Biology subjects

Gaucherel, C.

Publications and source records attributed to Gaucherel, C..

8 recordsLinked to original sources

bioRxiv2026

Questioning the G2 phase in the budding yeast cell cycle with a qualitative and possibilistic model

The budding yeast S. cerevisiae, a foundational model for cell cycle studies, exhibits a complex phase organisation (G1, S, G2/M) governed by checkpoints ensuring faithful cellular inheritance. However, the existence of a distinct G2 phase in yeast remains debated, with some advocating for a prometaphase instead. To address this issue, we developed a discrete-event, qualitative, and possibilistic model, the first one to our knowledge, to integrate organelle-level components (replication forks, sister chromatids, mitotic spindle, bud) while remaining parsimonious. Unlike molecular-centred or overly complex whole-cell models, this approach bridges broad systemic and finer mechanistic scales. Our results demonstrate that the model faithfully recapitulates cell cycle progression and supports the dispensable G2 phase. This possibilistic model inspired from recent applications in ecology advocates in favor of the necessity of prometaphase. This study thus provides a unifying and flexible framework to resolve long-standing ambiguities in yeast cell dynamics, while avoiding the pitfalls of excessive complexity or reductionism.

systems biology↗

bioRxiv2025

Biomes do not show a clear-cut phylogeny

Shifts between biomes, the broad types of ecosystems, are far less understood than other ecological transitions. In this study, we aimed at identifying a possible and preliminary lineage between terrestrial biomes on Earth by using up-to-date phylogenetic methods. Although a deep statistical power was lacking, we built on expert knowledge a double-entry table filled by a variety of traits characterizing all the 14 terrestrial biomes on Earth. As a central result, the biome phylogeny computed here is a clear approximation of the dominant biome successions observed on Earth. Latitudes (i.e., locations), not included into the trait table, may first explain the computed phylogeny. In particular, tropical biomes appeared well related and exhibited a possible common ancestor (higher than 50% confidence level). We discussed this possible history of terrestrial biome on Earth, along to the involved processes over the long term. This helped explaining how biomes differ from purely biological materials and partly why ecosystems are not evolving as life does.

ecology↗

bioRxiv2024

Inference of ecological networks and possibilistic dynamics based on Boolean networks from observations and prior knowledge

Being able to infer the interactions between a set of species from observations of the system is of paramount importance to obtain explaining and predictive models in ecology. We tackled this challenge by employing qualitative modeling frameworks and logic methods for the synthesis of mathematical models that can integrate both observations and expert knowledge on the system. After devising a formal link between ecological networks and the causal structure of Boolean networks, we applied a generic model synthesis engine to infer Boolean models that are able to reproduce the observed dynamics of a protist community. Our inference method supports optimization criteria to derive most parsimonious and most precise models. It is also able to integrate prior knowledge on the ecological network, adding constraints on impossible interactions, which is necessary to obtain realistic predictions. Such constraints may however prove to be too strict, in which case our method is able to conclude on the absence of a model compatible with both the observations and the input hypotheses. We demonstrated our methodology on experimental data of a protist system, and showed its ability to recover essential and sufficient ecological interactions to explain the observed dynamics.

ecology↗

bioRxiv2023

A single changing hypernetwork to represent (social-)ecological dynamics

AO_SCPLOWBSTRACTC_SCPLOWTo understand and manage (social-)ecological systems, we need an intuitive and rigorous way to represent them. Recent ecological studies propose to represent interaction networks into modular graphs, multiplexes and higher-order interactions. Along these lines, we argue here that non-dyadic (non-pairwise) interactions are common in ecology and environmental sciences, necessitating fresh concepts and tools for handling them. In addition, such interaction networks often change sharply, due to appearing and disappearing species and components. We illustrate in a simple example that any ecosystem can be represented by a single hypergraph, here called the ecosystem hypernetwork. Moreover, we highlight that any ecosystem hypernetwork exhibits a changing topology summarizing its long term dynamics (e.g., species extinction/invasion, pollutant or human arrival/migration). Qualitative and discrete-event models developed in computer science appear suitable for modeling hypergraph (topological) dynamics. Hypernetworks thus also provide a conceptual foundation for theoretical as well as more applied studies in ecology (at large), as they form the qualitative backbone of ever-changing ecosystems.

ecology↗

bioRxiv2023

Diagnosis of planktonic trophic network dynamics with sharp qualitative changes

AO_SCPLOWBSTRACTC_SCPLOWTrophic interaction networks are notoriously difficult to understand and to diagnose (i.e., to identify contrasted network functioning regimes). Such ecological networks have many direct and indirect connections between species, and these connections are not static but often vary over time. These topological changes, as opposed to a dynamic on a static (frozen) network, can be triggered by natural forcings (e.g., seasons) and/or by human influences (e.g., nutrient or pollution inputs). Aquatic trophic networks are especially dynamic and versatile, thus suggesting new approaches for identifying network structures and functioning in a comprehensive manner. In this study, a qualitative model was devised for this purpose. Applying discrete-event models from theoretical computer science, a mechanistic and qualitative model was developed that allowed computation of the exhaustive dynamics of a given trophic network and its environment. Once the model definition is assumed, it provides all possible trajectories of the network from a chosen initial state. In a rigorous and analytical approach, for the first time, we validated the model on one theoretical and two observed trajectories recorded at freshwater stations in the La Rochelle region (Western France). The model appears to be easy to build and intuitive, and it provides additional relevant trajectories to the expert community. We hope this formal approach will open a new avenue in identifying and predicting trophic (and non-trophic) ecological networks.

