Modeling the effect of soil meso- and macropores topology on the biodegradation of a soluble carbon substrate

Soil structure and interactions between biotic and abiotic processes are increasingly recognized as important for explaining the large uncertainties in the outputs of macroscopic SOM decomposition models. We present a numerical analysis to assess the role of meso- and macropore topology on the biodegradation of a soluble carbon substrate in variably water saturated and pure diffusion conditions . Our analysis was built as a complete factorial design and used a new 3D pore-scale model, LBioS, that couples a diffusion Lattice-Boltzmann model and a compartmental biodegradation model. The scenarios combined contrasted modalities of four factors: meso- and macropore space geometry, water saturation, bacterial distribution and physiology. A global sensitivity analysis of these factors highlighted the role of physical factors in the biodegradation kinetics of our scenarios. Bacteria location explained 28% of the total variance in substrate concentration in all scenarios, while the interactions among location, saturation and geometry explained up to 51% of it

In situ and ex situ bioremediation of heavy metals: the present scenario

Enhanced population growth, rapid industrialization, urbanization and hazardous industrial practices have resulted in the development of environmental pollution in the past few decades. Heavy metals are one of those pollutants that are related to environmental and public health concerns based on their toxicity. Effective bioremediation may be accomplished through “ex situ” and “in situ” processes, based on the type and concentration of pollutants, characteristics of the site but is not limited to cost. The recent developments in artificial neural network and microbial gene editing help to improve “in situ” bioremediation of heavy metals from the polluted sites. Multi-omics approaches are adopted for the effective removal of heavy metals by various indigenous microbes. This overview introspects two major bioremediation techniques, their principles, limitations and advantages, and the new aspects of nanobiotechnology, computational biology and DNA technology to improve the scenario

The environmental fate of organic pollutants through the global microbial metabolism

The production of new chemicals for industrial or therapeutic applications exceeds our ability to generate experimental data on their biological fate once they are released into the environment. Typically, mixtures of organic pollutants are freed into a variety of sites inhabited by diverse microorganisms, which structure complex multispecies metabolic networks. A machine learning approach has been instrumental to expose a correlation between the frequency of 149 atomic triads (chemotopes) common in organo-chemical compounds and the global capacity of microorganisms to metabolise them. Depending on the type of environmental fate defined, the system can correctly predict the biodegradative outcome for 73–87% of compounds. This system is available to the community as a web server (http://www.pdg.cnb.uam.es/BDPSERVER). The application of this predictive tool to chemical species released into the environment provides an early instrument for tentatively classifying the compounds as biodegradable or recalcitrant. Automated surveys of lists of industrial chemicals currently employed in large quantities revealed that herbicides are the group of functional molecules more difficult to recycle into the biosphere through the inclusive microbial metabolism

The logic layout of the TOL network of Pseudomonas putida pWW0 plasmid stems from a metabolic amplifier motif (MAM) that optimizes biodegradation of m-xylene

<p>Abstract</p> <p>Background</p> <p>The genetic network of the TOL plasmid pWW0 of the soil bacterium <it>Pseudomonas putida </it>mt-2 for catabolism of <it>m-</it>xylene is an archetypal model for environmental biodegradation of aromatic pollutants. Although nearly every metabolic and transcriptional component of this regulatory system is known to an extraordinary molecular detail, the complexity of its architecture is still perplexing. To gain an insight into the inner layout of this network a logic model of the TOL system was implemented, simulated and experimentally validated. This analysis made sense of the specific regulatory topology out on the basis of an unprecedented network motif around which the entire genetic circuit for <it>m-</it>xylene catabolism gravitates.</p> <p>Results</p> <p>The most salient feature of the whole TOL regulatory network is the control exerted by two distinct but still intertwined regulators (XylR and XylS) on expression of two separated catabolic operons (<it>upper </it>and <it>lower</it>) for catabolism of <it>m</it>-xylene. Following model reduction, a minimal modular circuit composed by five basic variables appeared to suffice for fully describing the operation of the entire system. <it>In silico </it>simulation of the effect of various perturbations were compared with experimental data in which specific portions of the network were activated with selected inducers: <it>m-</it>xylene, <it>o-</it>xylene, 3-methylbenzylalcohol and 3-methylbenzoate. The results accredited the ability of the model to faithfully describe network dynamics. This analysis revealed that the entire regulatory structure of the TOL system enables the action an unprecedented metabolic amplifier motif (MAM). This motif synchronizes expression of the <it>upper </it>and <it>lower </it>portions of a very long metabolic system when cells face the head pathway substrate, <it>m-</it>xylene.</p> <p>Conclusion</p> <p>Logic modeling of the TOL circuit accounted for the intricate regulatory topology of this otherwise simple metabolic device. The found MAM appears to ensure a simultaneous expression of the <it>upper </it>and <it>lower </it>segments of the <it>m-</it>xylene catabolic route that would be difficult to bring about with a standard substrate-responsive single promoter. Furthermore, it is plausible that the MAM helps to avoid biochemical conflicts between competing plasmid-encoded and chromosomally-encoded pathways in this bacterium.</p

