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∞ Game Strategy of Evolutionary Biological Network in Carcinogenic Process
Published in Bor-Sen Chen, Stochastic Game Strategies and Their Applications, 2019
The onset of cancer is characterized by an accumulation of genetic mutations and epigenetic alterations that are caused by different environmental stresses, including tobacco, chemical agents, radiation, and viruses. These mutations and alterations could typically modify the structures of DNA and chromatin, and consequently alter the gene products or the regulation of gene expression [466]. Molecular biologists have long recognized carcinogenesis as a somatic evolutionary process that involves genetic mutations, epigenetic alterations and natural selection. Indeed, cancer is driven by the somatic evolution of cell lineages [438,467–479]. In this chapter, the somatic evolution of cell lineages in an organ was modeled as a nonlinear stochastic biological network to study natural selection in the evolutionary carcinogenesis. The somatic evolutionary biological network of an organ is driven by intrinsic random fluctuations because of genetic and epigenetic variations and external disturbances attributed to carcinogens and other stressors in the ambient microenvironment. Therefore, we modeled the intrinsic random fluctuations because of genetic and epigenetic variations as a Poisson counting process, and the external disturbances were described by an uncertain signal. Therefore, an organ with different cell species undergoing carcinogenesis was modeled by a nonlinear stochastic system with an intrinsic Poisson counting process and external random disturbances during somatic evolutionary process [17,443].
Proteins and proteomics
Published in Firdos Alam Khan, Biotechnology Fundamentals, 2018
Like genetic mapping, it is also important to know the methods and procedures to identify and quantify proteins. One of the interesting features of proteomics is that it gives a much better understanding of an organism than genomics. First, the level of transcription of a gene gives only a rough estimate of its level of expression into a protein. An mRNA produced in abundance may be degraded rapidly or translated inefficiently, resulting in a small amount of protein. Second, as mentioned earlier, many proteins experience PTMs that profoundly affect their activities. For example, some proteins are not active until they become phosphory-lated (addition of phosphate molecules). Methods such as phosphopro-teomics and glycoproteomics are used to study PTMs. Third, many transcripts give rise to more than one protein, through alternative splicing or alternative PTMs. Fourth, many proteins form complexes with other proteins or RNA molecules and only function in the presence of these other molecules. Finally, the protein degradation rate plays an important role in protein content. In this section, we will learn various techniques and tools used in protein identification and analysis. We will specifically deal with tools and techniques that are being used to analyze the protein structurally, morphologically, and functionally. Ideally, measurement of expression is done by detecting the final gene product (for many genes, this is the protein). However, it is often easier to detect one of the precursors, typically mRNA, and infer gene expression level.
Devising and Synthesis of NEMS and MEMS
Published in Sergey Edward Lyshevski, Nano- and Micro-Electromechanical Systems, 2018
The information content of DNA (genetic material) is in the form of specific sequences of nucleotides (nucleic acids) along the DNA strands. The DNA inherited by an organism results in specific traits by dictating the synthesis of particular proteins. Proteins are the links between genotype and phenotype. Nucleic acids and proteins have specific sequences of monomers that comprise information, and this information from genes to proteins is provided in the linguistic form. Note that the accurate statement is one gene, one polypeptide. However, most proteins consist of single polypeptide. Following the common terminology, we will refer to proteins as the gene product.
A gravity inspired clustering algorithm for gene selection from high-dimensional microarray data
Published in The Imaging Science Journal, 2023
P. Jayashree, V. Brindha, P. Karthik
DNA (Deoxyribonucleic Acid) is a molecule composed of two chains of complex organic molecules called nucleotides. A gene is a sequence of nucleotides within a DNA molecule which stores the content about the synthesis of a ‘gene product’, determining the way a living organism is built and the way it functions. There are four types of nucleotides, by interleaving of which, a DNA strand is formed. These types are designated A (adenine), G (guanine), C (cytosine), and T (thymine). A DNA strand contains chains of nucleotides (called polynucleotide strands) which coil around each other and are bound by hydrogen bonds between complementary nucleotide pairs (called base pairs). The nucleotide types A and T are complementary to each other, with G and C being the other complementary pair. In order for the information present in genes to be expressed, a process called transcription occurs, which converts the encoded information into physical or chemical artefacts in the body of the organism.
Deep multi-modal fusion network with gated unit for breast cancer survival prediction
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Our study uses the METABRIC dataset, from the International Breast Cancer Society’s Molecular Classification Database, which aims to further classify breast cancer tumors based on molecular features that help determine the best course of treatment (METABRIC Group, 2012). This dataset has metabolic tracking data for 1980 authentic breast cancer patients, details of which are presented in Table 1. The information for each breast cancer patient is derived from data in three modalities: clinical data, CNA data, and gene expression data. Clinical characteristics include patient age, tumor size, tumor stage and grade, receptor status, etc., as detailed in Table 2. The copy number of the genome changes during cancer development, and each CNA data represents the copy number of a specific gene in a specific sample. Gene expression is the process of synthesizing genetic information from genes into functional gene products. Different types of cancer cause different gene expression patterns in humans, and each gene expression data indicates the expression level of a specific gene in a specific sample.
Existence and global exponential stability of almost periodic solutions of genetic regulatory networks with time-varying delays
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2020
Lian Duan, Fengjun Di, Zengyun Wang
It is known that living organisms respond to external signals due to a huge variety of genetically precoded responses which is achieved through networks of genes of high connectivity and complexity. Long coils of DNA (deoxyribonucleic acid) in the cell form the chromosomes that contain encoded information necessary for the organism to develop within a changing external environment. The DNA information also controls the expression of gene, whether a specific gene is expressed at a given time depends on the need for its gene product within the cell. The mechanisms that genes encode proteins and some of which in turn regulate gene expression are known as genetic regulatory networks (GRNs). On the other hand, since genetic regulatory networks have high dimensionality and nonlinearity, which can describe the highly complex interactions between mRNAs and proteins, it is meaningful to investigate the network dynamics from the viewpoint of nonlinear system theory. It is also expected that such theoretical studies on genetic networks may promote engineering developments of circuits and systems such as biotechnological design principles of synthetic genetic regulatory networks (Elowitz & Leibler, 2000) and new types of integrated circuits like neurochips learnt from biological neural networks. Consequently, the study of dynamical behaviors of genetic regulatory networks rapidly attracted and focused in the field of research, see (Huang, 1999; Huang & Zhang, 2019; Jiang, Liu, Yu, & Shen, 2015; Ren & Cao, 2008; Sakthivel, Mathiyalagan, Lakshmanan, & Park, 2013; Somogyi & Sniegoski, 1996) and the references therein.