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[hal-05694938] Galaxy-BioProd: An Integrated FAIR Platform for Synthetic Biology, Biotechnology and Life Cycle Analysis
The "Bioproductions (B-BEST): Biomass, Biotechnologies, and Sustainable Technologies for Chemistry and Fuels" research program is part of the national acceleration strategy "Bio-based Products and Industrial Biotechnologies - Sustainable Fuels" under the France 2030 plan. Co-led by INRAE and IFPEN, this program supports the transition from a petrochemical economy to bio-based products and services by aiming to better understand and utilise biomass to produce bio-based products and sustainable fuels. A secondary objective is to establish common practices for bio-refineries, which are currently not implemented at national level, thereby limiting the efficiency and reproducibility of developments. To address these issues, the Galaxy-BioProd project aims to develop a centralised portal providing standardised digital tools and resources to simplify, connect and accelerate industrial biotechnology projects. The tools and pipelines are developed by the members of the project. Based on the Galaxy environment and adhering to FAIR principles, the B-BEST Galaxy lab deployed on usegalaxy.fr offers an accessible and unified platform that can serve various communities in the fields of synthetic biology, biocatalysis and industrial biotechnology. Seventeen tools ranging from synthetic biology to Life Cycle Analysis are currently under development and will be deployed on B-BEST lab of usegalaxy.fr. Galaxy-BioProd project is part of the PEPR Bioproductions (B-BEST) program, supported by the France 2030 investment plan and co-led by INRAE and IFPEN [ANR-22-PEBB-0008].
ano.nymous@ccsd.cnrs.fr.invalid (Anthony Pragassam) 16 Jul 2026
https://hal.science/hal-05694938v1
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[hal-05693548] Lignocellulose-powered bioelectrochemical systems: microbial communities, controlling factors, and enhancement strategies
Lignocellulosic biomass (LCB) is the world’s most abundant renewable carbon source, yet its potential to drive a circular bioeconomy remains largely untapped. Microbial electrochemical technologies (METs) offer a promising route for converting this complex feedstock into electricity or valuable chemicals. However, LCB-MET advancement is hindered by a fundamental challenge: LCB recalcitrance necessitates depolymerization, a process mismatched with the metabolic capabilities of most electroactive microorganisms (EAMs). While EAMs excel at oxidizing simple substrates, most lack the hydrolytic machinery to break down LCB, creating a critical performance bottleneck. Addressing this requires a multi-disciplinary approach. At the heart of the biological challenge lie two core paradigms, each drawing on microorganisms sourced from nature or artificially engineered: (i) specialized strains capable of both hydrolytic and electrogenic functions, or (ii) synthetic consortia establishing division of labor between fermentative microbes and EAMs. These strategies do not operate in a vacuum; their performance is constrained by materials and environment. To evaluate the progress and potential of these interdependent biological, material, and engineering strategies, this review examines the landscape of LCB utilization in METs. It synthesizes recent advances in coupling depolymerization with extracellular electron transfer, critically evaluate microbial players from pure strains and mixed communities to genetically modified organisms and synthetic consortia, and assesses the key operational parameters, challenges, and potential solutions that define this field. Moving beyond, the review provides graphic representations and statistical analyses of recent publications to establish quantitative performance benchmarks.
ano.nymous@ccsd.cnrs.fr.invalid (Animut Assefa Molla) 15 Jul 2026
https://hal.inrae.fr/hal-05693548v1
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[hal-05692403] Caproate production from electro-fermentation of wine lees and waste activated sludge under applied potential
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ano.nymous@ccsd.cnrs.fr.invalid (Luisa Barbonaglia) 15 Jul 2026
https://hal.inrae.fr/hal-05692403v1
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[hal-05676003] Teindre durable sans se tromper de ton. Représentations des professionnels et consommateurs sur l'adoption de colorants biosourcés dans l'industrie de la mode
La couleur, critère clé dans le choix des vêtements, engendre une forte pollution liée aux teintures textiles, rendant indispensable une transition vers une mode plus éthique et durable (Lara et al., 2022; Niinimäki et al., 2020). Alors que les colorants synthétiques pétrosourcés dominent encore le marché, des alternatives biosourcées issues de biotechnologies émergent et peuvent s’inscrire dans une démarche circulaire en valorisant des biodéchets (Fried et al., 2022; Mazotto et al., 2021). Ces solutions innovantes offrent un potentiel environnemental et social important pour la mode, mais les perceptions des professionnels et consommateurs restent peu étudiées, tant concernant la teinture que les biotechnologies « vertes » (Mabuza et al., 2023). Cette recherche vise à comprendre comment les colorants biosourcés issus de biotechnologie peuvent s’intégrer dans la chaîne de valeur tout en conciliant les intérêts divergents des acteurs, des intermédiaires aux consommateurs. Elle mobilise deux cadres théoriques complémentaires : le modèle TOE pour analyser les déterminants de l’adoption par les professionnels (Tornatzky et Fleischer, 1990) et la théorie de la résistance à l’innovation (TRI) pour étudier les freins à l’acceptation chez les consommateurs (Ram et Sheth, 1989). Une enquête qualitative par entretiens semi-directifs a été réalisée auprès de professionnels de la filière (n=11) et de consommateurs ou influenceurs de mode (n=18), à partir de guides adaptés à leurs pratiques mais incluant un tronc commun sur la mode durable et les colorants biosourcés. Les entretiens, enregistrés et analysés thématiquement avec MAXQDA, ont permis d’identifier les bénéfices et obstacles perçus selon les cadres du modèle TOE pour les professionnels et de la théorie de la résistance à l’innovation (TRI) pour les consommateurs. Les professionnels perçoivent les colorants biosourcés comme un levier potentiel de différenciation et de communication, mais doutent de leurs réels bénéfices environnementaux et soulignent plusieurs obstacles techniques, économiques et structurels. Leur adoption semble conditionnée à la compatibilité avec les procédés existants et à des gains mesurables en efficacité (eau, énergie, coûts). Enfin, les contraintes réglementaires et la pression contradictoire des consommateurs entre durabilité et prix limitent encore leur déploiement. Les consommateurs accordent une grande importance à la couleur mais peu au type de colorant utilisé, exprimant globalement une confiance dans les procédés industriels et la réglementation. Si l’aspect circulaire des colorants biosourcés est perçu positivement, certaines réticences subsistent face à l’usage de biodéchets et à une technologie jugée compatible avec la surconsommation. Enfin, la valorisation de procédés traditionnels perçus comme plus “propres” peut constituer une barrière culturelle à l’adoption. Ces résultats impliquent que le développement de ces biocolorants doit répondre aux attentes environnementales des consommateurs et aux contraintes techniques et économiques des professionnels, la seule circularité ne suffisant pas sans impacts environnementaux positifs clairement identifiés. L’adoption de vêtements bio-colorés passerait surtout par des stratégies de marque, compte tenu du faible intérêt direct des consommateurs pour les colorants.
ano.nymous@ccsd.cnrs.fr.invalid (Arnaud Lamy) 01 Jul 2026
https://hal.science/hal-05676003v1
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[hal-05674613] Isobutanol Dehydration to Linear Butenes on Ferrierite: Mechanistic Insights from operando FTIR, Chemometrics and Kinetic Modelling
The selective dehydration of isobutanol to linear butenes catalyzed by acidic ferrierite (H-FER) has been investigated by operando IR spectroscopy under close batch conditions and along a temperature ramp. The employed apparatus permits to alternatively acquire spectra of both the gas phase composition and the species adsorbed on the H-FER surface, thus providing fundamental insights onto the products formation and the reaction intermediates formed on the catalyst surface. In the gas phase, the high selectivity for linear butenes was confirmed in the first phases of the reaction, followed by a slower isomerization to isobutene and by the formation of heavier compounds due to secondary processes. Detailed MCR-ALS analysis of the adsorbed species’ spectra permitted to also identify and quantify adsorbed 2-butanol and trans-2-butene on the H-FER surface, providing a first indication of the alcohol isomerization as a key step in the reaction mechanism. The proposed mechanism was evaluated by microkinetic modelling of both gas phase and surface concentration profiles, showing that the consecutive isomerization of isobutanol to adsorbed 2-butanol followed by its dehydration to trans-2-butene is more favorable than the one-step dehydration-isomerization of isobutanol. In contrast, the direct dehydration of isobutanol to isobutene was found to be slower over the entire investigated temperature range.
