Better monitoring and diagnostics tackle algae biofuel pond crash problem

Sandia National Laboratories is developing a suite of complementary technologies to help the emerging algae industry detect and quickly recover from algal pond crashes, an obstacle to large-scale algae cultivation for future biofuels.

Because of the way algae is grown and produced in most algal ponds, they are prone to attack by fungi, rotifers, viruses or other predators. Consequently, algal pond collapse is a critical issue that companies must solve to produce algal biofuels cost-effectively.

Quick identification of organisms in ponds is key to mitigation. To help pinpoint the problems, a Sandia team led by researcher Todd Lane recently developed a process to quickly and accurately identify pond crash agents through ultra-high-throughput sequencing using RapTOR.

RapTOR, originally developed for homeland security purposes, was developed to solve the "unknown unknowns" problem – lethal agents that could be weaponized from ordinary viruses or disguised to look harmless. It was designed to serve as a tool to rapidly characterize a biological organism with no pre-existing knowledge.