* what is evolution? Examples of what evolution is and is not, experimental and theoretical approaches. * interactions between genes (epistasis), synthetic/suppressive effects, and gene action (example of processes that set up evolution). * graphs and evolutionary representations (example of theory).
Genes and Systems
Genes and Systems - Introduction Goal of this section:
To learn about evolution and biology as a complex system.
* introduction to biological methods, concepts, and current issues.
* three lectures: Genes and Systems, Organismal Self-organization I, and
Organismal Self-organization II.
Evolutionary systems biology compendium:
* please read through and keep as a reference document (bibilography of selected
topics, schedule for lectures).
Homework:
* phylogenetic and population genetics exercise.
Genes and Systems - Introduction Evolution not “survival of the fittest”, but “descent with modification”:
Evolution is an adaptive process, but stochastic (random process).
* evolution is not always optimal.
* involves mutation (changes in gene sequences), recombination of genetic material
during sexual reproduction, the flow of genes in a population, and the restriction of
genes - reproductive isolation - in a population (drift).
Evolution is an unfolding process (over multiple generations).
* however, the signature of evolution is apparent in every individual (variation,
biodiversity).
* evolution involves a process called natural selection, which results in differential
reproduction.
* evolution also involves neutral processes, which are driven by population
processes.
Genes and Systems - Introduction Why do biologists use trees to
describe the diversity of life?
Tree of Life: http://www.tolweb.org
* lineage sorting (species diverge from
one another, lineages remain separate).
IN GENERAL – a tree is a useful way to
characterize biodiversity.
BUT – there are several processes that
violate this assumption:
* within-species sexual reproduction,
lateral gene transfer, cultural evolution,
hybridization.
* nevertheless, we still have a directed network to work with (heuristic for capturing
structure of biodiversity).
Ideas that define modern evolutionary theory
Darwin: introduces the idea of natural selection
(or descent with modification).
Mendel: introduces the idea of independent
assortment (single copy of a gene is inherited
from either parent).
Wright, Haldane, Fisher, Morgan: introduces
methods for population and experimental genetics.
E.O. Wilson, Richard Dawkins: extended
evolutionary ideas to the realm of groups
and environments.
Sean Carroll, Brian K. Hall: evolution of development
approach (builds on earlier work of Gould and
embryologists).
AVIDA/artificial life: digital organisms that behave
and replicate like bacterial populations
(http://devolab.cse.msu.edu).
Evolution: an experimental approach Experimental Evolution: Observing evolution directly is hard!
Most of evolutionary biology is done via inference (gene sequences, fossils).
Problems with experimental approach:
* generation times (mostly too long to observe).
* until recently, control of genes and expression.
Successful experimental endeavors:
Rich Lenski (genes in E. coli): > 50K generations!
Theodore Garland (physiological traits in mice)
Digital Evolution Group (evolution of complex
traits – AVIDA)
Andrew Murray (transgene vs. control - yeast)
Tadeusz Kawecki (learning and memory in
Drosophila – fruit fly)
Evolution: an experimental approach (con’t)
Artificial Selection:
The other need for experimental work is an adequate form of selection. Impose
selection in lab or via animal husbandry.
Examples:
Selective breeding of wild type animals (dog domestication, fruit flies): results in
fixation of phenotypic/behavioral traits, but much hitchhiking of other genes
(unintended consequences).
* nutrients in a bacterial medium: changing nutrient
conditions in vivo.
* in AVIDA, selection coefficient is set by metabolic
conditions; more successful genotypes consume
greater number of resources.
* selective breeding of knockout organisms:
breeding genetically altered strains (fly, mouse, yeast).
Evolution: a theoretical approach Neutral evolution (or “survival of the
good enough”):
* evolutionary changes that don’t result from
natural selection (no selective advantage per
se).
* neutral drift example: changes in gene
frequencies resulting from a restriction of
gene pool (e.g. reproductive isolation).
What is a neutral space?
A space of all possible genotypes and the number of mutational steps needed to get
there (genotypes = gene sequences).
When selection (variable intensity) is applied, anywhere from one to n possible
outcomes (weak selection, many possible outcomes, strong selection, only one path
to a “fit enough” genotype).
Evolution: a theoretical approach (con’t)
* Archaea (microbial organisms, live in extreme environments).
* Eubacteria (microbial organisms like bacteria).
* Eukaryota (yeast, plants, and animals).