ano.nymous@ccsd.cnrs.fr.invalid (Eleonora Vottero) 30 Jun 2026
https://hal.science/hal-05674613v1
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[hal-05673913] Isobutanol dehydration catalyzed by bridging OH groups at the (100) surface of ferrierite: From static DFT to machine learning accelerated molecular dynamics
Ferrierite is industrially used as a catalyst for the dehydration of isobutanol into butenes and the critical part of this transformation is presumably catalyzed by Brønsted acid (BA) sites located on its external surface. In this work, we present a DFT investigation of a complete reaction network over the T1O3 BA site at the (100) surface of this zeolite. Exploration of reaction mechanisms by means of the static approach revealed that the transformation towards all products proceeds in two steps - dehydration and deprotonation - with the former step being rate determining for all competing reaction channels. Free energetics of the dehydration reaction was therefore investigated using ab initio molecular dynamics accelerated by machine-learned force fields with accuracy controlled via a correction scheme based on machine learning perturbation theory. The activation free energies for transformations towards branched and linear products computed using the PBE+D2 functional (111.3 and 125.7 kJ/mol, respectively) suggest a preference of the former reaction channel. This conclusion was found to be independent of the dispersion correction used with the PBE functional (no correction, D3, D3(BJ), and D4) and of the choice of bridging OH group, as shown for the alternative T3O1 and T4O7 sites. Interestingly, our results are in agreement with our previous investigation of analogous reactions catalyzed by bulk chabazite. At the same time, however, they do not reproduce the experimental selectivities observed for ferrierite, which suggests that the bridging OH groups are not at the origin of the specific selectivity of ferrierite for linear alkenes.
ano.nymous@ccsd.cnrs.fr.invalid (Katarína Skladanová) 30 Jun 2026
https://ifp.hal.science/hal-05673913v1
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[hal-05673026] Open data for publication : Isobutanol Dehydration to Linear Butenes on Ferrierite: Mechanistic Insights from operando FTIR, Chemometrics and Kinetic Modelling
Dataset associated with the analysis described in the paper "Isobutanol Dehydration to Linear Butenes on Ferrierite: Mechanistic Insights from operando FTIR, Chemometrics and Kinetic Modelling". The folder includes: raw spectra of the operando FTIR experiment under batch conditions reference spectra measured in situ the python script containing the MCR-ALS analysis of the spectra associated with the species adsorbed on H-FER the measured concentration profiles of all quantified species the python script containing the kinetic model fit calculations
ano.nymous@ccsd.cnrs.fr.invalid (Eleonora Vottero) 29 Jun 2026
https://hal.science/hal-05673026v1
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[hal-05673023] Open data for publication: Multivariate analysis coupled to infrared spectroscopy unravels the diversity of adsorption sites and strengths of a zeolite surface.
Raw IR spectra and scripts for their processing and MCR analysis
ano.nymous@ccsd.cnrs.fr.invalid (Reda Aboulayt) 29 Jun 2026
https://hal.science/hal-05673023v1
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[hal-05662647] A Nanocatalyst‐Based Approach to Sustainable HMF Oxidation
In the urgent shift toward fossil-free chemistry, 5-hydroxymethylfurfural (HMF) stands out as a pivotal bio-based platform molecule for producing high-value furanic derivatives, including 2,5-furandicarboxylic acid (FDCA), a key precursor for next-generation bio-plastics like polyethylene furanoate (PEF). However, conventional FDCA synthesis often relies on harsh conditions (high pressure, toxic oxidants, or complex catalytic systems), limiting scalability and/or sustainability. In this study, we demonstrated that colloidal Pd nanoparticles enable efficient HMF oxidation under mild conditions (atmospheric air, 80°C), achieving full HMF conversion with 80% FDCA selectivity, a competitive performance under milder conditions. Through detailed mechanistic investigations, we elucidated the catalytic cycle of Pd nanoparticles while demonstrating the critical role of the soluble base, which generates hydroxyl groups with water to drive the reaction. Our findings conclusively established that the base must be introduced stoichiometrically to achieve optimal performance. Colloidal Pd system overcomes the limitations of traditional heterogeneous catalysts by eliminating support interference, providing clear mechanistic insights. Its scalable and tunable nature allows for future immobilization to enhance recyclability and ensures a controlled oxidation reaction. These advantages, mild conditions, mechanistic insights, and tunability, position our system as a promising, sustainable alternative for FDCA production, paving the way for industrial high-value-added biobased chemicals production.
ano.nymous@ccsd.cnrs.fr.invalid (Lénaïck Hervé) 25 Jun 2026
https://hal.science/hal-05662647v1
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[hal-05667759] Production of biopolymers from denitrifying microorganisms in granular sequencing batch reactor
Granular sequencing batch reactors allows aggregation of microorganisms through granule shape thanks to high selection pressure [1]. These granules are aggregates of microorganisms embedded in a network of highly cohesive exopolymers (EPS). The cohesive property of these polymers called structural EPS (sEPS) led to develop extraction protocols [2] and understand their properties [3] which allows to develop a new pathway for matter recycling inside wastewater treatment field. We aim to increase our knowledge in this field by studying the role of specific populations in the production of sEPS. The case of PAOs having been studied recently [4]and we focus here on denitrifying populations. This study aims to link sEPS production and characteristics to GSBR operating conditions and sludge properties. Based on the work from a previous thesis, [5], a 17L GSBR was operated during 3 months with alternating anoxic/aerobic conditions and a 4 carbon sources synthetic effluent composed of volatile fatty acids (Acetate and propionate), glucose and peptones. Nitrates were added to ensure anoxic conditions during non-aerated phase. The reactor followed 3.5-hour operating cycles consisting of 25 minutes of anoxic feed, 20 minutes of additional anoxic phase under slow agitation, 155 minutes of aerobic phase under rapid agitation, 6.5 minutes of decantation and 3.5 minutes of purging. The Volume Exchange Ratio (VER) was 50%. Typical ratios encountered in urban wastewater were applied: COD/P and COD/NTK ratios of 48 and 8, respectively. After inoculating with an activated sludge, the denitrifying microorganisms were selected by injecting the substrate during the non-aerated phase, allowing maximum COD consumption by the denitrification reaction in the anoxic phase and only slow growth in the aerated phase. In order to assess the activity and proportion of denitrifying populations, COD and nitrate balances were carried out in the anoxic and aerobic phases. The properties of the sludge in terms of settling and treatment of carbon, nitrogen and phosphorus were monitored dynamically. The stability and robustness of EPS production was assessed by quantifying the dynamics of polymer extraction yields, as well as their structures and rheological qualities. This made it possible to relate the structure of the aggregates to the production of sEPS. Ultimately, we hope to compare the results of this study with those obtained from the selection of other dominant populations and pure cultures. References [1] E.Morgenroth, T.Sherden, M.C.M Van Loosdrecht, Water Research, volume 31 (1997), p3191-3194). [2] Y.Lin, M. De Kreuk, M.CM. Van Loosdrecht, Water research, volume 44 (2010), p3355-3364 [3] T.Seviour, B.Donose, M.Pijuan, Z.Yuan, Environmental Science & Technology, vol44 (2010) [4] L.B.Guimaraes, N.R Gubser, Y.Lin, preprint (2024) [5] A.Filali, A. Mañas, M. Mercade, Y.Bessière, Biochemical Engineering Journal, vol. 67 (2012), p. 10-19
ano.nymous@ccsd.cnrs.fr.invalid (Lucas Berti) 24 Jun 2026
https://hal.science/hal-05667759v1
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[hal-05667005] An extended 1D dynamic model of biological methanation considering water production, variable height and maintenance heterogeneity
Numerical simulation of biological methanation remains challenging due to the strong coupling between mass transfer, hydrodynamics, and bioreaction in such gas-fed bioreactors. This work presents an extension of a 1D spatio-temporal gas-liquid model for bubble columns to the case of biological methanation. To this end, three major improvements are added to the previously published model: (i) the consideration of water production due to the biological methanation reaction, (ii) the implementation of an additional equation for the liquid height, (iii) the introduction of a variable maintenance model based on the spatial heterogeneity of the H2 mass transfer. The model was validated with experimental data from literature. Results indicate that water production acts as a dilution term, as revealed by simulations performed under both constant and variable height conditions. This led to a reduced biomass concentration while preserving methane production owing to a redistribution of H2 consumption between maintenance and growth. These intriguing results were confirmed by analytical solutions at steady-state. The variable maintenance model further allows connecting the decrease in performances through scale-up to increased local deviations between H2 mass transfer and cell demand. Also, a basic moving mesh method now extends the gas-liquid dynamic 1D model capabilities to any fed-batch (bio) reactor.
ano.nymous@ccsd.cnrs.fr.invalid (Julian Federico Sanchez Caldas) 23 Jun 2026
https://hal.inrae.fr/hal-05667005v1
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[hal-05662964] MetExplore 3: a web solution for network-based interpretation of metabolomics data
Introduction: Metabolomics experiments routinely generate lists of metabolites (metabolic profiles), but transforming these results into biologically interpretable metabolic mechanisms remains challenging. Existing workflows or web solutions often rely on disconnected tools for identifier search, pathway enrichment, network extraction, visualization, and dissemination of results. Aim: We developed MetExplore 3 to provide an integrated web platform for the exploration, contextualization, visualization, and sharing of graphical representation of metabolomics data mapping within genome-scale metabolic networks. Methods: MetExplore 3 is an open access web server based on modern web components that provides interactive tools for metabolic network analysis and visualization. The platform includes published curated genome-scale metabolic networks for several organisms and also allows users to import their own metabolic networks from SBML files. A dedicated metabolite identification module maps experimental metabolites onto metabolic networks using ChEBI identifiers. Once mapped, metabolites can be analyzed through pathway overrepresentation approaches and used to extract biologically contextualized subnetworks. These subnetworks can then be interactively visualized, edited, and shared through reproducible graphical representations. In addition to the graphical interface, MetExplore 3 provides an API enabling access to data structures and analytical methods from external environments and programming languages, facilitating integration into custom bioinformatics workflows. Results: MetExplore 3 enables users to move from metabolite identification to interpretable and shareable metabolic network representations within a unified environment, providing an alternative to widely used pathway enrichment interpretations. The platform integrates metabolite mapping, pathway overrepresentation, subnetwork extraction, and interactive visualization into a unique web solution. The ability to save and share curated network layouts facilitates collaborative interpretation of metabolomics data. Conclusion: By integrating analytical, visualization, and sharing functionalities within the same web solution, MetExplore 3 helps bridge the gap between metabolite lists and biologically meaningful network interpretations. The platform provides the metabolomics community with an accessible and extensible environment for network-based analysis of metabolomics data.
ano.nymous@ccsd.cnrs.fr.invalid (Ludovic Cottret) 19 Jun 2026
https://hal.inrae.fr/hal-05662964v1
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[hal-05661923] Volatile Fatty Acids (VFAs) as a sustainable carbon source for bioproduction by unconventional yeasts
In order to answer the major societal and environmental challenges of our time, we strive to replace fossil-derived molecules with sustainable alternatives. Biotechnological approaches offer promising opportunities but require sustainable, low-cost, and renewable carbon sources to achieve an economically viable and sustainable bioeconomy. One such alternative carbon source of great interest are volatile fatty acids (VFAs), produced naturally during the anaerobic digestion (AD) of organic waste. Although inexpensive and readily available, VFAs have long been overlooked as substrates for bioproduction due to their general toxicity towards microorganisms. However, recent insights into unconventional yeasts have revealed the potential of their diverse metabolisms to efficiently utilize VFAs as sole carbon sources and convert them into molecules of higher value. This review synthesizes the latest advancements in VFAs utilization by unconventional yeasts, focusing on VFAs production and composition, the diversity of unconventional yeasts, metabolic optimization, and bioproduction. It further discusses the current limitations in strain engineering and process integration for certain unconventional yeasts, while highlighting the emerging perspectives in process engineering and co-culture systems. Together, these insights shed light on the potential for unconventional yeasts to become established platforms for the revalorization of organic wastes, adding a crucial building block to the future circular bioeconomy.
ano.nymous@ccsd.cnrs.fr.invalid (Erik Habek) 18 Jun 2026
https://hal.science/hal-05661923v1
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[hal-05648227] Multi-scale characterization of porosity and diffusion in maize STEM internodes
The transition towards sustainable bio-based processes relies on the efficient conversion of lignocellulosic biomass, a renewable and abundant carbon resource. For instance, biotechnological transformation of lignocellulose into chemicals and energy, combining pretreatment, enzymatic hydrolysis and fermentation steps, has been explored for decades and is close to maturity. But underlying mechanisms during hydrolysis remain complex as they depend on many physical and chemical parameters which are deeply impacted by the pretreatment type and severity. In this study, we aim to develop multi-scale approaches to highlight markers of biomass properties and reactivity, focusing on biomass accessibility and porosity. Indeed, enzyme activity and efficiency strongly rely on their diffusion towards their substrate in hydrated plant cell wall substrates. To investigate porosity and molecular transport, our strategy was to combine complementary approaches applied to a reference biomass such as maize stem internodes, considering different genotypes, cell tissues, and pre-treatment conditions. First, the diffusion of water and larger molecular probes such as polyethylene glycols (PEGs) within the biomass global matrix was studied by pulsed-field gradient NMR (PFG-NMR). Then, textural analyses, based on physisorption and pycnometry techniques, provided insights into surface properties, meso and macropore size distribution and overall porosity. Finally, fluorescent PEGs diffusion was followed by Fluorescence recovery after photobleaching (FRAP) to investigate local diffusion, by selecting different cell walls in different tissues. Considering our results, they confirm that pretreated samples enhance molecular probe diffusion, indicating a better surface accessibility. The diffusion behaviour is strongly influenced by pore structure variations across different tissues and samples types. Diffusion is also related to probe size, revealing mesoporosity’s role in enzyme accessibility. Overall, by integrating advanced structural imaging techniques, we aim to refine our understanding of cell wall organization (nanoporosity). The combined use of diffusion-based and textural characterization approaches provides valuable insights into the structural evolution of lignocellulosic biomass, contributing to the optimization of efficient biorefinery processes.
ano.nymous@ccsd.cnrs.fr.invalid (Firat Goc) 08 Jun 2026
https://hal.inrae.fr/hal-05648227v1
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[hal-05638036] Poster, liker, responsabiliser ? Les pratiques des influenceuses engagées dans la mode éthique et durable sur Instagram
Cette recherche explore les pratiques des influenceuses spécialisées dans la mode durable sur Instagram en mobilisant la théorie des pratiques. À travers une analyse netnographique de 220 publications issues de 13 comptes francophones et cinq entretiens semi-directifs, l’étude met en évidence deux registres centraux : la mise en scène vestimentaire (valorisation des tenues éthiques et durables) et la prescription de conseils (achat de seconde main, entretien, tri, sensibilisation aux impacts environnementaux et sociaux). Ces pratiques reposent sur un assemblage de matérialités (vêtements, marques, plateformes numériques), de compétences (savoirs techniques, pédagogiques et créatifs) et de significations (valeurs éthiques, sobriété, transparence). L’analyse révèle une tension entre la promotion d’une consommation responsable et les logiques de renouvellement propres à Instagram, pouvant stimuler le désir de consommer.
ano.nymous@ccsd.cnrs.fr.invalid (Arnaud Lamy) 29 May 2026
https://hal.science/hal-05638036v1
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[hal-05563371] Multi-scale modelling of enzymatic hydrolysis of biomass using numerical homogenization
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ano.nymous@ccsd.cnrs.fr.invalid (Emma Berson) 27 May 2026
https://hal.science/hal-05563371v1
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[hal-05630861] Multi-modal and multi-scale analysis of plant biomass for a better understanding of its degradability
Biomass valorization plays a key role in sustainable development and renewable energy production strategies. It is crucial to better understand biomass structure to identify factors affecting its degradability. While their quantities and proportions can be determined by destructive chemical analysis, the spatial distribution of these components and their interactions at the nanometer scale remain poorly understood. In this poster, we present results obtained using Magnetic Resonance Imaging (MRI), complemented by an original correlative methodological approach that integrates various techniques. These include atomic force microscopy (AFM), which enables topographical and mechanical characterization at the nanometric scale; Raman spectroscopy, which provides information on chemical composition and molecular organization; and mass spectrometry imaging, which enables the identification and localization of specific biomolecules and the detection of local enzymatic activity. We used different types of biomass, such as poplar wood, maize and Brachypodium stem sections.
