"QuizID"	"AppID"	"AppTitle"	"TaskID"	"TaskText"	"RecordID"	"Record"	"Type"	"Note"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	1	"We'll begin with the basic virus model by turning off any immune response related component. Set all parameter values related to the immune response such that there is no __F__ and __A__ present at any time during the simulation. Set initial conditions to 10^5^ uninfected cells, no infected cells, and 10 virions. Set the infection rate to 10^-5^, no production or death of uninfected cells, lifespan of infected cells and virus of 1 day and 6 hours, respectively. The rate of virus production should be 100 per day. You should get a single acute viral infection with no immune response present and a maximum of 70217 infected cells."	"T1R1"	"Day at which the viral load peaks"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	2	"Let's explore different mechanisms for the innate response. Keep initial level of innate response at 0, but set innate production and removal rate to 1. Set all innate growth parameters (the _f~i~_) and all innate actions (_k~1~_, _k~2~_, _k~3~_) to 0. Run the simulation, confirm that you get an innate response that settles at a steady state (balance between production and removal), but that there is no further increase and that the rest of the dynamics doesn't change (i.e. you should get the same maximum number of infected cells as above)."	"T2R1"	"Maximum number of infected cells"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	2	"Let's explore different mechanisms for the innate response. Keep initial level of innate response at 0, but set innate production and removal rate to 1. Set all innate growth parameters (the _f~i~_) and all innate actions (_k~1~_, _k~2~_, _k~3~_) to 0. Run the simulation, confirm that you get an innate response that settles at a steady state (balance between production and removal), but that there is no further increase and that the rest of the dynamics doesn't change (i.e. you should get the same maximum number of infected cells as above)."	"T2R2"	"Value for innate response at steady state"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R1"	"Peak of **F** with _f~1~_ = 10^-5^ and _F~max~_ = 1000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R2"	"Day at which **F** peaks with _f~1~_ = 10^-5^ and _F~MAX~_ = 1000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R3"	"Peak of **F** with _f~1~_ = 10^-6^ and _F~max~_ = 1000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R4"	"Day at which **F** peaks with _f~1~_ = 10^-6^ and _F~max~_ = 1000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R5"	"Peak of **F** with _f~1~_ = 10^-5^ and _F~max~_ = 10000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	3	"Now, let's explore the different types of innate response induction by playing with the _f~i~_ parameters and the saturation parameter, _s~V~_. First, set _f~1~_ to 10^-5^ and _F~max~_ to 1000. Relate what you see in the plots to the equations so you get an idea of how different terms in the equations behave. Continue to keep the _k~i~_ at 0. This means the rest of the variables should not change. Make ten-fold changes to _f~1~_ and _F~max~_. Try to predict what will happen and then test your predictions by running the simulation."	"T3R6"	"Day at which **F** peaks with _f~1~_ = 10^-5^ and _F~max~_ = 10000"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	4	"Let's switch to the alternative innate response induction proceses. Set _f~1~_ back to zero. Then, set _f~2~_ to 3 and _s~V~_ to 10^5^. Play around with different values for _f~2~_ and _s~V~_, each time trying to predict how changes in the parameters will affect the model.  


Next, set _f~2~_ to zero and set _f~3~_ to 2. For any given value of _s~V~_, how does switching between innate response induction via _f~2~_ differ from _f~3~_? (*Hint: take another look at the model equations.*) Play around with different values for _f~3~_ and _s~V~_, again trying to predict the output and then running the simulation to confirm. Remember, each time you switch processes, 'turn off' the alternative ones."	"T4R1"	"Peak of **F** with _f~2~_ = 3 and _s~V~_ = 10^5^"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	4	"Let's switch to the alternative innate response induction proceses. Set _f~1~_ back to zero. Then, set _f~2~_ to 3 and _s~V~_ to 10^5^. Play around with different values for _f~2~_ and _s~V~_, each time trying to predict how changes in the parameters will affect the model.  


