Gabi Escuela

Learn More
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)
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)
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)
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)
Distributed Genetic Algorithm (DGA) is one of the most promising choices among the optimization methods. In this paper we describe DGAFrame, a flexible framework for evolutionary computation, written in Java. DGAFrame executes GAs across a range of machines communicating through RMI network technology, allowing the implementation of portable, flexible GAs(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)
We apply molecular code theory to a rule-based model of the human inner kinetochore and study how complex formation in general can give rise to molecular codes. We analyze 105 reaction networks generated from the rule-based inner kinetochore model in two variants: with and without dissociation of complexes. Interestingly, we found codes only when some but(More)