In a Isothermal Titration Calorimetry experiment, If the **injected volume** or any of the **concentrations** is too small, or if the **ratio between both concentration** values is not appropriate, **then the signal-to-noise ratio will be low and the uncertainty of any result will be high**.

### Determinant factors of poor quality results in Isothermal Titration Calorimetry Experiments

The **control** of these factors might be limited by the available amount or the corresponding solutes (they can be expensive or difficult to synthesize/purify). If the total number of titrations is low then the solute in the sample cell will not be saturated and **the quality of the results will be poor**. Additionally, in reactions for which multiple chemical species can be formed it is always better to simultaneously fit several experimental data series, each focusing the sampling in a different concentration region which is more sensitive to any of the species.

### Advantages of prior Isotherm Simulation

The simulator tool provided by AFFINImeter **allows you to test** the effect of the parameters listed above on your experiment in order to **optimize its design, thus saving time and samples.**

### Dealing with multiple curves and parameters

Within the domain of second and higher order chemical species, the number of **thermodynamic** parameters involved in the **analysis** of the corresponding systems is significant (normally 2 parameters per reaction).

**A single experimental curve is usually insufficient** to justify the **fitting** of more than a couple of parameters and so the simultaneous **analysis** of several curves is commonly required.

If the same **binding** model applies to all the measurements, the parameters of the different curves should be identical to each other. There are also situations where a different **binding** model applies to each experimental curve but one or more parameter is shared. This happens, for instance, when combining an experiment involving two competing molecules and a host with another experiment where one of the ligands is absent. Additionally, mainly when applying complex **binding** models, you may want to **relate one or more parameters between them by means of analytical or logical equations. All the situations described can be easily solved using AFFINImeter**.