Information Processing in Cells and Tissues

Name: Information Processing in Cells and Tissues | 10th International Conference, IPCAT 2015, San Diego, CA, USA, September 14-16, 2015, Proceedings
Brand: Springer
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10th International Conference, IPCAT 2015, San Diego, CA, USA, September 14-16, 2015, Proceedings

Michael Lones Andy Tyrrell Stephen Smith Gary Fogel(Herausgeber*in)

Springer (Verlag)

Erschienen am 2. September 2015

VIII, 233 Seiten

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978-3-319-23108-2 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Biochemical Information Processing
Surface-Immobilised DNA Molecular Machines for Information Processing
1 Information Processing with Bioelectronic Systems
2 Experimental Studies of DNA Machines on Surfaces
2.1 Materials and Methods
2.2 Experimental Results
3 Conclusion
References
Scalable Design of Logic Circuits Using an Active Molecular Spider System
1 Introduction
2 Model Description
3 Logic Circuit Construction
3.1 Normal Sites and Functional Sites
3.2 Designs of the AND and OR Gates
3.3 Design of the NOT Gate
3.4 Gate Cascades
3.5 Complexity Analysis
4 Formal Definition of the Model
4.1 Site Types and Transition Rules
4.2 Model Definition
5 Conclusions and Discussions
References
Organic Mathematics: On the Extension of Logics from Physical Atoms to Cellular Information Processes
Abstract
1 Introduction
1.1 The Symbolic Gap
1.2 The Logical Gap
2 A Novel Electrical Notation for the Bio-chemical Sciences
2.1 Origins of Atomic Numbers
2.2 Extensions of the Atomic Numbers
3 Perplex Notation and Physical Dependence of Organic Operands and Operations
3.1 Physically Independent and Interdependent Electrical Particles
3.2 Constraints on Formal Electrical Logics
4 Mereology and Electrically Labeled Bipartite Graphs
4.1 Ratiocinations
4.2 Mereology
5 Conclusions
5.1 The Role of Hybridizations of Logics in Organic Mathematics
5.2 Future Work and International Collaboration
6 Summary
References
Collective and Distributed Behaviour
An Ecosystem Algorithm for the Dynamic Redistribution of Bicycles in London
1 Introduction
2 Background
3 Problem Description
3.1 Problem Overview
3.2 Performance Indicators
3.3 Datasets
4 Method
4.1 Assumptions and Restrictions
4.2 Bicycle Distribution Simulator
4.3 Truck Scheduling Algorithms
5 Experiments
6 Results and Analysis
7 Conclusion and Future Work
7.1 Conclusion
7.2 Future Work
References
Evolving Ensembles: What Can We Learn from Biological Mutualisms?
1 Introduction
2 Evolving Ensembles
3 Biological Mutualism
4 Mutualistic Evolutionary Algorithms
5 Evolving Mutualistic Ensembles
5.1 Promoting Mutuality
5.2 Identifying Partners
6 Conclusions
References
An Artificial Immune System for Self-Healing in Swarm Robotic Systems
1 Introduction
2 Robotic Swarm Taxis
2.1 Anchoring of the Swarm
3 Initial Investigations into the -algorithm
3.1 Experimental Protocol
4 Immune Inspiration: Granuloma Formation
4.1 Granuloma Formation Algorithm
5 Experimental Investigation: Granuloma Formation Algorithm
5.1 Results
5.2 Maintaining Energy Across the Swarm
6 Conclusion
References
Team Search Tactics Through Multi-Agent HyperNEAT
1 Introduction
2 Background
2.1 NeuroEvolution of Augmenting Topologies (NEAT)
2.2 CPPNs and HyperNEAT
2.3 Multi-Agent HyperNEAT
3 Experimental Method
3.1 Search Task
3.2 Agent Simulator
3.3 Sensing Tactical Environments
3.4 MA-HyperNEAT Agents
3.5 Scripted Agents
3.6 Evolution and Evaluation
4 Results
5 Conclusion
References
Patterning and Rhythm Generation
miRNA Regulation of Human Embryonic Stem Cell Differentiation
Abstract
1 Introduction
1.1 miRNA Dynamics and During hESC Differentiation
1.2 Chemical Regulators of Pluripotency
2 Methods
2.1 Stem Cell Culture and Differentiation
2.2 RNA Preparation
2.3 Network Analysis
3 Results
3.1 Correlated Expression of miRNAs During hESC Differentiation
3.2 Global Network Analysis
3.3 Effect of Gö6976 on miRNA Expression During DE Formation
4 Discussion
Acknowledgements
Motifs Within Genetic Regulatory Networks Increase Organization During Pattern Formation
1 Introduction
2 Motifs Within Gene Regulatory Networks
3 Multicellular Model and Implementation
4 Methodology
4.1 Information Complexity
5 Results
6 Discussion
References
Harmonic Versus Chaos Controlled Oscillators in Hexapedal Locomotion
1 Introduction
2 Central Pattern Generators
2.1 Hopf Oscillator
2.2 Wu Oscillator
3 Interlimb Coordination
3.1 Hopf Oscillator
3.2 Chaos Controlled Wu Oscillator
4 Coupled Artificial Signalling Network
5 Controlling Legged Robot Locomotion
5.1 Gait Generation
5.2 Evolving Coupled Artificial Signalling Networks
6 Results
7 Conclusions
References
Biochemical Regulatory Networks
Switching Gene Regulatory Networks
1 Introduction
2 Background
3 Switching Gene Regulatory Networks
4 SGRN Model Synthesis
5 Experimental Results
5.1 Benchmark Design
5.2 Results
6 Related Work
7 Conclusion
References
The Role of Ago2 in microRNA Biogenesis: An Investigation of miR-21
Abstract
1 Introduction
1.1 Ago2
1.2 miR-21
1.3 Summary
2 Methods
3 Results
4 Discussion
Acknowledgements
Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network
1 Introduction
2 Background
3 The Artificial Epigenetic Network
3.1 Artificial Genes
3.2 Artificial Epigenetics
3.3 Adaptations
4 Methodology
4.1 Coupled Inverted Pendulums
4.2 Processor Scheduling
4.3 Towers of Hanoi
5 Results
5.1 Coupled Inverted Pendulums
5.2 Processor Scheduling
5.3 Towers of Hanoi
6 Conclusions
References
Metabolomics and Phenotypes
Sensitivity of Contending Cellular Objectives in the Central Carbon Metabolism of Escherichia Coli
1 Introduction
2 Materials and Methods
3 Results
References
Towards a Graph-Theoretic Approach to Hybrid Performance Prediction from Large-Scale Phenotypic Data
1 Introduction
2 Dataset
3 Method
3.1 Problem Definition
3.2 Data Normalization
3.3 A Graph-Theoretic Approach for Predictive Breeding
4 Results
4.1 Biomass Performance Prediction
5 Conclusion and Future Work
References
Automated Motion Analysis of Adherent Cells in Monolayer Culture
Abstract
1 Introduction
2 Methods
2.1 Cell Culture and Videomicroscopy
2.2 Cell Tracking
2.3 Feature Extraction
2.4 Choice of Features
2.4.1 Cell Migration Speed
2.4.2 Cell Migration Persistence
3 Results
4 Conclusion and Discussion
References
Neural Modelling and Neural Networks
Community Detection as Pattern Restoration by Attractor Neural-Network Dynamics
Abstract
1 Introduction
2 Methods
2.1 Neural-Network Dynamics
2.2 Imperfect and Inaccurate Set of Source Nodes: Deteriorated Pattern
2.3 Local Community Detection
2.4 State-of-the-Art Study
2.5 Biological Relevance
3 Results
3.1 Local Detection of Communities from Synthetic Benchmark Networks
3.2 Local Detection of Communities from Real Social Networks
4 Discussion
Acknowledgments
Appendix: Synthetic Benchmark Network
References
Feature Learning HyperNEAT: Evolving Neural Networks to Extract Features for Classification of Maritime Satellite Imagery
1 Intro
2 Background
2.1 BCCT200
2.2 NeuroEvolution of Augmenting Topologies (NEAT)
2.3 CPPNs and HyperNEAT
3 Feature Learning HyperNEAT
4 Experimental Setup
5 Results
6 Discussion and Conclusion
A Result Standard Deviations
References
Improving Crossover of Neural Networks in Evolution Through Speciation
1 Introduction
2 NeuroEvolution of Augmenting Topologies (NEAT)
3 Speciation Heuristics
4 Experimental Approach
5 Results
6 Discussion
7 Conclusion
A NEAT Parameters Shared Across Experiments
References
Author Index

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