Stefano Benedettini

The detection of intermediate-level emergent structures and patterns.

Artificial life is largely concerned with systems that exhibit different emergent phenomena; yet, the identification of emergent structures is frequently a difficult challenge. In this paper we introduced a system to identify candidate emergent mesolevel dynamical structures in dynamical networks. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks; its main novelty in comparison to previous application of similar measures is that we used it to consider truly dynamical networks, and not only fluctuations around stable asymptotic states. The identified structures are clusters of elements that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. We have evidence that our approach is able to identify these “emerging things” in some artificial network models and in more complex data coming from catalytic reaction networks and biological gene regulatory systems (A.thaliana). We think that this system could suggest interesting new ways in dealing with artificial and biological systems.

Two-Level ACO for Haplotype Inference Under Pure Parsimony

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information enables researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem under certain hypotheses (such as the pure parsimonycriterion) and to solve it using off-the-shelf combinatorial optimization techniques. At present, the main methods applied to Haplotype Inference are either simple greedy heuristic or exact methods, which are adequate only for moderate size instances. In this paper, we present an iterative constructive approach to Haplotype Inference based on ACO and we compare it against a state-of-the-art exact method.

Leveraging saving-based algorithms by master-slave genetic algorithms

Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms.

A randomized iterated greedy algorithm for the founder sequence reconstruction problem

The problem of inferring ancestral genetic information in terms of a set of founders of a given population arises in various biological contexts. In optimization terms, this problem can be formulated as a combinatorial string problem. The main problem of existing techniques, both exact and heuristic, is that their time complexity scales exponentially, which makes them impractical for solving large-scale instances. Basing our work on previous ideas outlined in [1], we developed a randomized iterated greedy algorithm that is able to provide good solutions in a short time span. The experimental evaluation shows that our algorithm is currently the best approximate technique, especially when large problem instances are concerned.

Identifying Emergent Dynamical Structures in Network Models

The identification of emergent structures in dynamical systems is a major challenge in complex systems science. In particular, the formation of intermediate-level dynamical structures is of particular interest for what concerns biological as well as artificial network models. In this work, we present a new technique aimed at identifying clusters of nodes in a network that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks. Even if our results are still preliminary, we have evidence for showing that our approach is able to identify these “emerging things” in some artificial network models and that it is way more powerful than usual measures based on statistical correlation. This method will make it possible to identify mesolevel dynamical structures in network models in general, from biological to social networks.

Large neighbourhood search algorithms for the founder sequence reconstruction problem

The reconstruction of founder genetic sequences of a population is a relevant issue in evolutionary biology research. The problem consists in finding a biologically plausible set of genetic sequences (founders), which can be recombined to obtain the genetic sequences of the individuals of a given population. The reconstruction of these sequences can be modelled as a combinatorial optimisation problem in which one has to find a set of genetic sequences such that the individuals of the population under study can be obtained by recombining founder sequences minimising the number of recombinations. This problem is called the founder sequence reconstruction problem. Solving this problem can contribute to research in understanding the origins of specific genotypic traits. In this paper, we present large neighbourhood search algorithms to tackle this problem. The proposed algorithms combine a stochastic local search with a branch-and-bound algorithm devoted to neighbourhood exploration. The developed algorithms are thoroughly evaluated on three different benchmark sets and they establish the new state of the art for realistic problem instances.

An Efficient Simulator for Boolean Network Models

Boolean networks (BNs), first introduced by Kauffman as genetic regulatory network models, are the subject of notable works in complex systems biology literature. BN models lately garnered much attention because it has been shown that BNs can capture important phenomena in genetics and biology in general. In this work, we illustrate the main properties and design principles of a new efficient, flexible and extensible BN simulator, named the Boolean Network Toolkit. This simulator makes it possible to easily set up experiments and analyse the most relevant features of BN’s dynamics.

Stochastic local search to automatically design Boolean networks with maximally distant attractors

In this work we address the issue of designing a Boolean network such that its attractors are maximally distant. The design objective is converted into an optimisation problem, that is solved via an iterated local search algorithm. This technique proves to be effective and enables us to design networks with size up to 200 nodes. We also show that the networks obtained through the optimisation technique exhibit a mixture of characteristics typical of networks in the critical and chaotic dynamical regime.

Automatic Design of Boolean Networks for Modelling Cell Differentiation

A mathematical model based on Random Boolean Networks (RBNs) has been recently proposed to describe the main features of cell differentiation. The model captures in a unique framework all the main phenomena involved in cell differentiation and can be subject to experimental testing. A prominent role in the model is played by cellular noise, which somehow controls the cell ontogenetic process from the stem, totipotent state to the mature, completely differentiated one. Noise is high in stem cells and decreases while the cell undergoes the differentiation process. A limitation of the current mathematical model is that RBNs, as an ensemble, are not endowed with the property of showing a smooth relation between noise level and the differentiation stages of cells. In this work, we show that it is possible to generate an ensemble of Boolean networks (BNs) that can satisfy such a requirement, while keeping the other main relevant statistical features of classical RBNs. This ensemble is designed by means of an optimisation process, in which a stochastic local search (SLS) optimises an objective function which accounts for the requirements the network ensemble has to fulfil.

Dynamical properties of artificially evolved Boolean network robots

In this work we investigate the dynamical properties of the Boolean networks (BN) that control a robot performing a composite task. Initially, the robot must perform phototaxis, i.e. move towards a light source located in the environment; upon perceiving a sharp sound, the robot must switch to antiphototaxis, i.e. move away from the light source. The network controlling the robot is subject to an adaptive walk and the process is subdivided in two sequential phases: in the first phase, the learning feedback is an evaluation of the robot’s performance in achieving only phototaxis; in the second phase, the learning feedback is composed of a performance measure accounting for both phototaxis and antiphototaxis. In this way, it is possible to study the properties of the evolution of the robot when its behaviour is adapted to a new operational requirement. We analyse the trajectories followed by the BNs in the state space and find that the best performing BNs (i.e. those able to maintaining the previous learned behaviour while adapting to the new task) are characterised by generalisation capabilities and the emergence of simple behaviours that are dynamically combined to attain the global task. In addition, we also observe a further remarkable property: the complexity of the best performing BNs increases during evolution. This result may provide useful indications for improving the automatic design of robot controllers and it may also help shed light on the relation and interplay among robustness, evolvability and complexity in evolving systems.