In this scenario, the experimental process and the computational model which
represents it are linked together and interact with each other in a truly
unified manner.
Entering the virtual experimentation scenario
To simulate an experiment numerically using the VERSE simulation code,
select BioSoftLab's virtual mode. BioSoftLab presents you with a software view
of the physical laboratory and its equipment. The instruments will be set
up and programmed exactly as if for a physical experiment; the functionality
of each virtual instrument is identical to that of its physical counterpart.
However, in the virtual mode, the physical input is then transformed to the
input required by the computational model.
Since there are additional numerical parameters and system characteristics
associated with simulation, special interfaces built into BioSoftLab's virtual
mode allow you to control the additional specifications. This information
consists of the following:
- specification of the physical makeup and characteristics of input
solutions and sorbents,
- properties of the input solutions needed by the computational model,
- identification of the reactions (if any) amongst the solutions, that
will occur in the column, and
- selection of the process model and associated numerical parameters.
With the physical and numerical input complete, the VERSE simulator is
started. Simulation output is collected in real time, and intermediate and
final results generated by the simulation can be visually represented on
BioSoftLab's virtual instruments. Data can be saved in various formats, and
can be loaded into PDELab for three-dimensional scientific visualization and
animation. When the simulation has finished, BioSoftLab allows you to
"save" the experiment to an experiment database. Saving a virtual
experiment means that the input configuration and output results are stored so
that they may later be retrieved to playback the experiment or to view
intermediate and final results.
In order to implement the above scenario, we encountered some major
difficulties. We had already built the software representation of the physical
instruments, and these virtual instruments were fully
"functional". However, there existed no mapping between input/setup
for the physical experiment and input to the computational model which was
used to simulate it. We had to identify and separate the kinds of
information required by the simulation code. A clear understanding of the
differences between the following categories of data enabled us to devise a
stable and consistent mapping.
- information related to the experimental procedure and instrument
setup. This information should be entered exactly as it is in the physical
experimentation scenario.
- information related to the physical makeup and characteristics of the
input solutions and sorbents. Here, the simulation setup must depart from
the physical setup. In the physical case, the experiment is driven by the
characteristics, properties and makeup of the solutions and sorbents
themselves. In the simulated experiment, the physical makeup and properties
must be described numerically to the simulator.
Instrument setup: programming the virtual pump controller
Therefore, users must specify the contents of the solutions and their physical
characteristics. If a reservoir is filled with a "taxol broth"
solution, the user must name (or tag) the solution and describe exactly what
is contained therein. Clearly, users should not be required to identify the
contents of tagged input more than once. Therefore, this information
required the introduction of a database.
Solution input: filling reservoir A with tissue culture broth
- information related to the properties of the input solutions which
are used to generate numerical parameters. There are several different
examples of this type of data. The most important is the specification of
which components from the input solutions are to be simulated. These are
special components whose journey through
Numerical properties of a component: describing taxol
numerically
the column is to be monitored by the simulation process. Special information
about these components is needed (for example, isotherm coefficients,
diffusivity, etc.). This again requires a database interface for storing and
retrieving such data.
- reaction information. Reactions occurring in the column require
particular attention. These are considered "components to be
monitored", and the component information described above is needed
here as well.
Reaction specification: identifying a dimerization reaction
- process model specification with associated numerical parameters.
This refers to the system of partial differential equations that will be
solved by the simulator, along with the numerical parameters required by the
Finite Element Method (FEM) which is used to solved them. There are several
"experiments" or "computational models" within VERSE,
and the application of a given model is governed by the type of experiment
which is being simulated. Ideally, expert system advice for the proper process
model should be available. Other FEM parameters, such as the mesh
specification, should be tied very closely to the column - since the
simulation actually represents what occurs in the column during the
experiment. To emphasize the relationship between the physical and
simulated experiments, the numerical parameters are tied wherever possible
to the virtual bioseparation column.
- specification of monitoring and output requirements. The simulation
produces concentration measurements of the specified components over time.
This data can be generated for concentrations at the end of the column or at
specified points along the length and radius of the column.
Probing the depths of the relationships between the physical process and the
computational model required many refinements to our virtual environment.
Since the theoretical model allows far greater flexibility in the
specification of input than the experiment itself, we had to extend the
concept of physical input to allow the experimenter to scale-up, scale-down,
optimize, mix or separate the input solutions in ways that were not feasible
in the physical laboratory. Since, however, these parameters represented
extrapolations of actual experimental input, they could easily be tied to
the physical devices from which they were extrapolated. In this way, users
of the numerical simulation model could understand how each parameter of the
theoretical input was associated with the actual experimental process.
Packing the column
There were also differences in the way physical and virtual experiments were
visualized in BioSoftLab. The simulated experiment is monitored and visualized
directly on the BioLabView instruments, just as in the physical experiment.
But in this case, the data used to "animate" the instruments is
generated by the simulation instead of by the actual devices.
Monitoring the column during simulation
This again allows the experimenter greater flexibility in visualizing the
experiment, since the simulation generates intermediate data which the
physical devices cannot produce. Column profiles generated by VERSE produced
three-dimensional time-dependent component concentration data along the length
of the column, which we visualized on special column-graphs within BioSoftLab.
We also animated this data using scientific visualization tools in PDELab. [5]
Graphs such as these were not possible before the advent of BioSoftLab.
Three-dimensional view of a reactant's concentration as it
moves down the column In this case, the concentration is uniform across the
column width.
