21.1: Regulatory Networks- Inference, Analysis, Application
- Page ID
- 41044
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Living systems are composed of multiple layers that encode information about the system. The primary layers are:
1. Epigenome: Defined by chromatin configuration. The structure of chromatin is based on the way that histones organize DNA. DNA is divided into nucleosome and nucleosome-free regions, forming its final shape and influencing gene expression.
- Genome: Includes coding and non-coding DNA. Genes defined by coding DNA are used to build RNA, and Cis-regulatory elements regulate the expression of these genes.
- Transcriptome RNAs (ex. mRNA, miRNA, ncRNA, piRNA) are transcribed from DNA. They have regulatory functions and manufacture proteins.
- Proteome Composed of proteins. This includes transcription factors, signaling proteins, and metabolic enzymes.
Interactions between these components are all different, but understanding them can put particular parts of the system into the context of the whole. To discover relationships and interactions within and between layers, we can use networks.
Introducing Biological Networks
Biological networks are composed as follows:
Regulatory Net – set of regulatory interactions in an organism.
- Nodes are regulators (ex. transcription factors) and associated targets.
- Edges correspond to regulatory interaction, directed from the regulatory factor to its target. They are signed according to the positive or negative e↵ect and weighted according to the strength of the reaction.
Metabolic Net – connects metabolic processes. There is some flexibility in the representation, but an example is a graph displaying shared metabolic products between enzymes.
- Nodes are enzymes.
- Edges correspond to regulatory reactions, and are weighted according to the strength of the reaction.
Signaling Net – represents paths of biological signals.
- Nodes are proteins called signaling receptors.
- Edges are transmitted and received biological signals, directed from transmitter to receiver.
Protein Net – displays physical interactions between proteins.
• Nodes are individual proteins.
• Edges are physical interactions between proteins.
Co-Expression Net – describes co-expression functions between genes. Quite general; represents functional rather than physical interaction networks, unlike the other types of nets. Powerful tool in computational analysis of biological data.
• Nodes are individual genes.
• Edges are co-expression relationships.
Today, we will focus exclusively on regulatory networks. Regulatory networks control context-specific gene expression, and thus have a great deal of control over development. They are worth studying because they are prone to malfunction and causing disease.
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(a) Interactions between biological networks.
Interactions Between Biological Networks
Individual biological networks (that is, layers) can themselves be considered nodes in a larger network representing the entire biological system. We can, for example, have a signaling network sensing the environment governing the expression of transcription factors. In this example, the network would display that TFs govern the expression of proteins, proteins can play roles as enzymes in metabolic pathways, and so on.
The general paths of information exchange between these networks are shown in figure 21.4.
Studying Regulatory Networks
In general, networks are used to represent dependencies among variables. Structural dependencies can be represented by the presence of an edge between nodes - as such, unconnected nodes are conditionally independent. Probabilistically, edges can be assigned a ”weight” that represents the strength or the likelihood of the interaction. Networks can also be viewed as matrices, allowing mathematical operations. These frameworks provides an effective way to represent and study biological systems.
These networks are particularly interesting to study because malfunctions can have a large effect. Many diseases are caused by rewirings of regulatory networks. They control context specific expression in development. Because of this, they can be used in systems biology to predict development, cell state, system state, and more. In addition, they encapsulate much of the evolutionary difference between organisms that are genetically similar.
To describe regulatory networks, there are several challenging questions to answer.
Element Identification What are the elements of a network? Elements constituting regulatory networks were identified last lecture. These include upstream motifs and their associated factors.
Network Structure Analysis How are the elements of a network connected? Given a network, structure analysis consists of examination and characterization of important properties. It can be done biological networks but is not restricted to them.
Network Inference How do regulators interact and turn on genes? This is the task of identifying gene edges and characterizing their actions.
Network Applications What can we do with networks once we have them? Applications include predict- ing function of regulating genes and predicting expression levels of regulated genes.
1More in the epigenetics lecture.