Identifiability problems in systems biology

August 19 to August 23, 2019

at the

American Institute of Mathematics, San Jose, California

organized by

Marisa Eisenberg and Nicolette Meshkat

Original Announcement

This workshop will be devoted to identifiability problems in systems biology. Identifiability is the problem of determining which unknown parameters of a mathematical model can be determined from known input-output data (i.e. from particular variables which are measured/observed). In the structural identifiability problem, data is assumed to be perfect, i.e. noise-free and of any time duration required. Structural identifiability is a necessary condition for the numerical or "practical" identifiability problem, which is the parameter estimation problem for real and often noisy data. A lack of appreciation of parameter identifiability and uncertainty has been pointed to on numerous occasions as hindering the progress of mathematical modeling in biology.

We hope to address identifiability problems in both the theoretical and application-driven side of mathematical modelling. We anticipate that this work will lead to new collaborations, formulation of scientific questions as precise mathematical problems, and the introduction and sharing of new algebraic and analytic techniques to address inference challenges in mathematical biology. Among the problems we may consider are the following:

Finally, we will synthesize the above problems to identify several key conjectures, problems, and open questions in the area of identifiability analysis of dynamic systems models. These problems will be both mathematical questions, as well as application-focused questions.

Material from the workshop

A list of participants.

The workshop schedule.

A report on the workshop activities.

Workshop Videos