Microbial communities can play important roles in both the health and disease of their hosts. However, measurements of these communities are often confounded by technical variation and bias introduced at a number of stages of sample processing and measurement. Here we develop a flexible class of Bayesian Multinomial-Logistic Normal state space models which explicitly controls for technical variation and bias. Paired with this modeling framework we discuss best practices for experimental design; in particular, the use of technical replicates for quantifying technical variation and calibration curves for measuring bias. We demonstrate our approach through both simulation studies and application to real data.