Narrow Results

In this work we present a data-driven modeling of the insulin dynamics in different in silico patients using a recurrent neural network with output feedback. The inputs for the identification is the rate of insulin (μU / dl / min) applied to the patient, and blood glucose concentration. The output is insulin concentration (μU / ml) present in the blood stream. Once completed the off-line modeling, this model could be used for on-line monitoring of the insulin concentration for a better treatment. The learning law of the recurrent neural network is inspired by adaptive observer theory, and proven to be convergent in the parameters and stable in the Lyapunov sense, even with only 13 samples available. Simulation results are shown to validate the presented modeling.

In self-propelled particle models, the interactions are generally subject to some type of noise, which is introduced either during or after the interactions between the particles. Here, self-propelled particles are subject to two different types of noise. If the orientation between them is less than a certain rate $\beta$, a vectorial noise is introduced in each particle–particle interaction, otherwise, an angular noise is introduced in the average direction of motion, already calculated, of neighboring particles. We vary the rate $\beta$ over a wide range ($\beta <\pi$) and study the change in the nature of the phase transition. The product-moment correlation coefficient between the order parameter and the hybrid noise, is calculated. We found that in the combination of the two types of noise, the critical noise decreases with increasing the parameter $\beta$, and by analysis of the Binder cumulant we estimate the value of $\beta$ as from which begins to appear the effects of the vectorial noise on the phase transition.

Circadian rhythms occupy an important role in daily biological activities of living species. Circadian disorder is a phenomenon of circadian rhythms which occurs when internal rhythms cannot keep up with the changes of external environment rhythms. Changes of environmental rhythms, presented by the change of light/dark cycles or by irregular rhythms, result in phase shifts between internal and external rhythms. The existence of these phase shifts in longer term has negative effect to health. Therefore, in biological study of circadian rhythms, finding a method to recover the shifted phases to their normal rhythms, which is also the treatment of circadian disorder, is an important required task. In this paper, we propose a control design method to reset the circadian phases. The phase restoration is carried out by the synchronization of trajectories generated from a controlled model with the trajectories of a reference system via nonlinear control design using only one measurement. Both reference and controlled systems are based on a given 3rd order model of Neurospora circadian rhythms. The two other unknown states are estimated using a recently developed nonlinear observer for the output-feedback control.

A modified semiempirical model named RM1_BH, which is based on RM1 parameterizations, is proposed to simulate varied biological hydrogen-bonded systems. The RM1_BH is formulated by adding Gaussian functions to the core–core repulsion items in original RM1 formula to reproduce the binding energies of hydrogen bonding of experimental and high-level computational results. In the parameterizations of our new model, 35 base-pair dimers, 18 amino acid residue dimers, 14 dimers between a base and an amino acid residue, and 20 other multimers were included. The results performed with RM1_BH were compared with experimental values and the benchmark density-functional (B3LYP/6-31G**/BSSE) and Möller–Plesset perturbation (MP2/6-31G**/BSSE) calculations on various biological hydrogen-bonded systems. It was demonstrated that RM1_BH model outperforms the PM3 and RM1 models in the calculations of the binding energies of biological hydrogen-bonded systems by very close agreement with the values of both high-level calculations and experiments. These results provide insight into the ideas, methods, and views of semiempirical modifications to investigate the weak interactions of biological systems.

Recently, the quantum formalism and methodology have been used in application to the modelling of information processing in biosystems, mainly to the process of decision making and psychological behaviour (but some applications in microbiology and genetics are considered as well). Since a living system is fundamentally open (an isolated biosystem is dead), the theory of open quantum systems is the most powerful tool for life-modelling. In this paper, we turn to the famous Schrödinger’s book “What is life?” and reformulate his speculations in terms of this theory. Schrödinger pointed to order preservation as one of the main distinguishing features of biosystems. Entropy is the basic quantitative measure of order. In physical systems, entropy has the tendency to increase (Second Law of Thermodynamics for isolated classical systems and dissipation in open classical and quantum systems). Schrödinger emphasized the ability of biosystems to beat this tendency. We demonstrate that systems processing information in the quantum-like way can preserve the order-structure expressed by the quantum (von Neumann or linear) entropy. We emphasize the role of the special class of quantum dynamics and initial states generating the camel-like graphs for entropy-evolution in the process of interaction with a new environment ${ℰ}$: 1) entropy (disorder) increasing in the process of adaptation to the specific features of ${ℰ}$; 2) entropy decreasing (order increasing) resulting from adaptation; 3) the restoration of order or even its increase for limiting steady state. In the latter case the steady state entropy can be even lower than the entropy of the initial state.

Nanotechnology is one of the most important emerging fields of science in this century. It deals with designing, construction, investigation, and utilization of systems at the nanoscale. Another interesting research discipline of current day is biotechnology, which gives us a way to understand biological system and to utilize our knowledge for industrial manufacturing. Nanobiotechnology lies at the interface of these two research fields. It exploits nanotechnology and biotechnology to analyze and create nanobiosystems to meet a wide variety of challenges and develops a wide range of applications.

The reduction method is used to obtain some optimal stability results in biomathematics: an epidemic model with diffusion and the May-Leonard system for competition between three species with diffusion. The existence of travelling waves solutions is proved for the epidemic model.

In this paper we want to extend the traditional idea of the gauge to different parts of the physics. And also to complex system of agents and also to the biological systems. After the introduction of the fundamental definitions of the Gauge Generalized Principle, we move to the different domain as physics, agents, symmetry and so on to show the applicability of the gauge general principle.

This paper deals with the problem of understanding anticipation in biological and cognitive systems. It is argued that a physical theory can be considered as biologically plausible only if it incorporates the ability to describe systems which exhibit anticipatory behaviours. The paper introduces a cognitive level description of anticipation and provides a simple characterization of anticipatory systems on this level. Specifically, a simple model of a formal anticipatory neuron and a model of an anticipatory neural network which is based on the former are introduced and discussed. Some learning algorithms are also discussed together with related experimental tasks and possible integrations. The outcome of the paper is a formal characterization of anticipation in cognitive systems which aims at being incorporated in a comprehensive and more general physical theory.

Bacteria, bees and birds often work together in groups to find food. A group of robots can be designed to coordinate their activities. Networked cooperative UAVs are being developed for commercial and military applications. Suppose that we refer to all such groups of entities as “social foraging swarms.” In order for such multi-agent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli. In this chapter we focus on discrete-time case and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive foraging even in the presence of sensing “noise.” The results quantify earlier claims that social foraging is in a certain sense superior to individual foraging when noise is present, and provide clear connections between local agent-agent interactions and emergent group behavior.

We first give a brief review on traveling waves, spreading speeds, and global stability for monotone evolution systems with monostable and bistable nonlinearities. Then we outline our recently developed theory and methods for general monotone semiflows and certain non-monotone systems, and their applications to some biological models.