Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers
in Endocrine-Related Cancer
Authors: Robert Clarke 1 , John J Tyson 2 , Ming Tan 3 , William T Baumann 4 , Lu Jin 1 , Jianhua Xuan 5 and Yue Wang 5
View Less
0 Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA 1 Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 2 Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA 3 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 4 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
Correspondence should be addressed to R Clarke: clarker@georgetown.edu
DOI: https://doi.org/10.1530/ERC-18-0309
Page(s): R345–R368
Volume/Issue: Volume 26: Issue 6
Article Type: Review Article
Online Publication Date: Jun 2019
Copyright: © 2019 Society for Endocrinology 2019
Free access
Download PDF
Check for updates
Citation Alert Citation Alerts
Get Permissions
Abstract/Excerpt
Full Text
PDF
Abstract
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.
in Endocrine-Related Cancer
Authors: Robert Clarke 1 , John J Tyson 2 , Ming Tan 3 , William T Baumann 4 , Lu Jin 1 , Jianhua Xuan 5 and Yue Wang 5
View Less
0 Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA 1 Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 2 Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA 3 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA 4 Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
Correspondence should be addressed to R Clarke: clarker@georgetown.edu
DOI: https://doi.org/10.1530/ERC-18-0309
Page(s): R345–R368
Volume/Issue: Volume 26: Issue 6
Article Type: Review Article
Online Publication Date: Jun 2019
Copyright: © 2019 Society for Endocrinology 2019
Free access
Download PDF
Check for updates
Citation Alert Citation Alerts
Get Permissions
Abstract/Excerpt
Full Text
Abstract
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.
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου