Envejecimiento Toward a Control Theory

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Toward a Control TheoryAnalysis of Aging

Michael P. Murphy1 and Linda Partridge2

1Medical Research Council Dunn Human Nutrition Unit, Cambridge CB2 0XY,United Kingdom; email: [email protected]

2Centre for Research on Aging, University College London, Department of BiologLondon WC1E 6BT, United Kingdom; email: [email protected]

Annu. Rev. Biochem. 2008. 77:77798

First published online as a Review in Advance onMarch 4, 2008

The Annual Review of Biochemistry is online at

biochem.annualreviews.org

This articles doi:10.1146/annurev.biochem.77.070606.101605

Copyright c 2008 by Annual Reviews.All rights reserved

0066-4154/08/0707-0777$20.00

Key Words

cell metabolic history, metabolic control analysis, nonspecific

damage

AbstractAging is due to the accumulation of damage over time that affecthe function and survival of the organism; however, it has prove

difficult to infer the relative importance of the many processes thcontribute to aging. To address this, here we outline an approac

that may prove useful in analyzing aging. In this approach, the funtion of the organism is described as a set of interacting physiologic

systems. Degradation of their outputs leads to functional decline andeath as a result of aging. In turn, degradation of the system ou

puts is attributable to changes at the next hierarchical level dowthe cell, through changes in cell number or function, which are

turn a consequence of the metabolic history of the cell. Within thframework, we then adapt the methods of metabolic control analy

(MCA) to determine which modifications are important for aginThis combination of a hierarchical framework and the methodol

gies of MCA may prove useful both for thinking about aging and fanalyzing it experimentally.

777

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Contents

INTRODUCTION... . . . . . . . . . . . . . . 778A HIERARCHICAL FRAMEWORK

FOR CONSIDERINGORGANISMAL AGING. . . . . . . . . 779

DYSFUNCTION OF

PHYSIOLOGICAL SYSTEMSDURING AGING . . . . . . . . . . . . . . . 780

CHANGES IN CELL NUMBER,

FUNCTION, AND

PHENOTYPE DURINGAGING . . . . . . . . . . . . . . . . . . . . . . . . . 781

Changes in Cell NumberDuring Aging . . . . . . . . . . . . . . . . . 783

Changes in Cell Function andPhenotype During Aging . . . . . . 784

CELL METABOLIC HISTORY . . . . 784

Nonspecific Damage . . . . . . . . . . . . . 785Gene Expression

and Cell Signaling . . . . . . . . . . . . . 786

Consequences of Cell MetabolicHistory for Cell Survival,

Replication, and Function. . . . . . 787

OVERVIEW OF THEHIERARCHICAL

DESCRIPTION OF AGING . . . . 788QUANTIFICATION OF THE

FACTORS CONTRIBUTING

TO AGING . . . . . . . . . . . . . . . . . . . . . 788Metabolic Control Analysis

a n d A g i n g . . . . . . . . . . . . . . . . . . . . . 7 8 9

Application of MCA to Aging . . . . . 790Practical Considerations for

Applying MCA to Aging . . . . . . . 792

INTRODUCTION

Extensive biochemical, organismal, popula-tion, and comparative studies on aging have

focused on qualitative and, sometimes, quan-

titative assessment of traits that contribute tonormal aging. It is hence evident that aging is

caused by accumulation of damage, resultingfrom a lack of capacity to protect, maintain,

and repair somatic tissues over time (17).

However, attempts to determine which pa

ticular types of aging-related damage are keto loss of function have been largely unsu

cessful because the diversity of sources antypes of damage is great and can vary wit

tissue, organism, and age (2, 810). It coul

reasonably, be argued that the developmen

of a general description of aging is prematubecause we lack both detailed descriptive daand a sufficiently mature understanding of a

ing to produce realistic models of the procesHowever, we believe that there is value in th

development of conceptual frameworks thhelp direct attention to the kinds of data an

experiments that could move toward a morquantitativedescription andanalysisof the a

ing process.Before we outline our approach, we fir

consider the properties of normal aging thit must accommodate. In nature, avoidindeath often depends on finding food, avoidin

predators, keeping warm, and surviving infections. However, when these extrinsic ha

ards are largely eliminated, the intrinsic aging process generally still leads to loss

function and death (1, 4, 11, 12), althougsome organisms age slowly and, in some case

seem hardly to age at all (13, 14). Averagand maximal life span under controlled con

ditions are broadly predictable for a givespecies. This is a familiar fact but, neverth

less, both intriguing and informative. Agin

reduces the genetic contribution of an indvidual to the next generation and is hence di

advantageous; no genes have evolved to caudeath. Instead, aging occurs through we

and tear that leads to the progressive accumlation of damage. However, different kinds

organisms evidentlyavoid, repair or withstandamage to different extents and hence diff

in their rates of aging. This biodiversitycan bdue to different genomes. For instance, ma

imum life spans can vary by orders of magnitude among different species of mamma

and birds (4, 1517). In addition, mutationin single genes can greatly increase life spa

in the budding yeast Saccharomyces cerevisia

the nematode worm Caenorhabditis elegans,th

778 Murphy Partridge


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fruitflyDrosophila melanogaster,andthemouse

(1820). How the genome is expressed withina species can also dramatically affect life span.

For example, queens of social insects, such asants, bees, and wasps, tend to age much more

slowly than genetically identical worker castes

(21).Thus,thegenomeandhowitisexpressed

constrain mortality and life span.However, genetic constraints on life spanare relatively loose because, even for genet-

ically identical animals in a standardized en-vironment, there is considerable variability in

life span (12, 2225), possibly in part owing toheterogeneity in robustness among individu-

als from stochastic events at the level of thecell, tissue, and organism, despite their sim-

ilar genotypes and environments (12, 22, 23,25, 26). Finally, several environmental inter-

ventions, such as decreased temperature (27),lowered oxygen tension (28), and dietary re-striction (DR) (29, 30), can also increase life

span.A combination of genetic determina-

tion, environmental variation, and stochasticevents thus contribute to the probability of

dying at each age, the age-specific mortality,P(t).ThereisnoapriorireasonforP(t)tohave

anyparticulardependenceonageortobesim-ilar for different organisms. However, as first

noted for humans by Gompertz (31), in manyorganisms, including the standard laboratory

model organisms, nematode, fruit fly, and

mouse, mortality increases roughly exponen-tially over the main part of adulthood (6, 12,

13). Thus, a Gompertz plot of Ln P(t) againsttime is approximately linear over this region,

although there are many exceptions (13).The challenges are to explain how normal

wear and tear at a metabolic level can accountforthegeneralpropertiesoforganismalaging,

how interventions act to alter life span, andwhy there is such variation in aging within and

between species. One obstacle is that muta-tions and environmental interventions affect

organisms at many levels of function, mak-

ing it difficult to pinpoint how an interven-tion affects aging. To illustrate, consider how

the poison cyanide leads to death. Is it due

Dietary restrictio(DR): decreasingthe nutrient supplyto organisms whileavoidingmalnutrition exten

life span

Age-specificmortality [P(t)]: tprobability of deathfor an organism atany given age

Metabolic controlanalysis (MCA): aexperimentalapproach developeto understand howcontrol is distribut

within metabolicpathways andnetworks

to inhibition of cytochrome oxidase? Loss of

mitochondrial proton motive force? Defec-tive ATP synthesis? Loss of control over ion

gradients in the cell? Defective action of acti-nomyosin? Defective muscle cell contraction?

Poor blood pumping by the heart? Clearly,

eveninasimple,acutesituation,itisnotpossi-

bletosaywhatkilledanorganismwithoutfirstdelineating the interacting biochemical andphysiological entities and considering eachin-

dependently to determine what precisely canlead to death. Similar complexities in biolog-

ical processes at different levels of organiza-tion should be addressed to understand how

normal aging occurs and to link biochemicalalterations to changes in function and in mor-

tality. A second major issue affecting our un-derstanding of aging is to detemine the rela-

tive significance or ranking of different typesof damage and biochemical modification fornormal aging. To address these two issues we

have developed a hierarchical description ofhow the various levels of organization within

an organism can contribute to aging. We thenuse this framework to show how it may be

possible to quantify and rank the factors thatcontribute to aging by applying concepts de-

rived from metabolic control analysis (MCA).Over the next few sections, the description

of the aging organism as a hierarchical sys-tem with interacting levels of organization is

developed.

A HIERARCHICAL FRAMEWORKFOR CONSIDERINGORGANISMAL AGING

We first place the physiological and metabolic

processes of an organism into interacting hi-erarchies so that it is clear how biochemical

alterations affect aging. In the top level ofthe hierarchy, all of an organisms functions

are ascribed to a set of physiological systems

that interact with each other and the environ-ment (Figure 1). Each physiological system

is considered to be a black box that onlycommunicates with other systems and the en-

vironment through inputs and outputs. The

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Organism

Environmental inputsIndependent outputsOutputs that are functions of inputs

System A System B

Figure 1

Interacting physiological systems are shown schematically as twointeracting systems. These systems are affected by inputs from theenvironment and by inputs from other physiological systems. System

outputs can originate within the system independently of other inputs orcan be dependent on the inputs from the environment and/or othersystems. Organisms can be divided into physiological systems in a numberof different ways; for example, human physiology can be described using11 systems: skin, respiratory system, circulatory system, central nervoussystem, endocrine system, reproductive system, lymphoid system,musculoskeletal system, urinary system, digestive system, and specialsense organs (33, 34). The increased mortality of the organism as it ages isdue to the decline over time of various systems outputs. These can beintrinsic where the system outputs become inappropriate during agingbecause of alterations within the system. Dysfunction in system outputscan also occur for undamaged systems when the inputs are inappropriate.

outputs can occur independently of external

inputs to the system, or they can be a func-tion of inputs from other systems or from

the environment (Figure 1). This leads to aninteracting network of physiological systems

that, in principle, gives a complete descrip-tion of the organisms functions. Although

each system is functionally discrete, interact-ing only through inputs and outputs, physi-

cal separation is not necessary. For example,the immune system is largely composed of in-

dividual cells that distribute throughout thebody and that can infiltrate other tissues dur-

ing inflammation and aging (32) but will still

only connect with other systems through out-puts such as cytokine release.

Each system can, in principle, be de-scribed by phenomenological models with-

out any knowledge of its internal workin

(Figure 1). System aging occurs over timthrough its output becoming inappropriat

thus impairing organismal function and elvating mortality rate. Damage within the sy

tem causes intrinsic dysfunction, leading

inappropriate system outputs. An undamage

system can also exhibit extrinsic dysfunctiowhen its outputs are inappropriate solely bcause the inputs from other physiological sy

tems are inappropriate. For this black box dscription, it is not necessary to understan

how the damage that leads to intrinsic dyfunction arises.

The progressive dysfunction with aging the organism is therefore due to dysfunction

ing of some or all of its systems as defineby their outputs. Of course, the practical di

ficulties of defining a physiological systemof knowing all the interactions, and of a

cidentally omitting unknown or unsuspecte

interactions are enormous. Even so, thinkinof aging in this way can be useful both as

heuristic exercise and to assist in identifyinfruitful avenues for experimental work. In th

next section, we consider the types of systedysfunction that can contribute to aging.

DYSFUNCTION OF

PHYSIOLOGICAL SYSTEMSDURING AGING

System function generally declines with aging;examples include humankidney glomer

larfiltration rate (35); musclestrengthin micflies, and humans (36, 37); mammalian neu

rological function as measured by memoformation (38, 39); -cell insulin secretio

in humans (40); and the mammalian immunsystem (41, 42). Studies of functionally iso

lated systems, such as pancreatic islets (40show that intrinsic system dysfunction can o

cur, but in vivo it is usually not clear if d

cline is due to intrinsic damage or defectivinputs. For example, stem cell activity in mi

declines on aging, but can be ameliorated bparabiotic pairings, whereby a young and a

old mouse share a circulatory system (43



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suggesting that at least part of thedecline with

age depends upon systemic factors from otherphysiological systems.

