scholarly journals Systems Biology and Experimental Model Systems of Cancer

2020 ◽  
Vol 10 (4) ◽  
pp. 180
Author(s):  
Gizem Damla Yalcin ◽  
Nurseda Danisik ◽  
Rana Can Baygin ◽  
Ahmet Acar
Keyword(s):  
Systems Biology ◽  
Experimental Model ◽  
High Throughput Sequencing ◽  
Critical Role ◽  
Genetic Alterations ◽  
Model Systems ◽  
Treatment Modalities ◽  
Omics Data ◽  
Cancer Systems Biology ◽  
Experimental Model Systems

Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.

Cooperativity and regulation through conformational changes as features of phage assembly

1976 ◽  
Vol 276 (943) ◽  
pp. 3-13 ◽  
Keyword(s):  
Experimental Model ◽  
Conformational Changes ◽  
Model Systems ◽  
Conformational States ◽  
Phage Assembly ◽  
Specific Stage ◽  
T4 Phage ◽  
Experimental Model Systems ◽  
Complete Dissociation

The assembly of bacteriophages provides experimental model systems for the study of regulation at the level of gene products. We discuss the hypothesis of regulation through sequentially induced conformational changes by which precursor-assemblies become ready at a specific stage of maturation to interact with an additional gene product or nucleic acids. Phage mutants provide excellent experimental model systems for studying, for example, the role and fate of the core in the prehead assembly. The conservative maturation of the prehead to the final, stable head consists of several steps whose complexity reflect that of the virus. It includes packaging of DNA. The surface lattice of maturing preheads apparently undergoes several steps characterized by different conformational states as suggested by in vitro studies on a morphological variant of the prehead, the polyhead of phage T4 (Steven, Couture, Aebi & Showe 1976; Laemmli, Amos & Klug 1976). Addition of a partly purified, enriched proteolytic fraction - which is gene 21-dependent - to empty purified polyheads leads to different conformational states. These seem to go in a direction approaching the structure of the surface of finished capsids as studied by Aebi et al . on gene 24 related (Bijlenga, Aebi & Kellenberger 1976) and other genetically defined giant-variants of T4 phage (Doermann, Eiserling & Boehner 1973). We show some experiments which suggest that high cooperativity is responsible for the stabilization of capsids. The activation energy necessary for dissociation of capsids is very high, 247 kJ for T4 capsids, and 10% smaller for T2. Once the energy barrier has been overcome, the capsids are first structurally modified before they undergo partial and finally complete dissociation.

scholarly journals CSAO-1. Interrogative Biology: Unraveling insights into causal disease drivers by use of a dynamic systems biology and Bayesian AI to identify the intersect of disease and healthy signatures

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii1-ii1
Author(s):  
Niven Narain ◽  
Michael Kiebish ◽  
Vivek Vishnudas ◽  
Vladimir Tolstikov ◽  
Gregory Miller ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Data Analytics ◽  
Large Scale ◽  
Model Systems ◽  
Dynamic Monitoring ◽  
Treatment Modalities ◽  
Data Sets ◽  
Health And Disease ◽  
Corrective Response

Abstract The past decade has been witness to an explosive proliferation of data analytics modalities, all seeking to unravel insight into large-scale data sets. Machine learning and AI methodologies now occupy a central role in analyses of data sets that range in nature from genomics, “omics”, clinical, real-world evidence, and demographic data. Despite advances in data analytics/machine learning, access to complex population level clinical and related datasets, translating information into actionable guidance in human health and disease remains a challenge. Interrogative Biology, a systems biology/AI platform generates an unbiased, data-informed network for identifying targets (disease drivers) and biomarkers for disease interception at the point of transition to dysregulation, preceding clinical phenotype. The data topology is enabled by a systematic acquisition and interrogation of longitudinal bio-samples of clinically annotated human matrices (e.g. blood, urine, saliva, tissues) subjected to comprehensive multi-omic (genomic, proteomics, lipidomics and metabolomics) profiling over time. The molecular profiles are integrated with clinical health information using Bayesian artificial intelligence analytics, bAIcis, to generate causal network maps of overall health. Differentials between “health” and “disease” network maps identifies drivers (targets and biomarkers) of disease and are rapidly validated in orthogonal wet-lab disease specific perturbed model systems. Target information imputed into the bAIcis framework can define therapeutic strategies including identification of existing drugs and bio-actives for corrective response. Using a combination of clinic based sampling and dried blood spot analysis for longitudinal dynamic monitoring of markers of health-disease status provides opportunity for proactive clinical management and intervention for corrective response in advance of major deterioration of health status. Taken together, the approach herein allows for health surveillance based on in-depth biological profiling of alterations in the patient narrative to guide treatment modalities and strategies in a longitudinal and dynamic manner to identify, track, intercept, and arrest human disease.

scholarly journals Uterine glands: development, function and experimental model systems

2013 ◽  
Vol 19 (9) ◽  
pp. 547-558 ◽  
Author(s):  
P. S. Cooke ◽  
T. E. Spencer ◽  
F. F. Bartol ◽  
K. Hayashi
Keyword(s):  
Experimental Model ◽  
Model Systems ◽  
Experimental Model Systems ◽  
Uterine Glands

The Validity of Models

1997 ◽  
Vol 11 (1) ◽  
pp. 150-159 ◽  
Author(s):  
J.W.T. Wimpenny
Keyword(s):  
Mathematical Models ◽  
Experimental Model ◽  
Scientific Research ◽  
Model Systems ◽  
Ecological Problem ◽  
Complete Understanding ◽  
Four Levels ◽  
Experimental Model Systems

The ubiquity of biofilm and its classification as a microbial aggregate is discussed. Investigations into any microbial ecological problem operate at four levels: (i) in situ investigations, (ii) the use of microcosms, (iii) experimental model systems, and (iv) mathematical models. Each of these is defined and their use in biofilm research illustrated. It is concluded that all these approaches are valid and that scientific research in general and biofilm research in particular must profit by the use widely different methods if a complete understanding of a system is to be achieved.

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