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Hippocrates introduced some level of philosophical reflection to the art of medical practice and made it a science for the very first time. This is how medicine was born. Introducing his theory of life, which was based on the principle of balance of four liquids (sc. humoral theory), Hippocrates created the first model of living organism in its proper physiological condition as well as in its pathological symptoms. The influence of Hippocratic thought on the development of European medicine was significant and became an inspiration for many generations of physicians seeking a final definition of health and disease. This, in consequence, resulted in the development of methodical tools that could be helpful in the description and interpretation of the phenomenon of life. The introduction of “numerical” methods followed by statistical methods to 19th-century medicine opened new ways for scientific investigation. One of most important pioneers in that field was French physician Pierre-Charles-Alexandre Louis. Statistics played an important role in the formation of new pathological and epidemiological models by Carl Rokitansky, Ignaz Semmelweis, and John Snow, became a standard procedure in clinical research of Carl Wunderlich, and finally found its full development in present-day evidence-based-medicine.

Neural networks become very popular as a tool for modeling of numerous systems, including technological, economical, sociological, psychological, and even political ones. On the contrary, neural networks are models of neural structures and neural processes observed in a real brain. However, for modeling of real neural structures and real neural processes occurring in a living brain, neural networks are too simplified and too primitive. Nevertheless, neural networks can be used for modeling the behavior of many biological systems and structures. Such models are not useful for explanation, taking into account the biological systems and processes, but can be very useful for the analysis of such system behavior, including the prognosis of future results of selected activities (e.g. the prognosis of results of different therapies for modeled illnesses). In this paper, selected examples of such models and their applications are presented.

This paper presents a new treatment of molecular evolutionary model as a product of intelligent changes. The aim of this paper is to obtain a life design system, drawing on processes occurring in nature regardless of explanations of the origins of life. The idea of intelligent design and molecular relationship is considered as a basic concept of the intelligent life design system, using some analogies taken from molecular evolutionary models. Three steps of life design system are outlined; however, the main subject is an attempt to find certain similar effects of the design system processes and the processes simulated with basic evolutionary substitution models: Jukes-Cantor; Felsenstein; and Hasegawa, Kishino, and Yano (HKY). An idea of gene reduction has been applied, from more complex (taking into account information density) biological systems to less complex, specialised biological systems. Two steps have been taken into consideration: a test stage in the virtual world and an adaptation finishing process after running the systems in the real world. Two algorithms have been applied. The first one has applied similarity related to an accommodation process to required conditions in the virtual and the real world. The second algorithm has applied accommodation to required conditions separately (expressed as amino acid substitution) in the first step, using a convenient criterion, and further (similar to observable) accommodation in the real world. A phylogenetic tree, similar to a real one, has been calculated using the above method for mammals, for mtDNA, with the maximum likelihood method, and with the aid of PhyML for the HKY model. This paper is an introduction showing an aspect of the life design system, related to phylogenetic relationships.

The shape of a protein chain may be analyzed at different levels of details. The ultimate shape description contains three-dimensional coordinates of all atoms in the chain. In many cases, a description of the local shape, namely secondary structure, is enough to determine some properties of proteins. Although obtaining the full three-dimensional (3D) information also defines the secondary structure, the problem of finding this precise 3D shape (tertiary structure) given only the amino acid sequence is very complex. However, the secondary structure may be found even without having the full 3D information. Many methods have been developed for this purpose. Most of them are based on similarities of the analyzed protein chain to other proteins that are already analyzed and have a known secondary structure. The presented paper proposes a method based on dictionaries of known structures for predicting the secondary structure from either the primary structure or the so-called structural code. Accuracies of up to 79% have been achieved.

The lipid-binding protein present in the human brain is the object of this analysis. The expression of these proteins is especially important for nervous cell differentiation and their migration in the process of the development of the brain. The “fuzzy oil drop” model applied to the analysis of these proteins may suggest the mechanism of complex generation. It is shown that this type of complex may appear spontaneously in water environment. The presence of ligand does not imply any form of adaptation of the polypeptide chain to the ligand molecule. It can be speculated that ligand binding is of a static character without the necessity for mutual structural fitting. The structures of polypeptide in the apo- and complexed forms do not differ in respect to hydrophobic core formation. Such an interpretation is different than that observed in other ligand-binding proteins where the binding cavity needs to be specially fitted to the specific ligand. It can also be concluded that the lipid-binding process is of low specificity in this case.

The fuzzy oil drop model was applied to analyze the structure of macromomycin, the apoprotein of the antitumor antibiotic auromomycin, revealing the differentiation of β-structural fragments present in β-sandwich. The seven-stranded antiparallel β-barrel and two antiparallel β-sheet ribbons represent the highly ordered geometry of the structure. However, participation in hydrophobic core formation appears different. The structure of the complete domain represents the status of the irregular hydrophobic core; however, some β-structural fragments appear to represent the hydrophobicity density distribution accordant with the idealized distribution of hydrophobicity as expected using the fuzzy oil drop model. Four β-structural fragments generating one common layer appear to be unstable in respect to the general structure of the hydrophobic core. This area is expected to be more flexible than other parts of the molecule. The protein binds the ligand – chromophore, two 2-methyl-2,4-pentanediol – in a well-defined cleft. The presence of this cleft makes the general structure of the hydrophobic core irregular (as it may be interpreted using the fuzzy oil drop model). Two short loops generated by two SS bonds fit very well to the general distribution of hydrophobicity density as expected for the model. No information about the potential amyloidogenic character of this protein is given in the literature; however, the specificity of the hydrophobicity distribution profile is found to be highly similar to the one observed in transthyretin (Banach M, Konieczny L, Roterman I. The fuzzy oil drop model, based on hydrophobicity density distribution, generalizes the influence of water environment on protein structure and function. J Theor Biol 2014;359:6–17), suggesting a possible tendency to turn to the amyloid form. A detailed analysis of macromomycin will be given, and a comparable analysis with other proteins of β-sandwich or β-barrel will be presented.

Currently, a disabled person can participate in social life and proceed with independent individual development with the assistance of intelligent support from modern technology. This paper reviews various approaches and recent achievements in designing and prototyping of interfaces for tetraplegic people originating from Biocybernetics Laboratory AGH. After a short review of other available systems, three categories of activities of daily living (ADL)-supporting solutions are presented: input devices, control software, and stand-alone systems. As a result of testing other alternatives such as touchpad, tongue presspad, eye tracker, blow sensor, and direct sensor of brain electrical activity, joysticks were selected as preferable input devices. They are free from hygienic issues, tolerant to involuntary use or excessive force, and easily operable by using the chin or lips. The operational flexibility required for personalization of input devices is achieved with dedicated software defining the rules of interpretation of operators’ motions. To adapt the final signal to the specificity of a supported human, the interpretation process takes into account the identification of gesture, its duration, and coincidence. The paper also presents two stand-alone systems dedicated to supporting the selected ADL. One of them is a smart interface for infrared-based remote control of home appliances, and the second is a joystick-based emulator of a computer mouse. Both devices provide a learning mode that allows for adaptation to the particular environment (i.e. set of devices to be controlled) and specific abilities of their human operator.