Cell energy metabolism: An update.

Green approach and strategies for wastewater treatment using bioelectrochemical systems: A critical review of fundamental concepts, applications, mechanism, and future trends.

In this paper, a relatively simple and ultra-sensitive Photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor is proposed for detecting different analyte refractive indices (RIs) ranging from 1.33 to 1.43 over a wide range of wavelength spectrum spanning <inline-formula> <tex-math notation="LaTeX">$0.55~\mu $ </tex-math></inline-formula>m to <inline-formula> <tex-math notation="LaTeX">$3.50~\mu $ </tex-math></inline-formula>m. The comprehensive finite-element simulations indicate that it is possible to achieve remarkable sensing performances such as wavelength sensitivity (WS) and figure of merit (FOM) as high as 123,000 nm/RIU and 683 RIU1, respectively, and extremely low value of wavelength resolution (WR) about <inline-formula> <tex-math notation="LaTeX">$8.13\times 10^{-7}$ </tex-math></inline-formula> RIU. A novel artificial neural network (ANN) model is proposed which helps to accurately predict the RIs by carefully examining the simulation data. The mean square error (MSE) and prediction accuracy (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) values for the ANN model are found about 0.0097 and 0.9987, respectively, indicating the high prediction capability of the proposed ANN model. Due to its exceptional sensitivity and precise detection capabilities, the proposed sensor has the potential to serve as a viable option for sensing analyte RI. Additionally, the sensor could be utilized for identifying cancerous cells and detecting urinary tract infections in humans.

Ultra-Sensitive Visible-IR Range Fiber Based Plasmonic Sensor: A Finite-Element Analysis and Deep Learning Approach for RI Prediction

Organisms have evolved a variety of mechanisms to cope with the unpredictability of environmental conditions, and yet mainstream models of metabolic regulation are typically based on strict optimality principles that do not account for uncertainty. This paper introduces a dynamic metabolic modelling framework that is a synthesis of recent ideas on resource allocation and the powerful optimal control formulation of Ramkrishna and colleagues. In particular, their work is extended based on the hypothesis that cellular resources are allocated among elementary flux modes according to the principle of maximum entropy. These concepts both generalise and unify prior approaches to dynamic metabolic modelling by establishing a smooth interpolation between dynamic flux balance analysis and dynamic metabolic models without regulation. The resulting theory is successful in describing 'bet-hedging' strategies employed by cell populations dealing with uncertainty in a fluctuating environment, including heterogenous resource investment, accumulation of reserves in growth-limiting conditions, and the observed behaviour of yeast growing in batch and continuous cultures. The maximum entropy principle is also shown to yield an optimal control law consistent with partitioning resources between elementary flux mode families, which has important practical implications for model reduction, selection, and simulation.

https://link.springer.com/content/pdf/10.1007/s00285-020-01499-6.pdf

Dynamic metabolic resource allocation based on the maximum entropy principle

https://biblio.ugent.be/publication/8724927/file/8724941.pdf

Microbial inefficient substrate use through the perspective of resource allocation models.

The spectacular physical phenomenon of surface plasmon resonance (SPR) is the essence of present‐day plasmonic sensors. Meanwhile, the unique properties of the interaction between light and matter have been carved out into the development of modern‐day diagnostic biosensors. Plasmons, in simple terms, are oscillating free electrons in metallic nano‐structures triggered by an incoming electromagnetic (EM) wave. With the advantages of real‐time and label‐free bio‐sensing, plasmonic sensors are being utilized in multiple diverse areas of food technology, the bio‐medical diagnostic sector, and even the chemical industry. Although this review will be brief, readers can gain a comprehensive picture of the essential elements by taking a broader look into the exploration of SPR sensor design via simulated studies and representative experimental plasmonic schemes developed for bio‐sensing. In short, the various SPR sensing schemes that researchers have explored to realize enhanced SPR sensitivity are reviewed and summarized. Different experimental plasmonic sensors are also examined in which new SPR excitation schemes have been adopted. These "unconventional" designs, specifically those involving hybrid localized surface plasmon resonance (LSPR)‐SPR excitation, may inspire those in the plasmonic field.

Computational Modeling for Intelligent Surface Plasmon Resonance Sensor Design and Experimental Schemes for Real‐Time Plasmonic Biosensing: A Review

The liver is the largest internal organ in the human body with largest mass of glandular tissue. Modeling the liver has been challenging due to its variety of major functions, including processing nutrients and vitamins, detoxification, and regulating body metabolism. The intrinsic shortfalls of conventional two-dimensional (2D) cell culture methods for studying pharmacokinetics in parenchymal cells (hepatocytes) have contributed to suboptimal outcomes in clinical trials and drug development. This prompts the development of highly automated, biomimetic liver-on-a-chip (LOC) devices to simulate native liver structure and function, with the aid of recent progress in microfluidics. LOC offers a cost-effective and accurate model for pharmacokinetics, pharmacodynamics, and toxicity studies. This review provides a critical update on recent developments in designing LOCs and fabrication strategies. We highlight biomimetic design approaches for LOCs, including mimicking liver structure and function, and their diverse applications in areas such as drug screening, toxicity assessment, and real-time biosensing. We capture the newest ideas in the field to advance the field of LOCs and address current challenges.

Liver-on-a-chip: Considerations, advances, and beyond.

Many differentiated cells rely primarily on mitochondrial oxidative phosphorylation for generating energy in the form of ATP needed for cellular metabolism. In contrast most tumor cells instead rely on aerobic glycolysis leading to lactate to about the same extent as on respiration. Warburg found that cancer cells to support oxidative phosphorylation, tend to ferment glucose or other energy source into lactate even in the presence of sufficient oxygen, which is an inefficient way to generate ATP. This effect also occurs in striated muscle cells, activated lymphocytes and microglia, endothelial cells and several mammalian cell types, a phenomenon termed the "Warburg effect". The effect is paradoxical at first glance because the ATP production rate of aerobic glycolysis is much slower than that of respiration and the energy demands are better to be met by pure oxidative phosphorylation. We tackle this question by building a minimal model including three combined reactions. The new aspect in extension to earlier models is that we take into account the possible uptake and oxidation of the fermentation products. We examine the case where the cell can allocate protein on several enzymes in a varying distribution and model this by a linear programming problem in which the objective is to maximize the ATP production rate under different combinations of constraints on enzymes. Depending on the cost of reactions and limitation of the substrates, this leads to pure respiration, pure fermentation, and a mixture of respiration and fermentation. The model predicts that fermentation products are only oxidized when glucose is scarce or its uptake is severely limited.

/pdf/linear-programming-model-can-explain-respiration-of-tmqwtzix2n.pdf

Linear programming model can explain respiration of fermentation products.

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Computational Modeling for Intelligent Surface Plasmon Resonance Sensor Design and Experimental Schemes for Real‐Time Plasmonic Biosensing: A Review

Liver-on-a-chip: Considerations, advances, and beyond.

Linear programming model can explain respiration of fermentation products.