparent fluctuations in interaction strength--and even shifts in interaction signs, often interpreted as transitions between competition and facilitation--can arise intrinsically from population dynamics with fixed ecological roles. Experimental protocols further shape these estimates: the duration of observation and the type of setup in microbial growth studies (e.g., chemostats, batch cultures, or resource conditions) systematically affect measured interactions. Considering interactions across timescales enhances interpretability: short-term measurements primarily capture direct species couplings, whereas long-term observations increasingly reflect indirect community feedback. Taken together, these results establish short-duration inferences, obtained either directly or extrapolated, as a principled way to disentangle direct from indirect interactions. Building on this insight, we propose a model-inference approach that leverages multiple short time series rather than extended longitudinal datasets.

ecology↗

bioRxiv2024

Towards universal models for collective interactions in biomolecular condensates

A wide-range of higher-order structures, including dense, liquid-like assemblies, serve as key components of cellular matter. The molecular language of how protein sequences encode the formation and biophysical properties of biomolecular condensates, however, is not completely understood. Recent notion on the scale invariance of the cluster sizes below the critical concentration for phase separation suggests a universal mechanism, which can operate from oligomers to non-stoichiometric assemblies. Here we propose a model for collective interactions in condensates, based on context-dependent, variable interactions. We provide the mathematical formalism, which is capable describing growing dynamic clusters as well as changes in their material properties. Furthermore, we discuss the consequences of the model to maximise sensitivity to the environmental signals and to increase correlation lengths.

systems biology↗

bioRxiv2024

Modelling post-disturbance empirical patterns in a forest ecosystem

We investigate the impact of disturbances on forest ecosystems by examining transient population dynamics in a controlled experiment carried out in a tropical rainforest. We first model the mean species abundance with a simple consumer-resource model, which is then extended into two multi-species frameworks for community dynamics: a neutral model, which emphasises the ability of any species to recover independently of the others; and a non-neutral one, where species interactions play an important role in reconstructing ecosystems structure and patterns. The results indicate that, while both frameworks accurately describe correlation functions and mean-variance relations, the non-neutral model more effectively captures community structure as revealed by an evenness indicator. This suggests that interspecific interactions significantly influence the ecosystems ability to respond to disturbances, providing deeper insights into the recovery dynamics of forest ecosystems.

ecology↗

bioRxiv2024

A Tale of Two Chains: Geometries of a Chain Model and Protein Native State Structures

Linear chain molecules play a central role in polymer physics with innumerable industrial applications. They are also ubiquitous constituents of living cells. Here we highlight the similarities and differences between two distinct ways of viewing a linear chain. We do this, on the one hand, through the lens of simulations for a standard polymer chain of tethered spheres at low and high temperatures and, on the other hand, through published experimental data on an important class of biopolymers, proteins. We present detailed analyses of their local and non-local structures as well as the maps of their closest contacts. We seek to reconcile the startlingly different behaviors of the two types of chains based on symmetry considerations.

biophysics↗

bioRxiv2023

III. Geometrical framework for thinking about globular proteins: turns in proteins

We have shown recently that the notion of poking pairwise interactions along a chain provides a unifying framework for understanding the formation of both secondary and the tertiary protein structure based on symmetry and geometry. -helices and {beta}-sheets are found to be special geometries that have systematic poking contacts in a repetitive manner with the contacts being local along the -helix and non-local along a pair of adjacent strands within a {beta}-sheet. Pairwise poking interactions also govern tertiary structure formation, but they are weaker and there are no special geometrical constraints as in secondary structure formation. Here we demonstrate that protein turns, the most prevalent non-repetitive structural element in proteins, are instances of local (as in -helices) and isolated (non-repetitive) poking pairwise contacts for which the geometrical constraints are partially relaxed. This simple and purely geometrical definition of protein turns (also sometimes known as reverse turns, {beta}-turns, {beta}-bends, hairpin bends, 310 bends, kinks, widgets, ...) provides a simple framework for unifying them. We present the results of a systematic analysis and identify their structural classes as well as their respective amino acid preferences. Statement for broader audiencePoking interaction along a discrete chain signals the affinity of one part of the chain with another. It has been shown that a simple and purely geometrical model based on poking interactions is able to capture the essential features of complex protein chains, especially their building blocks and their assembly. We demonstrate that poking interactions also provide a simple framework to describe protein turns as isolated (not coordinated) local poking interactions. This framework allows one to comprehend the structural variety of protein turns that have been extensively studied in the past.

