Living systems are characterized by dynamic changes. Temporal evolution of complex interactions drives many biological processes from cellular functions to organism development. While omics approaches typically provide component lists and snapshots of a system, imaging methods are well suited to capturing how interactions manifest in space and time.
Increases in quality and throughput of image data acquisition allow for unprecedented levels of spatial and temporal resolution. From visualization to data integration and analysis to modelling, the need and opportunities for the development of new computational methods capable of dealing with the dynamic data generated by imaging technologies are plenty.
We welcome manuscripts dealing, but not limited to the following areas:
• New concepts in tracking
• causality
• fate analysis
• phenotype prediction
• time series clustering
• Applications of tensor methods (e.g. factorization) to time data
• Modeling, including: time-series, single-cell, population and spatiotemporal modeling
• Shape dynamics / phenotype dynamics
• Visualization
• Data handling : compression / databases for time-resolved data
• Data integration
Living systems are characterized by dynamic changes. Temporal evolution of complex interactions drives many biological processes from cellular functions to organism development. While omics approaches typically provide component lists and snapshots of a system, imaging methods are well suited to capturing how interactions manifest in space and time.
Increases in quality and throughput of image data acquisition allow for unprecedented levels of spatial and temporal resolution. From visualization to data integration and analysis to modelling, the need and opportunities for the development of new computational methods capable of dealing with the dynamic data generated by imaging technologies are plenty.
We welcome manuscripts dealing, but not limited to the following areas:
• New concepts in tracking
• causality
• fate analysis
• phenotype prediction
• time series clustering
• Applications of tensor methods (e.g. factorization) to time data
• Modeling, including: time-series, single-cell, population and spatiotemporal modeling
• Shape dynamics / phenotype dynamics
• Visualization
• Data handling : compression / databases for time-resolved data
• Data integration