modeling of human aging using a systems approach dissertation proposal for glenn booker june 5, 2008
TRANSCRIPT
Modeling of Human Aging using a Systems Approach
Dissertation proposal for
Glenn Booker
June 5, 2008
Glenn Booker Dissertation Proposal 2
Presentation Outline
OverviewAging TheoriesGene Expression DataFuzzy Modeling Approach
Vicious Cycle ModelRetrograde Response Model
Network Theory of AgingDemographic ImplicationsSummary
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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 gene-disease networks and in biodemographics
Glenn Booker Dissertation Proposal 4
Overview
There are three main elements of this research Cellular system modeling using fuzzy logicInvestigation 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 dataThis 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 longerLike 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 itselfBasis 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 lifeA 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 cross-sectional study of varying age subjects (age 17 to 94), and identified genes which are significantly (>2.5 or <1/2.5) up- or down-regulatedOf 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 ageROS did not increase with ageGlycolysis activity increases with ageATP and biosynthesis decrease with age Inflammation and apoptosis-inhibiting genes
increased with ageMany 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
▲Glycolysis▲Glycolysis
▲ Calcineurin▲ Calcineurin
▲Akt▲Akt IkBαIkBα IkBβIkBβ
▲ NF-kB▲ NF-kB
▼ PTEN▼ PTEN
▲Ca2+▲Ca2+
▼ Δψm▼ ATP▼ ATP
Krebs Cycle
ETC▼ Δψm
ETC▼ Δψm
? NADH? NADH
▼ Biosynthesis▼ Biosynthesis
? ROS? ROS
Retrograde Response - Differential Gene ExpressionRetrograde Response - Differential Gene Expression
ApoptosisApoptosis
▲ BCL2L1BCL6
▲ BCL2L1BCL6
InflammationInflammation
▲ CytokinesInterleukinsComplement
▲ CytokinesInterleukinsComplement UPSUPS
OxidatedProteins
OxidatedProteins
▲ Glucose Proteins▲ Glucose Proteins
▼ Ribosomal +Structural Proteins
▼ Ribosomal +Structural Proteins
▼ ATP - Synthase▼ ATP - Synthase
▼ Enzymes
▼ NADH -Dehydrogenases
▼ NADH -Dehydrogenases
▲Glycolysis▲Glycolysis
▲ Calcineurin▲ Calcineurin
▲Akt▲Akt IkBαIkBα IkBβIkBβ
▲ NF-kB▲ NF-kB
▼ PTEN▼ PTEN
▲Ca2+▲Ca2+
▼ Δψm▼ ATP▼ ATP
Krebs Cycle
ETC▼ Δψm
ETC▼ Δψm
? NADH? NADH
▼ Biosynthesis▼ Biosynthesis
? ROS? ROS
Retrograde Response - Differential Gene ExpressionRetrograde Response - Differential Gene Expression
ApoptosisApoptosis
▲ BCL2L1BCL6
▲ BCL2L1BCL6
InflammationInflammation
▲ CytokinesInterleukinsComplement
▲ CytokinesInterleukinsComplement UPSUPS
OxidatedProteins
OxidatedProteins
▲ Glucose Proteins▲ Glucose Proteins
▼ Ribosomal +Structural Proteins
▼ Ribosomal +Structural Proteins
▼ ATP - Synthase▼ ATP - Synthase
▼ Enzymes
▼ NADH -Dehydrogenases
▼ NADH -Dehydrogenases
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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
Up-regulated
Down-regulated
No change
Shape Legend:
Reaction types are: ap = activator-product, ip = inhibitor-product, sp = substrate-product
Krebs Cycle (Oxphos)
ROS SOD7.ap8.ip
ATP3.ap0.01
2.ap0.01
18.sp0.03
5.sp0.01
Mitochondrial Electron
Transport Chain (ETC)
DYm
16.ip10.
Bio-synthesisATP demand
1.sp7.0
All reaction rates not
stated are 1.0
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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
Krebs Cycle (Oxphos)
ROS SOD
UPS
7.ap
8.ip
IkB IkB
NF-B
Nucleus
Ca2+
Calcineurin
Akt
PTEN
ATP
13.ip0.5
12.ip
0.5
11.ip0.5
3.ap0.01
14.ap0.09
tbd
10.ip
6.ip
2.ap0.01
18.sp0.2
15.ip0.1
5.ap0.01
4.sp
9.sp
Mitochondrial Electron
Transport Chain (ETC)
m
16.ip
Bio-synthesisATP demand
1.sp7.0
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Fuzzy Modeling Goals
The goals of the fuzzy modeling portion of this research are toExpand 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 down-regulated 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 toConduct more detailed investigation of the
diseases associated with aging, as they relate to the connections with gene dysregulationSupplement 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
femaley = 0.0403x - 1.4812
R2 = 0.9977
maley = 0.0368x - 1.0667
R2 = 0.9975
-0.50
0.00
0.50
1.00
1.50
2.00
2.50
3.00
30 50 70 90
Age, yrs
Lo
g1
0(d
ea
th r
ate
)
logmale
logfemale
Linear (logfemale)
Linear (logmale)
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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
-0.10
-0.05
0.00
0.05
0.10
30 40 50 60 70 80 90 100
Age
Res
idu
al =
act
ual
-
esti
mat
ed
Rmale
Rfemale
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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 toFurther investigate trends in death rate at high
agesFind data for other developed countries and
compare to US resultsDetermine if they support the hypothesis that
the retrograde response is being activatedInvestigate how diseases and chronic
inflammation play a role in this behavior
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Summary
This research uses changes in gene expression with age to support fuzzy logic and network modeling, then see if those results help explain biodemo-graphic trends
Gene Expression Data
Biodemographic Analysis
Vicious Cycle
Retrograde Response
Network Model
Fuzzy Models
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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?