  Like distributed systems, biological multicellular processes are subject to
dynamic changes and a biological system will not pass the
survival-of-the-fittest test unless it exhibits certain features that enable
fast recovery from these changes. In particular, a question that is crucial in
the context of biological cellular networks, is whether the system can keep the
changing components \emph{confined} so that only nodes in their vicinity may be
affected by the changes, but nodes sufficiently far away from any changing
component remain unaffected.
  Based on this notion of confinement, we propose a new metric for measuring
the dynamic changes recovery performance in distributed network algorithms
operating under the \emph{Stone Age} model (Emek \& Wattenhofer, PODC 2013),
where the class of dynamic topology changes we consider includes
inserting/deleting an edge, deleting a node together with its incident edges,
and inserting a new isolated node. Our main technical contribution is a
distributed algorithm for maximal independent set (MIS) in synchronous networks
subject to these topology changes that performs well in terms of the
aforementioned new metric. Specifically, our algorithm guarantees that nodes
which do not experience a topology change in their immediate vicinity are not
affected and that all surviving nodes (including the affected ones) perform
$\mathcal{O}((C + 1) \log^{2} n)$ computationally-meaningful steps, where $C$
is the number of topology changes; in other words, each surviving node performs
$\mathcal{O}(\log^{2} n)$ steps when amortized over the number of topology
changes. This is accompanied by a simple example demonstrating that the linear
dependency on $C$ cannot be avoided.
