Biological and medical sciences gained considerably with technology and new scientific paradigms.
Step by step, it is transforming itself from a “stamp collection”-based area (a scientific field based on individual experiments), to a scientific field based on models and general theories, nowadays called theoretical and mathematical biology. In the figures, we have the new scientific cycle for scientific investigation.
Essentially, models are built, models are analyzed, hypothesis are formulated, experiments are done, data are analyzed, datasets are used for better understanding individual experiments; all that was made possible due computers, with recent technologies.
That created a new “cycle of knowledge” (Figure b): biology, computation, and technology. Biology generates new ideas in its traditional and old ways, nowadays called wet-biology, computers are used to analyze data and generating models, called dry biology, and new technologies allow new experiments, such as studying cells outside its natural environment.
Taken from the thesis -> introduction.
According to Cobelli and Carson (2008), other system-model relationship are possible, e.g. when we observe the system working without interference (electrophysiology); one example is the nighty behavior of ghrelin dynamics.
We may, as represented in the scheme, create an input with the goal to study the system, how it respond to certain input profiles. One example is the ghrelin dynamic herein, other example is pharmacokinetics and pharmacodynamics.
“The second type of situation is that where we are again interested in modeling a single variable. However, this time we are viewing it as the response of a physiological system to a specific stimulus or perturbation that has been applied to that system.” Cobelli and Carson (2008)
Optimal control in medicine and biology
Optimal control in medicine and biology
Jorge Guerra Pires
Internal Seminar (DISIM): prof. Elena De Santis
DIPARTIMENTO DI INGEGNERIA E SCIENZE DELL'INFORMAZIONE E
MATEMATICA (DISIM), Università degli Studi dell'Aquila (UNIVAQ)
20th March 2017, L’Aquila, Italy
evolutionary Algorithms, and optimal control theory:
A numerical-based approach.
Accessed November 30, 2016.
Life Sciences (i.e., medical and biological sciences) might be seen as the connections between
medicine, biology, mathematics, physics, and computer sciences.
Furthermore, optimal control might be defined as the extension of static optimization, or even as
some comments, the new face of Variational Calculus.
A straightforward definition of life sciences is no longer simple, since it is an inter- and multi-
It might be said that in the past, this scientific domain comprised of a set of united field such as
medicine and biology that rarely interfered with each other (in fact, the name seems to be
originated in the last decades); nonetheless, in the present it is a “unique” branch comprised of
researches from a variety of field such as mathematics, medicine, and biology.
The inclusion of mathematics and other fields such as computer science (and information
sciences) came as a “rebirth” of the field.
Source: J G Pires. "On the mathematical modeling in gene expression estimation: an initial discussion on PBM and BM". September 24th 2014, Como
Lake (Poster Session). 4
Source: J G Pires. "On the mathematical modeling in gene expression estimation: an initial discussion on PBM and BM". September 24th 2014,
Como Lake (Poster Session).
Physical System modeling. A) the dichotomy ‘measure-analysis’; b) the dilemma of controlled and
noisy state variables
Basically, one have a system, then we created a model for this system. The system generates data for
our model, and our model generate possible analytical analysis for our physical system Figure a.
Furthermore, Figure b, in most of the cases when we model a system, we apply a stimulus, an input,
that supposes to trigger dormant properties of our system under question.