diff --git a/figures/doe_responses.svg b/figures/doe_responses.svg
new file mode 100644
index 0000000..2434394
--- /dev/null
+++ b/figures/doe_responses.svg
@@ -0,0 +1,2660 @@
+
+
+
+
diff --git a/figures/modeling_flower.svg b/figures/modeling_flower.svg
new file mode 100644
index 0000000..14904b7
--- /dev/null
+++ b/figures/modeling_flower.svg
@@ -0,0 +1,5980 @@
+
+
+
+
diff --git a/figures/modeling_overview.svg b/figures/modeling_overview.svg
new file mode 100644
index 0000000..4818f0b
--- /dev/null
+++ b/figures/modeling_overview.svg
@@ -0,0 +1,1572 @@
+
+
+
+
diff --git a/tex/thesis.tex b/tex/thesis.tex
index 890b9e0..f399ede 100644
--- a/tex/thesis.tex
+++ b/tex/thesis.tex
@@ -1550,6 +1550,77 @@ provide these benefits.
\section{introduction}
\section{methods}
\section{results}
+
+\subsection{DOE shows optimal conditions for expanded potent T cells}
+
+\begin{figure*}[ht!]
+ \begingroup
+
+ \includegraphics{../figures/modeling_overview.png}
+ \phantomsubcaption\label{fig:mod_overview_flow}
+ \phantomsubcaption\label{fig:mod_overview_doe}
+
+ \endgroup
+ \caption[Modeling Overview]
+ {Overview of modeling experiments.
+ \subcap{fig:mod_overview_flow}{Relationship
+ between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
+ be used to determine optimal process input conditions, and longitudinal
+ multiomics data will be used to fit predictive models. Together, these
+ will reveal predictive species that may be used for \glspl{cqa} and
+ \glspl{cpp}.}
+ \subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
+ initial \gls{doe} is given by the blue points and the augmented \gls{doe}
+ is given by the blue points.}
+ }
+ \label{fig:mod_overview}
+\end{figure*}
+
+\begin{figure*}[ht!]
+ \begingroup
+
+ \includegraphics{../figures/doe_responses.png}
+ \phantomsubcaption\label{fig:doe_responses_mem}
+ \phantomsubcaption\label{fig:doe_responses_cd4}
+
+ \endgroup
+ \caption[T cell optimization through Design of Experiments]
+ {\gls{doe} methodology reveals optimal conditions for expanding T cell
+ subsets. Responses vs IL2 concentration, \gls{dms} concentration, and
+ functional \gls{mab} percentage are shown for
+ \subcap{fig:doe_responses_mem}{total \ptmem{} T cells} and
+ \subcap{fig:doe_responses_cd4}{total \pth{} T cells}. Each point represents
+ one run.
+ }
+ \label{fig:doe_responses}
+\end{figure*}
+
+% DOE regression tables
+
+% TODO this section header sucks
+\subsection{AI modeling reveals highly predictive species}
+
+% model summary table
+
+\begin{figure*}[ht!]
+ \begingroup
+
+ \includegraphics{../figures/modeling_flower.png}
+ \phantomsubcaption\label{fig:mod_flower_48r}
+ \phantomsubcaption\label{fig:mod_flower_cd4}
+
+ \endgroup
+ \caption[Data-Driven \gls{cqa} identification]
+ {Data-driven modeling using techniques with regularization reveals species
+ predictive species which are candidates for \glspl{cqa}. Flower plots are
+ shown for \subcap{fig:mod_flower_48r}{CD4:CD8 ratio} and
+ \subcap{fig:mod_flower_cd4}{total \ptmemh{} cells}. The left and right
+ columns includes models that were trained only on the secretome and
+ metabolome respectively. Each flower on each plot represents one model,
+ moving toward the center indicates higher agreement between models.}
+ \label{fig:mod_flower}
+\end{figure*}
+
\section{discussion}
\chapter{aim 2b}\label{aim2b}
@@ -1615,7 +1686,6 @@ between survival groups.
\label{fig:mouse_dosing_overview}
\end{figure*}
-
\begin{table}[!h] \centering
\caption{Results for \gls{car} T cell \invivo{} dose study}
\label{tab:mouse_dosing_results}