ADD figs to aim 2a

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}