Kinetic Description of Social Dynamics: From Consensus to Flocking

Bacterial swarming logistics and cancer navigation strategies

Eshel Ben-Jacob

Tel Aviv University


Metastasis colonization of distant organs is responsible for 90% of cancer-related deaths. Yet, the process by which this occurs is unclear. The metastasis exodus begins when cells with unique traits leave the primary tumor and migrate towards the vascular or lymph system. Finding the way in the maze of the extracellular matrix (ECM) is a complex task requiring motility skills and navigation strategies. Of particular relevance for cancer navigation are our recent discoveries of swarming logistics in which the mother colony send out pioneering swarms in search of food. As the swarms migrate further away from the mother colony, they leave behind a network of swarms with complex internal traffic. The traffic is organized into lanes of bacteria traveling along the network from the mother colony to the front and back, so they can share resources and risks (much like social insects do). The intricate structure of the network and the complex internal traffic organize to maximize the swarm search for food. We have associated the swarm navigation and swarm-swarm interactions with the internal traffic. I will present a new “adaptable smart agents” (ASA) multi-level approach including 1. Adaptable motility and cell-cell interactions. 2. Capacity for sensing, signaling, communication, and distributed information processing. 3. Internal energy and internal clocks with adaptable rates. Modeling the bacteria led to a riveting realization: the collective interaction of the bacteria with the swarm boundary (the boundary of the lubricating ECM secreted by the bacteria) has a singular effect on the traffic organization inside the swarm. I will explain that this realization has important implications on the way the traffic of migrating cancer cells can be organized by interaction with the ECM and present modeling of cancer cell migration in the ECM as cell navigation in a maze to compare the success rates of path finding vs. path generating strategies of cancer navigation.