Design principles and phenotypic landscapes of biological networks
- Submission status
- Closed
- Submission deadline
Biological functions are governed by regulatory networks. Understanding how network topology relates to its function is a crucial challenge in systems biology. This involves investigating how the network is designed to fulfill different functions and generate diverse phenotypes. Computational models offer essential tools for comprehending the function and dynamic behavior of biological systems. In the past decade, various molecular and cellular profiling technologies have been developed to explore biological networks, including genomic, epigenomic datasets, etc. This collection focuses on combining these datasets to unravel the mechanisms of phenotypic stability associated with the topology and function of biological networks. We are interested in manuscripts that use dynamical modeling methods (deterministic and stochastic approach) to explore the structure and functional mechanism, as well as the phenotypic fate transitions of biological networks. Furthermore, this collection emphasizes advances in computational methods connecting data analysis and dynamical modeling as well as their applications on biological network systems.
This Collection supports and amplifies research related to SDG 3.
Editors
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Reka Albert, PhD
Pennsylvania State University, USA.
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Chunhe Li, PhD
Fudan University, China
Prof. Reka Albert is a distinguished professor at the Pennsylvania State University. Her research has two main thrusts: collaborating with experimental groups to develop predictive dynamic models of biological systems at various levels of organization and developing network-based dynamical methodologies to drive biological systems to beneficial attractors (phenotypes).
Prof. Chunhe Li is an associate professor in Shanghai Center for Mathematical Sciences at Fudan University. His research focuses on computational systems biology, gene regulatory networks, and computational neuroscience. He develops novel approaches for quantifying stochastic dynamics of gene networks to understand the underlying mechanisms of cell fate decisions in various biological systems, such as cell cycle, development, and cancer.