Important questions are whether all sys-tems within an individual decline and whether

the relative declines in different systems con-

tribute similarly to mortality. From an evo-

lutionary perspective, natural selection mayadjust costly tissue maintenance to maximizereproductive success and thereby lead to sim-

ilar rates of functional decline for all tissues(44). Many or all physiological systems within

an organism would then wear out at sim-ilar rates, but with those rates determined

at the level of the individual system. In ad-dition, similar rates of functional decline of

systems within an organism could occur be-cause the outputs of a dysfunctioning sys-

tem caused dysfunction in connected systems(Figure 1), even though their intrinsic ratesof decline were different. An alternative ex-

planation for parallel rates of aging in differ-ent tissues would be the existence of an ag-

ing process common to differentphysiologicalsystems (45). Supporting this idea, mutations

in single genes can extend healthy life spanby ameliorating many forms of aging-related

damage(e.g.,1820),pointingtotheexistenceof a single common aging process. Further-

more, the evolutionary conservation of the ef-fects on aging of some of these mutations be-

tween yeast, worms, flies, and mice raises the

possibility of a similar underlying aging pro-cess in these very different organisms. Alter-

natively, the rate of aging of individual phys-iological systems could vary idiosyncratically,

according to genetic susceptibility and envi-ronmental and stochastic events, with no sin-

gle biological age attributable to an individualorganism (46). Clearly, some aspects of ag-

ing can be organ specific and caused, for ex-ample, entirely by differences in environment

as with skin aging and exposure to sunlight(47). Determining whether physiological sys-

tems within an individual organism decline in

function at similar rates, and whether declinein all systems or in only a few key systems

contributes to aging, is basic to understand-

Oxidative damagenonspecific damageto biologicalmolecules caused breactive derivativesof oxygen

ing aging. However, multiple traits in single

individuals are seldom investigated during ag-ing, and even fewer studies have examined

functional decline in different physiologicalsystems. Markers of aging have been investi-

gated, such as common changes in RNA tran-

script profiles during aging in different tissues

(37, 48) andthe accumulation of similar mark-ers of oxidative damage (49). However, therelevance of these markers to system function

is unclear (50).Measurements are needed of the rela-

tive rates of decline in physiological systemswithin individual organisms over time to com-

pare how they vary from individual to individ-ual within a population and how they respond

to interventions that extend life span. An evengreater challenge is in comparing the relative

importanceofdeclineinthefunctionofdiffer-ent systems to functional decline of the organ-ism and probability of death. At the moment

there are no methods to quantify, or even torank, the relative roles of the functional de-

clines of different physiological systems in thefunctioning and probability of death of the

whole organism. Consequently, although ag-ing can be ascribed to the relative decline in

various systems, as is outlined in Figure 2,which shows that changes in system function

lead to alterations in P(t), considerable chal-lenges remain in quantifying how the various

systems contribute to normal aging. In addi-

tion, so far each system has been treated asa black box. To understand how dysfunction

occurs within systems during aging, we lookat the next level down in the hierarchy at the

constituent cells of the systems.

CHANGES IN CELL NUMBER,FUNCTION, AND PHENOTYPE

DURING AGING

Many kinds of changes to cells occur during

aging, but only those that affect the functionaloutputs of physiological systems will influence

aging (Figure 2). Most changes in the outputsof systems are due to alterations in their con-

stituent cells. Even changes outside cells, such



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P(t)Age-specificmortality

Changes in thephysiologicalsystem

Changes in cellpopulation andfunction

Cellmetabolichistory

Changes incell function

Cell death

Senescence

Proliferation

Changes incell number

Protectivemechanisms

Gene expression/cell signaling

Nonspecific damageOxidative damage

RadiationProtein unfolding

Changes insystem function

Figure 2

Aging of an organism is due to the decline in function of the top-level physiological systems into whichthe organism has been divided. This dysfunction leads to changes in system outputs that have a greater olesser influence on the age-specific mortality [P(t)], as indicated by the variable width of the arrowslinking to mortality. Dysfunction of each system is in turn due to changes in either the number orfunction of its constituent cells. The changes to cells are caused by their metabolic history and are due tnonspecific damage and to changes in signaling pathways and gene expression. These in turn lead toeffects on cell function and on cell number.



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as in the extracellular matrix or exoskeleton,

can often be ascribed to changes in cell func-tion. For example, the accumulation of ex-

tracellular debris such as fatty plaques can beassigned to the dysfunction of macrophages.

Bone is a dynamic systemcontrolled by the ac-

tivity of osteoclasts and osteoblasts. The form

of structures that no longer contain livingcells, such as tooth enamel or hair, are ascrib-able to the cells that constructed them. Even

so, some age-related modifications, such asglycation-mediated loss of elasticity of blood

vessel walls or damage to lens proteins, may bedifficult to ascribe to cell function. However,

these modifications occur at the same hierar-chical level as cell changes; therefore, they af-

fect system function in much the same way aschanges to cell function. Thus, the functional

declines of physiological systems during agingare caused by changes in cell number or func-tion or by changes in noncellular components

of the systems.

Changes in Cell NumberDuring Aging

During aging many tissues undergo changes

in cell number owing to cell death and to dis-ruption of the mechanisms that maintain cell

number (51). The change in cell content withaging varies tremendously with tissue and or-ganism, with both cell loss and hyperplasia

possible (51), as well as infiltration of somecell types, such as adipocytes or lymphocytes,

into other tissues (32, 42, 52).In postmitotic tissues, including most tis-

sues in adult flies and all tissues in nematodesapart from the gonad, cells that are lost are not

replaced (5356). In mammals, therefore, thenumber of cells in many postmitotic tissues

decreases with age, and this cell loss often cor-relates with a decline in system function. For

example, the loss of functioning glomeruli in

aging kidney correlates with decreased organfunction with age (35). However, cell loss with

aging is not general for all postmitotic tissues.One example is the mammalian brain where

there is no evidence for neuron loss with age

(38,39),eventhoughintheadultbrainthereisvery limited capacity to replace lost cells (57),

and this is restricted to the dentate gyrus andhippocampus (58, 59).

