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Multi-bit Boolean model for chemotactic drift of Escherichia coli.

ABSTRACT: 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.

SUBMITTER: Deshpande A

PROVIDER: S-EPMC8687284 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Multi-bit Boolean model for chemotactic drift of Escherichia coli.

Deshpande Anuj A Samanta Sibendu S Govindarajan Sutharsan S Layek Ritwik Kumar RK

IET systems biology 20201201 6

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 c ...[more]

PMID: 33399098

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Project description:Periplasmic binding proteins such as ribose-binding proteins (RBPs) are involved in the bacterial chemotaxis two-component system. RBP selectively identifies and interacts with ribose to induce a conformational change that leads to chemotaxis. Here, we report the development of an engineered Escherichia coli (E. coli) strain expressing a redesigned RBP that can effectively sense cadmium ions and regulate chemotactic movement of cells toward a cadmium ion gradient. RBP was computationally redesigned to bind cadmium ions and produce the conformational change required for chemoreceptor binding. The successful design, CdRBP1, binds to cadmium ions with a dissociation constant of 268 nM. When CdRBP1 was expressed in the periplasmic space of E. coli, the bacteria became live cadmium ion hunters with high selectivity over other divalent metal ions. This work presents an example of making cadmium ions, which are toxic for most organisms, as an attractant to regulate cells movement. Our approach also demonstrates that RBP can be precisely designed to develop metal-detecting living systems for potential applications in synthetic biology and environmental studies. IMPORTANCE Cadmium pollution is one of the major environmental problems due to excessive release and accumulation. New technologies that can auto-detect cadmium ions with good biocompatibility are in urgent need. In this study, we engineered the bacterial chemotaxis system to positively sense cadmium ions by redesigning ribose-binding protein (RBP) to tightly bind cadmium ion and produce the right conformational change for receptor binding and signaling. Our engineered E. coli cells can auto-detect and chase cadmium ions with divalent metal ion selectivity. Many attempts have been carried out to redesign RBP at the ribose binding site with little success. Instead of the ribose binding site, we introduced the cadmium binding site in the opening of the ribose binding pocket by a specially developed computational algorithm. Our design strategy can be applied to engineer live bacteria with autonomous detection and remediation abilities for metal ions or other chemicals in the future.

Project description:Escherichia coli chemotactic motion in spatiotemporally varying environments is studied by using a computational model based on a coarse-grained description of the intracellular signaling pathway dynamics. We find that the cell's chemotaxis drift velocity v(d) is a constant in an exponential attractant concentration gradient [L] proportional, variantexp(Gx). v(d) depends linearly on the exponential gradient G before it saturates when G is larger than a critical value G(C). We find that G(C) is determined by the intracellular adaptation rate k(R) with a simple scaling law: G(C) infinity k(1/2)(R). The linear dependence of v(d) on G = d(ln[L])/dx directly demonstrates E. coli's ability in sensing the derivative of the logarithmic attractant concentration. The existence of the limiting gradient G(C) and its scaling with k(R) are explained by the underlying intracellular adaptation dynamics and the flagellar motor response characteristics. For individual cells, we find that the overall average run length in an exponential gradient is longer than that in a homogeneous environment, which is caused by the constant kinase activity shift (decrease). The forward runs (up the gradient) are longer than the backward runs, as expected; and depending on the exact gradient, the (shorter) backward runs can be comparable to runs in a spatially homogeneous environment, consistent with previous experiments. In (spatial) ligand gradients that also vary in time, the chemotaxis motion is damped as the frequency omega of the time-varying spatial gradient becomes faster than a critical value omega(c), which is controlled by the cell's chemotaxis adaptation rate k(R). Finally, our model, with no adjustable parameters, agrees quantitatively with the classical capillary assay experiments where the attractant concentration changes both in space and time. Our model can thus be used to study E. coli chemotaxis behavior in arbitrary spatiotemporally varying environments. Further experiments are suggested to test some of the model predictions.

Dataset Information

Multi-bit Boolean model for chemotactic drift of Escherichia coli.

Publications

Multi-bit Boolean model for chemotactic drift of <i>Escherichia coli</i>.

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