ano.nymous@ccsd.cnrs.fr.invalid (Oriane Morel) 22 May 2026
https://hal.inrae.fr/hal-05630861v1
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[hal-05625620] Biotechnology for Europe’s agri-food sector and bioeconomy
This May 2026 policy brief by INRAE, B-BEST, and IBISBA argues that Europe's first Biotech Act is too narrowly focused on health, and calls for a broader frame…This May 2026 policy brief by INRAE, B-BEST, and IBISBA argues that Europe's first Biotech Act is too narrowly focused on health, and calls for a broader framework spanning the entire agri-food and bioeconomy sector. While the bio-based market could double by 2032 and cut up to 2.5 Gt CO₂e per year by 2030, Europe is held back by six key obstacles: gaps in fundamental research, a weak lab-to-market transition, fossil-fuel subsidies distorting competition, fragmented R&D funding, poor data interoperability, and low public acceptance of biotechnology. (Contributors by alphabetic order)
ano.nymous@ccsd.cnrs.fr.invalid (Stéphane Aymerich) 18 May 2026
https://hal.inrae.fr/hal-05625620v1
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[hal-05622341] Selective dehydration of isobutanol on H-FER: operando IR, multivariate analysis and kinetic modeling
Introduction Acidic ferrierite (H-FER) is able to dehydrate isobutanol to linear butenes with high selectivity, but the reaction mechanism explaining such selectivity remains elusive.1 This work aims to elucidate the reaction mechanism and provide kinetic rate constants for the main reaction. Results & Discussion The measurements were performed in a custom-made IR cell operating as a closed batch operando reactor and allowing to measure simultaneously the gas phase composition and the species adsorbed on the catalyst. Isobutanol dehydration on HFER was followed along temperature ramps from 40 to 260°C. Intermediates and products were identified and quantified by MCR-ALS analysis of the surface spectra (Figure 1B) and by fitting gas spectra with known references. The high selectivity to linear butenes was confirmed, and 2-butanol was found as an intermediate adsorbed at the HFER surface, suggesting that the key reaction step is the isomerization of the alcohol to the linear isomer. The validity of the proposed mechanism was confirmed by microkinetic modelling of both surface and gas phase concentration profiles, which also showed that the rate limiting step is the alcohol isomerization step. This is the first experimental confirmation of previous computational predictions on a comparable system. Significance This work demonstrates the potential of closed-batch operando IR for identifying reaction intermediates and enabling kinetic modeling of both gas-phase and surface concentrations in catalytic reactions. Applied to the selective dehydration of isobutanol on HFER, it provides the first experimental evidence that the key reaction step is the isomerization of isobutanol to 2-butanol.
ano.nymous@ccsd.cnrs.fr.invalid (Eleonora Vottero) 13 May 2026
https://hal.science/hal-05622341v1
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[hal-05622338] Modulation Excitation Spectroscopy beyond Phase-Sensitive Detection
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ano.nymous@ccsd.cnrs.fr.invalid (Eleonora Vottero) 13 May 2026
https://hal.science/hal-05622338v1
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[hal-05592447] What Makes a Bacterial Model a Good Reservoir Computer? Predicting Performance from Separability and Similarity
Biological systems are promising substrates for computation because they naturally process environmental information through complex internal dynamics. In this study, we investigate whether bacterial metabolic models can act as physical reservoirs and whether their computational performance can be predicted from dynamical properties linked to separability and similarity. We simulated the growth dynamics of five bacterial species, one yeast species, and 29 Escherichia coli single-gene deletion mutants using dynamic flux balance analysis (dFBA), with glucose and xylose concentrations as inputs and growth curves as reservoir states. Computational performance was assessed on random nonlinear classification tasks using a linear readout, while reservoir properties linked to separability and similarity were characterised through kernel and generalisation ranks computed from growth-curve state matrices. Several microbial models achieved high classification accuracy, showing that bacterial metabolic dynamics can support nonlinear computation. Clear differences were observed between species, with some models converging more rapidly and others reaching higher maximum accuracy, revealing a trade-off between convergence speed and peak performance. In contrast, all E. coli mutants were dominated by the wild-type model, suggesting that gene deletions reduce the dynamical richness required for efficient computation. The difference between kernel and generalisation ranks was generally associated with improved accuracy, but deviations across models and sensitivity at low rank values limited its predictive power in practice. Overall, these results show that bacterial metabolic models constitute promising substrates for reservoir computing and provide a first step towards identifying microbial strains with favourable computational properties for future experimental implementations.
ano.nymous@ccsd.cnrs.fr.invalid (Laura Alonso Bartolomé) 16 Apr 2026
https://hal.science/hal-05592447v1
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[hal-05585455] Unbiased molecular dynamics for the direct determination of catalytic reaction times: Paving the way beyond transition state theory
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ano.nymous@ccsd.cnrs.fr.invalid (Thomas Pigeon) 09 Apr 2026
https://ifp.hal.science/hal-05585455v1
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[hal-05584551] Fluorescence lifetime imaging microscopy of lignocellulosic biomass: principles, applications, and related techniques
Lignocellulosic biomass is a renewable carbon source that could help replacing fossil carbon feedstocks which cause many ecological concerns. However, to improve its bioconversion, the complex microstructure and chemistry of biomass needs thorough characterization. Emerging techniques like Fluorescence Lifetime Imaging Microscopy are particularly promising and this review aims to cover all aspects related to the use of lifetime microscopy for lignocellulosic biomass analysis. First, the mechanisms involved in fluorescence emission and atomistic properties influencing fluorescence lifetime are detailed. Then the three main instrumentations of lifetime microscopy are compared and the decay fitting function of fluorescence lifetime is presented. Numerous examples exposing the relevance of fluorescence lifetime imaging microscopy for biomass analysis are provided. Lifetime microscopy allows for cellulose, hemicelluloses, and lignins differential localization and syringyl / guaiacyl lignin ratio mapping. Fluorescence lifetime imaging microscopy can also provide insights on the effects of pretreatment and hydrolysis on the microstructure and chemistry of lignocellulosic biomass. Additionally, lifetime microscopy can inform on growth conditions like geographical origin or reaction wood formation as a response to gravitropic perturbations. Also, Förster Resonance Energy Transfer, being able to explore lignocellulosic biomass’s interactions with molecular probes, can be based on fluorescence imaging as well. Finally, other fluorescence-lifetime-related techniques having the potential to be implemented on lignocellulosic biomass are discussed.
ano.nymous@ccsd.cnrs.fr.invalid (Noah Remy) 08 Apr 2026
https://hal.science/hal-05584551v1
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[hal-05581048] Multivariate analysis coupled to infrared spectroscopy unravels the diversity of adsorption sites and strengths of a zeolite surface
Quantifying adsorption thermodynamics on heterogeneous catalysts remains challenging because macroscopic techniques yield averaged parameters, while vibrational spectra often contain strongly overlapping contributions from multiple adsorbed species. Here we introduce an infrared (IR)-chemometric framework that extracts site-specific adsorption thermodynamics directly from experimental IR isotherms and we benchmark it against independent microcalorimetry and density functional theory (DFT) calculations. Difference IR spectra recorded for isobutanol adsorption on H-ZSM-5 (MFI) were analysed by principal component analysis and multivariate curve resolution (MCR-ALS) under soft constraints (monotonic concentration profiles, spectral normalisation), and further refined using a hard–soft strategy in which concentration profiles are constrained by an adsorption model. The analysis reveals three adsorption modes associated with bridging Brønsted OH groups, extra-framework Al–OH species, and silanols, providing representative pure-component spectra and site-resolved adsorption isotherms. The thermodynamic trends and site hierarchy obtained from IR–MCR-ALS are consistent with independent microcalorimetry measurements and density functional theory calculations, validating the approach. More broadly, IR–MCR-ALS offers a transferable route to quantitative, site-resolved adsorption thermodynamics in complex catalysts.
