The response of microbes to external signals is mediated by biochemical networks with intrinsic timescales. These timescales give rise to a cellular memory that plays a pivotal role in controlling cellular behaviour. Here we study  the role of cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from the correlationsin their environment beyond local gradients. The improvement is maximal when the cellular memory is comparable to the timescale of the fluctuations perceived during swimming. We then extend coarse-grained population models and derive an analytical approximation for the drift velocity in rugged landscapes that includes the relevant time and length scales. Our model explains the improved velocity, and recovers standard Keller-Segel gradient-sensing results in the limits of no cellular memory, no ruggedness of the landscape, and when memory and fluctuation timescales are well separated. Our numerics also show that cellular memory can be used to induce bet-hedging at the population level by developing distinct sub-populations in response to heterogeneous chemoattractant landscapes.
The response of microbes to external signals is mediated by biochemical networks with intrinsic time scales. These time scales give rise to a memory that impacts cellular behaviour. Here we study theoretically the role of cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from environmental correlations. Maximal advantage is achieved when the memory is comparable to the time scale of fluctuations as perceived during swimming. We derive an analytical approximation for the drift velocity in rugged landscapes that explains the enhanced velocity, and recovers standard Keller-Segel gradient-sensing results in the limits when memory and fluctuation time scales are well separated. Our numerics also show that cellular memory can induce bet-hedging at the population level resulting in long-lived multi-modal distributions in heterogeneous landscapes.
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cellular memory that plays a pivotal role in controlling
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theoretically
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timescale of the fluctuations
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then extend coarse-grained population models and
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includes the relevant time and length scales. Our model explains the improved
explains the enhanced
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of no cellular memory, no ruggedness of the landscape, and
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timescales
time scales
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by developing distinct sub-populations in response to heterogeneous chemoattractant
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