Transcript Presentation

Modeling of Human Aging
using a Systems Approach
Dissertation proposal for
Glenn Booker
June 5, 2008
Presentation Outline
 Overview
 Aging Theories
 Gene Expression Data
 Fuzzy Modeling Approach
Vicious Cycle Model
Retrograde Response Model
 Network Theory of Aging
 Demographic Implications
 Summary
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Dissertation Proposal
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Overview
 My dissertation research will investigate whether
changes observed at the cellular level support
the existence of a retrograde response defense
mechanism in human aging and whether these
changes show on higher levels such as in genedisease networks and in biodemographics
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Overview
There are three main elements of this
research
Cellular system modeling using fuzzy logic
Investigation of network connections among
changes in gene expression with age, and
increased occurrence of certain diseases
Biodemographic analysis to investigate the
plateau effect and its possible connection to
the retrograde response
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Overview
The retrograde response (RR) is when a
cell slows using oxidative phosphorylation
(oxphos) to produce energy (ATP), and
instead uses the anaerobic and much less
efficient glycolysis to produce ATP
The RR has been observed in yeast and
other organisms, but not in humans or
other mammals (Butow, 2004)
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Overview
This research is described as a ‘systems
approach’ because it integrates cellular
level gene expression data with larger
scale biological pathways, organism-level
diseases and population-scale
demographic data
This multiscale perspective is analogous
to efforts such as the Physiome project
(Hunter, 2005)
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Aging Theories
Dozens of theories and models have been
proposed to explain why organisms age
(Kirkwood, 2003; Kirkwood, 2005)
Rate of living theory (1908) – long-lived
animals exert less energy per unit mass
Caloric restriction (1935) – Eating less
helps you live longer
Like Mechanistic theories (1963), which say
specific metabolic pathways cause aging
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Aging Theories
Free radical theories (1956) - Free radicals
and reactive oxidative species (ROS) damage
cell components (deMagalhaes, 2006; Fleury, 2002)
Shortening of telomeres theory is related to this
DNA damage (1959) – damage accumulates
to DNA until the body can’t repair itself
Basis for the Vicious Cycle (VC) model
Programmed Senescence (1961) – the body
is programmed to kill itself off
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Aging Theories
Disposable Soma Theory (1977) –focus
resources on reproduction, at the expense
of shorter life
A similar concept is Pleiotropy; genes which
help early in life, later cause damage
Network theory of aging (1992) – which
combines models of mitochondrial ROS
production, aberrant proteins, free radicals,
and scavengers (MARS) (Kowald, 1996)
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Gene Expression Data
The lab of Dr. Kriete has conducted
genome-wide gene expression studies
from human fibroblasts in a crosssectional study of varying age subjects
(age 17 to 94), and identified genes which
are significantly (>2.5 or <1/2.5) up- or
down-regulated
Of the 16,220 genes analyzed, 504 were
up-regulated, and 224 were down-regulated
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Gene Expression Data
 The data produced suggests:
Calcium homeostasis changes with age
ROS did not increase with age
Glycolysis activity increases with age
ATP and biosynthesis decrease with age
Inflammation and apoptosis-inhibiting genes
increased with age
 Many of these point to the possibility of a
retrograde response defense mechanism,
potentially mediated by the transcription
factor Nf-ĸB (Giardina, 2002)
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Schematic Model Summarizing Gene
Expression Data
▼ Enzymes
Krebs
Cycle
▼ETC
Δψm
??NADH
NADH
Apoptosis
Apoptosis
ETC
▼
▼Δψm
Δψm
▼
▼NADH
NADH- -
▼
▼ATP
ATP- -Synthase
Synthase
Dehydrogenases
Dehydrogenases
Inflammation
Inflammation
??ROS
ROS
Oxidated
Oxidated
Proteins
Proteins
▲
▲Cytokines
Cytokines
Interleukins
Interleukins
Complement
Complement
UPS
UPS
▲
▲BCL2L1
BCL2L1
BCL6
BCL6
▼
▼PTEN
PTEN
▲Akt
▲Akt
▼
▼ATP
ATP
2+
▲Ca
▲Ca2+
▲
▲Calcineurin
Calcineurin
▲Glycolysis
▲Glycolysis
▲
▲Glucose
GlucoseProteins
Proteins
IkBα
IkBβ
IkBα
IkBβ
▲
▲NF-kB
NF-kB
▼
▼Biosynthesis
Biosynthesis
▼
▼Ribosomal
Ribosomal++
Structural Proteins
Structural Proteins
Retrograde
RetrogradeResponse
Response- -Differential
DifferentialGene
GeneExpression
Expression
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Fuzzy Modeling Approach
Modeling of cellular behavior can be done
in great detail quantitatively (Kowald, 1996;
Werner, 2005; Wallace, 2005)
To compare against gene expression data,
which is statistically quite variable, a fuzzy
logic approach is being used to assess
qualitative behavior of cells (Center, 1998;
Franco-Lara, 2007)
Allows larger scale models to be created from
qualitative gene expression data (Nicholls, 2004)
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Fuzzy Modeling Approach
The model used for preliminary analysis
is the Bionet tool (Bosl, 2007; Doi, 2004)
A Java-based application, it uses fuzzy logic
to model cellular pathways
Developed by Dr. William Bosl of Harvard
Medical School
Nodes often represent the quantity of a species,
scaled from 0 to 1 in six fuzzy ranges (near
zero, plus very low to very high)
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Fuzzy Modeling Approach
While the Bionet tool has been adequate
for preliminary analysis of the Vicious
Cycle and Retrograde Response, it is
expected that a custom tool will be
developed using Matlab
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Vicious Cycle Model
The Free Radical and DNA damage
theories of aging both indicate that
damage to the cell occurs and
accumulates throughout life
That damage, whether from ROS or
genetic disturbances, leads to an
exponential decay in the body due to
deterioration of the mitochondria
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Vicious Cycle Model
Bio-synthesis
1.sp
7.0
ATP demand
18.sp
0.03
Krebs Cycle
(Oxphos)
2.ap
0.01
Mitochondrial
Electron
Transport
Chain (ETC)
DYm
3.ap
0.01
5.s 1
0.0 p 6.ip
1 10 .
