Predicting Escherichia coli's chemotactic drift under exponential gradient

Effect of receptor clustering on chemotactic performance of E. coli: Sensing versus adaptation

We show how the competition between sensing and adaptation can result in a performance peak in Escherichia coli chemotaxis using extensive numerical simulations in a detailed theoretical model. Receptor clustering amplifies the input signal coming from ligand binding which enhances chemotactic efficiency. But large clusters also induce large fluctuations in total activity since the number of clusters goes down. The activity and hence the run-tumble motility now gets controlled by methylation levels which are part of adaptation module rather than ligand binding. This reduces chemotactic efficiency.

Multi-bit Boolean model for chemotactic drift of Escherichia coli

Dynamic biological systems can be modelled to an equivalent modular structure using Boolean networks (BNs) due to their simple construction and relative ease of integration. The chemotaxis network of the bacterium Escherichia coli (E. coli) is one of the most investigated biological systems. In this study, the authors developed a multi-bit Boolean approach to model the drifting behaviour of the E. coli chemotaxis system. Their approach, which is slightly different than the conventional BNs, is designed to provide finer resolution to mimic high-level functional behaviour. Using this approach, they simulated the transient and steady-state responses of the chemoreceptor sensory module. Furthermore, they estimated the drift velocity under conditions of the exponential nutrient gradient. Their predictions on chemotactic drifting are in good agreement with the experimental measurements under similar input conditions. Taken together, by simulating chemotactic drifting, they propose that multi-bit Boolean methodology can be used for modelling complex biological networks. Application of the method towards designing bio-inspired systems such as nano-bots is discussed.

A hydrodynamic-stochastic model of chemotactic ciliated microorganisms

Biological systems like ciliated microorganisms are capable of responding to the external chemical gradients, a process known as chemotaxis. In this process, the internal signaling network of the microorganism is triggered due to binding of the chemoattractant molecules with the receptors on the surface of the body. This can alter the activity at the surface of the microorganism. We study the chemotaxis of ciliated microorganisms using the chiral squirmer model, a spherical body with a surface slip velocity. In the presence of a chemical gradient, the coefficients of the slip velocity get modified resulting in a change in the path followed by the body. We observe that the strength of the gradient is not the only parameter which controls the dynamics of the body but also the adaptation time plays a very significant role in the success of chemotaxis. The trajectory of the body is smooth if we ignore the discreteness in the ligand-receptor binding which is stochastic in nature. In the presence of the latter, the path is not only irregular but the whole dynamics of the body changes. We calculate the mean first passage time, by varying the strength of the chemical gradient and the adaptation time, to determine the success rate of chemotaxis.