Effect of Poly(acrylic acid) on Crystallization of Calcium Carbonate in a Hydrogel

In a biological system, biomineral is regulated by a controlled mass transfer as well as an assistance of soluble and insoluble macromolecules. Inspired by biomineralization, calcium carbonate morphologies were controlled...

Metabolism of Phytochemicals

Several phytochemicals have been developed as medicinal compounds. Extensive research has recently been conducted on phytochemicals such as curcumin, resveratrol, catechin, gallic acid, humulone, quercetin, rutin, diosgenin, allicin, gingerenone-A, caffeic acid, ellagic acid, kaempferol, isorhamnetin, chlorogenic acid, and others. All of these phytochemicals are metabolized in the biological system. To study the metabolic pathways of phytochemicals, studies are done using both in vitro and in vivo techniques. Metabolism is critical in determining phytochemical bioavailability, pharmacokinetics, and effectiveness. Metabolism can occur in organs such as the intestine, liver, gut, and spleen. The metabolic process is aided by a variety of enzymes, including cytochrome P450 enzymes found in the organs. This study outlines a few phytochemicals metabolic pathways. Tannic acid, ellagic acid, curcumin, quercetin, and resveratrol are selected and explained as examples.

Bifurcations in a Time-Delayed Birhythmic Biological System with Fractional Derivative and Lévy Noise

The birhythmic oscillation is of great significance in biology and engineering, and this paper presents a bifurcation analysis in a time-delayed birhythmic oscillator containing fractional derivative and Lévy noise. The numerical method is used to explore the influence of various parameters on the bifurcation of the birhythmic system, and the role of fractional derivative and Lévy noise in inducing or inhibiting birhythmicity in a time-delayed birhythmic biological system is examined in this work. First, we use a numerical method to calculate the fractional derivative, which has a fast calculation speed. Then the McCulloch algorithm is employed to generate Lévy random numbers. Finally, the stationary probability density function graph of the amplitude is obtained by Monte Carlo simulation. The results show that the fractional damping and Lévy noise can effectively control the characteristics of the birhythmic oscillator, and the change of the parameters (except the skewness parameter) can cause the system bifurcation. In addition, this article further discusses the interaction of fractional derivative and time delay in a birhythmic system with Lévy noise, proving that adjusting parameters of time delay can lead to abundant bifurcations. Our research may help to further explore the bifurcation phenomenon of birhythmic biological system, and has a practical significance.

Antioxidative Role of Synthetic and Natural Ascorbic Acid (Vitamin C) in Metabolic Pathways of A Biological System

The properties of a substance are determined by the structure of its component molecules. Ascorbic acid occurs abundantly in fresh fruit, especially blackcurrants, citrus fruit and strawberries, and in most fresh vegetables; good sources are broccoli and peppers. It is destroyed by heat and is not well stored in the body3. Ascorbic acid is a good reducing agent and facilitates many metabolic reaction and repair processes. In pharmaceutical preparations and fruit juices, ascorbic acid is readily separated from other compounds by TLC on silica gel and quantitated directly by absorption at 254nm. Serum and plasma may be deproteinized with twice the volume of methanol or ethanol.

DeepSNEM: Deep Signaling Network Embeddings for compound mechanism of action identification

The analysis and comparison of compounds' transcriptomic signatures can help elucidate a compound's Mechanism of Action (MoA) in a biological system. In order to take into account the complexity of the biological system, several computational methods have been developed that utilize prior knowledge of molecular interactions to create a signaling network representation that best explains the compound's effect. However, due to their complex structure, large scale datasets of compound-induced signaling networks and methods specifically tailored to their analysis and comparison are very limited. Our goal is to develop graph deep learning models that are optimized to transform compound-induced signaling networks into high-dimensional representations and investigate their relationship with their respective MoAs. We created a new dataset of compound-induced signaling networks by applying the CARNIVAL network creation pipeline on the gene expression profiles of the CMap dataset. Furthermore, we developed a novel unsupervised graph deep learning pipeline, called deepSNEM, to encode the information in the compound-induced signaling networks in fixed-length high-dimensional representations. The core of deepSNEM is a graph transformer network, trained to maximize the mutual information between whole-graph and sub-graph representations that belong to similar perturbations. By clustering the deepSNEM embeddings, using the k-means algorithm, we were able to identify distinct clusters that are significantly enriched for mTOR, topoisomerase, HDAC and protein synthesis inhibitors respectively. Additionally, we developed a subgraph importance pipeline and identified important nodes and subgraphs that were found to be directly related to the most prevalent MoA of the assigned cluster. As a use case, deepSNEM was applied on compounds' gene expression profiles from various experimental platforms (MicroArrays and RNA sequencing) and the results indicate that correct hypotheses can be generated regarding their MoA.

Lattice-based microenvironmental uncertainty driven phenotypic decision-making: a comparison with Notch-Delta-Jagged signaling

Phenotypic decision-making is a process of determining important phenotypes in accordance with the available microenvironmental information. Although phenotypic decision at the level of a single cell has been precisely studied, but the knowledge is still imperceptible at the multicellular level. How cells sense their environment and adapt? How single cells change their phenotype in a multicellular complex environment (without knowing the interactions among the cells), is still a rheotorical question. To unravel the fragmental story of multicellular decision-making, Least microEnvironmental Uncertainty Principle (LEUP) was refined and applied in this context. To address this set of questions, we use variational principle to grasp the role of sensitivity, build a LEUP driven agent-based model on a lattice which solely hinges on microenvironmental information and investigate the parallels in a well-known biological system, viz., Notch-Delta-Jagged signaling pathway. The analyses of this model led us to interesting spatiotemporal patterns in a population of cells, responsive to the sensitivity parameter and the radius of interaction. This resembles the tissue-level pattern of a population of cells interacting via Notch-Delta-Jagged signaling pathway in some parameter regimes.