Next, set _f~2~_ to zero and set _f~3~_ to 2. For any given value of _s~V~_, how does switching between innate response induction via _f~2~_ differ from _f~3~_? (*Hint: take another look at the model equations.*) Play around with different values for _f~3~_ and _s~V~_, again trying to predict the output and then running the simulation to confirm. Remember, each time you switch processes, 'turn off' the alternative ones."	"T4R2"	"Peak of **F** with _f~2~_ = 3 and _s~V~_ = 10^7^"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	4	"Let's switch to the alternative innate response induction proceses. Set _f~1~_ back to zero. Then, set _f~2~_ to 3 and _s~V~_ to 10^5^. Play around with different values for _f~2~_ and _s~V~_, each time trying to predict how changes in the parameters will affect the model.  


Next, set _f~2~_ to zero and set _f~3~_ to 2. For any given value of _s~V~_, how does switching between innate response induction via _f~2~_ differ from _f~3~_? (*Hint: take another look at the model equations.*) Play around with different values for _f~3~_ and _s~V~_, again trying to predict the output and then running the simulation to confirm. Remember, each time you switch processes, 'turn off' the alternative ones."	"T4R3"	"Peak of **F** with _f~3~_ = 2 and _s~V~_ = 10^5^"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	4	"Let's switch to the alternative innate response induction proceses. Set _f~1~_ back to zero. Then, set _f~2~_ to 3 and _s~V~_ to 10^5^. Play around with different values for _f~2~_ and _s~V~_, each time trying to predict how changes in the parameters will affect the model.  


Next, set _f~2~_ to zero and set _f~3~_ to 2. For any given value of _s~V~_, how does switching between innate response induction via _f~2~_ differ from _f~3~_? (*Hint: take another look at the model equations.*) Play around with different values for _f~3~_ and _s~V~_, again trying to predict the output and then running the simulation to confirm. Remember, each time you switch processes, 'turn off' the alternative ones."	"T4R4"	"Peak of **F** with _f~3~_ = 2 and _s~V~_ = 10^7^"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	5	"Explore the alternative representations of innate response induction by switching modeled process. Do you think that any of the modeled processes is more ***biologically reasonable*** than the others? Why or why not? Are you able to recreate similar dynamics regardless of which process is modeled? What happens if you turn more than one of the alternative processes on at once; is *this* ***biologically reasonable***? Can you think of improvements to the parameterization?"	"T5R1"	"Nothing"	"None"	""
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	6	"Now, let's explore what happens when you have non-zero _k~i~_. First, set the innate induction parameters as follows: _f~3~_ = 5, _f~1~_ = _f~2~_ = 0, and _s~V~_ = 10^5^ (this way we can explore the _k~i~_ effects using the same innate activation processes). Try different values for the _k~i~_ parameters. At first, turn on one process at a time; after, mix and match. 


You'll find that the action of the innate response now impacts the other variables, which in turn can impact further innate activation. Some of the resulting dynamics can get complex. Pay attention to how different processes of innate activation and innate action do or don't produce different overall dynamics."	"T6R1"	"Peak of **I** with _k~1~_ = 0.001, other _k~i~_ = 0"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	6	"Now, let's explore what happens when you have non-zero _k~i~_. First, set the innate induction parameters as follows: _f~3~_ = 5, _f~1~_ = _f~2~_ = 0, and _s~V~_ = 10^5^ (this way we can explore the _k~i~_ effects using the same innate activation processes). Try different values for the _k~i~_ parameters. At first, turn on one process at a time; after, mix and match. 


You'll find that the action of the innate response now impacts the other variables, which in turn can impact further innate activation. Some of the resulting dynamics can get complex. Pay attention to how different processes of innate activation and innate action do or don't produce different overall dynamics."	"T6R2"	"Peak of **I** with _k~2~_ = 0.001, other _k~i~_ = 0"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	6	"Now, let's explore what happens when you have non-zero _k~i~_. First, set the innate induction parameters as follows: _f~3~_ = 5, _f~1~_ = _f~2~_ = 0, and _s~V~_ = 10^5^ (this way we can explore the _k~i~_ effects using the same innate activation processes). Try different values for the _k~i~_ parameters. At first, turn on one process at a time; after, mix and match. 


