These processes require transiting through an environment consisting of other cells and extracellular matrix. The chemotactic process therefore involves both a response to the external signal, and the handling of mechanical constraints on the motion. From the computational point of view, much research has been devoted to the study of autonomous motion planning. An important part of autonomous taxis is the ability to independently navigate, namely to find a path to a defined target under possible constraints. This ability, which is essential for cellular translocation and for the study of animal behavior, is also important for successful robotic exploration. For individual agent-based navigation, one obvious way of encoding target information is by having the target emit a signal, which allows the agent to determine a locally favorable direction. But, it is clear that the locally best SCH727965 cost direction may not be the overall best choice, as this may lead to trapping of the agent by large obstacles. Optimally, the agent should balance this target-based information with local structural information so as to navigate around these traps. The conceptual view that cells should integrate multiple sources of information can lead to new predictions regarding cellular chemotaxis; this will be seen below. In this work, we will study these questions by use of a simplified model of cellular navigation capabilities. Efforts in the biological and biophysical community have elucidated the basic elements underlying how cells are able to navigate via chemical gradients. First, the external signal influences the cell orientation by various signal transduction pathways, highly conserved between different cell types. Consequently, the cell polarizes and different chemicals accumulate at the front versus the back of the cell. Motility is typically obtained by f-actin polymerization at the cell‘s front, leading to membrane protrusions such as pseudopods, lamellipods and ruffles. Beyond individual propulsion, the cell interacts with its environment by various passive as well as active processes: The cell can adhere or de-adhere to the extra -cellular matrix or to neighboring cells, apply forces and even actively degrade the ECM by proteases. Many attempts have been made to model different aspects of directed migration and chemotaxis. Most models to date have addressed distinct parts of motility, including retraction and protrusion, but are unable to describe the entire motility process; other models use ad-hoc rules to describe the motion. Many studies, both theoretical and experimental, have also been devoted to the question of collective motion and how it emerges from individual interactions, from the cellular to the animal scale. In this work we focus on single cell motility, but our results can be extended to the case of collective motion by adding intra-cellular.