Chemotaxis, the movement of URLanism in response to a chemical stimulus , is a typical feature of many microbiological systems. In particular, the social amoeba Disctyostelium discoideum is widely used as a URLanism, but it is not still clear how it behaves in heterogeneous environments. A few models focused on mechanical features have already addressed the question; however, we propose that phenomenological models focusing on the  dynamics may provide new meaningful data. Consequently, by means of a specific Multi-agent system model, we studied the dynamical features emerging from complex social interactions among individuals belonging to amoeboids colonies.\\ After defining an appropriate metric to perform meaningful measurements, we found that: a) obstacles play the role of local topological perturbation, as they alter the flux of chemical signals; b) that obstacle that physically block the cellular motion as well as the chemicals elicit dynamical evolutions that are not statistically distinguishable from the case where obstacles that do not interfere physically with said motion ; c) that fluctuations of the dynamics can lead to better exploration of the physical space, thus preventing multiple stable aggregates. From previous results, we may speculate about the fact that chemotactic cells , in many cases, can avoid obstacles by simply following the altered chemical gradient : social interactions seem to be sufficient to guarantee the aggregation of the whole colony past numerous obstacles . It is then unlikely that cells have developed special mechanisms to cope with the presence of topological perturbation sources. Nevertheless, we suggest that further studies can provide better understanding and that, in order to gain deeper knowledge, mechanical models should be coupled with phenomenological, system-oriented ones .
Chemotaxis, the movement of URLanism in response to chemical stimuli , is a typical feature of many microbiological systems. In particular, the social amoeba Disctyostelium discoideum is widely used as a URLanism, but it is not still clear how it behaves in heterogeneous environments. A few models focusing on mechanical features have already addressed the question; however, we suggest that phenomenological models focusing on the population dynamics may provide new meaningful data. Consequently, by means of a specific Multi-agent system model, we study the dynamical features emerging from complex social interactions among individuals belonging to amoeba colonies.\\ After defining an appropriate metric to quantitatively estimate the gathering process, we find that: a) obstacles play the role of local topological perturbation, as they alter the flux of chemical signals; b) physical obstacles (blocking the cellular motion and the chemical flux) and purely chemical obstacles (only interfering with chemical flux) elicit similar dynamical behaviors ; c) a minimal program for robustly gathering simulated cells does not involve mechanisms for obstacle sensing and avoidance; d) fluctuations of the dynamics concur in preventing multiple stable clusters. Comparing those findings with previous results, we speculate about the fact that chemotactic cells can avoid obstacles by simply following the altered chemical gradient . Social interactions are sufficient to guarantee the aggregation of the whole colony past numerous obstacles .
R
a chemical stimulus
chemical stimuli
R
focused
focusing
R
propose
suggest
A
population
R
studied
study
R
amoeboids colonies.
amoeba colonies.
R
perform meaningful measurements, we found
quantitatively estimate the gathering process, we find
R
that obstacle that physically block
physical obstacles (blocking
R
as well as the chemicals elicit dynamical evolutions that are not statistically distinguishable from the case where obstacles that do not interfere physically with said motion
and the chemical flux) and purely chemical obstacles (only interfering with chemical flux) elicit similar dynamical behaviors
R
that
a minimal program for robustly gathering simulated cells does not involve mechanisms for obstacle sensing and avoidance; d)
R
can lead to better exploration of the physical space, thus
concur in
R
aggregates. From
clusters. Comparing those findings with
D
may
D
, in many cases,
R
: social interactions seem to be
. Social interactions are
D
. It is then unlikely that cells have developed special mechanisms to cope with the presence of topological perturbation sources. Nevertheless, we suggest that further studies can provide better understanding and that, in order to gain deeper knowledge, mechanical models should be coupled with phenomenological, system-oriented ones