In mitotic tissues when differentiated cellsare lost, they can be replenished by divi-

sion of other differentiated cells (e.g., mam-malian liver) or from a pool of pluripotent,self-replenishing stem cells (e.g., mammalian

gut endothelium, fly ovary) (60, 61). How-ever, many differentiated cells in mitotic tis-

sues divide less readily with age in vivo, withan increasing proportion entering replicative

senescence (51, 6264). For example, in oldbaboons, more than 15% of skin fibroblasts

exhibit markers of senescence (62). Yet, theextent to which the accumulation of senescent

cells contributes to diminished mitotic capac-ity and to decreased cell function with aging is

uncertain. In addition, mammalian stem cells

are less effective at replacing lost cells as theorganism ages (57, 60, 65). However, it is un-

clear if decline in stem cell function causes de-cline in number of differentiated cells. There-

fore in mitotic tissues, cell number can declineon aging through increased cell death, dimin-

ished replacement of lost cells, or both.Cell number can also increase with age.

For example, the number of adipocytes in hu-man visceral adipose tissue increases (52), and

the increased tissue mass can raise produc-tion of proinflammatory cytokines that influ-

ence the function of other systems (52). Cell

hyperplasia also occurs in many tissues dur-ing aging, producing nodules that can dis-

rupt system function (e.g., 66, 67) and thatcan also develop into cancers. Indeed, aging

has been described as the most potent of allcarcinogens. Metastatic cancers can undergo

extensive genetic and epigenetic alterations,change their location in the body, and inter-

fere in diverse ways with the function of otherphysiological systems. Similarly, some cells

of the immune systems also infiltrate tissues,and during aging a proinflammatory state de-

velops (e.g., 6870) whereby several tissues



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are invaded by various classes of immune

cells, whichcontribute to pathogenesis duringaging (e.g., 71, 72).

Change in cell number during aging de-pends on physiological context and on the

signals and contacts received in vivo and is

hence difficult to study in vitro. More experi-

ments are needed to measure change in cellnumber in tissues in different systems dur-ing aging as well as in different individuals

and species. It is also important to determinewhether changes are due to increased loss or

defective replacement, which is often unclear.Most importantly, it is vital to determine how

much cell loss or gain can occur before it im-pairs system functions and thereby determine

how important change in cell number is fornormal aging.

Changes in Cell Functionand Phenotype During Aging

Changes in cell function with age could also

affect system outputs. A decline in cell func-tion with age is often found, for example, in

the synaptic transmission in neurons (38) andthe rates of contraction by musculoskeletal

motor units (36). The finding that decline inneuronal function in the aging mammalian

brain is associated with decreased numbersof synaptic connections and conduction, butnot with decreased cell number, indicates

the importance of loss of function indepen-dent of cell loss (38). Cell phenotype has

been investigated more often than has cellfunction. For example, muscle fiber size de-

creases, with a decrease in myofilament num-bers and poor packing of sarcomeres in both

fly and human muscle (73). Neurons shrinkand have fewer spines and dendrites, lower

synapse concentrations, and myelin dystro-phy (38). Changes in gene expression, in-

creased numbers of senescent cells, morpho-

logical changes, and accumulation of damagemarkers with age (e.g., 49) can also occur.

With age, there is a gradual divergence of cellphenotypes within a tissue, perhaps because

of stochastic events (24, 74, 75). For example,

gene expression varies more between individ

ual cardiomyocytes in aging mouse heart thin young cells (73, 76), and small changes

expression of many genes in the kidney cumulatively correlate with a small change

cell function (32). However, the relevance these markers to loss of cell function is un

clear. More importantly, the ways in whicdeclines in cell function cause systemdysfuntion have not been investigated systematical

Changes in cell number and functiowithin a system are probably intimately r

lated. Loss or dysfunction of cells with agcould have deleterious effects on the remain

ing cells in the system, because they are likeon average to be working harder, spendin

more time trying to restore themselves thomeostasis, and thus increasing the prob

bility of cell death or dysfunction. In addtion, senescent and other damaged cells ca

have a deleterious bystander effect by secre

ing factors that enhance local inflammatioand tissue structure changes that may lead

more cell death (51). These mechanisms mexplain why loss and dysfunction of cells in

system should accelerate with age.Therefore, changes in cell number, fun

tion, or phenotype can contribute to aginby altering the function of their physiolo

ical systems (Figure 2). However, there aconsiderable uncertainties as to how these a

terations may contribute to system functioand thus to mortality. To understand the bio

chemical processes that lead to the loss of cel

and the change in function that occur durinaging, we have to drop down to the next lev

in the hierarchy, that of the individual cell.

CELL METABOLIC HISTORY

Cell dysfunction and death are attributabto the cells metabolic history, which is

combination of the initial state of the ce

and subsequent cumulative changes. Thewill lead on to the changes in cell fun

tion and number that affect system functioand thereby mortality (Figure 2). The in

tial state of the cell is due to its genom



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developmental history, physical niche occu-

pied within the organism, and epigenetic fac-tors that affect genome expression (77). All

these factors combine to lead to a particu-lar state of its functional components. The

cells subsequent metabolic history can cause

permanent DNA sequence modification, al-

ter gene expression, and change the func-tional machinery of the cell by nonspecificdamage, posttranslational modification, and

environmental factors. These factors com-bine to determine the cells intrinsic proba-

bility of dying, proliferating, or malfunction-ing over time. Similar factors affect the func-

tion of noncellular components of systems,such as the extracellular matrix, that change

their function on aging. Extrinsic factors fromother cells and the environment will also alter

the cells chances of dying or malfunctioning.Factors clearly identified as important in thecells metabolic history are nonspecific dam-

age and changes in gene expression and sig-naling pathways.

Nonspecific Damage

Accumulation of diverse forms of nonspecific

damage to biomolecules with age is a majorcontributor to cell loss and dysfunction. Ox-

idative damage has attracted the most inter-est (49, 78, 79), but processes such as thermaldenaturation, misincorporation of monomers

into biopolymers, radiation, and inappropri-ate chemical reactions are also likely to be

important. These damage processes are aninevitable consequence of carrying out thou-

sands of chemical reactions in an enclosedspace containing many reactive molecules, re-

sulting in a range of damaged andpoorlyfunc-tioning biomolecules and subcellular struc-

tures during aging.A range of processes reduces formation

and duration of damaging agents, protects

the cell against the damage, and repairsor degrades the altered target biomolecules.