You'll find that the action of the innate response now impacts the other variables, which in turn can impact further innate activation. Some of the resulting dynamics can get complex. Pay attention to how different processes of innate activation and innate action do or don't produce different overall dynamics."	"T6R3"	"Peak of **I** with _k~3~_ = 0.001, other _k~i~_ = 0"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	6	"Now, let's explore what happens when you have non-zero _k~i~_. First, set the innate induction parameters as follows: _f~3~_ = 5, _f~1~_ = _f~2~_ = 0, and _s~V~_ = 10^5^ (this way we can explore the _k~i~_ effects using the same innate activation processes). Try different values for the _k~i~_ parameters. At first, turn on one process at a time; after, mix and match. 


You'll find that the action of the innate response now impacts the other variables, which in turn can impact further innate activation. Some of the resulting dynamics can get complex. Pay attention to how different processes of innate activation and innate action do or don't produce different overall dynamics."	"T6R4"	"TRUE or FALSE: you can track the extent of the infection (i.e., how many total cells were infected) by calculating the difference between the number of ***uninfected cells*** at the beginning and the end of the simulation for all non-zero values of _k~1~_, _k~2~_, and _k~3~_."	"Logical"	"Report TRUE or FALSE"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	7	"Finally, turn on the adaptive response. Note that in this model, there is no adaptive response without innate response (check it by setting the innate response to 0 while having non-zero adaptive growth rates, _a~i~_). Also, note that the alternative adaptive growth processes for _a~1~_ and _a~3~_ further need an initial non-zero value for the adaptive immune response to be able to grow (again, check it for yourself either by reviewing the model equations or running the simulation). 


Turn the innate response back on as in Task 6 with _k~2~_ = 0.001 and _k~1~_ = _k~3~_ = 0. Explore how different non-zero adaptive growth rates, _a~i~_, affect the adaptive response dynamics. Start by leaving the adaptive action parameters at 0. Then, fiddle with those parameters as well and set them to non-zero values. Also, pay attention to the impact of the saturation constants."	"T7R1"	"Nothing"	"None"	""
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	8	"Now that we have played with each of the immune response processes, let's see if we can put together a bit more complicated model. 


Set _d~F~_ = 10, the innate response growth parameters to _f~3~_ = 15, _f~1~_ = _f~2~_ = 0, and _s~V~_ = 10^5^, and the innate response action parameters to _k~2~_ = 0.001 and _k~1~_ = _k~3~_ = 0. Set the adaptive response initial value to 1, adaptive response growth parameters to _a~3~_ = 5, _a~1~_ = _a~2~_ = 0, and _h~V~_ = 10^7^, and the adaptive response action parameters to _k~4~_ = 0, _k~5~_= 0.01, _k~6~_ = 0.1, and _s~A~_ = 1000. Can you intuit what the model output will look like? Run the simulation for 100 days. You should have about 3 uninfected cells remaining at the end of the simulation.


Now, let's add some processes for the birth and death of uninfected cells. Set the rate of uninfected cell production, _n_, to 10000 and the rate of natural death of uninfected cells to a value that would, in the absence of infection, equate to a constant 10^5^ number of uninfected cells throughout the simulation. If you have difficulty determining the corresponding value for _d~U~_, you can review steady states in the ***Basic Virus*** app. Run the simulation. You should get a pretty interesting pattern. How would you interpret this? Does this model, parameter or dynamics, seem ***biologically reasonable***?"	"T8R1"	"Peak of **A**"	"Rounded_Integer"	"Report the rounded integer"
"DSAIRM_modelvariants"	"modelvariants"	"Model Variant Exploration"	9	"As you'll notice, some of the specific model choices (i.e. specific parameters/terms in the model being turned on or off) lead to similar results, while other times results are quite different. For a specific system under study, some ways to formulate the immune response and the dynamics one gets might be better than others. Think about a system you are familiar with and consider which (if any) of the possible mechanisms implemented in this model might best describe that system. Keep playing with the model and see if you can recreate the system!"	"T9R1"	"Nothing"	"None"	""