Both archaea and eubacteria are
prokaryotic, which is defined
by a cell type and level of
complexity.
What do we do when our
concept of “life” is altered?
In 1971, Carl Woese proposed that three
(rather than two) domains of “life” exist.
* a reclassification based on new
evidence.
* not all new evidence warrants a change
in classification (e.g. sequenced
genomes).
What is epistasis? Epistasis is the interactions between genes:
* some traits monogenic (sickle cell anemia, hemophilia). Basic Hardy-Weinberg (H-W).
* many traits multigenic (multiple genes, copy number variation). Epistasis plays role.
Genes interact with each other during expression:
* have suppressive, additive, and synergistic effects
(not gene action).
* many mechanisms of interaction (posttranscriptional
and posttranslational).
* interact in a network; one gene might regulate another
through 1) facilitating or 2) inhibiting activity.
* epistasis does not involve environment, may indirectly
affect phenotypic expression.
What is epistasis? (con’t)
Heredity and Gene Action (diploid genome):
* gene action: copies of a gene combine, express trait (physical, biochemical, behaviors).
* alternate forms of locus = alleles. Alleles take two forms: dominant (A), recessive (a).
* heterozygote (Aa) = beneficial, homozygous recessive (aa) = deleterious.
Monogenic trait:
* one parent has genotype Aa, other has
genotype Aa. Parents = F1
* law of independent assortment: 3
combinations of genotypes in offspring
(F2): AA, Aa, aa, varying frequencies.
* in this case, allele A = dominant. AA
and Aa = outcome in phenotype. Only
aa = recessive phenotype.
A (dominant) a (recessive)
A (dominant) AA Aa
a (recessive) Aa aa
Processes that set up Evolution 2) How can gene action affect the phenotype
post-translationally?
Matzke and Matzke, PLoS Biology, 2(5), 2004:
Basic mechanism of microRNAs (at right).
* short (<100bp) RNA sequences bind up/
degrade viruses, extra transcripts.
Petunia Example: create “more purple” petunia.
* add a transgene (third allele).
* if purple = dominant allele, then
third purple allele = more purple.
Flowers turned out differently. Why?
* violation of gene action, normal copy number variation: short interfering RNAs
silences both enhanced and normal expression.
Graphs and evolutionary representations
Coalescent Theory:
* gene geneaologies (tree-like structure).
* upside-down statistical gene-based phylogeny (each branch = # of changes).
Method:
* select a specific gene or locus.
* sample from population (N).
* infer relationships between taxa (tips of tree) statistically.
Q: in each previous generation [T(2),T(3)],
what is the likelihood of two lineages converging (see frame B of figure)?
Graphs and evolutionary representations (con’t)
Coalescent graph, branches based on union
of different sequence n generations in past:
Calculate parameters: * Ne = initial population size (theoretical minimal pop. size
required for breeding).
* μ =mutation rate (change per unit time).
* θ = 4(Ne)μ (parents are diploid, measure of variation).
Determine coalescence:
* Pc(t) = [1 – 1 / 2N] t-1 * [1 / 2N]
[1 – 1 / 2N] t-1 = prob. lineage DOES NOT coalesce at t-1.
[1 / 2N] = prob. lineage DOES coalesce at t – 0.
Graphs and evolutionary representations (con’t)
Four-taxon case: line between x and y is root, or origin point of the graph.
Lines to A, B, O, and C represent “branches” to individual taxa.
* length of branches ~ taxon has undergone n amount of evolution (each step/length of
branch = number of mutations, character state changes).
* x and y represent ancestral species (branching points between two related species).
* often used to test search algorithms.
QUIZ: in tree #1, which taxa are closer
to ancestral state? They are equidistant.
In tree #2, which taxa are closer to
the ancestral state? O and C.
Other Data Structures for Natural Variation
ENCODE project browser (http://genome.ucsc.edu/ENCODE/):
Project started to understand the functional significance of genomic elements.
* many genomes have been sequenced
(plants, animals, bacteria).
* human genome sequenced in 2001.
ENCODE project tried to distinguish “biochemical function” from “biological
role” (Nature, 447, 799-816, 2007). Used multiple sampling methods and
annontation (bioinformatics).
* 1% of human genome (survey).
* IDed novel non-protein coding transcripts (some formerly thought of as “junk” DNA).
* “junk” DNA not junk – diverse set of genetic elements, some serve no purpose.
