A good model includes incorrect assumptions. Practical models have to be simple enough that the number of parameters does not outstrip the available data. Theoretical models have to simple enough that you can figure out what they are doing and why. The real world unfortunately, lacks these properties. So in order to be useful, a model must ignore some known biological details, and replace these with simpler assumptions that are literally false.

Cheat

More precisely, do things with data that would make a statistician nervous, such as using univariate data to fit a multivariate rate equation by multiplication of limiting factor or Liebig’s law of the minimum, and choosing between those options based on your biological knowledge or intuition. Statisticians like to let data “speak for themselves”. Modelers should do that when it is possible, but more often the data are only one input into decisions about models structure, the rest coming from the experience and subject-area knowledge of the scientist and modelers.

Steal

Take ideas from others modelers and models, regardless of discipline. Cutting-edge original science is often done with conventional kind of models using conventional functional forms for rate equations- for example, compartment models abound in the study of HIV/AIDS. If someone else have developed s sensible looking model for a process that appears in the model, try it. If somebody else invested time and effort to estimate a parameter in a reasonable way, use it. Of course you need to be critical, and don’t hesitate to through out what you have stolen if it does not fit what you know about your system.

Reference:
Dynamic Models in Biology by Stephen P. Ellner and John Guckenheimer