AgentCell: a digital single-cell assay for bacterial chemotaxis

PLoS ONE, 2010

We report the application of agent-based modeling to examine the signal transduction network and receptor arrays for chemotaxis in Escherichia coli, which are responsible for regulating swimming behavior in response to environmental stimuli. Agent-based modeling is a stochastic and bottom-up approach, where individual components of the modeled system are explicitly represented, and bulk properties emerge from their movement and interactions. We present the Chemoscape model: a collection of agents representing both fixed membrane-embedded and mobile cytoplasmic proteins, each governed by a set of rules representing knowledge or hypotheses about their function. When the agents were placed in a simulated cellular space and then allowed to move and interact stochastically, the model exhibited many properties similar to the biological system including adaptation, high signal gain, and wide dynamic range. We found the agent based modeling approach to be both powerful and intuitive for testing hypotheses about biological properties such as selfassembly, the non-linear dynamics that occur through cooperative protein interactions, and non-uniform distributions of proteins in the cell. We applied the model to explore the role of receptor type, geometry and cooperativity in the signal gain and dynamic range of the chemotactic response to environmental stimuli. The model provided substantial qualitative evidence that the dynamic range of chemotactic response can be traced to both the heterogeneity of receptor types present, and the modulation of their cooperativity by their methylation state.

Proceedings of the National Academy of Sciences, 2012

Individual neuronal, signal transduction, and regulatory pathways often control multiple stochastic downstream actuators, which raises the question of how coordinated response to a single input can be achieved when individual actuators fluctuate independently. In Escherichia coli, the bacterial chemotaxis pathway controls the activity of multiple flagellar motors to generate the run-and-tumble motion of the cell. High-resolution microscopy experiments have identified the key conformational changes adopted by individual flagella during this process. By incorporating these observations into a stochastic model of the flagellar bundle, we demonstrate that the presence of multiple motors imposes a trade-off on chemotactic performance. Multiple motors reduce the latency of the response below the time scale of the stochastic switching of a single motor, which improves performance on steep gradients of attractants. However, the uncoordinated switching of multiple motors interrupts and shortens cell runs, which thereby reduces signal detection and performance on shallow gradients. Remarkably, when slow fluctuations generated by the adaptation mechanism of the chemotaxis system are incorporated in the model at levels measured in experiments, the chemotactic sensitivity and performance in shallow gradients is partially restored with marginal effects for steep gradients. The noise is beneficial because it simultaneously generates long events in the statistics of individual motors and coordinates the motors to generate a long tail in the run length distribution of the cell. Occasional long runs are known to enhance exploration of random walkers. Here we show that they have the additional benefit of enhancing the sensitivity of the bacterium to very shallow gradients.

Siam Journal on Applied Mathematics, 2004

Bacterial chemotaxis is widely studied from both the microscopic (cell) and macroscopic (population) points of view, and here we connect these different levels of description by deriving the classical macroscopic description for chemotaxis from a microscopic model of the behavior of individual cells. The analysis is based on the velocity jump process for describing the motion of individuals such as

Artificial Life and Robotics, 2014

We present in this paper an artificial life ecosystem in which the genes in the genome encode chemotaxis of bacteria that aim at: detecting resources (or sensing the environment), controlling the bacteria motion and producing a foraging behavior, and allowing bacteria to communicate together to obtain more sophisticated behaviors. The chemotaxis network of a cell is modulated by a hybrid approach that uses an algebraic model for the receptor clusters activity and an ordinary differential equation for the adaptation dynamics, and a metabolism model that is based on the transformation of matter from 'food'. The results show analysis of the motion obtained by some bacteria and their effects on the population behaviors generated by evolution. This evolution allows bacteria to have the ability to adapt themselves to better growth in the environment and to survive. As future work, we aim to improve the effect of the communication between bacteria to obtain bacteria that can emerge as new species, and to integrate the concept of colonies.