Genetically modified organisms for the environment: stories of success and failure and what we have learned from them

The expectations raised in the mid-1980s on the potential of genetic engineering for in situ remediation of environmental pollution have not been entirely fulfilled. Yet, we have learned a good deal about the expression of catabolic pathways by bacteria in their natural habitats, and how environmental conditions dictate the expression of desired catalytic activities. The many different choices between nutrients and responses to stresses form a network of transcriptional switches which, given the redundance and robustness of the regulatory circuits involved, can be neither unraveled through standard genetic analysis nor artificially programmed in a simple manner. Available data suggest that population dynamics and physiological control of catabolic gene expression prevail over any artificial attempt to engineer an optimal performance of the wanted catalytic activities. In this review, several valuable spin-offs of past research into genetically modified organisms with environmental applications are discussed, along with the impact of Systems Biology and Synthetic Biology in the future of environmental biotechnology. [Int Microbiol 2005; 8(3):213-222

Modeling microbial regulation of pesticide turnover in soils

Pesticides are widely used for pest control in agriculture. Besides their intended use, their long-term fate in real systems is not well understood. They may persist in soils, thereby altering ecosystem functioning and ultimately affecting human health. Pesticide fate is assessed through dissipation experiments in the laboratory or the field. While field experiments provide a close representation of real systems, they are often costly and can be influenced by many unknown or uncontrollable variables. Laboratory experiments, on the other hand, are cheaper and have good control over the governing variables, but due to simplification, extrapolation of the results to real systems can be limited. Mechanistic models are a powerful tool to connect lab and field data and help us to improve our process understanding. Therefore, I used mechanistic, process-based models to assess key microbial regulations of pesticide degradation. I tested my model hypotheses with two pesticide classes: i) chlorophenoxy herbicides (MCPA (2-methyl-4-chlorophenoxyacetic acid) and 2,4-D (2,4-Dichlorophenoxyacetic acid)), and ii) triazines (atrazine (AT)), in an ideal scenario, where bacterial degraders and pesticides are co-localized. This thesis explores some potential controls of pesticide degradation in soils: i) regulated gene expression, ii) mass-transfer process across the bacterial cell membranes, iii) bioenergetic constraints, and iv) environmental factors (soil temperature and moisture). The models presented in this thesis show that including microbial regulations improves predictions of pesticide degradation, compared to conventional models based on Monod kinetics. The gene-centric models achieved a better representation of microbial dynamics and enable us to explore the relationship between functional genes and process rates, and the models that used transition state theory to account for bioenergetic constraints improved the description of degradation at low concentrations. However, the lack of informative data for the validation of model processes hampered model development. Therefore, in the fourth part of this thesis, I used atrazine with its rather complex degradation pathway to apply a prospective optimal design method to find the optimal experimental designs to enable us identifying the degradation pathway present in a given environment. The optimal designs found suggest to prioritize determining metabolites and biomass of specific degraders, which are not typically measured in environmental fate studies. These data will lead to more robust model formulations for risk assessment and decision-making. With this thesis, I revealed important regulations of pesticide degradation in soils that help to improve process understanding and model predictions. I provided simple model formulations, for example the Hill function for gene expression and transition state theory for bioenergetic growth constraints, which can easily be integrated into biogeochemical models. My thesis covers initial but essential steps towards a predictive pesticide degradation model usable for risk assessment and decision-making. I also discuss implication for further research, in particular how mechanistic process-based modeling could be combined with new technologies like omics and machine learning.Pestizide sind weit verbreitet in der landwirtschaftlichen Schädlingsbekämpfung. Anders als ihre Wirkungsweise, ist ihr Langzeitverbleib in der Umwelt nicht gut verstanden. Sie gelangen in den Boden und können sich dort anreichen und die Bodenfunktionen beeinträchtigen und letzendlich auch die menschliche Gesundheit gefährden. Die Ausbreitung von Pestiziden wird anhand von