Update tutorial

doc/snapshot.md

@@ -5,11 +5,15 @@ Here, we describe the second modeling problem in our composite workflow, how to
 
 # Snapshot modeling step 1: gather information {#snapshots1}
 
+We begin snapshot modeling with the first step of integrative modeling, gathering information. Snapshot modeling utilizes structural information about the complex. In this case, we utilize heterogeneity models, the X-ray crystal structure of the fully assembled Bmi1/Ring1b-UbcH5c complex from the protein data bank (PDB), synthetically generated electron tomography (ET) density maps during the assembly process, and physical theories.
+
 \image html Input_snapshot.png width=600px
 
+The heterogeneity models inform protein copy numbers for the snapshot models. The PDB structure of the complex informs the structure of the individual proteins. The time-dependent ET data informs the size and shape of the assembling complex. Physical theories inform connectivity and excluded volume.
+
 # Snapshot modeling step 2: representation, scoring, and search process {#snapshots2}
 
-Navigate to the `Snapshots/Snapshots_Modeling/` folder. Here, you will find two python scripts. The first, `static_snapshot.py`, uses IMP to represent, score, and search for models of a single static snapshot. The second, `start_sim.py`, automates the creation of a snapshot model for each heterogeneity model.
+Next, we represent, score and search for snapshot models. To do so, navigate to the `Snapshots/Snapshots_Modeling/` folder. Here, you will find two python scripts. The first, `static_snapshot.py`, uses IMP to represent, score, and search for models of a single static snapshot. The second, `start_sim.py`, automates the creation of a snapshot model for each heterogeneity model.
 
 ## Modeling one snapshot
 

doc/trajectory.md

@@ -5,8 +5,12 @@ Here, we describe the final modeling problem in our composite workflow, how to b
 
 # Trajectory modeling step 1: gather information {#trajectories1}
 
+We begin trajectory modeling with the first step of integrative modeling, gathering information. Trajectory modeling utilizes dynamic information about the bimolecular process. In this case, we utilize heterogeneity models, snapshot models, physical theories, and synthetically generated small-angle X-ray scattering (SAXS) profiles.
+
 \image html Input_trajectories.png width=600px
 
+Heterogeneity models inform the possible compositional states at each time point and measure how well a compositional state agrees with input information. Snapshot models provide structural models for each heterogeneity model and measure how well those structural models agree with input information about their structure. Physical theories of macromolecular dynamics inform transitions between states. SAXS data informs the size and shape of the assembling complex and is left for validation.
+
 # Trajectory modeling step 2: representation, scoring, and search process {#trajectories2}
 
 Trajectory modeling connects alternative snapshot models at adjacent time points, followed by scoring the trajectories based on their fit to the input information, as described in full [here](https://www.biorxiv.org/content/10.1101/2024.08.06.606842v1.abstract).