* abundance of transcriptional start sites (epigenetic mechanisms – activity of start sites).
The myth of “Junk” DNA? Yes and no.
Food for thought (until next time): what is the function of our genome?
Historically, “junk” DNA was used to describe all non-coding sequences.
* recently, an increasing amount of this “junk” has been found to have
regulatory functions (although much also ~ transcriptional noise).
Genome size = # of nucleotide bases.
# of genes in genome = number of sequences
that code for proteins.
No correleation between organismal complexity and genome size or function.
However, much of the formerly defined “junk” have a regulatory function,
particularly “jumping genes” (transposons).
Organismal Self-organization I
* myth of “junk” DNA (genome content and structure). * gene networks and biocomplexity (genome function). * synthetic life and bioengineering (relevance of evolutionary systems biology to regenerative medicine and systems engineering).
The myth of “Junk” DNA? Yes and no (con’t).
Biemont and Vieira (2006). Nature, 443, 521-524.
Transposable elements (TEs): “jumping genes”
(translocate from one location to another).
* can disrupt a gene’s function during transcription.
* can significantly influence gene regulation.
* can contribute to the appearance of mutations,
which can result in disease.
* TEs are under epigenetic control, may contribute
to cancers and other diseases (not mutational!).
Transposable elements (and genome size) can also be influenced by population
processes (historically small, large population size).
* population bottlenecks (neutral processes) can affect # transposons in genome.
Example of “yes and no”: Retrotransposons
Retrotransposons: retroviral (e.g. HIV) elements, insert into host genome, may
even become heritable (exact mechanisms are controversial).
* HIV virus is a retrotransposon. Inserts itself into genome at active transcription site and uses machinery
to make copies of itself.
* retrotransposons violate the central dogma, which states that genomic information unfolds by being
transferred from genes to RNA to proteins.
* retrotransposons are frequently used for introducing genes in creation of transgenic cell lines. The
delivery of genes to cells is often done using these viral elements.
In situ retrotransposons: humans inherit a significant number of ancient
retrotransposons (just like genes) from mother and father, have been in genome for
generations.
* mostly non-functional, but may contribute to disease phenotypes (if actively transcribed).
* underscores the complexity of the genome, but just one example (for another example, look up Jonathan
Widom’s work on the nucleosome code).
Gene Networks and Biocomplexity
From Lecture #1:
Gene action is dependent on many genetic elements interacting.
* how do we characterize these interactions computationally?
* how do we recreate the higher-order interactions (e.g. rate-limiting, saturation,
feedbacks)?
Genetic regulatory networks (GRNs):
* gene regulation is the key to this model. Instead of a locus with multiple alleles,
the unit of analysis is the operon (regulatory elements, coding sequences).
* stoichiometry is another quality (a certain amount of gene product expressed,
degraded, contributes to function).
Gene Networks and Biocomplexity (con’t)
In silico GRN components (see lower left):
* regulatory interactions (set W).
* i is a specific operon (single gene and
regulatory elements such as activators,
repressors, etc).
* j is a specific element within the operon.
* S is the phenotype, in this case a gene
product.
* Sq is the gene product quantity.
Problems:
1) is this experimentally tractable? Does it map
to biologically-realistic functions?
2) What kinds of outputs does the network
produce? Sufficiently complex? Robust when
gene function is altered?
Jacob and Monod (discovered operon
using bacteria) – 1960s: complex
networks of genes interact to regulate
cell differentiation.
Stu Kauffman (1960s): interactions
understood using computational tools
(simple epistasis not enough).
Gene Networks and Biocomplexity (con’t)
Genetic regulatory networks in vivo: Britten/Davidson model (1973)
Inducer: small molecule, large number of enzymes, external signals producing a
pleiotropic (one gene, many effects) response.
cis-element: regulatory element in same operon as gene it regulates.
trans-element: regulatory element located in different operon as gene it regulates.
Sensor Gene: inducer binds to promoter.
Integrator Gene Set: gene being regulated.
Activator RNA: translational agent.
Receptor: structural gene site, activators bind
receptors in combinatoric fashion.
From Latchman, D.S. Gene
Regulation. Routledge.
Gene Networks and Biocomplexity (con’t)
Example: Espinosa-Soto et.al, The Plant Cell, 16, 2923–2939 (2004).
* constructed a GRN using experimental
data for floral organ formation in
Arabidopsis thaliana.
