In our work, we have employed the chemotaxis network responsible for the motion of E. coli bacteria as a model system for the general study of signal transduction networks. We asked whether there were specific molecular events that could cause behavioral variability in an individual cell. By developing a single-cell approach we were able to characterize the design principles of the signal transduction network in bacteria (chemotaxis). We found that the inherent randomness (noise) in the chemical reactions taking place in the intracellular signal processing was a "tuneable" source of behavioral variability. Moreover, the signal transduction network determining cellular behavior was found to be tuned to maximize its sensitivity. We used a combination of computer simulations and genetic experiments to identify the key molecular components that allow the cell to adjust signal transduction sensitivity. We found as a consequence of the network design that maximum sensitivity would be also accompanied with maximum behavioral variability, Korobkova et al., Nature (2004). We believe that such interplay between sensitivity and variability in signal transduction is shared by many other signaling systems.
The purpose of this project is to develop a software framework to study the design principles of simple intracellular computations in living organisms. A multi-level software, named AgentCell, has been developed to study important properties of the E. Coli chemotaxis network and the coordinated motion of bacterial cells in direct response to environmental stimuli, Emonet et al., bioinformatics (2005). The chemotaxis network for E. coli serves as a model system because it is well characterized and experimentally accessible. The multi-level simulation models the dynamics of the signal transduction networks within cells at one (micro-) level, and simulates the movement of the cells through a medium at the other (macro-) level.Repast is the agent-based simulation framework used for the upper level simulation that treats each of the cells as an individual agent. Cells (agents) interact dynamically with the environment (for example, sensing concentrations of attractants) and with other cells. An existing molecular simulation, StochSim (D. Bray Lab), is used to model the molecular reactions comprising the chemotaxis network. Each molecule is modeled individually as an object, and StochSim models each molecular reaction within a cell as it occurs over time. The likelihood of a molecular reaction is proportional to the rate constants of the kinetic reaction equations. This requires millions of reactions to be simulated per second of simulated time for each cell. Repast defines a class for the chemotaxis network simulation, and each StochSim run is represented as an instance of that class. The Repast simulation defines several other classes in addition to this to control and coordinate the cell simulations and translate the simulated chemical reactions into cell motion. The higher-level Repast simulation performs several functions. It controls the operation of the individual cell simulations and translates the concentrations of molecular species within each of the cells at any point in time into the state of motion for the cell, run or tumble. The motion state of the cell is translated into movement over time and space in terms of cell location and cell orientation. Finally, the Repast simulation models the motion of the cell as it moves through the representation of the larger space. The spatial representation assures adherence to spatial constraints such as boundaries, and potentially is a basis for modeling cell congestion. The Repast simulation synchronizes the flow of time across and within cells and also seamlessly coordinates the cell movement space and the molecular interaction space within cells. This project is in collaboration with Charles Macal and Mike North from Argonne National lab.
Using fluorescence correlation spectroscopy (FCS), we monitored in real-time and within a single bacterium of E. coli the dynamics of synthesis and the degradation of a specific RNA transcript. The goal of this study is to characterize the relationship between the dynamics and the design of simple transcriptional networks at the single cell level.Nearly half a century ago the discovery of messenger RNA as an "unstable intermediate" established RNA instability as a key dynamical property in the molecular organisation of life. The balance between the kinetics of synthesis and the degradation of RNA transcripts exhibits a primitive example of molecular adaptation of gene expression.This molecular adaptation also allows rapid responses to regulatory and environmental signals. But the difficulty in studying RNA dynamics lies in the real-time dissection of each dynamical process that a specific RNA transcript has to undergo. Expression levels of a specific RNA transcript extracted from ensemble measurements come generally from cells that exist in slightly different states of growth and that exhibit also different behavior due to the variations of their internal biochemical parameters. Due to the transient nature of RNA transcripts, it has been technically challenging to monitor in real-time how the response to an environment signal is directly reflected in the transcription activity of a given gene within an individual cell.
