Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation

Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.

Simulation study of crowd dynamics in pedestrian evacuation concerning panic contagion: A cellular automaton approach

The study of the panic evacuation process is of great significance to emergency management. Panic not only causes negative emotions such as irritability and anxiety, but also affects the pedestrians decision-making process, thereby inducing the abnormal crowd behavior. Prompted by the epidemiological SIR model, an extended floor field cellular automaton model was proposed to investigate the pedestrian dynamics under the threat of hazard resulting from the panic contagion. In the model, the conception of panic transmission status (PTS) was put forward to describe pedestrians' behavior who could transmit panic emotions to others. The model also indicated the pedestrian movement was governed by the static and hazard threat floor field. Then rules that panic could influence decision-making process were set up based on the floor field theory. The simulation results show that the stronger the pedestrian panic, the more sensitive pedestrians are to hazards, and the less able to rationally find safe exits. However, when the crowd density is high, the panic contagion has a less impact on the evacuation process of pedestrians. It is also found that when the hazard position is closer to the exit, the panic will propagate for a longer time and have a greater impact on the evacuation. The results also suggest that as the extent of pedestrian's familiarity with the environment increases, pedestrians spend less time to escape from the room and are less sensitive to the hazard. In addition, it is essential to point out that, compared with the impact of panic contagion, the pedestrian's familiarity with environment has a more significant influence on the evacuation.

Optimising Pedestrian Flow Around Large Stadiums

This study proposes a method that combines the cellular automaton model and the differential evolution algorithm for optimising pedestrian flow around large stadiums. A miniature version of a large stadium and its surrounding areas is constructed via the cellular automaton model. Special mechanisms are applied to influence the behaviour of an agent that leaves from a certain stadium gate. The agent may be attracted to a nearby business facility and/or guided to uncongested areas. The differential evolution algorithm is then used to determine the optimal probabilities of the influencing agents for each stadium gate. The main goal is to reduce the evacuation time, and other goals such as reducing the costs for the influencing agents’ behaviours and the individual evacuation time are also considered. We found that, although they worked differently in different scenarios, the attraction and guidance of agents significantly reduced the evacuation time. The optimal evacuation time was achieved with moderate attraction to the business facilities and strong guidance to the detouring route. The results demonstrate that the proposed method can provide a goal-dependent, exit-specific strategy that is otherwise hard to acquire for optimising pedestrian flow.

A METHOD FOR CHOOSING A STRATEGY FOR THE BEHAVIOR OF A CELLULAR AUTOMATON WHEN SOLVING THE PROBLEM OF FINDING TARGETS BY A GROUP OF MOVING OBJECTS

Currently, a large number of different mathematical models and methods aimed at solving problems of multidimensional optimization and modeling of complex behavioral systems have been developed. One of the areas of search for solutions is the search for solutions in conditions of incomplete information and the need to take into account changing external factors. Often such problems are solved by the method of complete search. In some conditions, the method of complete search can be significantly improved through the implementation and use of behavioral models of natural formations. Examples of such formations can be group behavior of insects, birds, fish, various flocks, etc. The idea of copying group activity of a shoal of fishes at the decision of problems of joint activity on extraction of food is used in work. The reasoning based on the simulation of the behavior of such a natural object allowed to justify the choice as a mathematical model - cellular automata. The paper examines the key features of such a model. Modeling of his work is carried out, strategies of behavior of group of mobile objects at search of the purposes are developed, key characteristics are investigated and the method of adaptive choice of strategy and change of rules of behavior taking into account features of the solved problem is developed. The search strategy is implemented in the work, which takes into account the need to solve the optimization problem on two parameters. The obtained results testify to the high descriptive possibility of such an approach, the possibility of finding the optimal strategy for the behavior of the cellular automaton and the formalization of the process of selecting the parameters of its operation. A further improvement of this approach can be the implementation of simulation to study the properties of the developed model, the formation of the optimal set of rules and parameters of the machine for the whole set of tasks.