Chemotaxis in uncertain environments: hedging bets with multiple receptor types
Eukaryotic cells are able to sense chemical gradients in a wide range of environments. We show that, if a cell is exposed to a highly variable environment, it may gain chemotactic accuracy by expressing multiple receptor types with varying affinities for the same signal, as found commonly in chemotaxing cells like Dictyostelium. As environment uncertainty is increased, there is a transition between cells preferring a single receptor type and a mixture of types - hedging their bets against the possibility of an unfavorable environment. We predict the optimal receptor affinities given a particular environment. In chemotaxing, cells may also integrate their measurement over time. Surprisingly, time-integration with multiple receptor types is qualitatively different from gradient sensing by a single type -- cells may extract orders of magnitude more chemotactic information than expected by naive time integration. Our results show when cells should express multiple receptor types to chemotax, and how cells can efficiently interpret the data from these receptors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Risk-Neutral Pricing and Hedging of In-Play Football Bets
A risk-neutral valuation framework is developed for pricing and hedging in-play football bets based on modelling scores by independent Poisson processes with constant intensities. The Fundamental Theorems of Asset Pricin…
Deep Hedging Under Non-Convexity: Limitations and a Case for AlphaZero
This paper examines replication portfolio construction in incomplete markets - a key problem in financial engineering with applications in pricing, hedging, balance sheet management, and energy storage planning. We model…
Modeling Uncertainty with Hedged Instance Embedding
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a singl…
ClusteringMetric LearningRetrievalModeling Uncertainty with Hedged Instance Embeddings
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a singl…
ClusteringMetric LearningRetrievalAdaptability of non-genetic diversity in bacterial chemotaxis
Bacterial chemotaxis systems are as diverse as the environments that bacteria inhabit, but how much environmental variation can cells tolerate with a single system? Diversification of a single chemotaxis system could ser…
Diversity