The sequencing of the first genomes in the mid to late 1990s fundamentally changed the life sciences.  Shortly after their appearance, the functional annotation of the ORFs in these genomes led to the first genome-scale metabolic network reconstructions.  These reconstructions were complete enough that they could be subjected to flux-balance analysis that could compute phenotypic states.  Genetic perturbations, changes in the growth medium, and outcomes of laboratory evolutions could be analyzed and even predicted.  Thus, the first full genome-scale mechanistic genotype-phenotype relationships were established.

One line of inquiry for the following two decades was to make genome-scale models (GEMs) ever more detailed, quantitative, and comprehensive. Such models can now compute optimal proteome composition, and thus study proteome allocation, and they can compute basic stress responses (thermal, ROS, pH) and enable the study of stress mitigation responses.  GEMs have been converted into whole cell models to study growth under a given condition in great mechanistic detail.  One might characterize this undertaking as a Descartian approach.  Its applications should reveal the nature of the growth process and play a role in designing de novo genomes.  The first lecture will be focused on these developments.

A massive drop in the cost of DNA sequencing towards the end of the 2000s enabled a second line of inquiry.  The big data world that this opened up, one that grew exponentially throughout the 2010s, led to the emphasis of the development of new data analytic tools, whose prowess grew with the growing size of public databases.  We now have a series of databases with data analytics for the major data types that constitute the basic ‘dogma of molecular biology’ in addition to metabolic features. These half a dozen or so databases are now being made interoperable with each other, as well as with GEMs.  We thus have a completely new set of tools to analyze biological features, such as diversity, adaptation, species differences, and inferring selection pressures.  One might characterize this undertaking as Darwinian, in contrast to the biophysical one encapsulated in GEMs.  The two approaches are complementary, and the second lecture will be focused on the latter approach.

Looking at these developments in the context of other technology drivers leads one to the hypothesis that before this decade is up, leading universities will establish Departments of Genome Engineering.