Systems biology describes a holistic and integrative approach to understand physiology and pathology. The "omic" disciplines include genomics, transcriptomics, proteomics, and metabolic profiling (metabonomics and metabolomics). By adopting a stance, which is opposing (yet complimentary) to conventional research techniques, systems biology offers an overview by assessing the "net" biological effect imposed by a disease or nondisease state. There are a number of different organizational levels to be understood, from DNA to protein, metabolites, cells, organs and organisms, even beyond this to an organism's context. Systems biology relies on the existence of "nodes" and "edges." Nodes are the constituent part of the system being studied (eg, proteins in the proteome), while the edges are the way these constituents interact. In future, it will be increasingly important to collaborate, collating data from multiple studies to improve data sets, making them freely available and undertaking integrative analyses.
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http://dx.doi.org/10.1177/1538574413510628 | DOI Listing |
J Mammary Gland Biol Neoplasia
January 2025
Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Fluorescent biosensors offer a powerful tool for tracking and quantifying protein activity in living systems with high temporospatial resolution. However, the expression of genetically encoded fluorescent proteins can interfere with endogenous signaling pathways, potentially leading to developmental and physiological abnormalities. The EKAREV-NLS mouse model, which carries a FRET-based biosensor for monitoring extracellular signal-regulated kinase (ERK) activity, has been widely utilized both in vivo and in vitro across various cell types and organs.
View Article and Find Full Text PDFEpigenetics
December 2025
Department of Anthropology, Dartmouth College, Hanover, NH, USA.
Menstrual effluent cell profiles have potential as noninvasive biomarkers of female reproductive and gynecological health and disease. We used DNA methylation-based cell type deconvolution (methylation cytometry) to identify cell type profiles in self-collected menstrual effluent. During the second day of their menstrual cycle, healthy participants collected menstrual effluent using a vaginal swab, menstrual cup, and pad.
View Article and Find Full Text PDFAdv Biol (Weinh)
January 2025
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
Synthetic cells offer a versatile platform for addressing biomedical and environmental challenges, due to their modular design and capability to mimic cellular processes such as biosensing, intercellular communication, and metabolism. Constructing synthetic cells capable of stimuli-responsive secretion is vital for applications in targeted drug delivery and biosensor development. Previous attempts at engineering secretion for synthetic cells have been confined to non-specific cargo release via membrane pores, limiting the spatiotemporal precision and specificity necessary for selective secretion.
View Article and Find Full Text PDFActa Physiol (Oxf)
February 2025
Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK.
Aim: Long QT syndrome (LQTS) and catecholaminergic polymorphism ventricular tachycardia (CPVT) are inherited cardiac disorders often caused by mutations in ion channels. These arrhythmia syndromes have recently been associated with calmodulin (CaM) variants. Here, we investigate the impact of the arrhythmogenic variants D131E and Q135P on CaM's structure-function relationship.
View Article and Find Full Text PDFHum Genomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Richards Building B304, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
Background: Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.
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