TY - JOUR
T1 - Systems biology
T2 - Perspectives on multiscale modeling in research on endocrine-related cancers
AU - Clarke, Robert
AU - Tyson, John J.
AU - Tan, Ming
AU - Baumann, William T.
AU - Jin, Lu
AU - Xuan, Jianhua
AU - Wang, Yue
N1 - Publisher Copyright:
© 2019 Society for Endocrinology Published by Bioscientifica Ltd.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - F systems biology f mathematical biology f computational biology f predictive modeling
UR - http://www.scopus.com/inward/record.url?scp=85067273121&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067273121&partnerID=8YFLogxK
U2 - 10.1530/ERC-18-0309
DO - 10.1530/ERC-18-0309
M3 - Article
C2 - 30965282
AN - SCOPUS:85067273121
SN - 1351-0088
VL - 26
SP - R345-R368
JO - Endocrine-related cancer
JF - Endocrine-related cancer
IS - 6
ER -