Increased steady-state levels of damagedbiomolecules could reflect elevated genera-

tion of damage, decreasedrepair and turnover,

or a combination of both. Some types of dam-

age, such as a misfolded protein, can in prin-ciple be dealt with by the cell, whereas oth-

ers cannot, such as fixation of a DNA mu-tation or accumulation of damaged material

that can be neither broken down nor removedfrom the cell. With damage repair, there is a

steady-state balance between impact of dam-age and its avoidance, repair, removal, orsequestration. Thissteady statecould theoret-

ically be set to prevent accumulation of non-specific damage to a level that affects function

by devoting a sufficientamount of thecells re-sources to maintain itself indefinitely. For ir-

reversible damage, which cannot be repaired,the cell could also decrease damage by com-

mitting more resources to prevention. How-ever, there may be no evolutionary advantage

in devoting resources to maintain an undam-aged somatic cell indefinitely at the expense of

reproduction, and mechanisms that prevent,

repair, or degrade damage are hence limiting,leading to cell dysfunction during aging.

Damage accumulation to cellular lipids,proteins, and nucleic acids during aging is

abundantly documented (e.g., reviewed in 49,8083). Furthermore, a role for nonspecific

damage in normal aging is supported by stud-ies where life span is increased by reducing

damage by, for example, overexpressing heatshock proteins in worms (84, 85) and increas-

ing antioxidant defenses in the mouse (86).Autophagy is essential for some forms of life

span extension in C. elegans (87). However,

caution in interpretation is warranted becauseit is often unproven that these manipulations

increase life span by decreasing damage accu-mulation, rather than by altering other pro-

cesses such as signaling pathways.Nonspecific damage, such as oxidative

damage, can impair the activities of enzymes,the fluidity of membranes, or the activity of

organelles (49), and thus impair cell function.However, to demonstrate a role in normal ag-

ing is less straightforward because we needto know whether damage affects cell survival

or function in vivo sufficiently to affect the

outputs of its physiological system and hence



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IGF: insulin-likegrowth factor

aging.For example, mutation to nuclear DNA

is an important candidate contributor to ag-ing because it is irreversible and it does oc-

cur during aging. However, its contributionto cellular systems and organismal aging is

still debated (88). Mutations to mitochondrial

DNA also accumulate with age (89), but their

role in cell dysfunction and organismal agingis more questionable because the normal mu-tation load to mitochondrial DNA during ag-

ing may be insufficient to explain functionaldecline (90). Yet, there have been few detailed

studies of the chain of events from accumu-lation of damage to biomolecules, to the ef-

fects on cell functions, and hence cell sur-vival or function, through to the physiological

system and aging. Cells may have consider-able thresholds for the accumulation of dam-

age before function or survival is impaired,and because a major consequence for cells ofdamage accumulation is death, damage could

have a major impactbut leave no obvious traceamong living cells in the organism.

Gene Expression and Cell Signaling

Changes in gene expression and cell signaling

during normal aging could contribute to ag-ing by affecting the rate of accumulation of

cell nonspecific damage or by independentlyaltering pathways that directly affect the abil-ity of the cell to function or survive. These

changes would then act to influence the out-puts of physiological systems in such a way as

to affect mortality (Figure 2).There is an extensive literature showing

changes in gene expression and cell signal-ing pathways in cells during aging (9196).

RNA transcript profiles have revealed a num-ber of changes in gene expression in mouse

muscle (91), including decreases in expres-sion of genes encoding proteins involved in

energy metabolism and increases in expres-

sion of stress response genes (91). In additionthere is increased stochasticity and variabil-

ity in expression between cells (73, 76). Thereare no systematic decreases in the expression

of defense, protective, or repair pathways, and

in fact, increases are often seen instead (91

consistent with a response to increased damage on aging. Thus, a systematic downregu

lation of protective pathways does not seeto account for aging, although it is possib

that increased variability in gene expressiomay lead to stochastic decreases in protectiv

systems in individual cells.Cellular signaling pathways are intimateinvolved in extension of organismal life spa

by single gene mutations and environmental interventions. For example, DR, a mod

erate reduction in food intake while avoiding malnutrition, extends life span in diver

organisms, including budding yeast, nemtodes, fruit flies, and rodents (97). Furthe

more, intensive study of DR in rodents hshown that it delaysor ameliorates theimpac

of multiple forms of damage, dysfunctioand disease (29). Although it is unclear if th

mechanisms by which DR extends life spa

are evolutionarily conserved, recent work himplicated several evolutionarily conserve

signaling pathways in the response to DRincluding the nutrient-sensing target of r

pamycin signaling pathway (98100) and thinsulin/insulin-like growth factor (IGF) pat

ways (101, 102). Mutations in genes encodincomponents of these same signaling pathwa

can also extend healthy life span in yeast (e.g103105), C. elegans(19), Drosophila (99, 106

and mouse (107, 108).The implication is that the altered acti

ity of these pathways ameliorates the kinds

damage that are normally limiting for organismal life span. For example, extension of li

span by reduced insulin/IGF signaling is ofteassociated with up-regulation of cellular pat

ways that increase the activity of stress resitance and cellular detoxification pathways

C. elegans, Drosophila, and mouse (e.g., 109111). However, interventions such as DR ar

associated with decreased damage accumulation, but this does not seem to be cause

by an increase in defensive pathways, manof which actually decline during DR (112

114). These interventions might also alt

the threshold for the amount of cell damag



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tumor load (51). The relationship between

cell damage, function, and survival and theireffects on aging are complex. For example, al-

though oxidative damage is often assumed tobe a major contributor to aging, in some mod-

els such as in mice heterozygous for the mito-

chondrial antioxidant enzyme manganese su-

peroxide dismutase(130)andin the long-livedrodent the naked mole rat (131), the relation-ship between oxidative damage and life span

is the opposite of that predicted. Thus, whatis required is a way of linking types of damage

and changes in cell signaling and gene expres-sion within cells to changes in cell survival and

cell function. In turn, these alterations need tobe linked to changes in system functions that

affect mortality (Figure 2). It seems probablethat, for example, large amounts of damage to

some cell types may be unimportant for mor-tality because they can withstand higher levelsof damage or because the damage does not af-

fect critical cell functions and system outputsthat affect aging. In contrast, small amounts

of damage in other cells may be critical foraging. More studies are required to measure

how factors such as damage affect outputs andhow these in turn affect aging.

