Today, we have only two complete WCMs. One for the small bacterium M. genitalium and the other for the minimal synthetic bacterial cell Syn3.0. Model organisms like the bacteria B. subtilis and Escherichia coli boast around 4,000 genes each. Eukaryotic cells, like the yeast Saccharomyces cerevisiae have approximately 6,000 genes, and humans a staggering 22,000 genes. This represents a significant increase in the number of proteins and their associated interactions compared to the simpler organisms for which WCMs exist. Despite the challenges posed by this complexity, we are determined to overcome them and create WCMs for any species.

In our quest to build WCMs, we face a daunting task: estimating the thousands or even hundreds of thousands of parameters involved in the intricate workings of a cell. To tackle this complexity, we employ a flexible Bayesian framework for statistical inference, allowing us to integrate prior knowledge from various sources like text mining and databases.