Memory shapes microbial populations

Abstract

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

Quantifying the impact of ecological memory on the dynamics of interacting communities

“…Fractional calculus allows introducing memory characterized by a power-law kernel, that is, a power-law decay of the influence of past states on the present state. It can be considered as a general approach to the modeling of gradually declining long-term memory, such as the one emerging from phenotypically heterogeneous bacterial populations [15,16,23]. Hence, our qualitative results on the influence of long-term memory on community dynamics are likely to hold more generally.…”

“…In contrast, long-term memory may arise if communities can alter their environment and thus modify environmental parameters in ways that reflect past events, or if organisms exhibit context-specific growth rates that reflect past adaptations [51,52]. It may also emerge only at the population or community level through phenotypic heterogeneity [23,24], which can be favored by dormancy, spatial structure, or adaptive bet-hedging mechanisms [15][16][17][18].…”

“…This is a fundamental and ubiquitous aspect of natural communities, and its influence on community dynamics has been widely recognized across ecological systems [10][11][12]. Memory can emerge at different time scales through a number of mechanisms, including the accumulation of abiotic and biotic material characterizing past legacies of the system, adaptations to past conditions, dormancy, or spatial structure [13][14][15][16][17][18]. Thus, developing and investigating new means to incorporate memory in dynamical models of ecological communities has the potential to yield more accurate mechanistic understanding and predictions.…”

Quantifying the impact of ecological memory on the dynamics of interacting communities

“…It is a phenomenological modeling tool that does not assume a specific ecological mechanism for the emergence of memory. It can be regarded as a general approach to the modeling of gradually declining long-term memory, such as the one emerging from phenotypically heterogeneous bacterial populations [ 15 , 16 , 23 ], for instance. Hence, our qualitative results on the influence of long-term memory on community dynamics are likely to hold more generally.…”

“…However, none of these approaches describes long-term memory with a power-law decay of the influence of past states. The lag times of antibiotic-tolerant persister cells have been shown to be power-law distributed in bacterial populations [ 23 ], and this type of long-term memory is likely to be common in microbial communities whenever memory emerges from phenotypic heterogeneity [ 16 , 24 ]. Furthermore, the impact of memory has not been systematically addressed in contemporary studies, and specific methods have been missing for incorporating memory into standard deterministic models of microbial community dynamics.…”