Biology Reference
In-Depth Information
phenotype reprogramming capacity will benefit regenerative medicine, where to repair tissue
deficits or correct organ dysfunction one requires specialized cells of certain types in large
quantities (such as heart muscle cells, skin stem cells, or pancreas insulin-producing cells).
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However, the design of reprogramming experiments remains largely empirical, typically
relying on brute-force trial and error and qualitative
to find how to
manipulate the regulatory genes in order to achieve a desired attractor state transition.
Here we present a formal framework for this purpose. It is based on our increasing
understanding of how gene-regulatory networks produce the stable network attractor states
that determine the cell-type-specific gene expression pattern, and will permit us to
quantitate the
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educated guesses
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'
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that separate the attractors and compute the most
efficient path from one attractor to another.
height of the barriers
We organize this chapter as follows: the second section will introduce the very basic concept
of dynamical systems and of the epigenetic or quasi-potential landscape in a qualitative
manner for experimental biologists. Engineers, computational, and physical biologists
can fast-forward to the central question of how to define a quasi-potential landscape and
to calculate transition dynamics that is addressed in detail in the third section. In the
fourth section a general step-by-step description of the principles for putting the theory into
practice is given, considering the paucity of information on gene regulatory networks
available. The fifth section offers examples for blood cell reprogramming and pancreas
beta-cell reprogramming. Conclusions and outlook end this chapter in the final section.
CONCEPTUAL FRAMEWORK: GENE REGULATORY NETWORKS,
NETWORK STATES, AND CELL TYPES
The Gene Regulatory Network and Gene Expression Patterns
as Network State
A gene regulatory network (GRN)
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consists of the regulatory interactions through which
regulatory genes control the expression behavior of their target genes. Since regulatory
genes are themselves also target genes, this leads to a network replete with feedback loops
that exhibits characteristic dynamics of the global gene expression changes. The gene
expression patterns and their temporal behavior are thus predestined by the network
structure which has evolved a
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wiring diagram
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to produce the
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meaningful,
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stable gene
expression states that govern cell phenotypes.
More specifically, the GRN controls cell differentiations during development. A GRN in which
fate-determining transcription factors (TFs) regulate each other drives the development of
tissues by orchestrating the activation or suppression of the appropriate genes across the
genome to establish the stable steady-state gene expression patterns that specify a given cell
type with its biological functions. These stable gene expression patterns
defined by the
N
gene of the genome, are the aforementioned attractor states. They guarantee the stability of
the cell-type-specific expression patterns.
5,17,18
The recent integrated analysis of gene expression
profiles of tens of thousands of genes across the genome based on microarrays has provided
evidence that cell types indeed represent high-dimensional attractor states of the dynamics
of GRNs.
5,19,20
If the cell-type-specific genomic expressions are attractors, then they are indeed
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preprogrammed
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by the genome acting as a blueprint because it specifies the particular
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which is defined by the invariant properties of which gene (conditionally)
regulates which one. The GRN is thus directly encoded in the genome, since molecular
interactions and their dependence on cooperative action (conditional binding, allostery, etc.)
are defined by protein and promoter DNA sequences. Yet, while this establishes the genome
as a blueprint, hence warranting the use of engineering metaphors in synthetic biology,
the occupation of the attractor states is modulated by noise and environmental signal in a
way that is unique to biological systems.
wiring diagram
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