From: W. Trevor King <wking@tremily.us>
Date: Fri, 21 Jun 2013 17:57:56 +0000 (-0400)
Subject: sawsim/discussion.tex: Rework sawsim Monte Carlo review
X-Git-Tag: v1.0~35
X-Git-Url: http://git.tremily.us/?a=commitdiff_plain;h=cdea7cbeb917120d09df674e2fdaa984a03e1df7;p=thesis.git

sawsim/discussion.tex: Rework sawsim Monte Carlo review

Start with the big picture, and zoom in on the finer points.
---

diff --git a/src/sawsim/discussion.tex b/src/sawsim/discussion.tex
index bf73a1f..bea8c66 100644
--- a/src/sawsim/discussion.tex
+++ b/src/sawsim/discussion.tex
@@ -759,29 +759,6 @@ solutions for unforced $k$ for cusp-like and quartic potentials.
 
 \section{Review of current research}
 
-There are two main approaches to modeling protein domain unfolding
-under tension: Bell's and Kramers'\citep{schlierf06,hummer03,dudko06}.
-Bell introduced his model in the context of cell
-adhesion\citep{bell78}, but it has been widely used to model
-mechanical unfolding in
-proteins\citep{rief97a,carrion-vazquez99b,schlierf06} due to its
-simplicity and ease of use\citep{hummer03}.  Kramers introduced his
-theory in the context of thermally activated barrier crossings, which
-is how we use it here.
-
-\subsection{Evolution of unfolding modeling}
-
-Evans introduced the saddle-point Kramers' approximation in a protein unfolding context in 1997 (\citet{evans97} Eqn.~3).
-However, early work on mechanical unfolding focused on the simpler Bell model\citep{rief97a}.%TODO
-In the early 2000's, the saddle-point/steepest-descent approximation to Kramer's model (\xref{hanggi90}{equation}{4.56c}) was introduced into our field\citep{dudko03,hyeon03}.%TODO
-By the mid 2000's, the full-blown double-integral form of Kramer's model (\xref{hanggi90}{equation}{4.56b}) was in use\citep{schlierf06}.%TODO
-
-There have been some tangential attempts towards even fancier models.
-\citet{dudko03} attempted to reduce the restrictions of the single-unfolding-path model.
-\citet{hyeon03} attempted to measure the local roughness using temperature dependent unfolding.
-
-\subsection{History of simulations}
-
 There is a long history of protein unfolding and unbinding
 simulations.  Early work by \citet{grubmuller96} and
 \citet{izrailev97} focused on molecular dynamics (MD) simulations of
@@ -797,14 +774,31 @@ validate their results.
   physical motion of atoms and molecules by numerically solving
   Newton's equations.}
 
-\subsection{History of experimental AFM unfolding experiments}
-
-\begin{itemize}
-  \item \citet{rief97a}: 
-\end{itemize}
-
-\subsection{History of experimental laser tweezer unfolding experiments}
+Within the Monte Carlo simulation approach, there are two main models
+for protein domain unfolding under tension: Bell's and
+Kramers'\citep{schlierf06,hummer03,dudko06}.  Bell introduced his
+model in the context of cell adhesion\citep{bell78}, but it has been
+widely used to model mechanical unfolding in
+proteins\citep{rief97a,carrion-vazquez99b,schlierf06} due to its
+simplicity and ease of use\citep{hummer03}.  Kramers introduced his
+theory in the context of thermally activated barrier crossings, which
+is how we use it here.
 
-\begin{itemize}
-  \item \citet{izrailev97}: 
-\end{itemize}
+Evans introduced the saddle-point Kramers' approximation in a protein
+unfolding context in 1997 (\xref{evans97}{equation}{3}).  However,
+early work on mechanical unfolding focused on the simpler Bell
+model\citep{rief97a}.  In the early 2000's, the
+saddle-point/steepest-descent approximation to Kramer's model
+(\xref{hanggi90}{equation}{4.56c}) was introduced into our
+field\citep{dudko03,hyeon03}.  By the mid 2000's, the full-blown
+double-integral form of Kramer's model
+(\xref{hanggi90}{equation}{4.56b}) was in use\citep{schlierf06}.
+
+There have been some tangential attempts towards even fancier models:
+\citet{dudko03} attempted to reduce the restrictions of the
+single-unfolding-path model and \citet{hyeon03} attempted to measure
+the local roughness using temperature dependent unfolding.  However,
+further work on these lines has been slow, because the Bell model fits
+the data well despite its simplicity.  For more complicated models to
+gain ground, we need larger, more detailed datasets that expose
+features which the Bell model doesn't capture.