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.

Impact of Molecular Electrostatics on Field-Coupled Nanocomputing and Quantum-Dot Cellular Automata Circuits

The molecular Field-Coupled Nanocomputing (FCN) is a promising implementation of the Quantum-dot Cellular Automata (QCA) paradigm for future low-power digital electronics. However, most of the literature assumes all the QCA devices as possible molecular FCN devices, ignoring the molecular physics. Indeed, the electrostatic molecular characteristics play a relevant role in the interaction and consequently influence the functioning of the circuits. In this work, by considering three reference molecular species, namely neutral, oxidized, and zwitterionic, we analyze the fundamental devices, aiming to clarify how molecule physics impacts architectural behavior. We thus examine through energy analysis the fundamental cell-to-cell interactions involved in the layouts. Additionally, we simulate a set of circuits using two available simulators: SCERPA and QCADesigner. In fact, ignoring the molecular characteristics and assuming the molecules copying the QCA behavior lead to controversial molecular circuit proposals. This work demonstrates the importance of considering the molecular type during the design process, thus declaring the simulators working scope and facilitating the assessment of molecular FCN as a possible candidate for future digital electronics.

Modeling the spread of an epidemic in presence of vaccination using cellular automata

The results of Kermack–McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of an epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like susceptible-infected-removed (SIR) model. The motion of the circular front of an infected cluster shows a linear behavior in time. The correlation of a particular site to be infected with respect to the central site is also studied. The outcomes of the CA model are in good agreement with those obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed a considerable amount of light on the physical properties of the spread of a typical epidemic in a simple, yet robust way.