The vast amount of knowledge in Cell Signaling gathered through reductionist efforts and omics technology is poised to approach a Systems Biology understanding of precise representations of cell structure and function and predictions at multi-scale levels despite the complexity. Super-resolution microscopy and single cell analysis are also providing opportunities to explore both spatial and temporal landscapes. Notably, many basic biological processes have been studied capturing mechanistic detail with the goal to understand cellular proliferation and differentiation, gene regulation, morphogenesis, metabolism, and cell-cell communication. Similarly, at the intracellular level, addressing functions such as self-assembly, phase separation, and transport is leading to insights not readily understood as linear pathways. Therefore, network-based mathematical modeling, delineating dynamic biochemical reactions through ordinary and partial differential equations, promises to discover emergent biological properties not heretofore expected.
Because omics technologies are advancing the fields of Cell and Developmental Biology, but frequently, do not consider Systems Biology theory, emergent properties are neither found nor considered. For example, it was only through mathematical modeling with differential equations describing p53 signaling was its oscillatory behavior at the level of protein expression discovered, and the nature of such in triggering apoptosis was better understood. In another report, two-fold changes in SHP-1 protein expression altered significant NK cell responsiveness and modeling was found to be consistent with a single cell analysis. These unexpected findings represent how a quantitative modeling approach leads to discoveries such phenomena as ultrasensitivity and all or none responses. Therefore, to gather both reviews and original research in a Research Topic on Network-based Mathematical Modeling in Cell and Developmental Biology would advance the leading edge of Systems Biology by determining similarities and differences across fields united by biological principles.
All types of articles are considered acceptable (reviews, original research, methodologies, etc.). While the topics in Cell and Developmental Biology described above are broad (cell proliferation and differentiation, gene regulation, morphogenesis, metabolism, and cell-cell communication), a cross-field emphasis will be placed on studies addressing emergent properties. Other biologic processes will also be considered such those addressing plasticity, morphogenesis, angiogenesis, and hematopoesis.
• Network-based modeling of signaling pathways that discover emergent properties such as EGF and BMP pathways but not limited to such
• Single cell modeling that captures spatial and temporal aspects of signaling
• Whole-cell modeling
• Studies that cross multiple scales such as protein molecular dynamics and ODE modeling and ODE modeling and 3D cell simulators. Other similar integrated studies will also be considered.
• Quantitative modeling enabling predictive outcomes
• Organoid studies addressing signaling in cell and developmental biology
The vast amount of knowledge in Cell Signaling gathered through reductionist efforts and omics technology is poised to approach a Systems Biology understanding of precise representations of cell structure and function and predictions at multi-scale levels despite the complexity. Super-resolution microscopy and single cell analysis are also providing opportunities to explore both spatial and temporal landscapes. Notably, many basic biological processes have been studied capturing mechanistic detail with the goal to understand cellular proliferation and differentiation, gene regulation, morphogenesis, metabolism, and cell-cell communication. Similarly, at the intracellular level, addressing functions such as self-assembly, phase separation, and transport is leading to insights not readily understood as linear pathways. Therefore, network-based mathematical modeling, delineating dynamic biochemical reactions through ordinary and partial differential equations, promises to discover emergent biological properties not heretofore expected.
Because omics technologies are advancing the fields of Cell and Developmental Biology, but frequently, do not consider Systems Biology theory, emergent properties are neither found nor considered. For example, it was only through mathematical modeling with differential equations describing p53 signaling was its oscillatory behavior at the level of protein expression discovered, and the nature of such in triggering apoptosis was better understood. In another report, two-fold changes in SHP-1 protein expression altered significant NK cell responsiveness and modeling was found to be consistent with a single cell analysis. These unexpected findings represent how a quantitative modeling approach leads to discoveries such phenomena as ultrasensitivity and all or none responses. Therefore, to gather both reviews and original research in a Research Topic on Network-based Mathematical Modeling in Cell and Developmental Biology would advance the leading edge of Systems Biology by determining similarities and differences across fields united by biological principles.
All types of articles are considered acceptable (reviews, original research, methodologies, etc.). While the topics in Cell and Developmental Biology described above are broad (cell proliferation and differentiation, gene regulation, morphogenesis, metabolism, and cell-cell communication), a cross-field emphasis will be placed on studies addressing emergent properties. Other biologic processes will also be considered such those addressing plasticity, morphogenesis, angiogenesis, and hematopoesis.
• Network-based modeling of signaling pathways that discover emergent properties such as EGF and BMP pathways but not limited to such
• Single cell modeling that captures spatial and temporal aspects of signaling
• Whole-cell modeling
• Studies that cross multiple scales such as protein molecular dynamics and ODE modeling and ODE modeling and 3D cell simulators. Other similar integrated studies will also be considered.
• Quantitative modeling enabling predictive outcomes
• Organoid studies addressing signaling in cell and developmental biology