ano.nymous@ccsd.cnrs.fr.invalid (Reda Aboulayt) 05 Apr 2026
https://hal.science/hal-05581048v1
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[hal-05240023] Chemical imaging of lignocellulosic biomass: Mapping plant chemistry
Lignocellulosic biomass (LB), which encompasses various plant samples, requires thorough characterization to optimize its use as a carbon resource. Chemical imaging simultaneously provides chemical and spatial information, offering significant benefits for LB analysis. This review presents an overview of the most advanced techniques for achieving this goal. By combining spectrometry and microscopy, microspectroscopy enables chemical imaging using various irradiation sources (IR, Raman, fluorescence, among others), allowing for the quantitative mapping of key LB components such as lignins, cellulose, and hemicelluloses. Mass Spectrometry Imaging (MSI) generates a mass spectrum for each spot of a sample thereby creating a chemical image pixel-by-pixel. MSI techniques like Matrix-Assisted Laser Desorption/Ionization (MALDI), down to 2–5 μm spatial resolution, and Secondary Ion Mass Spectrometry (SIMS), down to 300 nm for molecular analysis, effectively map small molecules in LB. In contrast, Desorption ElectroSpray Ionization (DESI) has been applied to plant extracts but remains largely unexplored for LB applications. Nuclear Magnetic Resonance (NMR) provides insight into various LB properties too. Solid-state NMR (ssNMR) and Dynamic Nuclear Polarization (DNP) help elucidate the structure of LB, sometimes aided by 3D atomistic modeling, whereas micro–Magnetic Resonance Imaging (micro-MRI) and Time-Domain (TD-NMR) probe the impact of water on LB properties.
ano.nymous@ccsd.cnrs.fr.invalid (Noah Remy) 09 Mar 2026
https://hal.inrae.fr/hal-05240023v1
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[hal-05543880] Unraveling the Mechanism of HMF Oxidation through a Colloidal Nanocatalyst Approach
The work presented here is performed in the context of the PEPR BBEST-FURFUN program, focused on the valorization of bio-sourced furanic derivatives. Our studies aim at developing efficient and sustainable catalytic oxidation of 5-hydroxymethylfurfural (HMF) for producing high-value compounds such as 2,5-furandicarboxylic acid (FDCA) and 2,5-diformylfuran (DFF). Most reported oxidation methods predominantly use efficient heterogeneous noble metal catalysts with soluble bases, but a deep understanding of the catalytic system is still needed to optimize the processes. Quasihomogeneous catalysis, based on stable metallic colloids as nanocatalysts, provides a well-defined environment to identify active sites to study reaction mechanisms, and has been applied for the first time to HMF oxidation under mild conditions. Nanocatalysts in the form of PVP-stabilized Pd nanoparticles (1.86 ± 0.07 nm) were synthesized in water.3 Catalytic oxidation of HMF was carried out under air flow in water (0.5 wt% HMF) with Na2CO3 (1.2 eq./HMF) at 80 °C. Complete HMF conversion was achieved within 48 h, with FDCA selectivity exceeding 80%. The colloidal system allows direct access to the intrinsic behavior of the active metal, free from support effects, enabling detailed mechanistic studies. Kinetic monitoring highlighted the critical role of the soluble base and clarified aspects of the reaction pathway towards selective oxidation. Moreover, the study demonstrates that quasi-homogeneous Pd nanocatalyst combines high efficiency and selectivity with minimal material use, making a promising system for sustainable biosourced chemical production. These findings lay the groundwork for optimization of catalytic processes, offering new perspectives for designing stable and environmentally benign catalysts for HMF valorization.
ano.nymous@ccsd.cnrs.fr.invalid (Lénaïck Hervé) 09 Mar 2026
https://hal.science/hal-05543880v1
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[hal-05523369] From Waste to Wardrobe: Exploring Consumer Acceptance of Biowaste-Dyed Sustainable Fashion
The study explores consumer acceptance of sustainable fashion innovations that use dyes made from biowaste, addressing a significant environmental challenge within the fashion industry. Through in-depth qualitative interviews with 18 participants (comprising both consumers and influencers engaged in sustainable fashion) the research dissects how intuitive judgment heuristics shape acceptance of biowaste-based dyeing. The analysis focuses on three key heuristics: affect (emotional response), trust (credibility and institutional assurance), and the natural-is-better bias (preference for perceived naturalness). Results reveal that while the valorisation of waste and the idea of circularity foster curiosity and positive reactions, acceptance is often limited by perceptions of unfamiliarity, safety concerns, and ambiguity about what is truly “natural.” Trust in regulatory frameworks, scientific validation, and transparent communication emerges as essential but is frequently conditional, requiring tangible proof before consumers feel comfortable. The findings highlight the complexity and ambivalence of consumer attitudes toward biowaste-based dyes, as intuitive heuristics can support both enthusiasm and scepticism, often hinging on how risks and benefits are framed by information sources. . Implications for sustainable innovation diffusion and managerial communication are discussed, alongside limitations and avenues for further research.
ano.nymous@ccsd.cnrs.fr.invalid (Arnaud Lamy) 23 Feb 2026
https://hal.science/hal-05523369v1
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[hal-05515316] RetroRules 2026: an expanded database combining biochemical and organic reaction templates for pathway discovery
Abstract RetroRules (https://retrorules.org) is an open resource of reaction templates, which are generic reaction representations that describe the atomic transformations underlying biochemical reactions. These templates are key to supporting metabolic pathway discovery, reaction prediction, and enzyme engineering. The 2026 release updates biochemical sources (MetaNetX, Rhea) and newly integrates organic chemistry reactions (USPTO), extending the scope of the database beyond enzymatic systems. The template encoding has been simplified by using implicit hydrogens and minimal atomic descriptors, resulting in faster and more compact representations. Radius range now spans 0–10, allowing finer control of reaction specificity. In addition, mass-imbalanced reactions are included, expanding the coverage of biochemically relevant transformations. Reaction mapping now relies on the transformer-based tool RXNMapper, improving accuracy. RetroRules 2026 comprises 1 174 216 templates derived from 92 698 reactions, covering 5796 fourth-level EC numbers. A redesigned website, updated Online Template Generator, and OpenAPI-defined API enable multi-criteria exploration (dataset, radius, and EC number), visualization, and data export in multiple formats. Sequence annotations from UniProt were refreshed and summarized as a normalized sequence-support score for ranking. Together, these updates establish RetroRules as a cross-domain resource bridging biochemistry and organic chemistry, offering broader coverage, controllable specificity, and enhanced usability for high-throughput pathway design, reaction prediction, and enzyme engineering.
ano.nymous@ccsd.cnrs.fr.invalid (Thomas Duigou) 17 Feb 2026
https://hal.science/hal-05515316v1
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[hal-05507094] Reverse engineering molecules from fingerprints through deterministic enumeration and generative models
Reverse engineering in molecular design aims to identify optimal structures based on activities, or properties, computed through molecular descriptors like fingerprints. This task is known to be particularly difficult for the widely used Extended-Connectivity Fingerprints (ECFPs), due to significant loss of structural information during vectorization. While recent artificial intelligence-based works have raised awareness about the privacy risks associated with ECFP-based data sharing, we contribute a more conclusive demonstration by introducing a deterministic algorithm that reconstructs molecular structures from ECFPs. Using MetaNetX and eMolecules as databases of natural compounds and commercially available chemicals, the deterministic algorithm benchmarks a Transformer-based generative model trained to predict SMILES from ECFPs. The generative model achieves a top-ranked retrieval accuracy of 95.64% but struggles with exhaustive enumeration. Additionally, applying the deterministic method to a drug dataset reveals its potential for de novo drug design, as many of the reverse-engineered structures are found to be patented or supported by bioassay data.
