Transcript What is systems biology? - McGraw Hill Higher Education

PowerPoint to accompany
Genetics: From Genes to
Genomes
Fourth Edition
Leland H. Hartwell, Leroy Hood,
Michael L. Goldberg, Ann E. Reynolds,
and Lee M. Silver
Prepared by Mary A. Bedell
University of Georgia
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Hartwell et al., 4th edition
1
PART
VI
Beyond the Individual Gene and Genome
CHAPTER
Systems Biology and
the Future of Medicine
CHAPTER OUTLINE
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21.1 What Is Systems Biology?
21.2 Biology as an Informational Science
21.3 The Practice of Systems Biology
21.4 A Systems Approach to Disease
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What is systems biology?
Biological system – collection of interacting elements that
carry out a specific biological task
• Can be interacting molecules; i.e. proteins, mRNAs,
metabolites, or control elements of genes
• Can be interacting cells; i.e. immune system cells,
hormonal network cells, or neuronal network cells
Systems biology – seeks to describe and analyze the
complex interactions of components within the system and
in relation to components of other systems
• Requires a cross-disciplinary approach – teams of
biologists, computer scientists, chemists, engineers,
mathematicians, and physicists
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Four questions to guide thinking
about biological systems
What are the elements of the system?
• Use data sets generated by genomic and proteomic
tools
What physical associations occur between the elements?
• e.g. Protein-protein, protein-DNA, cell-cell, etc.
What happens when the system is perturbed?
• Genetic or environmental perturbations
What gives rise to a system's emergent properties?
• Can sometimes be greater than the sum of individual
components
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Representation of a biological network
Nodes represent molecules,
metabolites, or cells
Lines represent relationships
between the nodes
Fig 21.2
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Biology as an informational science
Biological information is hierarchical
In systems biology, information from as many different
hierarchical levels must be captured and integrated
Digital genomic information has two types of sequences:
• Genes that encode protein and untranslated RNAs
• DNA sequences that are cis-control elements
All networks are dynamic – able to respond to conditions
when activated
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An example of a complex molecular machine
Drawing of a nuclear
pore in yeast
This complex contains
~ 60 proteins
Fig 21.3
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Example of a protein network in yeast
This network contains
~2500 proteins and
7000 linkages
Fig 21.4
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Gene regulatory networks control
information transmission
Gene regulatory networks receive diverse inputs of
information, integrate and modify the inputs, then transmit
the altered information to protein networks
Each gene has 3 - 30 (or more) cis-control elements
Some transcription factors control expression of two or
more genes that encode other transcription factors
• May generate complex feed-forward and feedback
regulatory loops
Complexity of a gene regulatory network is specified by the
number of layers in each network and the number of genes
in each layer
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Multiple transcription factors regulate
gene expression
In this example, six transcription factors bind to six ciscontrol elements to regulate when, where, and how much
mRNA from this gene is transcribed
Fig 21.5
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Gene regulatory network involving
three layers of genes
Transcription factor interactions may be
positive or negative and can interact with
other transcription factors in a lower layer
or can feedback to another layer
Fig 21.6
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Gene regulatory network for development
of the gut in sea urchins
Fig 21.7
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Larval development of the sea urchin
Fig 21.8
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The practice of systems biology
High throughput platforms for genomics and proteomics
(Chapter 10)
Powerful computational tools
Studies of simple model organisms; e.g. E. coli and yeast
Comparative genomics
Employs both discovery science and hypothesis-driven
science
Acquisition of global data sets and integration of different
types of data
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An algorithmic approach to systems biology
Scan the biological literature and databases to discover all
genes, mRNAs, and proteins in a cell or organism
Develop a preliminary model (descriptive, graphic, or
mathematical)
Formulate a hypothesis-driven query and test through
genetic or environmental manipulations
Integrate different types of graphical or mathematical data
Perform iterative perturbations with a second round of
genetic and environmental manipulations
Evaluate whether the refined model can predict the behavior
of the system
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Systems approach to reveal the process of
galactose utilization in yeast
Fig 21.9
GAL 1, GAL 5, GAL 7, and GAL 10 genes encode four enzymes
One transporter molecule carries galactose into cell
Four transcription factors that turn the system on and off
Nine genetically perturbed yeast strains, each has a single gene
knocked out, and a wild type strain
Global microarrays from cells grown in the presence and absence
of galactose (all 6000 yeast genes)
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Observations on systems approach to
galactose utilization in yeast
More than 8 unexpected gene expression patterns were noted
Expression patterns of 997 could be clustered into 16 groups
• Each group had a similar pattern of changes in gene
expression, some of which were known to be involved in
other pathways
• Suggested that these other pathways were directly or
indirectly connect to galactose-utilization pathway
Second round of analyses of protein-protein and protein-DNA
interactions confirmed the interactions
For 15 genes, found evidence for posttranscriptional regulation
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Modeling and experimental tests of the
galactose utilization system in yeast
Fig 21.10
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Interactions
between
networks
Genetic perturbations of
the galactose-utilizing
system in yeast affect the
network of interactions
with other metabolic and
functional systems
Fig 21.11
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A systems approach to disease
Disruptions that result in disease may arise from mutated
genes (e.g. cancer), or from infection by foreign agents (e.g.
AIDS, smallpox, the flu)
Identification of biomarkers is a first step
• Molecular footprints - patterns of mRNAs and proteins
in disease vs normal tissues/cells
Disease stratification may be identified
• Many diseases have different subtypes within the same
general phenotype
• Improved diagnostic and treatment potential for
different subtypes
Knowledge of protein interactions can identify drug targets
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Altered cellular network can lead to disease
Nondiseased
Diseased
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Fig 21.12
21
The systems approach leads to predictive,
preventive, personalized medicine
Prediction
• Individual genome sequence can be used to determine
chance of developing a particular disease
• Blood fingerprints will allow early detection and stratification
of disease types
New prevention strategies
• Better understanding of networks will lead to more effective
therapeutic agents and drugs to prevent disease
Personalization
• Apply power of predictive and preventive medicine to
individual needs
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