Abbauversuchen in Labor- und Feldexperimenten ermittelt. Feldexperimente bieten ein relativ genaues Abbild natürlicher Systeme, sind jedoch meist teuer und können durch unbekannte oder nicht kontrollierbare Faktoren stark beeinflusst werden. Laborexperimente sind in dieser Hinsicht kostengünstiger und bieten eine gute Kontrolle der einwirkenden Faktoren. Allerdings lassen sich die Ergebnisse nur begrenzt auf natürliche Systeme übertragen. Mechanistische Modelle sind ein mächtiges Werkzeug, um Labor- und Felddaten zusammenzuführen und helfen uns dabei, die mikrobiellen Regulationsmechanismen des Pestizidabbaus im Boden besser zu verstehen. Aus diesem Grund habe ich mechanistische, prozess basierte Modelle eingesetzt. Ich habe meine Modellhypothesen bei zwei Pestizidgruppen getestet: i) Chlorphenoxyherbiziden (MCPA (2-Methyl-4-chlorphenoxyessigsäure) und 2,4-D (2,4-Dichlorphenoxyessigsäure)) und ii) Triazinen (Atrazin (AT)), in einem Idealszenario, wo bakterielle Abbauer und Pestizid kolokalisiert auftreten. Meine Doktorarbeit konzentriert sich auf einige der potenziellen Kontrollmechanismen des Pestizidabbaus im Boden: i) regulierte Genexpression, ii) Massetransferprozesse durch die Zellmembran, iii) bioenergetische Limitierungen und iv) Umweltfaktoren (Bodentemperatur und Bodenfeuchte). Die in dieser Doktorarbeit vorgestellten Modelle zeigen, dass die Berücksichtigung mikrobieller Regulationen Vorhersagen des Pestizidabbaus verbessert, gegenüber herkömmlichen, auf Monod-Kinetik-basierenden Modellen. Die gen-basierten Modelle erreichten eine bessere Repräsentation der mikrobiellen Dynamik und geben uns die Möglichkeit, den Zusammenhang zwischen funktionellen Genen und Prozessraten herzustellen, wohingegen Modelle, die die Abbaugeschwindigkeit auf Grundlage der Theorie des Übergangszustandes limitieren, eine genauere Konzentrationen liefern. Der Mangel an Messdaten zur Validierung behinderte allerdings die Modellentwicklung. Daher benutzte ich ich im vierten Teil dieser Arbeit, am Beispiel von Atrazin, mit seinem eher komplexen Abbauweg, eine Methode des prospective optimal design, um das bestmögliche Experimentaldesign zu finden, mit dem wir den in einer bestimmten Umgebung vorherrschenden Abbauweg identifizieren können. Die gefundenen optimalen Designs weisen auf die Erfordenis hin, die Messung von Hauptmetaboliten und Biomasse von spezifischen Abbauern zu priorisieren, welche in Abbauversuchen typischerweise nicht gemessen werden. Die Informationen aus diesen Daten werden zu besseren Modellformulierungen führen, die sich für Risikoabschätzung und Entscheidungsfindung nutzen lassen. Mit dieser Doktorarbeit konnte ich für den Pestizidabbau im Boden wichtige Regulationsmechanismen aufdecken, und so, unser Verständnis und Vorhersagen solcher Prozesse verbessern. Ich stelle einfache Modellformulierungen bereit, beispielsweise die Hill-Funktion für Genexpression und eine Implementierung der Theorie des Übergangszustands, welche sich einfach in biogeochemische Modelle integrieren lassen. Meine Arbeit liefert grundlegende und entscheidende Schritte zur Entwicklung eines Vorhersagemodells für den Pestizidabbau und dessen Einsatz in Risikoabschätzung und Entscheidungsfindung. Darüber hinaus gebe ich einen Ausblick auf weiterführende Forschungsansätze, insbesondere wie sich mechanistische, prozess-basierte Modellansätze mit neuen Technologien wie omics und Machine Learning verbinden lassen könnten

Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis

The pressure to search effective bioremediation methodologies for contaminated ecosystems has led to the large-scale identification of microbial species and metabolic degradation pathways. However, minor attention has been paid to the study of bioremediation in marine food webs and to the definition of integrated strategies for reducing bioaccumulation in species. We propose a novel computational framework for analysing the multiscale effects of bioremediation at the ecosystem level, based on coupling food web bioaccumulation models and metabolic models of degrading bacteria. The combination of techniques from synthetic biology and ecological network analysis allows the specification of arbitrary scenarios of contaminant removal and the evaluation of strategies based on natural or synthetic microbial strains. In this study, we derive a bioaccumulation model of polychlorinated biphenyls (PCBs) in the Adriatic food web, and we extend a metabolic reconstruction of Pseudomonas putida KT2440 (iJN746) with the aerobic pathway of PCBs degradation. We assess the effectiveness of different bioremediation scenarios in reducing PCBs concentration in species and we study indices of species centrality to measure their importance in the contaminant diffusion via feeding links. The analysis of the Adriatic sea case study suggests that our framework could represent a practical tool in the design of effective remediation strategies, providing at the same time insights into the ecological role of microbial communities within food webs