OVERVIEW OF THE

HIERARCHICAL DESCRIPTIONOF AGING

The hierarchical description of aging is sum-marized in Figure 2. The increase in P(t) of

the organism with time is due to system dys-functions that lead to changes over time in

their functional outputs. These changes havea greater or lesser influence on the P(t), as in-

dicated by the variable width of the arrowslinking to mortality. System dysfunction is,

in turn, due to either changes in the numberor in the function of its constituent cells or

equivalent components, owing to metabolic

history. Interventions can only affect agingby changing system functions that alter the

P(t) of that organism, and many changes thatoccur during aging will have no impact on

mortality.

The hierarchical approach helps clarify th

contribution of various factors to aging, provides a framework for discussing aging that

internally consistent, and accommodates thfact that different tissues and organisms ma

age through quite different pathways. Eve

so, considerable challenges remain, particu

larly in determining which factors withinhierarchy are most critical for aging and thuwhere we should focus interventions and e

perimental effort. Approaches that can quantify, or at least rank, the contributions of va

ious factors to aging at various levels of thhierarchies are required. How the hierarch

cal description can be extended to be quantfied in ways that are useful for experimenta

ists investigating aging is the topic of the nesection.

QUANTIFICATION OF THEFACTORS CONTRIBUTING

TO AGING

The hierarchical description of aging prvides a framework that helps us pose appr

priate questions about the kinds of processthat contribute to aging and clarifies how bi

chemical alterations within cells impact oaging through their effects on physiologic

systems (Figure 2). However, even when cosidering the changes that occur during aging in a hierarchical context, we continual

come up against the problem of how to dtermine whether a process contributes to ag

ing or not. Even if it does, the challenges athen to quantify or rank the relative contribu

tions of different processes, at different levein the hierarchy, to aging within an organis

and determine how these contributions vawithin and between species and are affected b

interventions that affect life span. There armany questions central to aging that requir

a quantitative answer. For example, we wou

like to be able to assess the relative impotance of different physiological systems an

system outputs for aging and to know whethcell dysfunction or loss of cells during aging

more important. For cell loss, is accumulatio



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of senescent cells, loss of stem cells, or the

increased rate of cell loss most important?Which processes within a cell contribute most

to its loss of function or inabilityto replicate asit ages? What forms of nonspecific damageare

of most importance to the decline in cell func-

tion during normal aging? What is required

is a way of incorporating quantification, or atleast ranking, into the hierarchical approachwe have described to determine which system

or process does contribute to aging, whichis most important, and, hence, where inter-

ventions are likely to have most impact onaging.

If they existed, robust, quantifiable mod-els of aging might be useful to determine the

processes of greatest significance. However,modeling aging is problematic at several lev-

els. An immediate hurdle is that our lack ofdetailed knowledge of the processes involvedmakes modeling of aging premature. A further

challenge is the dynamic aspect; during aging,the systems themselves change with time, and

any modeling approach has to accommodatethis. In addition, time lags are likely to be im-

portant. Processes such as cell loss may occurdecades before they affect function or proba-

bility of death. Therefore, although there area number of interesting approaches under de-

velopment to model aging (e.g., 2, 810), ro-bust and quantifiable models of aging that can

rank the importance of systems, cells, and cel-

lular components to aging are not yet on thehorizon.

Therefore, we require an empirical ap-proach that would enable us to identify the

factors that contribute to aging and to deter-mine the relative importance of these factors

to aging in experimental animals, despite ourincomplete knowledge of the system and lim-

ited means of intervening to alter the rate ofaging. We think a promising way to do this is

to adapt aspects of metabolic control analysis(MCA). In the following sections, we describe

MCA and show how it may be used to design

and interpretexperiments, usingcurrent tech-nologies to answer important questions about

aging.

Metabolic Control Analysisand Aging

MCA was initially developed independently

by Kacser & Burns (132, 133) and Heinrich& Rappoport (134) and has since been de-

veloped and used to describe the control and

regulation of a range of metabolic pathwaysand networks (135139). In considering the

control of a metabolic pathway by MCA, thefirst step is to develop an explicit definition

of the limits of the system and of the measur-ablevariables,such as metabolicintermediates

and pathway fluxes. Importantly, apart fromclearly defining its limits, there is no require-

ment for a complete description of the system,and sections of it can be treated as black boxes

to accommodate measurable variables. Once

these measurable variables and their interac-tions are defined, the system is manipulated in

small ways, andthe changing relationships be-tween the variables reveal the extent to which

each step is controlling.Consider the analysis by MCA of a sim-

ple metabolic pathway of intermediates con-nected by enzyme-catalyzed reactions to ad-

dress which enzymatic steps exert control overthe overall pathway flux. To do this, the activ-

ity of each enzymatic step in the pathway isvaried very slightly, independently of changes

in other components of the pathway, and theeffect of this on the overall flux is determined.

This simple example yields several interesting

conclusions. A major one is a simple defini-tion of control, where the greater the change

in the overall flux on altering the activity ofan enzyme, then the greater the control of

that enzyme over flux. However, this changein overall flux will be the result of the change

in enzymeactivityin thecontextof the system,as altering its activity impacts on the over-

all flux by changing the concentrations of themetabolic intermediates that link it to the rest

of the pathway. Thus, the control over fluxis a property of the pathway, not of the en-

zyme in isolation. An important consequence

of control being a system property is that sev-eral steps in a pathway can share control, and



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Increasing levelVariableDecreasing level

ABCIncreasingmortality

Mortalityreadout

Decreasingmortality

Levels lower thanseen in normal aging

Range of levelsin normal aging

Levels higher thanseen in normal aging

Normal aging

Figure 3

Analysis of aging by metabolic control analysis. Here a generic indicator of mortality, the mortalityreadout, is plotted against a variable that is both decreased and increased relative to its level in normalpopulations. Each value of the mortality readout is determined for a separate population in which thevalue of the variable is altered. The central shaded area indicates how the variable alters in normal aging.Three scenarios are shown. In curve A, the variable has no impact on aging as varying it over the rangethat occurs in normal aging does not affect mortality. At high and low levels, it does impact on mortality,

but because these occur outside the range found in normal aging, they do not contribute to aging. CurveB shows a variable that contributes to aging; increasing it in the central shaded area raises the level ofmortality, whereas decreasing it lowers mortality. In contrast, in curve C, increasing the variable in thecentral shaded area decreases mortality, and decreasing it increases mortality, as might happen if thisvariable were protective. For both curves B and C, the steepness of the slope as the curve passes throughthe point of unmodulated aging gives an indication of how controlling the two processes are over aging.In this example, the process described by curve B is more controlling over aging than that described bycurve C.

