Epidermal Growth Factor Signaling towards Proliferation: Modeling and Logic Inference Using Forward and Backward Search

Metabolic Shades of S-D-Lactoylglutathione

S-D-lactoylglutathione (SDL) is an intermediate of the glutathione-dependent metabolism of methylglyoxal (MGO) by glyoxalases. MGO is an electrophilic compound that is inevitably produced in conjunction with glucose breakdown and is essentially metabolized via the glyoxalase route. In the last decades, MGO metabolism and its cytotoxic effects have been under active investigation, while almost nothing is known about SDL. This article seeks to fill the gap by presenting an overview of the chemistry, biochemistry, physiological role and clinical importance of SDL. The effects of intracellular SDL are investigated in three main directions: as a substrate for post-translational protein modifications, as a reservoir for mitochondrial reduced glutathione and as an energy currency. In essence, all three approaches point to one direction, namely, a metabolism-related regulatory role, enhancing the cellular defense against insults. It is also suggested that an increased plasma concentration of SDL or its metabolites may possibly serve as marker molecules in hemolytic states, particularly when the cause of hemolysis is a disturbance of the pay-off phase of the glycolytic chain. Finally, SDL could also represent a useful marker in such metabolic disorders as diabetes mellitus or ketotic states, in which its formation is expected to be enhanced. Despite the lack of clear-cut evidence underlying the clinical and experimental findings, the investigation of SDL metabolism is a promising field of research.

Artificial intelligence for modeling uveal melanoma

Understanding of the cellular signaling pathways involved in cancer disease is of great importance. These complex biological mechanisms can be thoroughly revealed by their structure, dynamics, and control methods. Artificial intelligence offers rule-based models that favor the research of human signaling processes. In this paper, we give an overview of the advantages of the formalism of symbolic models in medical biology and cell biology of the uveal melanoma. A language is described that allows us: (1) To define the system states and elements with their alterations; (2) To model the dynamics of the cellular system; and (3) To perform inference-based analysis with the logical tools of the language.

Soft Set Theory for Decision Making in Computational Biology under Incomplete Information

The study of biological systems is complex and of great importance. There exist numerous approaches to signal transduction processes, including symbolic modeling of cellular adaptation. The use of formal methods for computational systems biology eases the analysis of cellular models and the establishment of the causes and consequences of certain cellular situations associated to diseases. In this paper, we define an application of logic modeling with rewriting logic and soft set theory. Our approach to decision making with soft sets offers a novel strategy that complements standard strategies. We implement a metalevel strategy to control and guide the rewriting process of the Maude rewriting engine. In particular, we adapt mathematical methods to capture imprecision, vagueness, and uncertainty in the available data. Using this new strategy, we propose an extension in the biological symbolic models of Pathway Logic. Our ultimate aim is to automatically determine the rules that are most appropriate and adjusted to reality in dynamic systems using decision making with incomplete soft sets.

The role of epithelial growth factors and insulin growth factors in the adrenal neoplasms

Human fetal and adult adrenal gland express both insulin growth factor-1 (IGF-1) and IGF-2, their receptors (IGF-Rs) and a variety of specific IGF binding proteins suggesting their potential role in the regulation of adrenal growth and function. IGF-2 overexpression is essential for the growth of monoclonal lesions, such as large benign adenomas (ACA) and adrenocortical carcinomas (ACC) and has been found to contribute to tumorigenesis in Beckwith-Wiedemann syndrome. IGF-2 is the most highly expressed gene observed in more than 85% of ACCs. However, no significant differences in clinical, biological and transcriptomic traits were found between tumors with high and low expression of IGF-2. On the contrary, the expression of IGF-1R, mediating the IGF-2 effects in vivo, was more discriminant between malignant (overexpression) and benign tumors. Data on the role of epithelial growth factor (EGF) and its receptor (EGF-R) in adrenocortical tumorigenesis are controversial. Several studies have shown EGF-R overexpression in ACCs but not in benign ACAs, suggesting that EGF-R could potentially be used as a marker for the differential diagnosis of ACAs and ACCs. Although, in vitro and animal studies provide promising results in the therapeutic role of IGF and EGF pathway inhibitors, the available data in humans are still not encouraging. Herein, we aim to present recent data on the role of IGF and EGF pathways in adrenal development and tumorigenesis and their potential implication in the treatment of the ACC, a rare malignancy with very poor prognosis.

Use of real-time cellular analysis and Plackett-Burman design to develop the serum-free media for PC-3 prostate cancer cells

In this study, we developed a rapid strategy to screen a serum-free medium for culturing the anchorage-dependent PC-3 prostate cancer cells, which was going to be prepared in large scale to generate GM-CSF/TNFα-surface-modified whole cell prostate cancer vaccine. Automated real-time cellular analysis as a rapid and non-invasive technology was used to monitor the growth of PC-3 cells in 16-well plates. At the same time, Plackett-Burman design was employed to identify the most influential formulation by integrating relevant information statistically. The effects of the 16 selected factors were evaluated during exponential cell growth and three medium constituents (EGF, FGF and linoleic acid) were identified to have significant effects on the cell growth. Subsequently, the response surface methodology with central composite design was applied to determine the interactions among the three factors so that these factors were optimized to improve cell growth. Finally, the prediction of the best combination was made under the maximal response to optimize cell growth by Design-Expert software 7.0. A total of 20 experiments were conducted to construct a quadratic model and a second-order polynomial equation. With the optimized combination validated by the stability test of serial passaging PC-3 cells, the serum-free medium had similar cell density and cell viability to the original serum medium. In summary, this high-throughput scheme minimized the screening time and may thus provide a new platform to efficiently develop the serum-free media for adherent cells.