A modified particle swarm optimization algorithm for parameter estimation of a biological system

Background

Mathematical modeling has achieved a broad interest in the field of biology. These models represent the associations among the metabolism of the biological phenomenon with some mathematical equations such that the observed time course profile of the biological data fits the model. However, the estimation of the unknown parameters of the model is a challenging task. Many algorithms have been developed for parameter estimation, but none of them is entirely capable of finding the best solution. The purpose of this paper is to develop a method for precise estimation of parameters of a biological model.

Methods

In this paper, a novel particle swarm optimization algorithm based on a decomposition technique is developed. Then, its root mean square error is compared with simple particle swarm optimization, Iterative Unscented Kalman Filter and Simulated Annealing algorithms for two different simulation scenarios and a real data set related to the metabolism of CAD system.

Results

Our proposed algorithm results in 54.39% and 26.72% average reduction in root mean square error when applied to the simulation and experimental data, respectively.

Conclusion

The results show that the metaheuristic approaches such as the proposed method are very wise choices for finding the solution of nonlinear problems with many unknown parameters.

     

Hybrid chitosan–ß‐glycerol phosphate–gelatin nano‐/micro fibrous scaffolds with suitable mechanical and biological properties for tissue engineering

Scaffold‐based tissue engineering is considered as a promising approach in the regenerative medicine. Graft instability of collagen, by causing poor mechanical properties and rapid degradation, and their hard handling remains major challenges to be addressed. In this research, a composite structured nano‐/microfibrous scaffold, made from a mixture of chitosan–ß‐glycerol phosphate–gelatin (chitosan–GP–gelatin) using a standard electrospinning set‐up was developed. Gelatin–acid acetic and chitosan ß‐glycerol phosphate–HCL solutions were prepared at ratios of 30/70, 50/50, 70/30 (w/w) and their mechanical and biological properties were engineered. Furthermore, the pore structure of the fabricated nanofibrous scaffolds was investigated and predicted using a theoretical model. Higher gelatin concentrations in the polymer blend resulted in significant increase in mean pore size and its distribution. Interaction between the scaffold and the contained cells was also monitored and compared in the test and control groups. Scaffolds with higher chitosan concentrations showed higher rate of cell attachment with better proliferation property, compared with gelatin‐only scaffolds. The fabricated scaffolds, unlike many other natural polymers, also exhibit non‐toxic and biodegradable properties in the grafted tissues. In conclusion, the data clearly showed that the fabricated biomaterial is a biologically compatible scaffold with potential to serve as a proper platform for retaining the cultured cells for further application in cell‐based tissue engineering, especially in wound healing practices. These results suggested the potential of using mesoporous composite chitosan–GP–gelatin fibrous scaffolds for engineering three‐dimensional tissues with different inherent cell characteristics. © 2015 Wiley Periodicals, Inc. Biopolymers 105: 163–175, 2016.     

Adipose tissue‐derived mesenchymal stem cells and keratinocytes co‐culture on gelatin/chitosan/β‐glycerol phosphate nanoscaffold in skin regeneration

Using cell‐based engineered skin is an emerging strategy for treating difficult‐to‐heal wounds. To date, much endeavor has been devoted to the fabrication of appropriate scaffolds with suitable biomechanical properties to support cell viability and growth in the microenvironment of a wound. The aim of this research was to assess the impact of adipose tissue‐derived mesenchymal stem cells (AD‐MSCs) and keratinocytes on gelatin/chitosan/β‐glycerol phosphate (GCGP) nanoscaffold in full‐thickness excisional skin wound healing of rats. For this purpose, AD‐MSCs and keratinocytes were isolated from rats and GCGP nanoscaffolds were electrospun. Through an in vivo study, the percentage of wound closure was assessed on days 7, 14, and 21 after wound induction. Samples were taken from the wound sites in order to evaluate the density of collagen fibers and vessels at 7 and 14 days. Moreover, sampling was done on days 7 and 14 from wound sites to assess the density of collagen fibers and vessels. The wound closure rate was significantly increased in the keratinocytes‐AD‐MSCs‐scaffold (KMS) group compared with other groups. The expressions of vascular endothelial growth factor, collagen type 1, and CD34 were also significantly higher in the KMS group compared with the other groups. These results suggest that the combination of AD‐MSCs and keratinocytes seeded onto GCGP nanoscaffold provides a promising treatment for wound healing.     

Modified thalamocortical model: A step towards more understanding of the functional contribution of astrocytes to epilepsy

It is evident that the cortex plays a primary role in seizure generation. At the same time, various experimental results clearly confirm that thalamic neurons are also actively involved in seizure generation and spreading. On the other hand, recent neurophysiologic findings suggest that astrocytes regulate dynamically the synaptic activity in neuronal networks. Therefore, in the present study, the thalamocortical neural population model (TCPM) is modified by embedding into the model the functional role of astrocytes in the regulation of synaptic transmission. Using the modified TCPM (MTCPM) we examined the hypothesis that one of the possible causes of neural hypersynchronization is the dysfunction of astrocytes in the regulatory feedback loop. Then, two MTCPMs are coupled via excitatory synapses and the astrocytes are also coupled together through gap junctions. Utilizing the MTCPM and CMTCPM, the transition from normal to malfunctioned states is analyzed using a dynamical system approach. In this way, the hypothesis is investigated and it is demonstrated that the healthy astrocytes provide feedback control to regulate neural activity. That is, the astrocytes compensate to a large extent variations in the coupling between neural populations and maintain the balance between the excitation and inhibition levels. However, the malfunctioned astrocytes are no longer able to regulate and/or compensate the excessive increase of the inter-population coupling strength. As a consequence, disruption of the signaling function of astrocytes could contribute to the neuronal hyperexcitability and generating epileptiform activity. These results suggest that astrocytes might be one of the potential targets for the treatment of epilepsy.     

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a “gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).