Modelling the Failure of Anaerobic Digestion

The overall goal of WP4 is to analyse the mechanisms underlying the biogas production decreases in anaerobic reactors (ARs) fed with microalgal substrates and identify early warning signals using an innovative method based on the analysis of causal interaction networks.

• Task 4.1. Linking community structure, gene expression and bioreactor functioning through state transitions
  In order to reveal ongoing mechanisms in the ARs during the transition from stable to critical to failure, we will integrate all the accumulated information from a variety of sources, including structure (metabarcoding), functional potential (metagenomics) and expressed functions (metaproteomics) from Task 2.4, as well as chemical data from Task 2.5. MetaOMICs data tables from perturbation experiments will be linked with data tables containing routine and chemical analyses. Metabolic reconstruction and functional pathways will be generated and extracted for all MAGs and expressed proteins belonging to these will be quantified across the AR transitions and linked with methane production levels and other key parameters of the ARs. Microbial interaction maps and food webs/substrate flow maps will be generated in line with our previous work. Key features (taxa, mechanisms, function, interactions) correlating with bioreactor transitions will be identified through a combination weighted correlation network analysis, multivariate analysis, eigengene networks and Sankey diagrams.
• Task 4.2. Modelling ARs systems
  Modelling can also be used as an effective tool to evaluate the impact of different process disturbances and operational variables on the anaerobic digestion performance. In this case, process efficiency dynamics will be evaluated based on the Anaerobic Digestion Model No. 1 (ADM1). This task will run simulations in ADM1 to explore the variability of key bioreactor parameters in the face of applied perturbations (inhibition scenarios).
• Task 4.3 Empirical dynamic modelling for early warning signals identification
  As described in Task 3.3, time-series data of chemical and biological data derived from the anaerobic reactors will be assembled and analysed using the Empirical Dynamic Modelling (EDM) framework described in section 1.3b Methodology (subsection AS2.3 Early warning signals identification). Massive data acquisition using metabarcoding (Task 2.4) and chemical fingerprinting (Task 2.5), along with routine measurements of system parameters, will provide i) an unprecedented analysis of interaction networks (the ‘interactomes’) in bioreactors, and ii) highly efficient early warning signals for process failure in anaerobic reactors. A qualitative analysis of warning indicators will be carried out to evaluate, from all identified early warnings, the best candidates according to the criteria of scalability, ease of determination (measurement), and affordability (cost of measurement). The reliability of the early warning indicators will be tested by repeating the analyses on time series derived from experimental replicates. Warning thresholds and warning times will be also calculated from the EDM results.