Slow and Steady Wins the Race: A Bacterial Exploitative Competition Strategy in Fluctuating Environments

Abstract

One promising frontier for synthetic biology is the development of synthetic ecologies, whereby interacting species form an additional layer of connectivity for engineered gene circuits. Toward this goal, an important step is to understand different types of bacterial interactions in natural settings, among which competition is the most prevalent. By constructing a two-species population dynamics model, here, we mimicked bacterial growth in nature with resource-limited fluctuating environments and searched for optimal strategies for bacterial exploitative competition. In a simple game with two strategy options (constant or susceptible growth), we found that the species playing the constant growth strategy always outplays or is evenly matched with its competitor, suggesting that constant growth is a "no-loss" good bet. We also showed that adoption of sophisticated strategies enables a species to maximize its fitness when its competitor grows susceptibly. The pursuit of fitness maximization is, however, associated with potential loss if both species are capable of strategy adjustment, indicating an intrinsic risk-return trade-off. These findings offer new insights into bacterial competition and may also facilitate the engineering of microbial consortia for synthetic biology applications.

Programming the group behaviors of bacterial communities with synthetic cellular communication

“…Difficulties in the efficient construction of engineered circuits often stem from a lack of biological knowledge. Specifically, to facilitate gene circuit engineering, it is needed to have a deep understanding of stochasticity in gene expression [104][105][106], the inherent interplay between a synthetic circuit and the host organism [1], and issues related to multicellular physiology and metabolism [107]. Another big challenge arises from the technical side of synthetic biology, which includes the lack of powerful rational design platforms, limited availability of parts and modules, efficient systematic optimization strategies and toolkits, and high-throughput assays for circuit validation.…”

Synthetic Ecology of Microbes: Mathematical Models and Applications

“…The complex balancing of benefits and costs associated with inter-species interactions in microbial communities can also be effectively addressed by using game theory and evolutionary game theory approaches 45; 87; 150; 151; 152; 153; 154; 155 (see also 156 and 157 for comprehensive reviews). Game theory is a general mathematical framework to model strategic interactions among a number of agents (players) where the payoff of each agent (i.e., how happy each agent is) is not only a function of its own strategy (action) but also a function of other players’ strategies.…”

“…This nonlinear model could explain the experimentally observed coexistence between cheaters and cooperators, which is the reminiscent of a classical game theory scenario, termed the snowdrift game. In another study, the dynamics of a game between two bacterial species competing for a limiting resource in a fluctuating environment was captured by an extension of the LV model that allows switching from one species (strategy) to another 151 . Each species was assumed to take either of the two strategies constant (environment-insensitive) growth and susceptible (environment-dependent) growth.…”

Fluctuation and noise propagation in phenotypic transition cascades of clonal populations

“…On the other hand, there are two conceivable phenotypic transition ways. One is the bidirectional transition cascade, such as the transitions between two species in a bacterial community with exploitative competition [24] and the interconversion between three cell states in breast cancer lines [16]. The other is the unidirectional transition cascade, such as the differentiation from stem cell to semidifferentiated cell and then to fully differentiated cell in a colonic crypt [17].…”

“…III, the theoretical formulas for noise propagation in phenotypic transition cascades are derived by using the logarithmic gain [19,21,[25][26][27]. By virtue of these theoretical formulas, the fluctuations and noise propagation in a bidirectional [24] and in unidirectional [17] phenotypic transition cascades are respectively studied in Secs. IV and V. We end with conclusions and discussions in Sec.…”