Modeling bacterial chemotaxis inside a cell

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.

2014

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15510 The contribution was presented at : https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=33185 To cite this version : Ouannes, Nesrine and Djedi, Nouredinne and Duthen, Yves and Luga, Hervé Modeling bacterial chemotaxis inside a cell. Abstract-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 ...

Biophysical Journal, 1995

We present a model of the chemotactic mechanism of Escherichia cofi that exhibits both initial excitation and eventual complete adaptation to any and all levels of stimulus ("exact" adaptation). In setting up the reaction network, we use only known interactions and experimentally determined cytosolic concentrations. Whenever possible, rate coefficients are first assigned experimentally measured values; second, we permit some variation in these rate coefficients by using a multiple-well optimization technique and incremental adjustment to obtain values that are sufficient to engender initial response to stimuli (excitation) and an eventual return of behavior to baseline (adaptation). The predictions of the model are similar to the observed behavior of wild-type bacteria in regard to the time scale of excitation in the presence of both attractant and repellent. The model predicts a weaker response to attractant than that observed experimentally, and the time scale of adaptation does not depend as strongly upon stimulant concentration as does that for wild-type bacteria. The mechanism responsible for long-term adaptation is local rather than global: on addition of a repellent or attractant, the receptor types not sensitive to that attractant or repellent do not change their average methylation level in the long term, although transient changes do occur. By carrying out a phenomenological simulation of bacterial chemotaxis, we find that the model is insufficiently sensitive to effect taxis in a gradient of attractant. However, by arbitrarily increasing the sensitivity of the motor to the tumble effector (phosphorylated CheY), we can obtain chemotactic behavior.

Biophysical Journal, 2003

The signaling apparatus mediating bacterial chemotaxis can adapt to a wide range of persistent external stimuli. In many cases, the bacterial activity returns to its prestimulus level exactly, and this perfect adaptability is robust against variations in various chemotaxis protein concentrations. We model the bacterial chemotaxis signaling pathway, from ligand binding to CheY phosphorylation. By solving the steady-state equations of the model analytically, we derive a full set of conditions for the system to achieve perfect adaptation. The conditions related to the phosphorylation part of the pathway are discovered for the first time, while other conditions are generalizations of the ones found in previous works. Sensitivity of the perfect adaptation is evaluated by perturbing these conditions. We find that, even in the absence of some of the perfect adaptation conditions, adaptation can be achieved with near-perfect precision as a result of the separation of scales in both chemotaxis protein concentrations and reaction rates, or specific properties of the receptor distribution in different methylation states. Since near-perfect adaptation can be found in much larger regions of the parameter space than that defined by the perfect adaptation conditions, their existence is essential to understand robustness in 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.

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

PloS one, 2013

Using a minimal model of metabolism, we examine the limitations of behavior that is (a) solely in response to environmental phenomena or (b) solely in response to metabolic dynamics, showing that basic forms of each of these kinds of behavior are incapable of driving survival-prolonging behavior in certain situations. Inspired by experimental evidence of concurrent metabolism-based and metabolism-independent chemotactic mechanisms in Escherichia coli and Rhodobacter sphaeroides, we then investigate how metabolism-independent and metabolism-based sensitivities can be integrated into a single behavioral response, demonstrating that a simple switching mechanism can be sufficient to effectively integrate metabolism-based and metabolism-independent behaviors. Finally, we use a spatial simulation of bacteria to show that the investigated forms of behavior produce different spatio-temporal patterns that are influenced by the metabolic-history of the bacteria. We suggest that these patterns...

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

Journal of Mathematical Analysis and Applications, 1999

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.