fastER: a user-friendly tool for ultrafast and robust cell segmentation in large-scale microscopy.

Motivation: Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.

Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome.

Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown.

Accurate cell segmentation in microscopy images using membrane patterns.

Motivation: Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries.

Near-optimal experimental design for model selection in systems biology.

Motivation: Biological systems are understood through iterations of modeling and experimentation. Not all experiments, however, are equally valuable for predictive modeling. This study introduces an efficient method for experimental design aimed at selecting dynamical models from data.

Using CellX to quantify intracellular events.

Methods to quantify features of individual cells using light microscopy have become widely used in biology. A multitude of computational tools has been developed for image analysis; however, they are often only for specific cell types and microscopy techniques. This unit describes CellX, an open-source software package for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images.

A specialized ODE integrator for the efficient computation of parameter sensitivities.

Background: Dynamic mathematical models in the form of systems of ordinary differential equations (ODEs) play an important role in systems biology. For any sufficiently complex model, the speed and accuracy of solving the ODEs by numerical integration is critical. This applies especially to systems identification problems where the parameter sensitivities must be integrated alongside the system variables.

Systems analysis of cellular networks under uncertainty.

Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems.

DNA replication in the fission yeast: robustness in the face of uncertainty.

DNA replication, the process of duplication of a cell's genetic content, must be carried out with great precision every time the cell divides, so that genetic information is preserved. Control mechanisms must ensure that every base of the genome is replicated within the allocated time (S-phase) and only once per cell cycle, thereby safeguarding genomic integrity. In eukaryotes, replication starts from many points along the chromosome, termed origins of replication, and then proceeds continuously bidirectionally until an opposing moving fork is encountered.