Igor Segota

Spontaneous emergence of large-scale cell cycle synchronization in amoeba colonies.

Unicellular eukaryotic amoebae Dictyostelium discoideum are generally believed to grow in their vegetative state as single cells until starvation, when their collective aspect emerges and they differentiate to form a multicellular slime mold. While major efforts continue to be aimed at their starvation-induced social aspect, our understanding of population dynamics and cell cycle in the vegetative growth phase has remained incomplete. Here we show that cell populations grown on a substrate spontaneously synchronize their cell cycles within several hours. These collective population-wide cell cycle oscillations span millimeter length scales and can be completely suppressed by washing away putative cell-secreted signals, implying signaling by means of a diffusible growth factor or mitogen. These observations give strong evidence for collective proliferation behavior in the vegetative state.

High fidelity information processing in folic acid chemotaxis of Dictyostelium amoebae

Living cells depend upon the detection of chemical signals for their existence. Eukaryotic cells can sense a concentration difference as low as a few per cent across their bodies. This process was previously suggested to be limited by the receptor–ligand binding fluctuations. Here, we first determine the chemotaxis response of Dictyostelium cells to static folic acid gradients and show that they can significantly exceed this sensitivity, responding to gradients as shallow as 0.2% across the cell body. Second, using a previously developed information theory framework, we compare the total information gained about the gradient (based on the cell response) to its upper limit: the information gained at the receptor–ligand binding step. We find that the model originally applied to cAMP sensing fails as demonstrated by the violation of the data processing inequality, i.e. the total information exceeds the information at the receptor–ligand binding step. We propose an extended model with multiple known receptor types and with cells allowed to perform several independent measurements of receptor occupancy. This does not violate the data processing inequality and implies the receptor–ligand binding noise dominates both for low- and high-chemoattractant concentrations. We also speculate that the interplay between exploration and exploitation is used as a strategy for accurate sensing of otherwise unmeasurable levels of a chemoattractant.

Extracellular Processing of Molecular Gradients by Eukaryotic Cells Can Improve Gradient Detection Accuracy

Eukaryotic cells sense molecular gradients by measuring spatial concentration variation through the difference in the number of occupied receptors to which molecules can bind. They also secrete enzymes that degrade these molecules, and it is presently not well understood how this affects the local gradient perceived by cells. Numerical and analytical results show that these enzymes can substantially increase the signal-to-noise ratio of the receptor difference and allow cells to respond to a much broader range of molecular concentrations and gradients than they would without these enzymes.

MADNet: A Web Server for Contextual Analysis and Visualization of High-Throughput Experiments

Efficient data integration and visualization represents an important component of any systems biology approach. With large datasets resulting from experiments on a complex biological system it is often impossible to analyze and interpret results one by one – rather, we require tools to help us understand the outcome of our experiment in a broader context and with as much visual information as possible. MADNet, the MicroArray Database Network web server, is a user-friendly data mining and visualization tool with a simple and straightforward interface for rapid analysis of diverse high-throughput biological experiment results, such as microarray, phage display, or even metagenome analysis. It visually presents experimental results in the context of metabolic and signaling pathways, transcription factors, and drug targets through minimal user input, consisting only of the file containing a list of genes and associated expression values. This data is integrated with information extracted from various biological databases such as NCBI nucleotide and protein sequence databanks, metabolic and signaling pathway databases (KEGG), transcription regulation (TRANSFAC©), and drug target database (DrugBank). MADNet is freely available for academic use at http://www.bioinfo.hr/madnet.