You are here because you want to optimize a complex experiment. You
have some way of judging the quality of your experiments, by measuring
an experimental response. Your experiment is complex, because it
has several experimental parameters (e.g., between four and twenty)
that interact with each other in unknown, unpredictable ways. You want
to optimize these parameters, i.e., find values of the experimental
parameters that lead to desired experimental responses. You will have
some way of executing many experiments in an automated or
semi-automated way (e.g. using a high-throughput experimental
We will consider your experiments as being grouped into generations.
PDT is a tool for designing experiments for each generation, based
on sophisticated machine-learning modeling of data from all past
generations, with the aim of optimizing experimental responses.
Your experiments are expensive, so that each generation typically
won't contain more than 10's, 100's, or few 1000's of experiments. Prediction of
the next generation of experiments is therefore a small data
problem. Building predictive models using machine-learning techniques
for small data problems requires special methods (in contrast to
methods used for big data), made conveniently available to you with
PDT's web interface.
Your use of PDT entails three basic phases, two for initialization, and one that is repeated
over and over with each generation:
1. Experimental space definition
You start by choosing the type of experimental space. Currently there are two options:
Factorial, where you specify the experimental space as a list of experimental parameters
(e.g. concentrations, temperatures, etc), and of the discrete values each parameter may take
when you execute an experiment. The experimental space is given by all possible combinations of values,
one for each parameter.
Mixture, where you specify the experimental space as a list of experimental parameters,
and a range of integer values that each parameter may take. These values correspond to the number of
units of that parameter in an experiment (for example, the number of drops of solution
in an experiment set up by a liquid handling robot). The number of units, summed over all parameters,
is equal to a constant usum for all experiments. That implies that if an experiment
contains u units of a certain parameter, the ratio u/usum represents the proportion of that
parameter in the mixture.
After choosing the type of experimental space, and specifying parameters and values, you then specify the
generation parameters, Np = population size and
Nr = number of replicates. These two numbers determine how
many experiments there will be in a generation: Nexp =
Np (Nr + 1).
2. Initial experiments
Before launching a PDT campaign, you may want to enter experimental results
(experiments and response measurements) that you have gathered in preliminary studies,
e.g., calibration runs, as initial experiments. To be usable by PDT, these experiments
must lie in the expanded experimental space determined in the previous experimental
space definition phase. This means that each experiment must have a specified value
for each of the parameters in the experimental space definition (and, if the space is a mixture, respect the
constraint on the total number of units), but it is not necessary that its parameter values correspond to
particular values specified in the experimental space definition.
3. Experiment design and response measurements
PDT iteratively chooses designs of Nexp experiments, each selected from
your experimental space, called generations. The first generation is typically chosen
at random, while keeping into account any initial experiments you may have
entered in the previous phase. At the end of every generation, PDT uses all available
experimental results to build a model that predicts which, among all untried experiments,
are those most likely to have good experimental response, and accordingly chooses the design
for the following generation.
In addition to the experiments specified by the PDT design, you may
optionally perform extra (additional) experiments, either to explore your own intuition,
or to further validate results from previous generations, e.g., for calibration reasons.