Investigating Variability in Microbial Fuel Cells

In scientific studies, replicas should replicate, and identical conditions should produce very similar results which enable parameters to be tested. However, in microbial experiments which use real world mixed inocula to generate a new "adapted" community, this replication is very hard to achieve. The diversity within real-world microbial systems is huge, and when a subsample of this diversity is placed into a reactor vessel or onto a surface to create a biofilm, stochastic processes occur, meaning there is heterogeneity within these new communities. The smaller the subsample, the greater this heterogeneity is likely to be. Microbial fuel cells are typically operated at a very small laboratory scale and rely on specific communities which must include electrogenic bacteria, known to be of low abundance in most natural inocula. Microbial fuel cells (MFCs) offer a unique opportunity to investigate and quantify variability as they produce current when they metabolize, which can be measured in real time as the community develops. In this research, we built and tested 28 replica MFCs and ran them under identical conditions. The results showed high variability in terms of the rate and amount of current production. This variability perpetuated into subsequent feeding rounds, both with and without the presence of new inoculate. In an attempt to control this variability, reactors were reseeded using established "good" and "bad" reactors. However, this did not result in replica biofilms, suggesting there is a spatial as well as a compositional control over biofilm formation. IMPORTANCE The research presented, although carried out in the area of microbial fuel cells, reaches an important and broadly impacting conclusion that when using mixed inoculate in replica reactors under replicated conditions, different communities emerge capable of different levels of metabolism. To date there has been very little research focusing on this, or even reporting it, with most studies using duplicate or triplicate reactors, in which this phenomenon is not fully observed. Publishing data in which replicas do not replicate will be an important and brave first step in the research into understanding this fundamental microbial process.