The Dual Role of Subaerial Biofilms through the Lens of AI: the case for Causal Networks and Targeted Learning

Subaerial biofilms (SABs) are microbial communities that form on surfaces exposed to both air and periodic moisture and that can adapt to harsh environmental conditions like UV radiation, and fluctuating temperatures. On the one hand they can protect built surfaces by forming a barrier against environmental stressors, on the other they can also cause deterioration through biological weathering. The balance is complex and depend on a large number of factors. Unfortunately, only a small part of the complex multiscale network of physical, chemical and biological processes is captured by existing mechanistic model; this prompts for the involvement of phenomenological models. In this work we point at the modeling advantages offered by Bayesian Networks (BNs), Causal Networks (or Structural Causal Models, SCMs) and Targeted Learning (TL). The three related frameworks can model the complex interactions between biofilms, substrates, and atmosphere by identifying cause-effect relationships. SCMs can help devise interventions able to keep the biofilm within a desirable range of conditions, furthermore they can help assess the transportability of the discovered causal models across settings; Targeted Learning helps focusing on a specific research question (such as estimating the influence of factors on a target variable; quantifying their interactions; distinguishing between direct and mediated influence) without requiring the full discovery of the joint variable distribution, and using a semiparametric approach, as free as possible from modeling biases. In this work we highlight the considerable potential of the use of those related frameworks to the study of SABs.