biophysics↗

bioRxiv2023

Amino acid characteristics in protein native state structures

We present a geometrical analysis of the protrusion statistics of side chains in more than 4,000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of C atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds.

biophysics↗

bioRxiv2023

Emergent Ecological Patterns and Modelling of Gut Microbiomes in Health and in Disease

Recent advancements in next-generation sequencing have revolutionized our understanding of the human microbiome. Despite this progress, challenges persist in comprehending the microbiomes influence on disease, hindered by technical complexities in species classification, abundance estimation, and data compositionality. At the same time, recently the existence of macroecological laws describing the variation and diversity in microbial communities irrespective of their environment has been proposed using 16s data and explained by a simple phenomenological model of population dynamics. We here investigate the relationship between dysbiosis, i.e. in unhealthy individuals there are deviations from the "regular" composition of the gut microbial community, and the existence of macro-ecological emergent law in microbial communities. We first quantitatively reconstruct these patterns at the species level using shotgun data, offering a more biologically interpretable approach, and addressing the consequences of sampling effects and false positives on ecological patterns. We then ask if such patterns can discriminate between healthy and unhealthy cohorts. Concomitantly, we evaluate the efficacy of different population models, which incorporate sampling and different ecological and statistical principles (e.g., the Taylors law and environmental noise) to describe such patterns. A critical aspect of our analysis is understanding the relationship between model parameters, which have clear ecological interpretations, and the state of the gut microbiome, thereby enabling the generation of synthetic compositional data that distinctively represent healthy and unhealthy individuals. Our approach, grounded in theoretical ecology and statistical physics, allows for a robust comparison of these models with empirical data, enhancing our understanding of the strengths and limitations of simple microbial models of population dynamics. Author summaryIn this study, we explore emerging ecological properties in gut microbiomes. Our aim here is to determine whether these patterns can be informative of the gut microbiome (healthy or diseased) and unveil essential ingredients driving its population dynamics. Leveraging on phenomenological models of species abundance fluctuations and metagenomics data, we highlight the pivotal role of Taylors law, a straightforward mathematical relation, in constructing theoretical models for the human gut microbiome. We thus explore such a general theoretical framework for investigating microbiome composition and show that not all ecological patterns are informative to characterize its states, while few are (e.g., species diversity). Eventually, thanks to the ecological interpretability of the inferred models parameters, our analysis provides insights into the role of environmental fluctuations and carrying capacities of the gut microbiomes in both health and disease. This study offers valuable knowledge, bridging theoretical concepts with practical implications for human health.

ecology↗

bioRxiv2023

Sperm length evolution in relation to body mass is shaped by multiple trade-offs in tetrapods

Sperm size is highly variable across species and is influenced by various factors including fertilization mode, female reproductive traits and sperm competition. Despite considerable efforts, many questions about sperm size variation remain open. Variation in body size may affect sperm size evolution through its influence on these factors, but the extent to which sperm size variation is linked to body mass remains elusive. In this study, we use the general theory of Pareto Optimality to investigate the relationship between sperm size and body mass across tetrapods. We find that tetrapods fall within a triangular-shaped Pareto front in the trait space of body mass and sperm length suggesting that the evolution of sperm size in relation to body size is shaped by trade-offs. We then explore the three main factors predicted to influence sperm size evolution, namely sperm competition, clutch size and genome size. Our results demonstrate that body mass optimally shapes sperm size evolution in tetrapods mainly through its association with sperm competition and clutch size. Finally, we show that the triangular-shaped Pareto front is maintained when tested separately within mammals, birds, endothermic species and internal fertilizers, suggesting that similar evolutionary trade-offs characterize the evolution of sperm size in relation to body size within taxonomic/phylogenetic and functional subgroups of tetrapods. This study provides insights into the evolutionary mechanisms driving interspecific sperm size variation and highlights the importance of considering multiple trade-offs in optimizing reproductive traits.