* used a dynamical network model (see
Kaufmann, 1993) to obtain attractor
points as output.
* epistasis is multiplicative (large # of
potential states) – but small # of
observed morphologies (conserved
but robust).
* model reduces large # of states to
tractable # of active states, corresponds
with transcriptional profiles.
Actual genes = nodes.
Interactions are inferred from
literature.
Logical rules govern
interactions between nodes
(activity, timing).
Plant morphologies match
active network states.
Synthetic Life and Bioengineering
What can we do with an understanding of genomic complexity? Bioengineering
applications.
What is Bioengineering?
* the harnessing of life to make useful things by modifying the structure of function
of the organism.
http://2008.igem.org
http://openwetware.org/wiki/Synthetic_Biology:BioBricks
* focus on microbial organisms and cellular models (for now).
Example: Craig Venter’s group engineered the genome of a microbe to perform
specific functional tasks (clean up toxic waste, medical applications).
* Student groups have also worked on projects involving things like programmed
bioluminescence (open-source biology).
Synthetic Life and Bioengineering (con’t)
Drew Endy approach to procedures/goals of synthetic biology (technique for apps e.g.
metabolic engineering, nanotech):
Traditional tools:
* recombinant DNA (create
sequences).
* PCR (amplify genetic material).
* automated sequencing (read
genetic material).
These define basic read/write
operations.
More advanced tools:
* automated construction (making things
out of DNA, proteins, secondary structure).
* standards (list of biological parts).
* abstraction (encode and compress
biological complexity).
Goal is to create a programming language
for self-assembly, creation of complex
objects.
Synthetic Life and Bioengineering (con’t)
How to design a customized microbial genome:
* microbes have a haploid, circular genome (right),
a few 1000 genes.
Combinatorial knockout experiments:
* brute force, knockout every gene, see if microbe dies.
If so, gene is essential. If not gene can be knocked out.
* result is skeletal genome with only essential genes
(necessary for survival). Applications come from “booting
up” microbes with genes only for specialized functions.
* works well in microbes, not a great approach for
plant or animal genomes.
Synthetic Life and Bioengineering (con’t)
Question: how does cellular differentiation occur in
nature and across phylogeny?
In C. elegans, cell fate maps have been worked out
(developmental model):
* cell at certain position in body (gut, head) will become specific cell type.
* differentiate during development
* good model for understanding links between structure and function.
Cell fate (undifferentiated cells): * local paracrine factors.
* gene expression gradients.
* fate of local population.
Synthetic Life and Bioengineering (con’t)
In vertebrates: cell fate can be more complex:
* in some systems (weakly electric fish tail repair)
localized dedifferentiation occurs naturally.
* transplanted stem/iPS cells take on fate of local
population.
* example: cardiac muscle.
* other processes (such as neural repair) not well
understood – may involve production of new
pluripotent cells in adulthood.
* in these cases, a “cell fate map” makes less
sense (differentiation indispensible part of life
cycle).
Story of Cellular Reprogramming Q: how do we create customized
stem cells for therapy and disease
research?
A: create iPS (or induced
pluripotent stem) cells.
Pluripotent cells: capable of
differentiating into any cell type.
* four transcription factors (Oct4,
Sox2, c-Myc, and Nanog) can be
used to “reprogram” differentiated
cells to pluripotent (stem-cell like).
* cells can be used as delivery
system for gene therapy, or as way
to repair damaged tissue (integrate
into cell population).
Shinya Yamanaka: discovered
four factor trigger using a
“high-throughput” genetic
screen.
Story of cellular reprogramming (con’t) Anatomy of a genetic circuit: Oct4, Sox2 artificially stimulated by transgenic
element.
* NANOG both triggered by Oct4/Sox2 and stimulates further expression (first-order
positive FB).
* presence of all three suppress differentiation genes and activate stem cell genes
(modules).
Hypothesis: Reprogramming is a critical process.
“Cellular-wide avalanche” occurs at level of gene
regulation (e.g. large-scale downregulation of
differentiation genes).
* critical process: one event (small magnitude) can trigger many large
events. Leaves a power-law signature (1/f noise).
* noise can trigger, drive reprogramming process in culture dish. In
general., external stimuli can facilitate reprogramming.
* perspective lacking from traditional biomedical approaches.
Story of cellular reprogramming (con’t)
Sandpile model is a metaphor for the
reprogramming process (several
avalanches occur during
reprogramming). See next slide.
