Ghrelin, paper review part I

For more: Book Review and Comment

Big glucose model (talk part 1)

See also

The big glucose model: the quest for unification

Food Intake vs. Metabolism: body weight control

In the following diagram, we organize several hormones and molecules (in general peptides) that play a central role in glucose control. The coming text dissert on each of them, and others. The most important to understand is that glucose is the main energy of the body and it must be refuelled from time to time, however this time to time is not always predictable, and how biological system must manage the energy already present until the next meal. Even in the meal time, it is necessary to "take it easy", it must be controlled how much to eat, and as well the absorption of the glucose. It is achieved through a network of interconnected hormones and vital molecules, some in the brain, some in strategic places such as the guts and pancreas. Body weight is a quite important parameter for the body: too much weight means too much energy to keep it, too less energy means not enough power in important situations. 

Schematic viewpoint of the hormones involved in the appetite control and metabolism. In red are important players, but are not hormones. The dashed arrows intend to show hormones that plays more then one control function on the diagram, the challenge is to build a complete diagram on this style, even in layers, that is, one hormone may control others such in gene expression, transcription networks; e.g. some researches show that leptin can control insulin⁽¹¹⁾. This diagram is mathematical modeling biased. The "fat" arrow between body weight and metabolism intends to say that the metabolism is a short-term dynamic process, from seconds to hours. whereas the body weight changes in a long-term scale, from days to months. Source: own elaboration.

See also

The big glucose model: the quest for unification

The big glucose model: the quest for unification

The coupling of hormonal responses to nutrient availability is fundamental for metabolic control⁽¹⁾. Metabolism is the important step in which living systems balance the energy available and the energy demanded, on such a way that the organism will not find itself in a situation of lacking energy after an abundance⁽¹⁾. Independent of scientific advances, our body works, it is a miracle of control system in practice. Glucose is constantly converted to glycogen, "the battery of living systems", constantly it is brought back to the bloodstream. 

Controversy underscores the fact that, despite the impressive progress made over the past few decades in unraveling many of the molecular pathways involved in energy regulation, we still have a rather murky understanding of how all the pieces fit together to function as an integrated system⁽³⁾. For instance, recently a new hormone long ago guessed was finally identified, called neuromedin U⁽¹⁾, firstly screened off in fruit fly, called limostatin. Basically this hormone works when we are fasting, it avoids glucose to be stored in situations in which it supposes to be available.

A literature analysis shows a considerable about of hormones and molecules involved in the complex process of eating and managing energy. Food is equal energy, energy is equal work. We do work from simples tasks such as sleeping to more complex ones and elaborated tasks such as playing out favorite sport game. 

References

1. Alfa RW, Park S, Skelly KR, Poffenberger G, Jain N, Gu X, Kockel L, Wang J, Liu Y, Powers AC, Kim SK. Suppression of insulin production and secretion by a decretin hormone. Cell Metab. 2015 Feb 3;21(2):323-33. doi: 10.1016/j.cmet.2015.01.006.

2. K. N. Frayn. Metabolic Regulation: A Human Perspective. Third Edition. Wiley-blackwell. 2010.

3. J. Tam, Dai Fukumura, and Rakesh K. Jain. A mathematical model of murine metabolic regulation by leptin: energy balance and defense of a stable body weight. Cell Metab. 2009 January 7; 9(1): 52–63. doi:10.1016/j.cmet.2008.11.005.

4. Pasquale Palumbo, Susanne Ditlevsen, Alessandro Bertuzzi, Andrea De Gaetano, Mathematical modeling of the glucose–insulin system: A review, Mathematical Biosciences 244 (2013) 69–81.

Artificial Intelligence in Medicine

Artificial Intelligence appeared in the 1950s as a term to designate a set of novel methods and philosophical attitudes toward problem solving. In the 1980s it had a serious falling in popularity, which was the period of methodologies such as Intellgent Control, fuzzy systems, and neural networks, nowadays composing computational intelligence, these are numerical.based methodolgies, so far artifiical intelligence was mainly worried about symbolic-based methodologies. 

Some says that artificial intelligence possesses too much Is, this is to highlight the problems faced by the same in the past. There are several definitions. Russel and Norvig (2010) categorizes them into: thinking humanly, thinking rationally, acting humanly, or acting rationally. Computational intelligence, a competitor for attention, is placed into acting rationally. 

Computer is without a doubt the revolution of the millennium. Medicine is not different from the other sciences, it is nowadays somehow slave of equipaments, some doctors will not move even an eye without the proper machinary. All these systems cannot be run and controlled just based on linear models or linearizations as it is done often by mathematician. Computer scientists and engineers are more practical and they have certainly been taking advantage of the changes so far.

