Distinguishing Modes of Eukaryotic Gradient Sensing

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The behaviors of biological systems depend on complex networks of interactions between large numbers of components. The network of interactions that allows biological cells to detect and respond to external gradients of small molecules with directed movement is an example where many of the relevant components have been identified. This behavior, called chemotaxis, is essential for biological functions ranging from immune response in higher animals to the food gathering and social behavior of ameboid cells. Gradient sensing is the component of this behavior whereby cells transduce the spatio-temporal information in the external stimulus into an internal distribution of molecules that mediate the mechanical and morphological changes necessary for movement. Signaling by membrane lipids, in particular 3' phosphoinositides (3'PIs), is thought to play an important role in this transduction. Key features of the network of interactions that regulates the dynamics of these lipids are coupled positive feedbacks that might lead to response bifurcations and the involvement of molecules that translocate from the cytosol to the membrane, coupling responses at distant point on the cell surface. Both are likely to play important roles in amplifying cellular responses and shaping their qualitative features.

 To better understand the network of interactions that regulates 3'PI dynamics in gradient sensing, we develop a computational model at an intermediate level of detail.  To investigate how the qualitative features of cellular response depend on the structure of this network, we define four variants of our model by adjusting the effectiveness of the included feedback loops and the importance of translocating molecules in response amplification.  Simulations of characteristic responses suggest that differences between our model variants are most evident at transitions between efficient gradient detection and failure.  Based on these results, we propose criteria to distinguish between possible modes of gradient sensing in real cells, where many biochemical parameters may be unknown.  We also identify constraints on parameters required for efficient gradient detection.  Finally, our analysis suggests how a cell might transition between responsiveness and non-responsiveness, and between different modes of gradient sensing, by adjusting its biochemical parameters.