Prediction and optimization of the laccase-mediated synthesis of the antimicrobial compound iodine (I2)

Schubert, Mark; Fey, A.; Ihssen, Julian; Civardi, Chiara; Schwarze, Francis W. M. R.; Mourad, Safer

doi:10.1016/j.jbiotec.2014.11.029

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Fungi are often considered in the context of wood degradation, but they can also be utilized to specifically degrade the wood in a controlled way. Fungi or enzyme systems can for instance be used to modify or open the wood structure and facilitate further functionalization treatments . For genetic modification of trees, a common aim is to reduce lignin content or ease lignin degradation for the purpose of pulp and biofuel production .…”

Section: Discussionmentioning

confidence: 99%

Bioinspired Wood Nanotechnology for Functional Materials

2018

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It is a challenging task to realize the vision of hierarchically structured nanomaterials for large-scale applications. Herein, the biomaterial wood as a large-scale biotemplate for functionalization at multiple scales is discussed, to provide an increased property range to this renewable and CO -storing bioresource, which is available at low cost and in large quantities. The Progress Report reviews the emerging field of functional wood materials in view of the specific features of the structural template and novel nanotechnological approaches for the development of wood-polymer composites and wood-mineral hybrids for advanced property profiles and new functions.

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Section: Discussionmentioning

confidence: 99%

Bioinspired Wood Nanotechnology for Functional Materials

2018

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“…Another potential application of laccases is in the oxidation of iodide to generate iodine (I 2 ), an inexpensive and efficient antimicrobial compound (Xu, ). Iodide oxidation by laccase was proposed to inactivate Bacillus anthracis spores (Niederwöhrmeier et al ., ), and in the past year, the laccase‐mediated synthesis of I 2 based on an artificial neural network was studied with a genetic algorithm (Schubert et al ., ).…”

Section: Laccases In the Cutting Edge Of Biotechnologymentioning

confidence: 97%

Laccase: a multi‐purpose biocatalyst at the forefront of biotechnology

Maté

Alcalde

2016

Microbial Biotechnology

439

228

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SummaryLaccases are multicopper containing enzymes capable of performing one electron oxidation of a broad range of substrates. Using molecular oxygen as the final electron acceptor, they release only water as a by‐product, and as such, laccases are eco‐friendly, versatile biocatalysts that have generated an enormous biotechnological interest. Indeed, this group of enzymes has been used in different industrial fields for very diverse purposes, from food additive and beverage processing to biomedical diagnosis, and as cross‐linking agents for furniture construction or in the production of biofuels. Laccases have also been studied intensely in nanobiotechnology for the development of implantable biosensors and biofuel cells. Moreover, their capacity to transform complex xenobiotics makes them useful biocatalysts in enzymatic bioremediation. This review summarizes the most significant recent advances in the use of laccases and their future perspectives in biotechnology.

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“…Other classical designs used for the detection of interactions and quadratic effects, which are built upon 2 k factorial or fractional factorial designs, include the central composite design (CCD) and Box–Behnken Design (BBD). Full factorials, fractional factorials, CCDs, and BBDs are all designs used in the context of response surface methodology (RSM). , In a DoE + ML framework, an ML model, often combined with an evolutionary algorithm such as the genetic algorithm (GA), replaces the typical methods for analyzing DoE data such as RSM when performing analysis. − The “classical” DoE + RSM/ML frameworks can be viewed as a linear pipeline from data to some end application of that data (Figure a). A recent trend in the topic of DoE and ML is based on an adaptive design paradigm in which the predictions of a surrogate model serve as input to define a utility or acquisition function, which dictates the next experimental configurations to be performed, thus introducing an iterative refinement process into the pipeline (Figure b).…”

Section: Managing Wood Complexity With Machine Learningmentioning

confidence: 99%

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

2022

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Wood is a renewable resource with excellent qualities and the potential to become a key element of a future bioeconomy. The increasing environmental awareness and drive to achieve sustainability is leading to a resurgence of research on wood materials. Nevertheless, the global climate changes and associated consequences will soon challenge the wood-value chains in several regions (e.g., central Europe). To cope with these challenges, it is necessary to rethink the current practice of wood sourcing and transformation. The goal of this review is to address the intrinsic natural diversity of wood, from its origin to its technological consequences for the present and future manufacturing of wood products. So far, industrial processes have been optimized to repress the variability of wood properties, enabling more efficient processing and production of reliable products. However, the need to preserve biodiversity and the impact of climate change on forests call for new wood processing techniques and green chemistry protocols for wood modification as enabling factors necessary for managing a more diverse wood provision in the future. This article discusses the past developments that have resulted in the current wood value chains and provides a perspective about how natural variability could be turned into an asset for making truly sustainable wood products. After briefly introducing the chemical and structural complexity of wood, the methods conventionally adopted for industrial homogenization and modification of wood are discussed in relation to their evolution toward increased sustainability. Finally, a perspective is given on technological potentials of machine learning techniques and of novel functional wood materials. Here the main message is that through a combination of sustainable forestry, adherence to green chemistry principles and adapted processes based on machine learning, the wood industry could not only overcome current challenges but also thrive in the near future despite the awaiting challenges.

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Prediction and optimization of the laccase-mediated synthesis of the antimicrobial compound iodine (I2)

Cited by 5 publications

References 11 publications

Bioinspired Wood Nanotechnology for Functional Materials

Bioinspired Wood Nanotechnology for Functional Materials

Laccase: a multi‐purpose biocatalyst at the forefront of biotechnology

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

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