Search

Synthetic Biology aspires to design, compose and engineer biological systems that implement specified behaviour. When designing such systems, hypothesis testing via computational modelling and simulation is vital in order to reduce the need of costly wet lab experiments. As a case study, we discuss the use of computational modelling and stochastic simulation for engineered genetic circuits that implement Boolean AND and OR gates that have been reported in the literature. We present performance analysis results for nine different state-of-the-art stochastic simulation algorithms and analyse the dynamic behaviour of the proposed gates. Stochastic simulations verify the desired functioning of the proposed gate designs.\ud

Research into molecular computation offers exciting possibilities for interfacing computation with biological systems. This could be achieved using light to switch photochromic molecules between states. For example, 6-Nitro-BIPS2 can be switched from a Spiropyran (SP) state to a Trans-Merocyanine (MC) state using UV photons while visible light switches from MC to SP. The MC state is also fluorescent. Modified spiropyrans targeted to proteins can improve imaging contrast3, alter enzyme activity4, alter protein interactions5 and switch vesicle permeability6.

In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin's wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.

Developing a problem-domain independent methodology to automatically generate high performing solving strategies for specific problems is one of the challenging trends on hyper-heuristics design. Designing hyper-heuristics is important because they raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem at hand. In this paper, we present a three-steps methodology that combines multiple sequence alignment and grammatical induction in order to automatically generate high performing solving strategies for a combinatorial optimisation problem. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

Automatic protein structure predictors use the notion of energy to guide the search towards good candidate structures. The energy functions used by the state-of-the-art predictors are defined as a linear combination of several energy terms designed by human experts. We hypothesised that the energy based guidance could be more accurate if the terms were combined more freely. To test this hypothesis, we designed a genetic programming algorithm to evolve the protein energy function. Using several different fitness functions we examined the potential of the evolutionary approach on a set of candidate structures generated during the protein structure prediction process. Although our algorithms were able to improve over the random walk, the fitness of the best individuals was far from the optimum. We discuss the shortcomings of our initial algorithm design and the possible directions for further research.

Over the recent years, there has been increasing research activities made on improving the efficacy of memetic algorithm (MA) for solving complex optimization problems. Particularly, these efforts have revealed the success of MA on a wide range of real world problems. MAs not only converge to high quality solutions, but also search more efficiently than their conventional counterparts. Despite the success and surge in interests on MAs, there is still plenty of scope for furthering our understanding on how and why synergy between population- based and individual learning searchers would lead to successful Memetic Algorithms. In this paper we outline several important design issues of Memetic Algorithms and present a systematic study on each. In particular, we conduct extensive experimental studies on the impact of each individual design issue and their relative impacts on memetic search performances by means of three commonly used synthetic problems. From the empirical studies obtained, we attempt to reveal the behaviors of several MA variants to enhance our understandings on MAs.

Self-assembly is a distributed, asynchronous mechanism that is pervasive across natural systems where hierarchical complex structures are built from the bottom-up. The lack of a centralised master plan, no external intervention, and preprogrammed interactions among entities are within its most relevant and technologically appealing properties. This paper tackles the self-assembly Wang tiles designability problem by means of artificial evolution. This research is centred in the use of tiles that are extended with rotation and probabilistic motion, and an evolutionary algorithm using the Morphological Image Analyses method as a fitness function. The obtained results support this approach as a successful engineering mechanism for the computer-aided design of self-assembled patterns.

This paper presents a model that simulates a self-assembly process for software components. Initial investigations on the automated self-assembly programming paradigm (ASAP2) is presented whereby software components are treated as a gas' molecules and their interactions, within a confined area with specific temperature and pressure constraints, give rise to a variety of program architectures. We present experimental results that show how different factors affect the efficiency of the software self-assembly process and the diversity of the self-assembled programs. To the best of our knowledge this is the first time that self-assembly - a ubiquitous natural phenomenon - has been proposed as a (potentially) viable alternative to other automated program synthesis methodologies like for example genetic programming

This paper presents an analysis of increased diversity in genetic programming. A selection strategy based on genetic lineages is used to increase genetic diversity. A genetic lineage is defined as the path from an individual to individuals which were created from its genetic material. The method is applied to three problem domains: artificial ant, even-5-parity and symbolic regression of the binomial-3 function. We examine how increased diversity affects problems differently and draw conclusions about the types of diversity which are more important for each problem. Results indicate that diversity in the ant problem helps to overcome deception, while elitism in combination with diversity is likely to benefit the parity and regression problems.