evolutionary biology↗

bioRxiv2023

A geometrical framework for thinking about proteins

We present a model, based on symmetry and geometry, for proteins. Using elementary ideas from mathematics and physics, we derive the geometries of discrete helices and sheets. We postulate a compatible solvent-mediated emergent pairwise attraction that assembles these building blocks, while respecting their individual symmetries. Instead of seeking to mimic the complexity of proteins, we look for a simple abstraction of reality that yet captures the essence of proteins. We employ analytic calculations and detailed Monte Carlo simulations to explore some consequences of our theory. The predictions of our approach are in accord with experimental data. Our framework provides a rationalization for understanding the common characteristics of proteins. Our results show that the free energy landscape of a globular protein is pre-sculpted at the backbone level, sequences and functionalities evolve in the fixed backdrop of the folds determined by geometry and symmetry, and that protein structures are unique in being simultaneously characterized by stability, diversity, and sensitivity. Statement for broader audienceWe present a simple geometrical model of a chain, which captures the essential features of globular proteins, and explore its consequences. Our model marries the ideas of Kepler, of objects touching each other, and Pauling, of hydrogen bonds providing scaffolding for helices and sheets. We suggest a poking potential for a chain, whose deployment yields the correct structures of both helices and sheets, while promoting the assembly of the building blocks into the tertiary structure.

biophysics↗

bioRxiv2023

II. Geometrical framework for thinking about globular proteins: the power of poking

Recently, we presented a framework for understanding protein structure based on the idea that simple constructs of holding hands or touching of objects can be used to rationalize the common characteristics of globular proteins. We developed a consistent approach for understanding the formation of the two key common building blocks of helices and sheets as well as the compatible assembly of secondary structures into the tertiary structure through the notion of poking pairwise interactions. Here we benchmark our predictions with a detailed analysis of structural data of over 4000 proteins from the Protein Data Bank. We also present the results of detailed computer simulations of a simplified model demonstrating a pre-sculpted free energy landscape, determined by geometry and symmetry, comprising numerous minima corresponding to putative native state structures. We explore the consequences of our model. Our results suggest that symmetry and geometry are a powerful guide to capture the simplicity underlying protein complexity. Statement for broader audienceA poking interaction in a chain causes two parts of a chain to poke towards each other. We use empirical data to establish a two-way link between geometry and chemistry through poking interactions and hydrogen bonds. We show that our simple and tractable geometrical model, superficially unrelated to proteins, can capture the essential features of a rich and incredibly complex protein molecule.

biophysics↗

bioRxiv2023

A scale-invariant log-normal droplet size distribution below the critical concentration for protein phase separation

Many proteins have been recently shown to undergo a process of phase separation that leads to the formation of biomolecular condensates. Intriguingly, it has been observed that some of these proteins form dense droplets of sizeable dimensions already below the critical concentration, which is the concentration at which phase separation occurs. To understand this phenomenon, which is not readily compatible with classical nucleation theory, we investigated the properties of the droplet size distributions as a function of protein concentration. We found that these distributions can be described by a scale-invariant log-normal function with an average that increases progressively as the concentration approaches the critical concentration from below. The results of this scaling analysis suggest the existence of a universal behaviour independent of the sequences and structures of the proteins undergoing phase separation. While we refrain from proposing a theoretical model here, we suggest that any model of protein phase separation should predict the scaling exponents that we reported here from the fitting of experimental measurements of droplet size distributions. Furthermore, based on these observations, we show that it is possible to use the scale invariance to estimate the critical concentration for protein phase separation.

biophysics↗

bioRxiv2021

Marginally compact phase and ordered ground states in a model polymer with side spheres

We present the results of a quantitative study of the phase behavior of a model polymer chain with side spheres using two independent computer simulation techniques. We find that the mere addition of side spheres results in key modifications of standard polymer behavior. One obtains a novel marginally compact phase at low temperatures, the structures in this phase are reduced in dimensionality and are ordered, they include strands assembled into sheets and a variety of helices, and at least one of the transitions on lowering the temperature to access these ordered states is found to be first order. Our model serves to partially bridge conventional polymer phases with biomolecular phases.

biophysics↗

bioRxiv2021

Spontaneous dimensional reduction and novel ground state degeneracy in a simple chain model

Chain molecules play a key role in the polymer field and in living cells. Our focus is on a new homopolymer model of a linear chain molecule subject to an attractive self-interaction promoting compactness. We analyze the model using simple analytic arguments complemented by extensive computer simulations. We find several striking results: there is a first order transition from a high temperature random coil phase to a highly unusual low temperature phase; the modular ground states exhibit significant degeneracy; the ground state structures exhibit spontaneous dimensional reduction and have a two-layer structure; and the ground states are assembled from secondary motifs of helices and strands connected by tight loops. We discuss the similarities and notable differences between the ground state structures (we call these PoSSuM - Planar Structures with Secondary Motifs) in the novel phase and protein native state structures.