at either end of curve A. However, as this

increase in mortality occurs outside the rangeof values for that variable during normal ag-

ing, illustrated by the central shaded area, this

factor does not contribute to normal aging.The effect of a variable that is harmful to the

organism is shown in curve B. Decreasing theamount of this variable increases longevity,

whereas increasing it will lead to increasedmortality. Most importantly, the changes in

the amount of the variable that affect agingoccur within the normal range of the vari-

able during aging. At some point, decreasingthis variable further will impact on mortality,

leading to increasing mortality, but this occursoutside the normal range of variation during

aging. The effect of a variable that protects

against aging is shown in curve C. Decreasingthe amount of the variable is harmful, but in-

creasing it is protective, and most importantly,the changes in the amount of the variable that

are sufficient to affect the mortality readout

occur during normal aging. Many combina-

tions of these three curves are possible, butthese illustrate the critical aspects. The most

important point is that if a factor controls ag-ingthen thecurveof mortalityreadout against

the variable of interest has a measurable slope

as it passes through the region of the normallyaging population.

Figure 3 indicates how ideas from MCAcan be used to determine whether or not a

process contributes to aging. There is a well-developed mathematical apparatus for MCA

that can be used to quantify and rank the con-tribution of various factors to the control of

metabolic fluxes (135139). It is clear that thegreater the control of a process over aging the

steeper the slope of curves such as B and C asthey pass through the point of normal aging in

Figure 3. For example, it is clear that allowing

for appropriate normalization, the process incurve B has more control over aging than that

in curve C. Quantification and comparison



16/25

of the relative control of different factors in

MCA are done by comparing the normalizedfractional changes in an output, such as a flux,

with the normalized fractional changes in thefactors being varied. This leads to dimension-

less quantities called control coefficients that

enable the proportion of control over a flux

to be assigned to each process. Analysis ofthe control of aging requires similar carefulnormalization of the changes relative to the

endogenous levels of the variable, requiring adetailed development of the parallels between

the mathematical formalism of MCA and thecontrol of aging that will be described in fu-

ture publications. Even so, the approach out-linedin Figure 3 showshowMCAcanbeused

to determine if an intervention affects normalaging and to rank and quantify the contribu-

tions of factors to aging. In the next section,the practical aspects of carrying out these ex-periments are considered.

Practical Considerationsfor Applying MCA to Aging

Here we consider how studies, such as thosedescribed in Figure 3, could be done using

currently available animal models and tech-nologies. This approach requires (a) factors

that may impact on aging to be modulated bya series of small increments and decrementsin different populations of experimental ani-

mals and (b) the effects of these changes ona mortality readout for each population to be

determined. In the first instance, such experi-ments can be done with populations of nema-

todes or Drosophila because these are alreadyroutinely used in aging research and have the

advantages of a short life span as well as easeof genetic manipulation and measurement of

mortality.A number of mortality readouts could be

chosen,butmeasuringtheslopeofaplotofLn

P(t) against time for a population has a num-ber of attractions. Over much of the life span,

it is linear; consequently, a large number of in-dividuals in a given population contribute to

thereadout,it is already routinelymeasuredin

aging research in worms and flies, and it gen

erates a single number for each populatioNevertheless, other readouts of aging, such

median life span or the intercept of curves Ln P(t) against time with the y-axisor dea

within or by a certain time interval, may alprove useful.

The variable whose effect on aging is beininvestigated will have to be increased and dcreased very slightly (perhaps by as little as

few percent) and incrementally. The mortaity readout would be determined in a seri

of populations of flies or nematodes whethe variable was modified slightly. In add

tion, the range of values of the variable durinnormal aging would have to be measured.

the variable was a protein, then in nematodand Drosophila, its expression level could b

modulated downward by standard RNAi approaches and modified so as to decrease e

pression of the protein by only a few pe

cent. For a small increase in expression the same protein, a number of current ap

proaches can be adapted to generate strainwith slightly increased expression levels of th

target protein. After generating several diffeent populations, each with a slight variatio

in the expression level of the protein of interest, their mortality readouts would then b

measured and plotted against the level of thprotein of interest. If a type of damage, suc

as oxidative damage or accumulation of mifolded protein was of interest, then this cou

be increased slightly by addition of pharm

cological or environmental stressors or bdecreasing expression of protective enzyme

Damage could be decreased by addition protective agents or by increasing expressio

of protective proteins. These approaches cabe extended by selectively expressing the pr

teinsinonlyonetissueorphysiologicalsysteor by only changing expression at differen

stages during the subjects life spans. Similarthe effect of cell number in a system cou

be addressed by increasing or decreasing thexpression of toxic or protective proteins t

modulate slightly the number of cells in

tissue.



17/25

Consider some examples:the DAF2 recep-

tor is known to impact on the life span of ne-matodes, and its activity is thought to corre-

late inversely with life span. If the amount ofthis receptor was altered and compared with

a mortality readout, we might predict a re-

sult, similar to curve B in Figure 3. However,

it could be that the effects of changing theamounts of these receptors on life span onlyoccur with large-scale changes, and the curve

might look more like curve A. With increasedantioxidant defenses we might predict a curve

such as C in Figure 3; however, it could bethat the thresholds for effects on mortality

are such that there is no change relative tothe normal range over aging. Another inter-

esting type of damage is increased mtDNAmutation load, where high levels clearly lead

to an aging phenotype, but it is unclear if thisonly occurs because the dependence of mor-

tality on mtDNA damage is a curve of type

A. The approaches outlined should help de-termine whether a single factor can impact

on aging. Many interesting questions in ag-ing research arise from environmental inter-

ventions, such as DR, that alter life span, but

it is difficult to determine which of the many

changes that occur during DR are importantfor aging and which are not. This issue can beaddressed by the MCA approach by selecting

plausible factors that change in DR and ma-nipulating these independently to see if they

contribute to the changes in aging seen duringDR.

