The CRISPR-Cas system has enabled the development of
sophisticated,
multigene metabolic engineering programs through the use of guide
RNA-directed activation or repression of target genes. To optimize
biosynthetic pathways in microbial systems, we need improved models
to inform design and implementation of transcriptional programs. Recent
progress has resulted in new modeling approaches for identifying gene
targets and predicting the efficacy of guide RNA targeting. Genome-scale
and flux balance models have successfully been applied to identify
targets for improving biosynthetic production yields using combinatorial
CRISPR-interference (CRISPRi) programs. The advent of new approaches
for tunable and dynamic CRISPR activation (CRISPRa) promises to further
advance these engineering capabilities. Once appropriate targets are
identified, guide RNA prediction models can lead to increased efficacy
in gene targeting. Developing improved models and incorporating approaches
from machine learning may be able to overcome current limitations
and greatly expand the capabilities of CRISPR-Cas9 tools for metabolic
engineering.