ano.nymous@ccsd.cnrs.fr.invalid (Philippe Meyer) 12 Feb 2026
https://hal.science/hal-05507094v1
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[hal-05500853] Living bacterial reservoir computers for information processing and sensing
We introduce a systems-level approach to sensing and computing in which Escherichia coli acts as a living reservoir computer, performing complex information processing through its native growth responses without requiring genetic modification or specialized instrumentation. We validate this framework by accurately classifying early-stage COVID-19 plasma samples (mild vs . severe) using only bacterial growth data, highlighting a diagnostic potential without infrastructure-dependent methods. By controlling nutrient media compositions, we also demonstrate that E. coli growth encodes nonlinear transformations that outperform linear regression, support vector machines, and multilayer perceptrons across diverse regression and classification tasks. Using simulations across genome-scale metabolic models from multiple bacterial species, we establish a strong link between phenotypic diversity and computational capacity, showing that learning capacities scale with the diversity of metabolic phenotypes. These findings position biological reservoir computing as a robust, scalable, and low-cost platform for intelligent biosensing, diagnostics, and hybrid bio-digital computation, while providing new mechanistic insights into the computational capabilities of living systems.
ano.nymous@ccsd.cnrs.fr.invalid (Paul Ahavi) 09 Feb 2026
https://hal.science/hal-05500853v1
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[hal-05484909] Impact of CP12 deletion on inorganic carbon acquisition and Rubisco partitioning in Chlamydomonas reinhardtii
The small chloroplastic protein CP12 has multiple functions, including the regulation of enzymes in the Calvin-Benson-Bassham cycle. Here, we investigated its role in the acclimation of Chlamydomonas reinhardtii to varying CO2 availability. We showed that phosphoribulokinase can interact with CP12 in conditions where the Calvin-Benson-Bassham cycle is active. Compared to the wild type, at high CO2, C. reinhardtii CP12 deletion mutants, or partially complemented mutants, have less phosphoribulokinase and ribulose-1,5-bisphosphate (RuBP), indicating that the regeneration of RuBP is regulated, in part, by CP12. C. reinhardtii has a CO2 concentrating mechanism that increases the supply of CO2 to ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and involves, among other features, the condensation of Rubisco within the pyrenoid via its interaction with a scaffold protein named Essential Pyrenoid Component 1 (EPYC1). In CP12 deletion mutants, the expected relocation of Rubisco towards the pyrenoid was not observed upon transition from high to very low CO2, contrary to WT cells. The CP12 deletion mutants are a unique example where the induction of CO2 concentrating mechanism at very low CO2 was not accompanied by Rubisco relocation. Altogether, these results suggest that CP12 contributes to the coordination between RuBP regeneration, Rubisco location, and CO2 acquisition.
ano.nymous@ccsd.cnrs.fr.invalid (Cassy Gérard) 30 Jan 2026
https://hal.science/hal-05484909v1
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[hal-05482986] 4D (space + time) datasets of spruce wood enzymatic hydrolysis
The conversion of lignocellulosic biomass from plant cell walls into bioproducts can contribute to reducing dependence on fossil sources and achieving sustainable development. Biotechnological conversion of lignocellulosic biomass has several advantages over other conversion approaches such as thermochemical and chemical conversions. These advantages include improved efficiency and specificity for desired products, ecological compatibility and reduced toxicity. Enzymatic transformation is a key step in biotechnological conversion. To achieve a cost-effective conversion, a comprehensive understanding of cell wall enzymatic hydrolysis is required. Despite progress, the enzymatic hydrolysis at microscale is comparatively understudied and lacks comprehensive investigation. Addressing this gap requires collection of time-lapse image datasets of cell wall enzymatic hydrolysis which is a technically demanding task. Furthermore, accurate processing of the time-lapse images to identify and track individual cell walls is particularly challenging, notably because of the sample drift present in the images. Recently, an efficient image processing pipeline, called AIMTrack, has been developed which uses an enhanced divide-and-conquer strategy to divide time-lapse images into clusters whose sizes are dynamically adjusted to the deconstruction extent. The image registrations are then limited to clusters and the resulting transformations are combined to correct sample drift across time-lapse images. Subsequently AIMTrack provides segmentation of time-lapse images where voxels belonging to the same cell walls are labelled with a unique identifier. The time-lapse image datasets presented here consist of time-lapse images of spruce wood cell walls acquired during enzymatic hydrolysis using a cellulolytic enzyme cocktail at two enzyme loadings of 15 and 30 FPU/g biomass. Control time-lapse datasets which are acquired under the identical conditions, but without addition of enzymes, are also included. Both control and hydrolysis datasets are processed using AIMTrack to track the cell walls from time-lapse images. The generated segmentations are also provided.
ano.nymous@ccsd.cnrs.fr.invalid (Solmaz Hossein Khani) 29 Jan 2026
https://hal.science/hal-05482986v1
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[hal-05477215] Linear versus branched products: how dynamical effects influence the transformations of isobutanol catalyzed by acidic zeolites
Ab initio molecular dynamics (AIMD) calculations were performed to study isomerization (I1), synchronous dehydration and isomerization (DHI1) and dehydration (DH1) reactions of isobutanol over acid chabazite leading to formation of branched products, i.e., tert-butanol, tert-butyl cation, and isobutene, respectively. Reactions I1 and DHI1 were shown to be variants of the same transformation, differing only in the relative position of water with respect to the carbenium cation formed after passing through a common transition state. The Bennett–Chandler approach predicted that DHI1 is strongly favored over variant I1, mainly due to stability of the tert-butyl intermediate interacting with water, which is further enhanced by the entropic effect. The relative importance of this class of transformations compared to the I2 and DHI2 reactions leading to the formation of linear products (n-butanol, n-butyl cation, n-butenes) was assessed. At the experimentally relevant temperature of 500 K, reactions ultimately yielding isobutene (DHI1 and I1) were found to be dominant, in line with experimental observations made for large pore zeolites. Although the transformations leading to linear butenes (DHI2, I2) are slower, with rate constants 1–2 orders of magnitude lower than those of DHI1/I1, they are still competitive. The importance of dynamic effects was underlined by comparison with a static approach, which strongly underrated importance of synchronous dehydration and isomerization reaction channels.
ano.nymous@ccsd.cnrs.fr.invalid (Monika Gešvandtnerová) 26 Jan 2026
https://ifp.hal.science/hal-05477215v1
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[hal-05469683] Following the Committor Flow: A Data-Driven Discovery of Transition Pathways
The discovery of transition pathways to unravel distinct reaction mechanisms and, in general, rare events that occur in molecular systems is still a challenge. Recent advances have focused on analyzing the transition path ensemble using the committor probability, widely regarded as the most informative one-dimensional reaction coordinate. Consistency between transition pathways and the committor function is essential for accurate mechanistic insight. In this work, we propose an iterative framework to infer the committor and, subsequently, to identify the most relevant transition pathways. Starting from an initial guess for the transition path, we generate biased sampling from which we train a neural network to approximate the committor probability. From this learned committor, we extract dominant transition channels as discretized strings lying on isocommittor surfaces. These pathways are then used to enhance sampling and iteratively refine both the committor and the transition paths until convergence. The resulting committor enables accurate estimation of the reaction rate constant. We demonstrate the effectiveness of our approach on benchmark systems, including a two-dimensional model potential, peptide conformational transitions, and a Diels--Alder reaction.
ano.nymous@ccsd.cnrs.fr.invalid (Cheng Giuseppe Chen) 21 Jan 2026
https://hal.science/hal-05469683v1
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[hal-05469667] From Static Pathways to Dynamic Mechanisms: A Committor-Based Data-Driven Approach to Chemical Reactions
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Radu Talmazan) 21 Jan 2026
https://hal.science/hal-05469667v1
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[hal-05469657] From Atoms to Dynamics: Learning the Committor Without Collective Variables
<div><p>We introduce a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach assists in understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes.</p></div>
ano.nymous@ccsd.cnrs.fr.invalid (Sergio Contreras Arredondo) 21 Jan 2026
https://hal.science/hal-05469657v1
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[hal-05459821] BIOMODLAB : a Platform for Analyzing 3D Images
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Maxime Corré) 15 Jan 2026
https://hal.science/hal-05459821v1
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[cea-05424045] Scalable model development of carbon photosynthetic assimilation and partitioning in a green microalga during nitrogen starvation
Lipid accumulation in green microalgae is induced by stresses (e.g. nitrogen starvation) which compromise photosynthetic activity resulting in significantly lower biomass productivity than under nutrient replete conditions. While algae photosynthetic growth has been well characterized and modelled under nutrient replete conditions, the loss of photosynthetic activity during nitrogen starvation lacks specific studies to determine suitable parameterisation. The loss of photosynthetic activity of the lipid-accumulating microalgae Chlorella vulgaris NIES 227 was studied under varying light intensities during nitrogen starvation. Partition of assimilated carbon between the different macromolecules pools (carbohydrates, lipids, and proteins) was concomitantly monitored. The results showed that the decrease of photosynthetic activity correlated well to the increase of cell C:N ratio $(R^2$ = 0,883, $N$=65) enabling to develop a model of microalgae growth and carbon partition under nitrogen starvation. Biomass dry-weight increase could be predicted with good accuracy ($R^2$ = 0,940, $N$ = 66), as total lipid and carbohydrate production could also be predicted with fair accuracy ($R^2$=0,841 and 0,618 respectively). The present study henceforth showed that modelling microalgae productivity based on photosynthetic activity inferred from local light intensity, as done in scalable models under nutrient replete conditions, may be extended to nitrogen starvation conditions and enabled the prediction of lipids and carbohydrates productivity. The model proposed should thus prove useful in optimizing photobioreactors design for the production of important energetic molecules based on light distribution knowledge.