ecology↗

bioRxiv2021

Model-checking ecological state-transition graphs

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, from state-and-transition models to assembly graphs. Thus, model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology. Here we promote the adoption of the model-checking toolbox in ecology through its application to an illustrative example. We assessed the dynamics of a vegetation model inspired from state-and-transition models by model-checking Computation Tree Logic formulas built from a proposed catalogue of patterns. Model-checking encompasses a wide range of concepts and available software, mentioned in discussion, thus its implementation can be fitted to the specific features of the described system. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarifying and comparing them. Author summaryEcologists have drawn state-transition graphs representing the dynamics of ecosystems for more than a century. Model-checking is an automated method for the analysis of such graphs developed in computer science and acknowledged by a Turing award in 2007. Ecologists appear to be mostly unaware of model-checking despite its successes in systems biology to assess the dynamics of biological networks. We promote model-checking of ecological state-transition graphs through its application to an illustrative vegetation model. We exemplify the insights provided by model-checking by assessing management policies aiming to tackle savanna encroachment. We also provide a catalogue of patterns to help ecologists with the difficulty of formally expressing dynamical properties. We also discuss the wide range of model-checking concepts and available software, enabling to fit the specific features of the studied system, such as durations or probabilities. Model-checking can be applied to both empirical data and theoretical models, as long as they sum up into state-transition graphs. It provides automated and accurate answers to complex questions that could barely be analysed through human examination, if not impossible to answer this way. In addition to the automated analysis of ecological state-transition graphs, we believe that formally defining ecological concepts within the model-checking framework could help in clarifying and comparing them.

bioinformatics↗

bioRxiv2021

East-African savanna dynamics: from a knowledge-based model to the possible futures of a social-ecological system

Sub-Saharan savanna ecosystems are undergoing transitions such as bush encroachment, desertification or agricultural expansion. Such shifts and persistence of land cover are increasingly well understood, especially bush encroachment which is of major concern in pastoral systems. Although dominant factors can explain such transformations, they often result from intertwined causes in which human activities play a significant role. Therefore, in this latter case, these issues may require integrated solutions, involving many interacting components. Ecosystem modelling has proved appropriate to support decision-makers in such complex situations. However, ecosystem models often require lots of quantitative information for estimating parameters and the precise functional form of interactions is often unknown. Alternatively, in rangeland management, States-and-Transitions Models (STMs) have been developed to organize knowledge about system transitions and to help decision-makers. However, these conceptual diagrams often lack mathematical analyzing tools, which strongly constrains their complexity. In this paper, we introduce the Ecological Discrete-Event Network (EDEN) modelling approach for representing the qualitative dynamics of an East-African savanna as a set of discrete states and transitions generated from empirical rules. These rules are derived from local knowledge, field observations and scientific literature. In contrast with STMs, EDEN generates automatically every possible states and transitions, thus enabling the prediction of novel ecosystem structures. Our results show that the savanna is potentially resilient to the disturbances considered. Moreover, the model highlights all transitions between vegetation types and socio-economic profiles under various climatic scenarios. The model also suggests that wildlife diversity may increase socio-economic resistance to seasonal drought. Tree-grass coexistence and agropastoralism have the widest ranges of conditions of existence of all vegetation types and socio-economic profiles, respectively. As this is a preliminary use of EDEN for applied purpose, analysis tools should be improved to enable finer investigation of desirable trajectories. By translating local knowledge into ecosystem dynamics, the EDEN approach seems promising to build a new bridge between managers and modellers.

ecology↗

bioRxiv2019

Long term development of a realistic and integrated socio-ecological system

We developed a discrete and qualitative model of integrated socio-ecosystems, with the help of formal Petri nets. We illustrated such Petri nets in the case study of temporary marshes in the Mediterranean part of France, the Camargue delta, by integrating biotic, abiotic and human-related components along with their processes into the same interaction network. The model demonstrated that when marshes are exposed to extensive grazing the presence of marsh heritage species is facilitated by opening up the vegetation through various trajectories. This supports the commonly used management practice of extensive grazing to conserve certain protected habitats. With this Possibilistic approach, we identified all potential ecosystem trajectories and provided their differential (non-systematic) impacts on heritage species richness (number). Hence, we rigorously demonstrate with this new type of model that grazing benefits marsh species which are faced with competition from common grassland species. The detailed analysis of the explicit state space and trajectories allows exploring simultaneously the identification of a range of recommendations for management strategies.

ecology↗