Sandpile model interactive demo:
http://www.cmth.phy.bnl.gov/~maslov/sandpile.htm
Noise may facilitate reprogramming process:
Computational approaches to gene expression include adding
noise (stochastic element) to model.
* non-specific noise in expression of four factors, other genes can trigger
reprogramming.
Black function: Oct4, Sox2. Blue function: NANOG. Red function: lineage-
specific master genes, σ: parameter value for amplitude of noise (same for
every genes).
Up next: “Reducible Complexity”
Evolvability: the capacity of an organism to evolve
traits. How easy is it to evolve a trait give the prior
state of the organism (OR why didn’t humans grow
horns, and why did rams)?
* historical contingency. (path-dependence – does one trait depend
on another?). Assumes common ancestry.
* hopeful monster (complex traits arise de novo). “X-Men” model.
Adami shows theoretical example of receptor
specificity (e.g. lock-and-key).
* preexisting redundancy, lack of specificity during evolution can
lead to functionally integrated traits/systems.
For next time, read Adami paper “Reducible Complexity” (Science, 2006):
Similar perspectives:
Lenski et.al "The evolutionary origin of complex features". Nature, 423, 139 (2003).
Clements et.al "The reducible complexity of a mitochondrial molecular machine". PNAS, 106(37), 15791 (2009).
Avida-ED Demonstration
http://avida-ed.msu.edu/
Organismal Self-organization II
* physiomics and scales of life (combining methods vertically- genes to organism) * modularity and evolution (functional subdivisions of life) * specific mechanisms and predictions (epigenetics, phenotypic capacitance, and facilitated variation).
Physiomics and Scales of Life
What is the Physiome? IUPS Physiome http://physiome.nz
Biocomplexity is organized vertically (from genes to morphology and behavior).
The physiome project is a means to better get at connection between these scales in a
computationally rigorous manner.
Model of the heart might combine:
* MRI imaging
* gene expression studies
* finite element analysis (FEA)
* information ontology
Physiomics and Scales of Life (con’t)
Cascade sampling (Weibel, Symmorphoses,
2000):
* recursively subsample tissues and their
component parts.
* in sample, muscles are composed of fibers,
which are composed of mitochondria, and so
on.
Principle: many membranes make up a single
mitochondria, many mitochondria make up
single fiber.
* form a hierarchical relationship, units at one
level contribute to structure and function
at a higher levels in different ways.
Physiomics and Scales of Life (con’t)
NSR Physiome project:
http://nsr.bioeng.washington.edu/
Stated goals of Physiome project:
* parameter sets for different cells,
tissues, and species.
* schema of interactions and types
of relationships.
* databases and models (model
archive).
* definitions of model standards and terminology.
Modularity and Evolution Modularity: the compartmentalization of biological structure and function (within and
across scales).
Why is it important?
Modularity of the organism (body plan) allows for parts
to evolve in parallel or be
conserved independently.
Example: Hox cluster in
Drosophila:
Insect body has segments,
each segment determined
by a Hox gene.
Hox gene family members
are tightly linked and
conserved (no mutation).
Modularity and Evolution (con’t)
Bithorax mutant: a case where Hox gene is not conserved (deleterious).
* in genotype, linkage of Hox genes is broken.
* in phenotype, two sets of wings are expressed.
* in this case, fitness would be severely reduced.
Vertebrate spinal cord: a case where segments can evolve independently.
* variable across a phylogeny (see right).
* different types of vertebrae occur in different numbers
(thoracic, lumbar, sacral, caudal)
* Hox10 triple mutant: lumbar ribs are expressed in mutant,
not in wild-type, mimics what occurs in evolution.
Modularity and Evolution (con’t) So far in this section:
* physiomics, scales of life (organized vertically – cells vs. tissues vs. organs).
* modularity (functional subdivisions of phenomena at one or many scales).
Put relationship between genotype and phenotype in context:
* organization of biocomplexity (skeleton), dynamics of biocomplexity.
* dynamics of biocomplexity = most interesting aspects of evolutionary systems.
Will lead us to:
* specific mechanisms (predictions?) and their theoretical implications.
* epigenetics, phenotypic capacitance, and facilitated variation.
Epigenetics and Phenotypic Capacitance
Epigenetics: the role of chromatin, methylation, and other
non-gene sequence mechanisms (responsible for specific
phenotypic outcomes – see table).
