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Robustness and evolvability of biological systems
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
While the genotype denotes the genetic makeup, the phenotype refers to higher-level observable characteristics or traits of an organism. At the molecular level, features such as protein structures, RNA structures or folds can be regarded as molecular phenotypes. At a higher level of organisation, namely the network level, more complex phenotypes can be considered. Table 13.1 lists some examples of genotype–phenotype mapping from RNA to metabolic networks to artificial human-made systems such as digital circuits. Once we determine the mapping between genotype and phenotype—this could be via an experiment, or theoretically, via computation—we can unravel the various genotype networks that span the genotype space. Figure 13.2b shows phenotypes mapped on to genotypes using a variety of colours. Evidently, the genotype space is laced with numerous genotype networks, of different sizes and connectivities.
Toxicology
Published in W. David Yates, Safety Professional’s Reference and Study Guide, 2020
Other classifications of chemicals or toxins include carcinogens, cocarcinogens, epigenetic, genotoxic, mutagen, clastogen, and teratogen, which are described in the following. Carcinogen: Any substance or agent known to cause cancer. Carcinogens do not adhere to the dose–response curve.Cocarcinogen: These agents, when applied immediately prior to or with a genotoxic carcinogen, enhance the oncogenic (cancerous) effect of the agent.Epigenetic: Changes in phenotype (appearance) or gene expression caused by mechanisms other than changes in the underlying DNA sequence.Genotoxic: These materials are known to be potentially mutagenic and carcinogenic in nature. They act directly by altering the DNA.Mutagen: A physical or chemical agent that changes the genetic material (usually DNA) of an organism and thus increases the frequency of mutagens above the natural background level.Teratogens: Any agent that can disturb the development of an embryo or fetus.
Resources and datasets for radiomics
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Ken Chang, Andrew Beers, James Brown, Jayashree Kalpathy-Cramer
Medical imaging allows for non-invasive evaluation of disease to aid with clinical decision-making. However, current imaging within typical clinical workflows is often qualitative and subject to variable interpretation. As such, there is need for more quantitative assessment of disease. Radiomics is a technique that allows for delineation of quantitative features extracted from single- or multi-modal imaging that describe disease imaging characteristics. These imaging features may include descriptors such as image intensity, shape, and texture. While the utility of individual features may be limited, the many imaging features extracted from a patient describe a disease phenotype that can be used collectively to predict clinical variables such as genomics, treatment response, and survival. 1–4 This is accomplished through the use of machine learning techniques that can extract highly predictive imaging phenotypes from high quantities of features. This approach is important in the era of “precision medicine,” which is focused on a more customized approach to patient care. In this chapter, we will discuss publicly available resources for practitioners of radiomics. This includes software, packages, and datasets. We will also briefly review best practices and lessons learned for radiomics research, developed through multi-institutional collaborative projects.
Epigenotoxicity: a danger to the future life
Published in Journal of Environmental Science and Health, Part A, 2023
Farzaneh Kefayati, Atoosa Karimi Babaahmadi, Taraneh Mousavi, Mahshid Hodjat, Mohammad Abdollahi
Epigenetic mechanisms are divided into DNA modifications, chromatin modifications, non-coding RNAs and RNA modifications, which produce a set of potential hereditary changes in gene expression;[2] thus, epigenetic markers can persevere throughout growth and likely pass from offspring to offspring. For example, the open state of chromatin is caused by chemical changes in histone proteins that facilitate gene expression by interacting with transcription factors and enzymes with DNA, or the closed state of heterochromatin, which prevents the initiation of transcription and suppresses gene expression. Although epigenetic markers are stable and regulate gene expression, environmental factors can act as stimuli; thus, altered epigenetic patterns may change phenotypic responses via different pathways and disruption of epigenetic modifiers.[3] These factors include air pollution, metals, pesticides, and electrical waste (E-waste), which have become more prevalent following urbanization and the expansion of industries. The role of environmental stimuli on epigenetic changes can be clearly understood in the case of identical twins. Although they have the same DNA sequence, epigenetic mechanisms such as DNA methylation and histone modification have led to phenotypic differences resulting from different exposure to environmental factors.[1]
Artificial intelligence: improving the efficiency of cardiovascular imaging
Published in Expert Review of Medical Devices, 2020
Andrew Lin, Márton Kolossváry, Ivana Išgum, Pál Maurovich-Horvat, Piotr J Slomka, Damini Dey
Cotemporary progress and studies in AI and cardiovascular imaging have been driven by several forces. First, rapid advances in imaging technology, EHR, and mobile solutions have made big data encompassing the heterogeneity of patient and disease features available. Second is the development of new and sophisticated AI algorithms and exploratory methods to analyze high-dimensional data. Third is hardware advances such as graphics processing units and other digital accelerators. Fourth, the emergence of cloud-based computing and open-source AI software packages have provided low-cost and portable solutions to complicated data processing. Cardiovascular medicine is primed for scalable AI applications which can interpret more imaging data in greater depth than ever before, via automated algorithms and advanced data analysis techniques. This will enable novel insights into disease genotypes and phenotypes and enhance outcome prediction.
Digital workflow for climate resilient building façade generation
Published in Building Research & Information, 2023
The use of data in performance-based design (PBD) represent one of the big opportunities offered by digital computation for design resilient buildings and façades. Unfortunately, due to methodology and software limitations, the performance evaluation through data analysis is not used as a generative element in the early-stage phase and as result we see the same identical archetypes applied in different climate zones. The computational approach offers the possibility to link a metadesign (Kolarevic, 2018), with quantitative data derived by simulation and allows to better understand of the relationship between geometry as well as materials and performative criteria defined as benchmark, from urban to skin scale. The metadesign describes the genotype of the project, that is its genetic makeup or rather the DNA. This DNA is represented by the parametric model that describes the geometry. The geometry requires careful consideration not only of its parameters, but the resulting parametric hierarchy, with parameters articulated at different levels, with a clear definition of interdependencies (Kolarevic, 2018). To fully exploit the design space, it is important to introduce the concept of phenotype as the set of morphological and functional characteristics of an organism, as they result from the expression of its genotype and from environmental influences. Through the approach proposed above, it is possible to define a different concept of resiliency and adaptation that considers façade design as part of the whole process and fully exploit the design complexity of the post-digital era enabled by digital computation and fabrication.