Signals and Noise in Complex Biological Systems

In every single living cell, countless different kinds of molecules continuously work together and react chemically in a complex system that may adjust to changing environments and extreme conditions, living to survive and reproduce itself. The information needed to create these components is saved in the genome, that is replicated in each cell division and transferred and combined with another genome from parent to child. The regulatory mechanisms which control biological systems, for example the regulation of expression levels for each gene, has changed so that global robustness and capability to survive under tough circumstances is a strength, simultaneously as biological tasks on a comprehensive molecular level should be carried out with good precision and without failures. It has led to systems which may be referred to as a hierarchy of levels of complexity: from the lowest level, where molecular mechanisms control other components at the same level, to pathways of synchronised interactions between components, formed to carry out particular biological tasks, and up to large-scale systems composed of all components, connected in a network with a topology which enables the system robust and flexible…

Contents: Signals and Noise in Complex Biological Systems

1 Introduction
1.1 Complexsystems
1.2 Genomebiology
1.3 Gene expression
1.4 Genetic variation and disease
1.5 Dynamicalsystemsandnoise
1.6 Outline
2 Measurement
2.1 Gene expression microarrays
2.2 Genotyping
2.2.1 Genotyping with Illumina BeadChip system
2.3 DNA-bindingproteins
2.4 Proteininteractions
2.5 Single-molecule measurement techniques
3 Low-level signals and noise
3.1 Control of gene expression
3.1.1 DNA motifs as signals for protein binding
3.2 Synthetic biology and randomness in gene expression
3.3 Signal transduction in stochastic bistable systems
3.3.1 Stochastic resonance
3.4 Effects on gene expression by genetic variation
4 Pathways and biological tasks
4.1 Pathway control
5 Large-scale gene networks
5.1 Topology and structural properties of complex biological system
5.2 Inferring large-scale systems from data
5.2.1 Networks from microarray data
5.2.2 Transcription factor binding networks
5.2.3 Protein-proteinnetworks
5.2.4 Integratingnetworks
5.3 Noise and modelling issues
5.3.1 Choosing the right level of model complexity
5.3.2 Artefacts in network modelling
6 Signalling between different levels of complexity
6.1 Genetic variation cause disease
6.1.1 Type 2 Diabetes Mellitus
6.1.2 Genome-wide association studies
7 Contributions
7.1 Paper I: General measures for signal-noise separation in nonlinear dynamical systems
7.1.1 Methods
7.1.2 Results
7.1.3 Discussion
7.2 Paper II: Building and analysing genome-wide disruption networks
7.2.1 Background
7.2.2 Methods
7.2.3 Results
7.2.4 Discussion…

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