Cells that reside within a community can cooperate and also compete with each other for resources. It remains unclear how these opposing interactions are resolved at the population level. Here we investigated such an internal conflict within a microbial biofilm community: Cells in the biofilm periphery not only protect interior cells from external attack, but also starve them through nutrient consumption. We discovered that this conflict between protection and starvation is resolved through emergence of long-range metabolic codependence between peripheral and interior cells. As a result, biofilm growth halts periodically, increasing nutrient availability for the sheltered interior cells. We show that this collective oscillation in biofilm growth benefits the community in the event of a chemical attack. These findings indicate that oscillations support population-level conflict resolution by coordinating competing metabolic demands in space and time, suggesting new strategies to control biofilm growth.

Bacteria within communities can interact to organize their behavior. It has been unclear whether such interactions can extend beyond a single community to coordinate the behavior of distant populations. We discovered that two Bacillus subtilis biofilm communities undergoing metabolic oscillations can become coupled through electrical signaling and synchronize their growth dynamics. Coupling increases competition by also synchronizing demand for limited nutrients. As predicted by mathematical modeling, we confirm that biofilms resolve this conflict by switching from in-phase to antiphase oscillations. This results in time-sharing behavior, where each community takes turns consuming nutrients. Time-sharing enables biofilms to counterintuitively increase growth under reduced nutrient supply. Distant biofilms can thus coordinate their behavior to resolve nutrient competition through time-sharing, a strategy used in engineered systems to allocate limited resources.

Cellular information processing is generally attributed to the complex networks of genes and proteins that regulate cell behavior. It is still unclear, however, what are the main features of those networks that allow a cell to encode and interpret its ever changing environment. Here, we address this question by studying the computational capabilities of the transcriptional regulatory networks of five evolutionary distant organisms. We identify in all cases a cyclic recurrent structure, formed by a small core of genes, that is essential for dynamical encoding and information integration. The recent history of the cell is projected nonlinearly into this recurrent reservoir of nodes, where it is encoded by its transient dynamics, while the rest of the network forms a readout layer devoted to decode and interpret the high-dimensional dynamical state of the recurrent core. In that way, gene regulatory networks act as echo-state networks that perform optimally in standard memory-demanding tasks, with most of their memory residing in the recurrent reservoir. The biological significance of these results is analyzed in the particular case of the bacterium Escherichia coli. Our work thus suggests that recurrent nonlinear dynamics is a key element for the processing of complex time-dependent information by cells.

Cellular information processing is generally attributed to the complex networks of genes and proteins that regulate cell behavior. It is still unclear, however, what are the main features of those networks that allow a cell to encode and interpret its ever changing environment. Here we address this question by studying the computational capabilities of the transcriptional regulatory networks of five evolutionary distant organisms. We identify in all cases a cyclic recurrent structure, formed by a small core of genes, that is essential for dynamical encoding and information integration. The recent history of the cell is encoded by the transient dynamics of this recurrent reservoir of nodes, while the rest of the network forms a readout layer devoted to decode and interpret the highdimensional dynamical state of the recurrent core. This separation of roles allows for the integration of temporal information, while facilitating the learning of new environmental conditions and preventing catastrophic interference between those new inputs and the previously stored information. This resembles the reservoir-computing paradigm recently proposed in computational neuroscience and machine learning. Our results reveal that gene regulatory networks act as echo-state networks that perform optimally in standard memory-demanding tasks, and confirms that most of their memory resides in the recurrent reservoir. We also show that the readout layer can learn to decode the information stored in the reservoir via standard evolutionary strategies. Our work thus suggests that recurrent dynamics is a key element for the processing of complex time-dependent information by cells. SummaryCells must monitor the dynamics of their environment continuously, in order to adapt to present conditions and anticipate future changes. But anticipation requires processing temporal information, which in turn requires memory. Here we propose that cells can perform such dynamical information processing via the reservoir computing paradigm. According to this concept, a structure with recurrent (cyclic) paths, known as the reservoir, stores in its dynamics a record of the cell's recent history. A much simpler feedforward structure then reads and decodes that information. We show that the transcriptional gene regulatory networks of five evolutionary distant organisms are organized in this manner, allowing them to store complex time-dependent signals entering the cell in a biologically realistic manner.