Research Project

Understanding signaling pathways regulating chemotaxis

Mathematical/computational models that help to understand how cells achieve chemotaxis effectively.


The figure above shows five snapshots at different time points from a chemotaxing cell moving towards a needle. They show the cell outline (white), the skeleton (green) and the tips (protrusions in red, retractions in blue) in each image. The times (in seconds) when each image was taken from the beginning of the movie are labeled at the lower-left corners. The scale bar denotes 5 µm. This image is from Ref. 5.

Chemotaxis is the movement of cells in response to spatial gradients of chemical cues. While single-celled organisms rely on sensing and responding to chemical gradients to search for nutrients, chemotaxis is also an essential component of the mammalian immune system. However, chemotaxis can also be deleterious, since chemotactic tumor cells can lead to metastasis. Due to its importance, understanding the process by which cells sense and respond to chemical gradients attracts considerable interest. Our primary goal is to develop mathematical/computational models that help to understand how cells achieve chemotaxis effectively. To this end we carry out research at a number of different levels. We develop image processing tools to characterize amoeboid motility. We build mathematical models that describe our findings, and simulate the ensuing motion using techniques developed to simulate systems evolving in deformable surfaces. Much of this work is done in collaboration with Peter Devreotes, of the Department of Cell Biology in the JH School of Medicine.


This image shows the simulated trajectory of a cell responding to gradients of chemoattractants. During the first 450 seconds, the gradient points to the right. Thereafter, it points to the left. Note the gradual realignment of the cell with the gradient after the change in direction. This figure is from Ref. 1.














Some recent publications related to this work are:
1. Shi C, Huang CH, Devreotes PN, Iglesias PA. Interaction of motility, directional sensing, and polarity modules recreates the behaviors of chemotaxing cells. PLoS Comput Biol; 9:e1003122, 2013.
2. Huang CH, Tang M, Shi C, Iglesias PA, Devreotes PN. An excitable signal integrator couples to an idling cytoskeletal oscillator to drive cell migration. Nat Cell Biol. 15:1307-16, 2013.
3. Iglesias PA, Devreotes PN. Biased excitable networks: how cells direct motion in response to gradients. Curr Opin Cell Biol. 24:245-53, 2012.
4. Xiong Y, Huang CH, Iglesias PA, Devreotes PN. Cells navigate with a local-excitation, global-inhibition-biased excitable network. Proc Natl Acad Sci U S A. 107:17079-86, 2010.
5. Xiong Y, Kabacoff C, Franca-Koh J, Devreotes PN, Robinson DN, Iglesias PA. Automated characterization of cell shape changes during amoeboid motility by skeletonization. BMC Syst Biol. 4:33, 2010.


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