2021

This thesis describes the development of an agent-based simulation of E. coli chemotaxis in C# and the Unity game engine. The agents use a mathematical model of the chemical pathway underlying chemotaxis to produce either forward-motion (running) or rotation (tumbling), in response to the concentration of ligand in their immediate environment. This model consists of a system of ODEs from Edgington and Tindall [1] and elements of survival analysis. A tool for analysing data from these simulations was also developed, and used to make quantitative comparisons between simulations. This is used to compare our model to a simplified model of chemotaxis, designed to always display chemotactic behaviour. It is concluded that both models display chemotactic movement, with the simplified model being more effective at finding the ligand source, but the ODE-based model being more adaptive

AIChE Journal, 1994

The motility of a population of swimming bacteria can be characterized by a random motility coefficient, p, the operational equivalent of a diffusion coefficient at the macroscopic level and in the absence of interacting chemical gradients. At the microscopic level, random motility is related to the single-cell parameters: speed, tumbling probability, and index of directional persistence (related to the angle a cell's path assumes following a change in direction). Various mathematical models have been proposed for relating the macroscopic random motility coefficient to these microscopic single-cell parameters. In separate experiments, we have measured motility at both the cell-population and single-cell levels for Escherichia coli. The agreement of these results shows that the macroscopic transport behavior of a population of motile bacteria can be predicted from straightforward microscopic observations on single cells.

2014 International Conference on Multimedia Computing and Systems (ICMCS), 2014

This paper describes a bacterial system that reproduces a population of bacteria that behave by simulating the internal reactions of each bacterial cell. The chemotaxis network of a cell is modulated by a hybrid approach that uses an algebraic model for the receptor clusters activity and an ordinary differential equation for the adaptation dynamics. The experiments are defined in order to simulate bacterial growth in an environment where nutrients are regularly added to it. The results show analysis of the motion obtained by some bacteria and their effects on the population behaviors generated by evolution. This evolution allows bacteria to have the ability to adapt themselves to better growth in the available food existed in its environment and to survive.

Computing Research Repository, 2009

The chemotactic pathway allows bacteria to respond and adapt to environmental changes, by tuning the tumbling and running motions that are due to clockwise and counterclockwise rotations of their flagella. The pathway is tightly regulated by feedback mechanisms governed by the phosphorylation and methylation of several proteins. In this paper, we present a detailed mechanistic model for chemotaxis, that considers all of its transmembrane and cytoplasmic components, and their mutual interactions. Stochastic simulations of the dynamics of a pivotal protein, CheYp, are performed by means of tau leaping algorithm. This approach is then used to investigate the interplay between the stochastic fluctuations of CheYp amount and the number of cellular flagella. Our results suggest that the combination of these factors might represent a relevant component for chemotaxis. Moreover, we study the pathway under various conditions, such as different methylation levels and ligand amounts, in order to test its adaptation response. Some issues for future work are finally discussed.

WIREs Systems Biology and Medicine, 2012

Research into understanding bacterial chemotactic systems has become a paradigm for Systems Biology. Experimental and theoretical researchers have worked handin-hand for over 40 years to understand the intricate behavior driving bacterial species, in particular how such small creatures, usually not more than 5 μm in length, detect and respond to small changes in their extracellular environment. In this review we highlight the importance that theoretical modeling has played in providing new insight and understanding into bacterial chemotaxis. We begin with an overview of the bacterial chemotaxis sensory response, before reviewing the role of theoretical modeling in understanding elements of the system on the single cell scale and features underpinning multiscale extensions to population models.

Scientific Reports, 2017

Bacteria are able to coordinate their movement, growth and biochemical activities through cell-cell communication. While the biophysical mechanism of bacterial chemotaxis has been well understood in individual cells, the role of communication in the chemotaxis of bacterial populations is not clear. Here we report experimental evidence for cell-cell communication that significantly enhances the chemotactic migration of bacterial populations, a finding that we further substantiate using numerical simulations. Using a microfluidic approach, we find that E. coli cells respond to the gradient of chemoattractant not only by biasing their own random-walk swimming pattern through the wellunderstood intracellular chemotaxis signaling, but also by actively secreting a chemical signal into the extracellular medium, possibly through a hitherto unknown communication signal transduction pathway. This extracellular signaling molecule is a strong chemoattractant that attracts distant cells to the food source. The observed behavior may represent a common evolved solution to accelerate the function of biochemical networks of interacting cells.