Methylation: addition of a methyl group at C-to-G
transitions in genome. Affects transcription (from DNA
to RNA, protein).
Chromatin state (figure): silent vs. transcriptionally
competent. Chromatin comformational state driven by
histones, affects if and how genes are expressed.
Epigenetics and Phenotypic Capacitance (con’t)
Epigenetic control of phenotypic
expression:
* insert IAP element into allele A.
* methylation now required for
normal expression (unmethylated
= deleterious).
Chromatin state diagram:
systems-level = feedback between
mechanisms.
* methylation of histone H3K9 further
suppresses H3K9 deacetylation/
methylation pathway.
* also work for activators.
Epigenetics and Phenotypic Capacitance (con’t)
In humans, a transgenerational
response has been found:
* consequence of nutritional
differences in development.
* sex-linked (differences between
males and females).
* scarcity in T0, obesity in T2;
abundance in T0, slim phenotypes
in T2.
* involve the resetting of methylation
patterns during gametogenesis of T0.
Evolutionary Capacitance Capacitor Example (Bergman and Siegal, 2003, Nature, 424, 549): Hsp90 (heat
shock protein). Active under “normal” environmental conditions.
Activity: “buffers” genotypic variation (multiple genotypes = single phenotype).
Inactivity: triggered by environment stress (overwhelms function, results in diverse
phenotypic effects). Multiple pleiotropic effects.
Drosophila (fruit fly) larvae example:
Buffering in context:
Left: larvae exposed to mild heat shock,
then to severe heat shock (expression).
Right: larvae exposed to severe heat shock
only (no expression).
Inducible tolerance ~ expression of
proteins in Hsp family (protective
function).
Evolutionary Capacitance (con’t) Wildtype (WT) Knockout (KO)
Before Evolution
(network state)
No compensatory
mechanism needed
Compensatory mechanism
due to loss of genes
After Evolution
(expression outcome)
Var. in gene expression =
lower
Var. in gene expression =
higher
Average variance in gene expression is lower for wildtype (WT) than for the knockout
(KO) given environmental noise.
KOs: when arbitrary genes chosen for deletion, the remaining genes in network =
increased variance in their expression. Leads to increased phenotypic variance.
In silico Evolution (remove buffering,
reset gene network):
KO populations = 314 generations
to new phenotypic optimum (N = 500).
WT populations = 391 generations
to a new phenotypic optimum (N = 500).
Evolutionary Capacitance (con’t) One take-home message:
Accumulation of variation (random mutation,
etc.) during phenotypic buffering ~ Loss-of-
function mutants with higher fitness.
At left:
Power-law distribution of distance (from
buffered phenotypes), very few viable
individuals at large phenotypic distance (see
‘lethal’ category).
Facilitated Variation (FV) and Evolution of Development (Evo-Devo): Parter et.al,
2008, PLoS Computatation Biology, Gerhart and Kirschner, 2006, PNAS USA.
Prediction of FV: evolution is directed by interaction between the demands of
environment and genetic mechanisms (existing variation can guide evolution).
Prediction of Evo-Devo: development “recapitulates” evolution, changes get there as
genes are expressed (gene action).
Facilitated Variation (FV) Review Facilitated variation (FV) can be described mathematically as
FV = (Mn/Ml) * (Dn/Dl)
* Mn are the number of non-lethal mutants.
* Ml is the number of lethal mutants.
* Dn is the phenotypic distance of non-lethal
mutants from the wildtype.
* Dl is the phenotypic distance of lethal mutants
from the wildtype.
As variation is facilitated, both Mn and Dn
become large.
* non-wildtype variants become more abundant and more dissimilar as FV is
maximized.
Facilitated Variation (FV) Review (con’t) Features of Facilitated Variation (comparable with Evo-Devo):
1) weak regulatory linkage: linkage = co-
expression, co-evolution of genes. Linkage
relaxed, relationships less predictable.
2) exploratory behavior: change in effects
of gene, hormonal effects on target tissues.
Genes more diverse in effects.
3) reduced pleiotropy: single gene =
multiple effects (pleiotropy). Genes =
more specific in effects on trait X.
4) modularity: segmental organization,
functional subsets (phenotypic) defined
by distinct gene networks, patterns of
gene expression.
Pleiotropy in
human system
Example of
Phenotypic
Modularity
If you have any further questions/want to
know more:
Bradly Alicea
Research Website
http://www.msu.edu/~aliceabr/
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