ano.nymous@ccsd.cnrs.fr.invalid (Paul Chambonniere) 18 Dec 2025
https://cea.hal.science/cea-05424045v1
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[hal-05412282] Calculating free-energy differences using an average force: A tutorial for adaptive biasing force simulations
The purpose of this tutorial is to get the reader familiarized with the calculation of a free-energy change along a reaction-coordinate (RC) model through a number of applications of variants of the importance-sampling adaptive biasing force algorithm. The reversible sodium-chloride ion pairing in aqueous solution serves as an introductory example, wherein the RC model is defined as the distance separating the ions. For the reversible folding of the short peptide deca-alanine, alternative collective variables are considered to map the conformational free-energy landscape. The importance-sampling algorithm is then applied to the transfer of an ethanol molecule across the water liquid-vapor interface to estimate its hydration free-energy. The results are compared to those of an alchemical transformation, using free-energy perturbation calculations. In the final application, the Ramachandran free-energy surface underlying the conformational equilibrium of N-methyl-N'-acetylalanylamide is determined in two dimensions, comparing single-and multiple-walker strategies.
ano.nymous@ccsd.cnrs.fr.invalid (Radu Talmazan) 11 Dec 2025
https://hal.science/hal-05412282v1
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[hal-05390663] Multi-scale characterisation of the porosity in pretreated lignocellulosic biomass
The transition towards sustainable bio-based processes relies on the efficient conversion of lignocellulosic biomass, a renewable and abundant carbon resource. The biochemical conversion of lignocelluloses into ethanol, through pretreatment, enzymatic hydrolysis and fermentation, has been explored for decades and is nearing maturity. However, hydrolysis remains complex, as its efficiency largely depends on the physico-chemical properties of pretreated biomass. This study develops multi-scale approaches to identify biomass property markers and reactivity, focusing on porous architecture, a key parameter for hydrolysis, as it determines enzyme diffusion before adsorption on cellulose and conversion into glucose. Understanding how porosity influences molecular transport in hydrated biomass is essential for optimizing biomass valorization. We investigate multi-scale porosity in maize stem internodes, considering different genotypes, tissues and pre-treatment conditions [1]. To assess porosity and molecular transport, we use pulsed-field gradient NMR (PFG-NMR) to analyze the diffusion of water and polyethylene glycols (PEGs) probes [2]. Complementary textural analyses, based on adsorption techniques, provide insights into surface properties and pore size distribution. Our results confirm that pretreated samples enhance diffusion, indicating greater cellulosic surface accessibility (fig.1). Diffusion behavior varies with pore structure across tissues, showing clear differences between native and pretreated biomass. It also depends on probe size, revealing mesoporosity’s role in enzyme accessibility. By integrating structural imaging techniques, we refine our understanding of cell wall organization [3]. The combined use of diffusion-based and textural characterization provides insights into the structural evolution of lignocellulosic biomass, helping optimize pre-treatment and cultivation strategies for efficient biorefinery processes.
ano.nymous@ccsd.cnrs.fr.invalid (Firat Goc) 04 Dec 2025
https://hal.science/hal-05390663v1
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[hal-05395884] How to deal with large multispectral images comparison? PCA score distribution as a tool
<div><p>Multispectral autofluorescence images can be acquired using automated microscopes with a spatial resolution of 1µm and a large field of view (1 to 2 cm 2 ). Such acquisition systems are well suited to observe whole organ sections, while enabling tissue identification. They allowed the acquisition of an increased number of images, although the sample preparation is still the bottelneck. These large image series pave the way for statistical analysis, and could be used to explore the diversity of plant structure and cell wall spectral properties as a function of different factors (genetic, environmental and agricultural conditions), but also on technological fractions obtained from these plant ressources. To quantify the differences between images we proposed to take advantage of the pixel score distributions after Principal Component Analysis (PCA). Series of 30-60 large images may contain millions to billions of pixels. Principal Component Analysis can be applied to image series by iteratively computing the variance-covariance matrix and the score images [1]. It is called large PCA. For each principal component, the pixel score distribution across the entire image series and its percentiles are determined. Local PCA score distributions are then calculated for each image using these percentiles. These local score distributions are regarded as quantitative characteristics enabling multispectral images to be compared. Two examples based on maize stem section and ground maize fractions are presented. Autofluorescence multispectral images were acquired and specific attention was paid to UV and visible fluorescence variability. First of all, 4 maize inbred lines were compared based on 40 stem imaged sections. The overall comparison was carried out by looking at the principal component analysis of the score distributions. Similar approach was used to compare 6 grinding fractions obtained from 6 maize inbred lines.</p><p>Pixel score distributions are found to be a promising tool for statistical comparison of multispectral images. The method is easy to implement, can be used as a first descriptive analysis of the image series, and can be easily extended to other spectral imaging techniques.</p></div>
ano.nymous@ccsd.cnrs.fr.invalid (Marie-Françoise Devaux) 03 Dec 2025
https://hal.science/hal-05395884v1
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[hal-05395839] Leveraging autofluorescence-based multispectral imaging to trace tissue origin in ground forage maize particles.
Maize is a pillar of the French agricultural system, providing biomass for animal feed which is crucial for milk production. The maize stem comprises distinct tissues with unique cell-wall matrices that affect dry matter digestibility, which impacts milk production, and respond differently to drought1. During harvest, the stem is ground into particles. These particles are either pure (composed of a single tissue) or mixed (composed of multiple tissues, whose proportion could explain a part of their digestibility). In direct relation to their specific biochemical composition, the unique autofluorescence properties of these tissues have been emphasised2. Multispectral imaging was successfully used to identify the given tissue on maize microscopic cross-sections by exploiting these autofluorescence properties, but its potential must be evaluated on dried particles found in the agricultural sector. To study this, particles were produced from isolated pure tissues extracted from stems grown from a panel of forage maize inbred lines and hybrid varieties cultivated in Southern France. In this poster, we present early results regarding differences between the tissue types observed through multispectral images. We then explore how these spectral differences could feed predictive models of particle tissular origin, and discuss how it could complement tissue-specific biochemistry and near-infrared spectroscopy methods routinely used in forage quality evaluation. We conclude that multispectral imaging could provide complementary information to breeding programs, offering a new dimension for improving forage maize digestibility.
ano.nymous@ccsd.cnrs.fr.invalid (Oscar Main) 03 Dec 2025
https://hal.inrae.fr/hal-05395839v1
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[hal-05392386] Crystal Digital PCR™ Enables Precise Quantification of Species Abundance in Microbial Mixtures
Accurate bacterial quantification is critical in many biological fields, from clinical diagnostics to environmental microbiology. Here, we establish a robust workflow for absolute quantification of bacterial species within mixed communities using Crystal Digital PCR™. Using a synthetic consortium of Clostridium acetobutylicum and Nitratidesulfovibrio vulgaris, we optimized primer design for species-specific detection and demonstrated that Crystal Digital PCR™ enables reliable quantification of low-abundance species, down to a 1:10,000 ratio. We further show that the presence of one species does not interfere with the quantification of another. Finally, we demonstrate that Crystal Digital PCR™ can also be used to determine plasmid-to-chromosome copy number ratios in bacteria carrying megaplasmids.
