link sections to notebooks 01-08

Author: thempel

manuscript/manuscript.tex

@@ -371,8 +371,9 @@ \subsection{Feature selection}
 and the more general variational approach for Markov processes (VAMP)~\cite{vamp-preprint}
 provide a systematic means to quantitatively compare multiple representations of the simulation data.
 In particular, we can use a scalar score obtained using VAMP to directly compare the ability of certain features to capture slow dynamical modes in a particular molecular system.
+In Notebook (01), we present in detail how to extract features from MD datasets and how to systematically compare them.
 
-Here, we utilize the VAMP-2 score, which maximizes the kinetic variance contained in the features~\cite{kinetic-maps}.
+Throughout this tutorial, we utilize the VAMP-2 score, which maximizes the kinetic variance contained in the features~\cite{kinetic-maps}.
 We should always evaluate the score in a cross-validated manner to ensure that we neither include too few features (under-fitting) or too many features (over-fitting)~\cite{gmrq,vamp-preprint}.
 To choose among three different molecular features reflecting protein structure,
 we compute the (cross-validated) VAMP-2 score (Notebook 00).
@@ -398,6 +399,8 @@ \subsection{Dimensionality reduction}
 Discrete jumps between the minima can be observed by visualizing the transformation of the first trajectory into these ICs (Fig.~\ref{fig:io-to-tica}d).
 We thus assume that our TICA-transformed backbone torsion features describe one or more metastable processes.
 
+We demonstrate how to apply TICA, suggest how to interpret the projected coordinates and compare the results to other dimension reduction techniques in Notebook (02).
+
 \begin{figure}
 \includegraphics{figure_3}
 \caption{Example analysis of the conformational dynamics of a pentapeptide backbone:
@@ -414,7 +417,8 @@ \subsection{Discretization}
 which can greatly facilitate the decomposition of our system into the discrete Markovian states necessary for MSM estimation.
 Here, we use the $k$-means algorithm to segment the four dimensional TICA space into $k=75$ cluster centers.
 The number of cluster centers has been chosen to optimize the VAMP-2 score in a manner identical to how the feature selection was carried out above,
-which is shown in the showcase notebook (00).
+which is shown in the showcase Notebook (00).
+A detailed comparison between different clustering techniques is provided in Notebook (02).
 
 \subsection{MSM estimation and validation}
 
@@ -447,6 +451,8 @@ \subsection{MSM estimation and validation}
 and shows that the MSM we have estimated at lag time $\tau=0.5$~ns indeed predicts the
 long-timescale behavior of our system within error (blue/shaded area).
 
+In Notebook (03), we demonstrate in detail how to estimate and validate MSMs with PyEMMA.
+
 \subsection{Analyzing the MSM}
 
 \begin{figure}
@@ -526,6 +532,8 @@ \subsection{Analyzing the MSM}
 The transition network can be additionally visualized by plotting representative structures of the five metastable states $\mathcal{S}_{(1-5)}$ according to their committor probability (Fig.~\ref{fig:tpt-network}).
 It is easy to see from this depiction that the dominant pathway from $\mathcal{S}_2$ to $\mathcal{S}_4$ proceeds through $\mathcal{S}_5$.
 
+More details about (spectral) properties of MSMs and how to analyze them with PyEMMA are discussed in Notebook (04) and Notebook (05).
+
 \subsection{Connecting the MSM with experimental data}
 
 \begin{figure}
@@ -560,6 +568,8 @@ \subsection{Connecting the MSM with experimental data}
 We see that the predicted relaxation signal has a much larger amplitude for the nonequilibrium initialization,
 making it more likely to be experimentally measurable.
 
+Besides a detailed demonstration of the above, Notebook (06) demonstrates how to compute J-couplings and dynamic fingerprints from MSMs.
+
 \subsection{Summary}
 
 In this section, we have summarized how to conduct an MSM-based analysis of biomolecular dynamics data using PyEMMA.
@@ -587,6 +597,7 @@ \subsection{Modeling large systems}
 we explain how to deal with those in the tutorials.
 
 More details on how to model complex systems with the techniques presented here are described, e.g., by~\cite{plattner_protein_2015,plattner_complete_2017}.
+We further demonstrate the symptoms of difficult data situations and how to deal with them in Notebook (08).
 
 \subsection{Advanced Methods}