biophysics↗

bioRxiv2020

Building blocks of protein structures - Physics meets Biology

The native state structures of globular proteins are stable and well-packed indicating that self-interactions are favored over protein-solvent interactions under folding conditions. We use this as a guiding principle to derive the geometry of the building blocks of protein structures - -helices and strands assembled into {beta}-sheets - with no adjustable parameters, no amino acid sequence information, and no chemistry. There is an almost perfect fit between the dictates of mathematics and physics and the rules of quantum chemistry. Our theory establishes an energy landscape that channels protein evolution by providing sequence-independent platforms for elaborating sequence-dependent functional diversity. Our work highlights the vital role of discreteness in life and has implications for the creation of artificial life and on the nature of life elsewhere in the cosmos.

biophysics↗

bioRxiv2020

Statistical Physics of DNA melting: Unveiling the artificial corrections for self-complementary sequences

DNA hybridization is at the heart of countless biological and biotechnological processes. Its theoretical modeling played a crucial role, since it has enabled extracting the relevant thermodynamic parameters from systematic measurements of DNA melting curves. However, in its current state, hybridization modelling requires introducing an extra entropic contribution in self-complementary sequences that lacks any biophysical meaning. In this article, we propose a framework based on statistical physics to describe DNA hybridization and melting in an arbitrary mixture of DNA strands. In particular, we are able to analytically derive closed expressions of the system partition functions for any number N of strings, and explicitly calculate them in two paradigmatic situations: (i) a system made of self-complementary sequences and (ii) a system comprising two mutually complementary sequences. We derive the melting curve in the thermodynamic limit (N [->] {infty}) of our description, which differs from the expression commonly used to evaluate the melting of self-complementary systems in that it does not require correcting terms. We provide a thorough study comprising limit cases and alternative approaches showing how our framework can give a comprehensive view of hybridization and melting phenomena. SIGNIFICANCEIn this study, we provide a transparent derivation of the melting curves of DNA duplexes using basic tools of statistical mechanics. We find that in the case of self-complementary sequences, our expression differs from the one used in literature, which is generally amended by the introduction of a phenomenological correction which in our approach becomes unnecessary. By offering a clean formal description of DNA hybridization, our approach sharpens our understanding of DNA interactions and opens the way to study the pairing of DNA oligomers away from any thermodynamic limit.

biophysics↗

bioRxiv2020

Proteome allocation influences population dynamics in microbial communities

Microbial communities are ubiquitous and play crucial roles in many natural processes. Despite their importance for the environment, industry and human health, there are still many aspects of microbial community dynamics that we do not understand quantitatively. Recent experiments have shown that the metabolism of species in a community is intertwined with its composition, suggesting that properties at the intracellular level such as the allocation of cellular proteomic resources must be taken into account when describing microbial communities with a population dynamics approach. In this work we reconsider one of the theoretical frameworks most commonly used to model population dynamics in competitive ecosystems, MacArthurs consumer-resource model, in light of experimental evidence showing how pro-teome allocation affects microbial growth. This new framework allows us to describe community dynamics at an intermediate level of complexity between classical consumer-resource models and biochemical models of microbial metabolism, accounting for temporally-varying proteome allocation subject to constraints on growth and protein synthesis in the presence of multiple resources, while preserving analytical insight into the dynamics of the system. We first show experimentally that proteome allocation needs to be accounted for to properly understand the dynamics of even the simplest microbial community, i.e. two bacterial strains competing for one common resource. We then study the model analytically and numerically to determine the conditions that allow multiple species to coexist in systems with arbitrary numbers of species and resources.

biophysics↗

bioRxiv2019

DNA energy constraints shape biological evolutionary trajectories

Most living systems rely on double-stranded DNA (dsDNA) to store their genetic information and perpetrate themselves. Thus, the biological information contained within a dsDNA molecule, in terms of a linear sequence of nucleotides, has been considered the main target of the evolution. However, in this information-centred perspective, certain DNA sequence symmetries are difficult to explain. Here we show that these patterns can emerge from the physical peculiarities of the dsDNA molecule itself and the maximum entropy principle alone, rather than from biological or environmental evolutionary pressure. Our predictions are valid for both prokaryotes and eukaryotes, and also inform the interpretation of observed codon biases and context-dependent mutation patterns in human populations. Our results suggest that the double helix energy constraints and, more generally, the physical properties of the dsDNA are the hard drivers of the overall DNA sequence architecture, whereas the biological selective processes act as soft drivers, which only under extraordinary circumstances overtake the overall entropy content of the genome.

molecular biology↗