scholarly journals Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers

2019 ◽  
Vol 26 (6) ◽  
pp. R345-R368 ◽  
Author(s):  
Robert Clarke ◽  
John J Tyson ◽  
Ming Tan ◽  
William T Baumann ◽  
Lu Jin ◽  
...  
Keyword(s):  
Growth Factors ◽  
Systems Biology ◽  
Multiscale Modeling ◽  
Cell Biology ◽  
Molecular Control ◽  
Control Networks ◽  
Systems Perspective ◽  
Cancer Systems Biology ◽  
Therapeutic Protocols ◽  
Estrogen Biosynthesis

Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.

scholarly journals An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

2018 ◽  
Vol 17 ◽  
pp. 117693511879975 ◽  
Author(s):  
Abdallah K Alameddine ◽  
Frederick Conlin ◽  
Brian Binnall
Keyword(s):  
Mathematical Modeling ◽  
Systems Biology ◽  
Cancer Progression ◽  
Malignant Tumors ◽  
Deterministic System ◽  
Introductory Overview ◽  
Cancer Systems Biology ◽  
Heterogeneous Processes ◽  
Global Systems ◽  
Deficient Mismatch Repair

Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention. Aims: We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features. Methods: Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical. Results: To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved. Conclusions: In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.

scholarly journals Bioelectrical understanding and engineering of cell biology

2020 ◽  
Vol 17 (166) ◽  
pp. 20200013 ◽  
Author(s):  
Zoe Schofield ◽  
Gabriel N. Meloni ◽  
Peter Tran ◽  
Christian Zerfass ◽  
Giovanni Sena ◽  
...  
Keyword(s):  
Systems Biology ◽  
Cell Biology ◽  
Genetic Basis ◽  
Control Cell ◽  
Status Quo ◽  
Cell Behaviour ◽  
Cellular Processes ◽  
Mechanical Responses ◽  
Predictive Understanding ◽  
Biology Research

The last five decades of molecular and systems biology research have provided unprecedented insights into the molecular and genetic basis of many cellular processes. Despite these insights, however, it is arguable that there is still only limited predictive understanding of cell behaviours. In particular, the basis of heterogeneity in single-cell behaviour and the initiation of many different metabolic, transcriptional or mechanical responses to environmental stimuli remain largely unexplained. To go beyond the status quo , the understanding of cell behaviours emerging from molecular genetics must be complemented with physical and physiological ones, focusing on the intracellular and extracellular conditions within and around cells. Here, we argue that such a combination of genetics, physics and physiology can be grounded on a bioelectrical conceptualization of cells. We motivate the reasoning behind such a proposal and describe examples where a bioelectrical view has been shown to, or can, provide predictive biological understanding. In addition, we discuss how this view opens up novel ways to control cell behaviours by electrical and electrochemical means, setting the stage for the emergence of bioelectrical engineering.

scholarly journals The people behind the papers – Kayt Scott and Bruce Appel

Development ◽  
2020 ◽  
Vol 147 (16) ◽  
pp. dev195735
Keyword(s):  
Motor Neurons ◽  
Cell Biology ◽  
Zebrafish Embryo ◽  
Cell Types ◽  
Colorado School ◽  
Molecular Control ◽  
University Of Colorado ◽  
School Of Medicine ◽  
The People ◽  
Developing Spinal Cord

ABSTRACTIn the developing spinal cord, progenitor cells sequentially give rise to motor neurons and precursors of one of the major glial cell types: oligodendrocytes. A new paper in Development unpicks the molecular control of the neuron-glia switch and the differentiation of oligodendrocyte precursors in the zebrafish embryo. To find out more about the work, we met first author and graduate student Kayt Scott and her supervisor Bruce Appel, who holds the Diane G. Wallach Chair of Pediatric Stem Cell Biology and is Professor and Head of the Section of Developmental Biology at the Department of Pediatrics, University of Colorado School of Medicine in Aurora.

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