2 research outputs found
Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities
We present an integrated framework for the online optimal experimental re-design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro-kinetic differential equation model for Escherichia coli fed-batch processes after 6 h of cultivation. The system includes two fully-automated liquid handling robots; one containing eight mini-bioreactors and another used for automated at-line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re-designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re-computation of the optimal experiment are proven by a 50-fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619. © 2016 Wiley Periodicals, Inc.BMBF, 02PJ1150, Verbundprojekt: Plattformtechnologien für automatisierte Bioprozessentwicklung (AutoBio); Teilprojekt: Automatisierte Bioprozessentwicklung am Beispiel von neuen Nukleosidphosphorylase
Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch
How to design a clinical test aimed at identifying
in the safest, most precise and quickest way the subject-
specific parameters of a detailed model of glucose
homeostasis in type 1 diabetes is the topic of this article.
Recently, standard techniques of model-based design of
experiments (MBDoE) for parameter identification have
been proposed to design clinical tests for the identification
of the model parameters for a single type 1 diabetic individual.
However, standard MBDoE is affected by some
limitations. In particular, the existence of a structural mismatch
between the responses of the subject and that of the
model to be identified, together with initial uncertainty in
the model parameters may lead to design clinical tests that
are sub-optimal (scarcely informative) or even unsafe (the
actual response of the subject might be hypoglycaemic or
strongly hyperglycaemic). The integrated use of two
advanced MBDoE techniques (online model-based redesign
of experiments and backoff-based MBDoE) is proposed in
this article as a way to effectively tackle the above issue.
Online model-based experiment redesign is utilised to
exploit the information embedded in the experimental data
as soon as the data become available, and to adjust the
clinical test accordingly whilst the test is running. Backoffbased
MBDoE explicitly accounts for model parameter
uncertainty, and allows one to plan a test that is both
optimally informative and safe by design. The effectiveness
and features of the proposed approach are assessed and
critically discussed via a simulated case study based on
state-of-the-art detailed models of glucose homeostasis. It is
shown that the proposed approach based on advanced
MBDoE techniques allows defining safe, informative and
subject-tailored clinical tests for model identification, with
limited experimental effort