Simulated evolution of mass conserving reaction networks

With the rise of systems biology, the systematic analysis and construction of behavioral mechanisms in both natural and artificial biochemical networks has become a vital part of understanding and predicting the inner workings of intracellular signaling networks. As a modeling platform, artificial chemistries are commonly adopted to study and construct artificial reaction network motifs that exhibit complex computational behaviors. Here, we present a genetic algorithm to evolve networks that can compute elementary mathematical functions by transforming initial input molecules into the steady state concentrations of output molecules. Morespecifically, the proposed algorithm implicitly guarantees mass conservation through an atom based description of the molecules and reaction networks. We discuss the adopted approach for the artificial evolution of these chemical networks, evolve networks to compute the square root function. Finally,we provide an extensive deterministic and stochastic analysis of a core square root network motif present in these resulting networks, confirming that the motif is indeed capable of computing the square root function

Multiple functionalities of biochemical reaction networks

We consider a biological cell as a highly nterconnected network of chemical reactions, which is constituted of a large number of semi-autonomous functional modules. Depending on the global state of the network, the separate functional modules may display qualitatively different behavior. As an example, we study a conceptual network of phosphorylation cycles, for which the steady-state concentration of an output compound depends on the concentrations of two input enzymes. We show that the input-output relation depends on the expression of the proteins in the network. Hence changes in protein expression, due to changes in the global regulatory network of the cell, can change the functionality of the module. In this specific example, changed expression of two proteins is sufficient to switch between the functionalities of various logical gates

From individuals to populations, approaches to the study of biological emergent phenomena

An assorted range of approaches have contributed to our understanding of the oscillatory behavior of population sizes in predation models. Among these are Mathematical Biology, Statistics and Artificial Life (ALife). In this paper, I will give a review of these different approaches. In addition, another approach, based on Evolutionary Game Theory, is proposed and discussed. This paper also suggests that a complementary study of both the Mathematical, Artificial Life and Game Theory approach is needed to explain some of the mysticism surrounding the global emergent behavior of local predator-prey relationships

From individuals to populations, approaches to the study of biological emergent phenomena

An assorted range of approaches have contributed to our understanding of the oscillatory behavior of population sizes in predation models. Among these are Mathematical Biology, Statistics and Artificial Life (ALife). In this paper, I will give a review of these different approaches. In addition, another approach, based on Evolutionary Game Theory, is proposed and discussed. This paper also suggests that a complementary study of both the Mathematical, Artificial Life and Game Theory approach is needed to explain some of the mysticism surrounding the global emergent behavior of local predator-prey relationships

GaMuSo: Graph base music recommendation in a social bookmarking service

In this work we describe a recommendation system based upon user-generated description (tags) of content. In particular, we describe an experimental system (GaMuSo) that consists of more than 140.000 user-defined tags for over 400.000 artists. From this data we constructed a bipartite graph, linking artists via tags to other artists. On the resulting graph we compute related artists for an initial artist of interest. In this work we describe and analyse our system and show that a straightforward recommendation approach leads to related concepts that are overly general, that is, concepts that are related to almost every other concept in the graph. Additionally, we describe a method to provide functional hypothesis for recommendations, given the user insight why concepts are related. GaMuSo is implemented as a webservice and available at: music.biograph.be

Finite population models of dynamic optimization with stochastically alternating fitness functions

We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can be utilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the stochastic and deterministic environment models can be applied to study the influence of the system's parameters - rate of mutations, rate of changes in the environment, population size and selective pressure - on the long run performance of GAs in the respective environments. A comparison of these conclusions between static and dynamic environments is give