Nowadays, a large part of the online biological data resides in the deep web. Lately, there have been several efforts focusing on integrating and providing search functionality for biological deep web data sources. Such systems often require data access involving a large number of remote data sources and the use of various communication links. Both the servers and networking links are vulnerable to congestion and failures. This can lead to an unpredictable unavailability or inaccessibility, which can disrupt access to the information. In this paper, we propose a solution to maintain query processing capability of an integrated biological deep web search system in the presence of unavailable or inaccessible data sources. Our solution involves dynamically adapting query processing when unexpected data source unavailability or inaccessibility is detected. We exploit the data redundancy that is found across biological deep web data sources. We incrementally generate a partial new query plan by bringing in new data sources that were not in the original query plan to replace the subplan that became inaccessible.