According to Fieschi (1990),  artificial intelligence, a strange phrase in which the two words taken

separately conjure up opposite meanings. Intelligence seems to us to be intimately associated with human behaviour. The idea of 'artificial', on the other hand, conjures up the idea of objects characteristically not natural but 'man-made'. To call 'artificial' a prime component of human nature seems a paradox. This term is badly chosen, particularly as the aim of artificial intelligence systems is to represent behaviour comparable to human behaviour. The same observation was done on Poole et al (1998). Further, Fieschi (1990) still pinpoint, artificial intelligence in medicine is going to take a very important place in the science of medical informatics.

In summary, artificial intelligence is changing, and it seems for better. Medicine is a field rich on tough problems, problems which solution could benefit several people. This field certainly will occupy the minds of several researches on the future. I am quite sure that computational intelligence will not be left behind, see for example Lam et al (2012).

References cited

RUSSELL, Stuart; NORVIG, Peter. Artificial Intelligence: A modern approach. Third edition. Prentice Hall Series in Artificial Intelligence: 2010.
Fieschi, M, Artificial Intelligence in medicine: expert systems, Translated by D Cramp, Spring-Science + Business Media, 1990.
David Poole; Alan Mackworth; Randy Goebel. Computational Intelligence: A Logical Approach. Oxford University Press. 1998.
Lam, HK; Ling, SH; Nguyen, HT (eds) (2012). Computational Intelligence and its applications: evolutionary Computation, Fuzzy Logic, Neural Network, and Support Vector Machine Technique. Imperial College Press.

External Links

- Conference on Artificial Intelligence in Medicine (Italy)

- Artificial Intelligence in Medicine (Portugal)

Systems Identification vs. Model identification

"However, still the problem of system identification has not been completely solved.
Consequently, nowadays new ideas and methods to solve the system identification
problem or parts of it are introduced." Keesman 2011.

We can say that the ultimate goal of science is building models, mathematical modeling is one of the means to achieve this goal, the promising is to apply more symbolic methodologies once this is the way we think. However, as always, life is not easy. Behind this challenge, new ones come out. One of the is system identification, sometimes referred to as model identification, apparently model identification is used more in Italy, further, it seems to be a broader concept, having system identification as a special case. 

The challenge that arises is based upon the fact that besides we might know how a system model looks like, in general we do not know the parameter values, for instance, what demands methods to find them from experiments. Maybe a nice way to see it is if you know the basics of music theory. Besides a symphony is complex, it is built upon simple chords, in general 8, but some musicians rely their works on just three chords; e.g. I, IV, and V. If you hear a song, you suppose to identify the chords, given you know them. However in real life it does not happen so easily. If you ask ten guitar players to transcribe a song you like, this is highly possible they will disagree, especially on subtle differences such as G and G7. In this case we can say they have failed to identify the system model, this is so an identifiability problem, they know the model in general, but details cannot be filtered out. 

In general, system identification consists of three basic steps: experiment design and data acquisition, model structure selection and parameter estimation, and model validation [1]. System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system [2]. 

Traditionally system identification is based on mathematics, however new trends are applying what can be kept by the name of computational intelligence such as neural networks.  

References cited

[1] Karel J. Keesman, System Identification: an introduction, Advanced books in control and signal processing, Spring, 2011.

[2] Lennart Ljung, System Identification: theory for user, Second edition, Practice hall information znd system science series, 1999. 

Pharmacokinetics and pharmacodynamics

"Pharmacokinetics and pharmacodynamics are the important fields of pharmaceutical sciences for investigating disposition profiles and the pharmacological efficacy of drugs in the body under various experimental and clinical conditions."

(Caldwell et

al., 1995 and Cocchetto and Wargin, 1980, cited by Kwon 2002).

Source: Pires et al 2014.

Source: Pires et al 2014.

References cited

Younggil Kwon, Handbook of Essential Pharmacokinetics, Pharmacodynamics and Drug Metabolism for Industrial Scientists, Kluwer Academic Publishers, 2002.

Caldwell J. et al., An introduction to drug disposition: the basic principles of absorption, distribution,

Cocchetto D. M. and Wargin W. A., A bibliography for selected pharmacokinetic topics, Drug Intel. Clin. metabolism and excretion, Toxicol. Pathol. 23: 102-114, 1995.

Pharmacol. 14: 769-776,1980.

JG Pires, R Maggio, C Manes, P Palumbo, On the importance of pharmacokinetics and pharmacodynamics in engineering sciences as an inter- and multidisciplinary field: an introductory analysis. SIMPEP 2014,