ano.nymous@ccsd.cnrs.fr.invalid (Louis Delecourt) 02 Dec 2025
https://hal.science/hal-05392386v1
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[hal-05371880] Plant cell wall enzymatic hydrolysis: Predicting yield dynamics from autofluorescence and morphological temporal changes
Enzymatic hydrolysis of plant cell walls into fermentable sugars is a critical step in biotechnological conversion, yet efficiency is limited by cell wall recalcitrance. Predicting conversion yields of cell wall-derived sugars during hydrolysis is challenging due to the complex underlying mechanisms and the labor-intensive nature of conventional methods. This study introduces an innovative pipeline that accurately quantifies cell wall autofluorescence intensity and morphological descriptors during enzymatic hydrolysis. The pipeline incorporates a novel adaptive drift compensation strategy that dynamically adjusts to the progression and extent of deconstruction, ensuring robust analysis. Applied to time-lapse images of spruce wood enzymatic deconstruction, the pipeline revealed strong negative correlations between conversion yields during hydrolysis and both the dynamics of cell wall autofluorescence intensity and morphological descriptors. Phase-specific analysis uncovered distinct correlation patterns dependent on hydrolysis stage and sugar type. This nondestructive pipeline eliminates the need for extensive sampling and time-consuming chemical assays, establishing plant cell wall autofluorescence and morphological descriptors as accurate predictive real-time biomarkers of dynamics of sugar conversion yields. The findings provide a framework for accelerating the development of optimized biotechnological conversion processes.
ano.nymous@ccsd.cnrs.fr.invalid (Solmaz Hossein Khani) 19 Nov 2025
https://hal.science/hal-05371880v1
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[hal-05371717] Modelling of biological methanation by CFD and 1D model in an industrial bubble column
<div><p>In this paper, a Euler-Euler CFD model based on the two-fluid model is developed for the industrial-scale biological methanation process. The model resolves the hydrodynamics of the bubbly flow, along with the effects of the multispecies mass transfer (H 2 , CO 2 and CH 4 ) and bioreaction kinetics. Three-dimensional simulations were performed with the commercial CFD code ANSYS Fluent® version 2021 R1 and compared with one-dimensional simulations solved with a previously validated in-house 1D code Matlab®. Both modelling tools implemented closure relations in a consistent manner, ensuring proper coupling in terms of interfacial force interactions, mass transfer, and bioreaction uptake rate. Following validation on a pilot methanation bubble column of 1 m high, the results focus on a 10 m high industrial bubble column where the hydrogen gas-liquid transfer rate is a limiting factor of the methanation process.</p><p>At this scale, hydrostatic pressure effects and longer gas residence times strongly intensify mass transfer and reduce gas hold-up and bubble size, leading to inverse density gradients and instability of the bubbly flow. Due to the substantial gas consumption, complex hydrodynamics prevail in large reactive bubble columns and significant concentration heterogeneities appear. The application of unsteady 3D two-phase flow simulations provides a more comprehensive description of these phenomena than reduced 1D models. Nevertheless, the 1D model remains a valuable tool for predicting overall performance, while its parameters can be adjusted to reflect hydrodynamic instabilities and to facilitate rapid investigation of operating scenarios.</p></div>
ano.nymous@ccsd.cnrs.fr.invalid (Vincent Ngu) 18 Nov 2025
https://insa-toulouse.hal.science/hal-05371717v1
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[hal-05333962] BioRGroup dataset: R-group expansion of ChEBI molecules referenced in the Rhea database
The application of artificial intelligence in cheminformatics highlights the necessity of comprehensive datasets that fully utilize all available chemical information. While generalist databases such as PubChem provide extensive compound coverage, specialised resources such as Rhea, which relies on the ChEBI ontology, are critical for the study of enzyme-catalysed reactions. A notable challenge arises from the presence of generic structures in ChEBI molecules, which incorporate R-groups as placeholders for various molecular fragments. This creates difficulties for their use in computational pipelines, such as those applied in retro-biosynthesis and biocatalysis. To address this issue, a curated dataset is presented that resolves R-group-containing ChEBI entries into fully defined molecular instances. The pipeline extracts generic molecules from Rhea, identifies compatible substitutions using PubChem and RDKit, and applies tailored filters to generate chemically valid enumerations. The dataset, BioRGroup, is delivered in a standard file format, thereby enabling the systematic integration of previously under-utilised generic structures into computational workflows, enhancing the scope and granularity of chemical data analysis.
ano.nymous@ccsd.cnrs.fr.invalid (Guillaume Gricourt) 27 Oct 2025
https://hal.science/hal-05333962v1
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[hal-05323974] Doomed by cross-feeding: How yeast supports contaminant LAB growth in modeled 2G ethanol fermentation
• Contamination by lactic acid bacteria (LAB) during fermentation is a major problem for biorefineries, resulting in yield losses of 7-22% in first-generation bioethanol with yeast viability decreasing by 83%. • LAB are known to be auxotrophous for some amino acids and vitamins (B-group). Moreover, yeast have been showed to promote LAB growth by providing essential amino acids. • Contamination by LAB was successfully simulated in 2G SScF. • Increasing yeast ratio led to quicker and stronger contamination. • Yeast provides essential amino acids to LAB contaminant in 2G fermentation improving growth and so increasing negative impact on fermentation. • Engineering yeast to limit secretion of certain amino acids, such as glutamine or aspartate, could help inhibit LAB growth and thereby reduce contamination. • A transcriptomic analysis of contaminated fermentations could provide deeper insights into yeast responses under LAB-induced stress.
ano.nymous@ccsd.cnrs.fr.invalid (Kyan Aligholi) 21 Oct 2025
https://ifp.hal.science/hal-05323974v1
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[hal-05323946] How yeast paves the way for lactic acid bacteria contamination in second generation ethanol fermentation
• Contamination by lactic acid bacteria (LAB) during fermentation is a major problem for biorefineries, resulting in yield losses of 7-22% in first-generation bioethanol production processes (Thomas et al., 2001, Narendranath et al., 1997), with yeast viability decreasing by 83% (Bayrock et al., 2004). • Key factors driving contamination in 2G biofuel context, with specific inhibitors. • Results suggest a boosting effect of yeast on the contaminant, potentially through release of essential growth factors. • Identifying metabolites fluxes between both actors through metabolomic studies will allow new contamination control strategies to disrupt this parasitic interaction.
ano.nymous@ccsd.cnrs.fr.invalid (Kyan Aligholi) 21 Oct 2025
https://ifp.hal.science/hal-05323946v1
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[hal-05323928] Innovative Solvent Production Through Genetic Engineering and Microbial consortium approaches
• Genetic engineering and cocultures are complementary approaches to improve fermentation yields • The asaccharolytic C3 producing strain Anaerotignum propionicum (Apr) was characterized in different conditions • A Δhbd mutant of Clostridium acetobutylicum (Cac) was obtained using a CRISPR-Cas9 genetic tool • Co-cultures designed to enhance propanol titer were conducted between the wild type or Δhbd mutant of Cac and Apr
ano.nymous@ccsd.cnrs.fr.invalid (Cyrielle Debeir) 21 Oct 2025
https://ifp.hal.science/hal-05323928v1
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[hal-05323908] Gene essentiality in the solventogenic Clostridium acetobutylicum DSM 792
Clostridium acetobutylicum is a solventogenic, anaerobic, gram-positive bacterium that is commonly considered the model organism for studying Acetone-Butanol-Ethanol (ABE) fermentation. The need to produce these chemicals sustainably and with a minimal impact on the environment has revived interest in research on this bacterium. The recent development of efficient genetic tools allows us to better understand the physiology of this microorganism, aiming to improve its fermentation capacities. Knowledge about gene essentiality would guide future genetic editing strategies and support the understanding of crucial cellular functions in this bacterium. In this work, we applied a transposon insertion site sequencing (TIS) method to generate large mutant libraries containing millions of independent mutants with unique insertion sites, allowing us to identify essential genes. In total, we identified a core group of 418 critical genes needed for in vitro development on rich media containing glucose as a carbon source.
ano.nymous@ccsd.cnrs.fr.invalid (Alexandre Delarouzée) 21 Oct 2025
https://ifp.hal.science/hal-05323908v1
