Modeling of Human Aging using a Systems Approach Dissertation proposal for Glenn Booker June 5,...

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


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


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


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)


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)


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


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


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)


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



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)


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


▼ ATP - Synthase▼ ATP - Synthase

▼ Enzymes

▼ 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



▼ ATP - Synthase▼ ATP - Synthase

▼ Enzymes




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)



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)



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


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


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


Vicious Cycle Model

ROS

biosynth

ATP


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



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



ROS

ATP

NF-kB

biosynth


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


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



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



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


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



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)



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



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)



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


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


Biodemographic Analysis

Vicious Cycle

Retrograde Response

Network Model

Fuzzy Models


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?

Modeling of Human Aging using a Systems Approach Dissertation proposal for Glenn Booker June 5,...

Documents

Transcript of Modeling of Human Aging using a Systems Approach Dissertation proposal for Glenn Booker June 5,...