Gabi Escuela

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A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated sub-structures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is(More)
The process of designing a P system in order to perform a task is a hard job. The researcher has often only an approximate idea of the design, but finding the exact description of the rules is a heavy handmade work. In this paper we introduce PSystemEvolver, an evolutionary algorithm based on generative encoding, that could help to design a P system to(More)
Cycles are abundant in most kinds of networks, especially in biological ones. Here, we investigate their role in the evolution of a chemical reaction system from one self-sustaining composition of molecular species to another and their influence on the stability of these compositions. While it is accepted that, from a topological standpoint, they enhance(More)
This deliverable complements the link to experimental results and differential equation models from ICFPAN that were missing in deliverable D3.1. Abstract Here we review and extend models on different scales for a computing architecture made from networks of excitable chemical droplets. The model system of the Belousov-Zhabotinsky (BZ) reaction enclosed in(More)
We investigate several evolutionary computation approaches as a mechanism to “program” networks of excitable chemical droplets. For this kind of systems, we assigned a specific task and concentrated on the characteristics of signals representing symbols. Given a Boolean function as target functionality, 2D networks composed of 10 × 10 droplets were(More)
In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network(More)
An encoding scheme for protein folding on lattice models, inspired by parametric L-systems, was proposed. The encoding incorporates problem domain knowledge in the form of predesigned production rules that capture commonly known secondary structures: α-helices and β-sheets. The ability of this encoding to capture protein native con-formations was tested(More)
Reconstruction of signal transduction network models based on incomplete information about network structure and dynamical behaviour is a major challenge in current systems biology. In particular, interactions within signalling networks are frequently characterised by partially unknown protein phosphorylation and dephosphory-lation cascades at a(More)