A complex adaptive systems approach to the kinetic folding of RNA

The kinetic folding of RNA sequences into secondary structures is modeled as a complex adaptive system, the components of which are possible RNA structural rearrangements (SRs) and their associated bases and base pairs. RNA bases and base pairs engage in local stacking interactions that determine the probabilities (or fitnesses) of possible SRs. Meanwhile, selection operates at the level of SRs; an autonomous stochastic process periodically (i.e., from one time step to another) selects a subset of possible SRs for realization based on the fitnesses of the SRs. Using examples based on selected natural and synthetic RNAs, the model is shown to qualitatively reproduce characteristic (nonlinear) RNA folding dynamics such as the attainment by RNAs of alternative stable states. Possible applications of the model to the analysis of properties of fitness landscapes, and of the RNA sequence to structure mapping are discussed.Comment: 23 pages, 4 figures, 2 tables, to be published in BioSystems (Note: updated 2 references

Pseudo-NK: an Enhanced Model of Complexity

This paper is based on the acknowledgment that NK models are an extremely usefu l tool in order to represent and study the complexity stemming from interactions among components of a system. For this reason NK models have been applied in many domains, such as Organizational Sciences and Economics, as a simple and powerful tool for the representation of complexity. However, the paper suggests that NK suffers from un-necessary limitations and difficulties due to its peculiar implementation, originally devised for biological phenomena. We suggest that it is possible to devise alternative implementations of NK that, though maintaining the core aspects of the NK model, remove its major limitations to applications in new domains. The paper proposes one such a model, called pseudo-NK (pNK) model, which we describe and test. The proposed model appears to be able to replicate most, if not all, the properties of standard NK models, but also to offer wider possibilities. Namely, pNK uses real-valued (instead of binary) dimensions forming the landscape and allows for gradual levels of interaction among components (instead of presence-absence). These extensions provide the possibility to maintain the approach at the original of NK (and therefore, the compatibility with former results) and extend the application to further domains, where the limitations posed by NK are more striking.NK model, Simulation models, Complexity, Interactions

Maximally rugged NK landscapes contain the highest peaks

NK models provide a family of tunably rugged fitness landscapes used in a wide range of evolutionary computation studies. It is well known that the average height of local optima regresses to the mean of the landscape with increasing epistasis, K. This fact has been confirmed using both theoretical studies of landscape structure and empirical studies of evolutionary search. We show that the global optimum behaves quite differently: the expected value of the global maximum is highest in the maximally rugged case. Furthermore, we demonstrate that this expected value increases with K, despite the fact that the average fitness of the local optima decreases. That is, the highest peaks are found in the most rugged landscapes, scattered amongst masses of low-lying peaks. We find the asymptotic value of the global optimum as N approaches infinity for both the smooth and maximally rugged cases. In evolutionary search, the optima that are found reflect the local optima that exist in the landscape, the size of these optima - which corresponds to the size of their basins of attraction, and the effort expended in the search process. Increasing the level of epistasis in an NK landscape stochastically introduces higher peaks, but renders them exponentially more difficult to find. Copyright 2005 ACM

Maximally Rugged NK Landscapes Contain the Highest Peaks”, In: Beyer et al

NK models provide a family of tunably rugged fitness landscapes used in a wide range of evolutionary computation studies. It is well known that the average height of local optima regresses to the mean of the landscape with increasing epistasis, K. This fact has been confirmed using both theoretical studies of landscape structure and empirical studies of evolutionary search. We show that the global optimum behaves quite differently: the expected value of the global maximum is highest in the maximally rugged case. Furthermore, we demonstrate that this expected value increases with K, despite the fact that the average fitness of the local optima decreases. That is, the highest peaks are found in the most rugged landscapes, scattered amongst masses of low-lying peaks. We find the asymptotic value of the global optimum as N approaches infinity for both the smooth and maximally rugged cases. In evolutionary search, the optima that are found reflect the local optima that exist in the landscape, the size of these optima – which corresponds to the size of their basins of attraction, and the effort expended in the search process. Increasing the level of epistasis in an NK landscape stochastically introduces higher peaks, but renders them exponentially more difficult to find

Decision making in supply risk and supply disruption management

The research questions addressed in the course of this dissertation revolve around important issues of decision making in supply risk and supply disruption management. These issues have received only limited research attention and benefit from novel insights to improve our understanding of how supply risks and disruptions can effectively be addressed. In the extant literature on supply risks and disruptions, there is an agreement that supply disruptions follow a typical profile with regard to their impact on firm performance over time (Sheffi, 2005). In case that a supply risk materializes, a subsequent supply disruption leads to a sudden drop in operating performance. This disturbance causes firms to initiate recovery efforts to return to normal performance levels. In particular, the first research question explores why some managers act proactively to mitigate the potential loss from future supply disruptions while others do not. The second research question aims to shed light on how prior engagement in corporate social responsibility (CSR) affects negative stakeholder reactions to a materialized CSR-related risk. Finally, the third research question addresses the issue of how quickly decision makers should and do actually initiate recovery efforts after their firm has been hit by a supply disruption. Each of these research questions was approached by means of carefully designed and executed experiments to enable a controlled test of the relationships investigated

Decision making in supply risk and supply disruption management

The research questions addressed in the course of this dissertation revolve around important issues of decision making in supply risk and supply disruption management. These issues have received only limited research attention and benefit from novel insights to improve our understanding of how supply risks and disruptions can effectively be addressed. In the extant literature on supply risks and disruptions, there is an agreement that supply disruptions follow a typical profile with regard to their impact on firm performance over time (Sheffi, 2005). In case that a supply risk materializes, a subsequent supply disruption leads to a sudden drop in operating performance. This disturbance causes firms to initiate recovery efforts to return to normal performance levels. In particular, the first research question explores why some managers act proactively to mitigate the potential loss from future supply disruptions while others do not. The second research question aims to shed light on how prior engagement in corporate social responsibility (CSR) affects negative stakeholder reactions to a materialized CSR-related risk. Finally, the third research question addresses the issue of how quickly decision makers should and do actually initiate recovery efforts after their firm has been hit by a supply disruption. Each of these research questions was approached by means of carefully designed and executed experiments to enable a controlled test of the relationships investigated

Utilising restricted for-loops in genetic programming

Genetic programming is an approach that utilises the power of evolution to allow computers to evolve programs. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. The work is to investigate a number of restricted looping constructs to determine whether any significant benefits can be obtained in genetic programming. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers and solving existing problems quicker by using fewer evaluations. In this thesis, a number of explicit restricted loop formats were formulated and tested on the Santa Fe ant problem, a modified ant problem, a sorting problem, a visit-every-square problem and a difficult object classificat ion problem. The experimental results showed that these explicit loops can be successfully used in genetic programming. The evolutionary process can decide when, where and how to use them. Runs with these loops tended to generate smaller sized solutions in fewer evaluations. Solutions with loops were found to some problems that could not be solved without loops. The results and analysis of this thesis have established that there are significant benefits in using loops in genetic programming. Restricted loops can avoid the difficulties of evolving consistent programs and the infinite iterations problem. Researchers and other users of genetic programming should not be afraid of loops