The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors

Background: A deep understanding of what causes the phenotypic variation arising from biological patterning processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the original model. A pattern descriptor is a variable that describes and quantifies a certain qualitative feature of the pattern, for example the degree to which certain macroscopic structures are present. There is today no general procedure for how to relate a set of patterns and their characteristic features to the functional relationships, parameter values and initial values of an original pattern-generating model. Here we present a new, generic approach for explorative analysis of complex patterning models which focuses on the essential pattern features and their relations to the model parameters. The approach is illustrated on an existing model for Delta-Notch lateral inhibition over a two-dimensional lattice. Results: By combining computer simulations according to a succession of statistical experimental designs, computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling, we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider of interest. The pattern descriptor model relates the values of the new, dedicated pattern descriptors to the parameter values of the original model, for example by predicting the parameter values leading to particular patterns, and provides insights that would have been hard to obtain by traditional methods. Conclusion: The results suggest that our approach may qualify as a general procedure for how to discover and relate relevant features and characteristics of emergent patterns to the functional relationships, parameter values and initial values of an underlying pattern-generating mathematical model

Design of a simulation environment for laboratory management by robot organizations

This paper describes the basic concepts needed for a simulation environment capable of supporting the design of robot organizations for managing chemical, or similar, laboratories on the planned U.S. Space Station. The environment should facilitate a thorough study of the problems to be encountered in assigning the responsibility of managing a non-life-critical, but mission valuable, process to an organized group of robots. In the first phase of the work, we seek to employ the simulation environment to develop robot cognitive systems and strategies for effective multi-robot management of chemical experiments. Later phases will explore human-robot interaction and development of robot autonomy

BRAHMS: Novel middleware for integrated systems computation

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological. At the same time, the rise of the philosophy of embodied modelling is generating a need to deploy biological models as controllers for robots in real-world environments. Finally, robotics engineers are beginning to find value in seconding biomimetic control strategies for use on practical robots. Together with the ubiquitous desire to make good on past software development effort, these trends are throwing up new challenges of intellectual and technological integration (for example across scales, across disciplines, and even across time) - challenges that are unmet by existing software frameworks. Here, we outline these challenges in detail, and go on to describe a newly developed software framework, BRAHMS. that meets them. BRAHMS is a tool for integrating computational process modules into a viable, computable system: its generality and flexibility facilitate integration across barriers, such as those described above, in a coherent and effective way. We go on to describe several cases where BRAHMS has been successfully deployed in practical situations. We also show excellent performance in comparison with a monolithic development approach. Additional benefits of developing in the framework include source code self-documentation, automatic coarse-grained parallelisation, cross-language integration, data logging, performance monitoring, and will include dynamic load-balancing and 'pause and continue' execution. BRAHMS is built on the nascent, and similarly general purpose, model markup language, SystemML. This will, in future, also facilitate repeatability and accountability (same answers ten years from now), transparent automatic software distribution, and interfacing with other SystemML tools. (C) 2009 Elsevier Ltd. All rights reserved

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Delivering building simulation information via new communication media

Often, the goal of understanding how the building works and the impact of design decisions is hampered by limitations in the presentation of performance data. Contemporary results display is often constrained to what was considered good practice some decades ago rather than in ways that preserve the richness of the underlying data. This paper reviews a framework for building simulation support that addresses these presentation limitations as well as making a start on issues related to distributed team working. The framework uses tools and communication protocols that enable concurrent information sharing and provide a richer set of options for understanding complex performance relationships

Delivering building simulation information via new communication media

Often, the goal of understanding how the building works and the impact of design decisions is hampered by limitations in the presentation of performance data. Contemporary results display is often constrained to what was considered good practice some decades ago rather than in ways that preserve the richness of the underlying data. This paper reviews a framework for building simulation support that addresses these presentation limitations as well as making a start on issues related to distributed team working. The framework uses tools and communication protocols that enable concurrent information sharing and provide a richer set of options for understanding complex performance relationships

Neuroethology, Computational

Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents