  While biomanufacturing plays a significant role in supporting the economy and
ensuring public health, it faces critical challenges, including complexity,
high variability, lengthy lead time, and very limited process data, especially
for personalized new cell and gene biotherapeutics. Driven by these challenges,
we propose an interpretable semantic bioprocess probabilistic knowledge graph
and develop a game theory based risk and sensitivity analyses for production
process to facilitate quality-by-design and stability control. Specifically, by
exploring the causal relationships and interactions of critical process
parameters and quality attributes (CPPs/CQAs), we create a Bayesian network
based probabilistic knowledge graph characterizing the complex causal
interdependencies of all factors. Then, we introduce a Shapley value based
sensitivity analysis, which can correctly quantify the variation contribution
from each input factor on the outputs (i.e., productivity, product quality).
Since the bioprocess model coefficients are learned from limited process
observations, we derive the Bayesian posterior distribution to quantify model
uncertainty and further develop the Shapley value based sensitivity analysis to
evaluate the impact of estimation uncertainty from each set of model
coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses
can identify the bottlenecks, guide the reliable process specifications and the
most "informative" data collection, and improve production stability.
