Updating tutorial

doc/heterogeneity.md

@@ -39,8 +39,8 @@ From the output of `prepare_protein_library`, we see that there are 3 heterogene
 
 # Heterogeneity modeling step 3: assessment {#heterogeneity_assess}
 
-Now, we have a variety of heterogeneity models. In general, there are four ways to assess a model: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. Here, we will focus specifically on comparing the model to experimental data, as other assessments will be performed later, when the [trajectory models are assessed.] (@ref trajectory_assess)
-
-In the `Heterogeneity/Heterogeneity_Assessment` folder, there is a single script, `plot_heterogeneity.m`. This script plots the modeled and experimental copy numbers simultaneously, as shown below for proteins A (a), B (b), and C (c). From these plots, we observe that the range of possible experimental copy numbers are well sampled by the heterogeneity models, indicating that we are prepared for [snapshot modeling.] (@ref snapshots)
+Now, we have a variety of heterogeneity models. In general, there are four ways to assess a model: estimate the sampling precision, compare the model to data used to construct it, validate the model against data not used to construct it, and quantify the precision of the model. Here, we will focus specifically on comparing the model to experimental data, as other assessments will be performed later, when the [trajectory model is assessed.] (@ref trajectory_assess)
 
 \image html Heterogeneity_Assessment.png width=600px
+
+In the `Heterogeneity/Heterogeneity_Assessment` folder, there is a single script, `plot_heterogeneity.m`. This script plots the modeled and experimental copy numbers simultaneously, as shown below for proteins A (a), B (b), and C (c). From these plots, we observe that the range of possible experimental copy numbers are well sampled by the heterogeneity models, indicating that we are prepared for [snapshot modeling.] (@ref snapshots)

doc/mainpage.md

@@ -7,10 +7,10 @@ Integrative spatiotemporal modeling in IMP {#mainpage}
 
 Biomolecules are constantly in motion; therefore, a complete depiction of their function must include their dynamics instead of just static structures. We have developed an integrative spatiotemporal approach to model dynamic systems.
 
-Our approach applies a composite workflow, consisting of three modeling problems to compute (i) heterogeneity models, (ii) snapshot models, and (iii) trajectory models.
+Our approach applies a composite workflow, consisting of three modeling problems to compute (i) heterogeneity models, (ii) snapshot models, and (iii) a trajectory model.
 Heterogeneity models describe the possible biomolecular compositions of the system at each time point. Optionally, other auxiliary variables can be considered, such as the coarse location in the final state when modeling an assembly process.
 For each heterogeneity model, one snapshot model is produced. A snapshot model is a set of alternative standard static integrative structure models based on the information available for the corresponding time point.
-Then, trajectory models are created by connecting alternative snapshot models at adjacent time points. These trajectory models can be scored based on both the scores of static structures and the transitions between them, allowing for the creation of trajectories that are in agreement with the input information by construction.
+Then, a set of trajectories ranked by their agreement with input information is computed by connecting alternative snapshot models at adjacent time points (*i.e.*, the “trajectory model”). This trajectory model can be scored based on both the scores of static structures and the transitions between them, allowing for the creation of trajectories that are in agreement with the input information by construction.
 
 If you use this tutorial or its accompanying method, please site the corresponding publications: