Persistent Cell Motion in the Absence of External Signals: A Search Strategy for Eukaryotic Cells

Background Eukaryotic cells are large enough to detect signals and then orient to them by differentiating the signal strength across the length and breadth of the cell. Amoebae, fibroblasts, neutrophils and growth cones all behave in this way. Little is known however about cell motion and searching behavior in the absence of a signal. Is individual cell motion best characterized as a random walk? Do individual cells have a search strategy when they are beyond the range of the signal they would otherwise move toward? Here we ask if single, isolated, Dictyostelium and Polysphondylium amoebae bias their motion in the absence of external cues. Methodology We placed single well-isolated Dictyostelium and Polysphondylium cells on a nutrient-free agar surface and followed them at 10 sec intervals for ∼10 hr, then analyzed their motion with respect to velocity, turning angle, persistence length, and persistence time, comparing the results to the expectation for a variety of different types of random motion. Conclusions We find that amoeboid behavior is well described by a special kind of random motion: Amoebae show a long persistence time (∼10 min) beyond which they start to lose their direction; they move forward in a zig-zag manner; and they make turns every 1–2 min on average. They bias their motion by remembering the last turn and turning away from it. Interpreting the motion as consisting of runs and turns, the duration of a run and the amplitude of a turn are both found to be exponentially distributed. We show that this behavior greatly improves their chances of finding a target relative to performing a random walk. We believe that other eukaryotic cells may employ a strategy similar to Dictyostelium when seeking conditions or signal sources not yet within range of their detection system.

[1]  H. Flyvbjerg,et al.  Power spectrum analysis for optical tweezers , 2004 .

[2]  Frederic Bartumeus,et al.  Erratum: Optimizing the Encounter Rate in Biological Interactions: Lévy versus Brownian Strategies [Phys. Rev. Lett.88, 097901 (2002)] , 2002 .

[3]  R. Mazo On the theory of brownian motion , 1973 .

[4]  Thomas Winckler,et al.  Molecular Phylogeny and Evolution of Morphology in the Social Amoebas , 2006, Science.

[5]  P. Devreotes,et al.  Signaling pathways mediating chemotaxis in the social amoeba, Dictyostelium discoideum. , 2006, European journal of cell biology.

[6]  T. C. B. McLeish,et al.  Polymer Physics , 2009, Encyclopedia of Complexity and Systems Science.

[7]  J. Xu,et al.  Polarity reveals intrinsic cell chirality , 2007, Proceedings of the National Academy of Sciences.

[8]  T. Coates,et al.  The fundamental motor of the human neutrophil is not random: evidence for local non-Markov movement in neutrophils. , 1994, Biophysical journal.

[9]  Lutz Schimansky-Geier,et al.  Optimal foraging by zooplankton within patches: the case of Daphnia. , 2007, Mathematical biosciences.

[10]  Marcos C. Santos,et al.  Dynamical robustness of Lévy search strategies. , 2003, Physical review letters.

[11]  A. M. Edwards,et al.  Revisiting Lévy flight search patterns of wandering albatrosses, bumblebees and deer , 2007, Nature.

[12]  M J Potel,et al.  Preaggregative cell motion in Dictyostelium. , 1979, Journal of cell science.

[13]  M. Shlesinger Mathematical physics: Search research , 2006, Nature.

[14]  G M Viswanathan,et al.  Optimization of random searches on regular lattices. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  F. Schweitzer,et al.  Brownian particles far from equilibrium , 2000 .

[16]  Natalie Andrew,et al.  Chemotaxis in shallow gradients is mediated independently of PtdIns 3-kinase by biased choices between random protrusions , 2007, Nature Cell Biology.

[17]  E. Cox,et al.  Electrophoretic karyotype for Dictyostelium discoideum. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[18]  T. Meyer,et al.  A local coupling model and compass parameter for eukaryotic chemotaxis. , 2005, Developmental cell.

[19]  John G Flanagan,et al.  Neural map specification by gradients , 2006, Current Opinion in Neurobiology.

[20]  F Bartumeus,et al.  Optimizing the encounter rate in biological interactions: Lévy versus Brownian strategies. , 2002, Physical review letters.

[21]  H. Stanley,et al.  Optimizing the success of random searches , 1999, Nature.

[22]  O Bénichou,et al.  Optimal search strategies for hidden targets. , 2005, Physical review letters.

[23]  A. Czirók,et al.  Exponential Distribution of Locomotion Activity in Cell Cultures , 1998, physics/9902022.

[24]  D. Kramer,et al.  The Behavioral Ecology of Intermittent Locomotion1 , 2001 .

[25]  O Bénichou,et al.  Two-dimensional intermittent search processes: An alternative to Lévy flight strategies. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Denis Boyer,et al.  Evidence for biological L\'evy flights stands , 2008, 0802.1762.