ROS
Shape
Legend:
Upregulated
ATP
All reaction
rates not
stated are 1.0
8.ip
7.ap
No
change
SOD
Downregulated
Reaction types are: ap = activator-product, ip = inhibitor-product, sp = substrate-product
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Vicious Cycle Model
ROS
ATP
biosynth
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Retrograde Response Model
In contrast, if a retrograde response is
being activated in the cell, it should slow
oxphos and increase glycolysis, in order to
prevent ROS damage from accumulating,
i.e. it’s a pro-survival mechanism
The increased activity of the transcription
factor NF-kB with age is believed to be the
major regulating mechanism for many
intracellular processes
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Retrograde Response Model
Bio-synthesis
1.sp
7.0
18.sp
0.2
ATP demand
Krebs Cycle
(Oxphos)
Mitochondrial
Electron
Transport
Chain (ETC)
 m
2.ap
0.01
3.ap
0.01
ATP
6.ip
4.sp
5.ap
0.01 16.ip
PTEN
ROS
7.ap
SOD
9.sp
UPS
11.ip
0.5
.ip
10
IkB
Akt
Ca2+
Calcineurin
8.ip
tbd
IkB
ip
12.
0.5
13.
ip
0.5
NF-B
15.ip
0.1
14.ap
0.09
Nucleus
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Retrograde Response Model
NF-kB
ROS
ATP
biosynth
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Fuzzy Modeling Goals
The goals of the fuzzy modeling portion of
this research are to
Expand and refine the fuzzy models of cellular
behavior to include other relevant processes,
such as glycolysis, apoptosis, and inflammation
Refine modeling parameters (e.g. initial
conditions, concentrations, organelle
descriptors) based on experimental assays
and published sources
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Network Theory of Aging
Biological pathways form networks, based
on gene expressions which produce the
species involved (Barabasi, 2004)
The diseasome maps which diseases are
associated with the genes which may
cause them (Goh, 2007)
The preliminary list of genes up- or downregulated with age were cross-referenced
with the diseasome
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Network Theory of Aging
Preliminary analysis identified 119
diseases, based on 81 genes
22 genes were down-regulated, 59 up
Many of these diseases are strongly
associated with increased age
For examples: deafness, diabetes,
cardiomyopathy, hypertension, leukemia,
gastric and colon cancers, ataxia, macular
degeneration, and muscular dystrophy
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Network Theory of Aging
Goals for this portion of my research are to
Conduct more detailed investigation of the
diseases associated with aging, as they relate
to the connections with gene dysregulation
Supplement other gene expression changes with
age, such as for brain, liver, and muscle tissue
Perform a pathway-based analysis by matching
diseases and aging-related pathways
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Demographic Implications
The third portion of this research is looking
for demographic evidence of the
retrograde response
US Census data was obtained for death
rates by single year age (Census, 2004)
From about age 30 on, an exponential increase
in death rate should be seen
However perturbations about the exponential
trend were seen
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Demographic Implications
Regression of male and female
death rates, ages 30-99
3.00
male
y = 0.0368x - 1.0667
Log10(death rate)
2.50
2
R = 0.9975
2.00
1.50
logmale
logfemale
1.00
Linear (logfemale)
Linear (logmale)
0.50
0.00
-0.50
30
50
70
90
Age, yrs
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Dissertation Proposal
female
y = 0.0403x - 1.4812
R2 = 0.9977
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Demographic Implications
The residual between the log-linear
regression and the actual death rate was
calculated
Residuals of log(death rate) by age
Residual = actual estimated
0.10
0.05
Rmale
0.00
Rfemale
-0.05
-0.10
30
40
50
60
70
80
90
100
Age
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Demographic Implications
This shows a surprisingly cyclical
behavior, with a period on the order
of 160 years
It ends with a strong downturn in death
rate, which agrees with observations of
a plateau in death rate at ages 90+
(Vaupel, 1998; Weitz, 2001)
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Demographic Implications
Goals for the demographic aspect of this
research are to
Further investigate trends in death rate at high
ages
Find data for other developed countries and compare
to US results
Determine if they support the hypothesis that
the retrograde response is being activated
Investigate how diseases and chronic
inflammation play a role in this behavior
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Summary
This research uses
Biodemographic
changes in gene
Analysis
expression with age to
support fuzzy logic
Network
Fuzzy
and network
Model
Models
modeling, then see if
Gene Expression
those results help
Data
explain biodemographic trends
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Dissertation Proposal
Vicious Cycle
Retrograde Response
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Credits
 Thanks to
 My advisor, Prof. Andres Kriete
 Dr. Kriete’s team who conducted the gene expression studies
(Nirupama Yalamanchili, William Beggs, Kelli Mayo, Ulrich
Rodeck)
 Team of Dr. Barabasi (Northwestern University) for data sharing
 Dr. William Bosl (Harvard Medical School) for Bionet
 My dissertation committee
 Drs. Aleister Saunders (Bioscience), Aydin Tozeren
(Biomed), Bahrad Sokhansanj (Biomed), Donald McEachron
(Biomed), Longjian Liu (Public Health)
 Questions?
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