Thus, by developing the MCA approachesand applying them to currently available ex-

perimental models of aging using experimen-tal approaches that are already developed, we

should be able to address a number of criticalquestions about the factors that control aging.

SUMMARY POINTS

1. Aging arises from the accumulation of damage resulting from a lack of capacity to

protect, maintain, and repair somatic tissues over time. Accumulation of damage leads

to loss of function and, ultimately, death.

2. The rate of aging of individuals can vary as a result of genetic, epigenetic, and envi-

ronmental variation as well as of stochastic events.3. The accumulation of damage during aging occurs at multiple levels, from the physi-

ological system, through organs, to cells, and individual biomolecules. Not all of the

changes that occur with age are likely to be causal in loss of function and increasedlikelihood of death, and it is often difficult to determine which factors are important

for aging.

4. One way of clarifying causality with events occurring at multiple levels during aging

is to make explicit the hierarchical level under consideration and its relationshipto other levels. The mortality of the individual is ultimately due to the change in

function of its physiological systems. These system changes are caused by changes in

the number or function of its component cells. Cell changes are themselves due to themetabolic history of the cell and to its impact on the ability of the cell to function and

survive. The aspects of the metabolic history of the cell that are important for agingare the accumulation of nonspecific damage and changes in cell signaling and gene

expression.



18/25

5. Even when the hierarchical description has been adapted, the remaining critical ques-

tion in aging research is to develop methodologies that will enable the relative con-

tributions of various metabolic changes to aging to be quantified and related to eachother. What is required is a methodology that can be used to address these questions

in the experimental animal systems currently in use using technologies now avail-able. We suggest that adaptation of MCA to aging will enable significant progress in

determining the relative importance of the factors that contribute most to aging.

FUTURE ISSUES

1. Can we successfully adapt the methodologies of MCA to aging in model organisms

such as worms, flies, and mice so as to determine the relative contribution of variousfactors to aging? In doing so, is it possible to use current approaches such as RNAi

to manipulate the levels of factors that are thought to contribute to aging? Is it alsopossible to use this approach to determinethe pathways through which changes during

interventions such as DR occur?

2. If the MCA approach proves fruitful in aging research, is it able to contribute toward

answering critical questions, including: Can we quantify or rank the relative impor-tance of the functional outputs of different physiological systems that are important

for aging? Can we quantify the contribution of changes in cell number and functionto the alteration in a systems functional outputs over aging? Is it possible to quantify

or rank the importance of nonspecific damage and changes in gene expression and cellsignaling pathways as well as to determine how they affect cell function and survival

in vivo?

3. If quantification of the contribution of various processes to aging proves feasible, then

are the critical factors for aging similar or different for individuals within a population

and also between different species? Is the hierarchical description and application ofthe MCA approach helpful in developing new insights into aging and in suggestingnovel interventions that may affect aging? Can this approach be usefully extended to

aging-associated degenerative diseases and to other complex, multifactorial diseases?

DISCLOSURE STATEMENT

The authors are not aware of any biases that might be perceived as affecting the objectivity this review.

ACKNOWLEDGMENTS

We thank Meredith Ross for drawing the figures and Martin Brand, Judith Campisi, HelenCocheme, David Gems, Aubrey de Gray, Andrew James, Nils-Goran Larsson, George Marti

Richard Miller, Meredith Ross, and Thomas Von Zglinicki for helpful advice. We are gratefto the BBSRC, MRC, Wellcome Trust, and the European Communitys sixth Framewor

Program for Research, Contract LSHM-CT-2004-503116, for financial support.



19/25

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Annual Review of

Biochemistry

Volume 77, 2008Contents

Prefatory Chapters

Discovery of G Protein Signaling

Zvi Selinger p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p1

Moments of Discovery

Paul Berg p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 14

Single-Molecule Theme

In singulo Biochemistry: When Less Is More

Carlos Bustamante p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 45

Advances in Single-Molecule Fluorescence Methods

for Molecular Biology

Chirlmin Joo, Hamza Balci, Yuji Ishitsuka, Chittanon Buranachai,

and Taekjip Ha p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 51

How RNA Unfolds and Refolds

Pan T.X. Li, Jeffrey Vieregg, and Ignacio Tinoco, Jr. p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 77

Single-Molecule Studies of Protein FoldingAlessandro Borgia, Philip M. Williams, and Jane Clarke p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 101

Structure and Mechanics of Membrane Proteins

Andreas Engel and Hermann E. Gaub p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p127

Single-Molecule Studies of RNA Polymerase: Motoring Along

Kristina M. Herbert, William J. Greenleaf, and Steven M. Block p p p p p p p p p p p p p p p p p p p p149

Translation at the Single-Molecule Level

R. Andrew Marshall, Colin Echeverra Aitken, Magdalena Dorywalska,

and Joseph D. Puglisi p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p177

Recent Advances in Optical Tweezers

Jeffrey R. Moffitt, Yann R. Chemla, Steven B. Smith, and Carlos Bustamante p p p p p p 205

Recent Advances in Biochemistry

Mechanism of Eukaryotic Homologous Recombination

Joseph San Filippo, Patrick Sung, and Hannah Klein p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 229

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Structural and Functional Relationships of the XPF/MUS81

Family of Proteins

Alberto Ciccia, Neil McDonald, and Stephen C. West p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 25

Fat and Beyond: The Diverse Biology of PPAR

Peter Tontonoz and Bruce M. Spiegelman p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 28

Eukaryotic DNA Ligases: Structural and Functional Insights

Tom Ellenberger and Alan E. Tomkinsonp p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p

31

Structure and Energetics of the Hydrogen-Bonded Backbone

in Protein Folding

D. Wayne Bolen and George D. Rose p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 33

Macromolecular Modeling with Rosetta

Rhiju Das and David Baker p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 36

Activity-Based Protein Profiling: From Enzyme Chemistry

to Proteomic Chemistry

Benjamin F. Cravatt, Aaron T. Wright, and John W. Kozarich p p p p p p p p p p p p p p p p p p p p p p 38

Analyzing Protein Interaction Networks Using Structural Information

Christina Kiel, Pedro Beltrao, and Luis Serrano p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 41

Integrating Diverse Data for Structure Determination

of Macromolecular Assemblies

Frank Alber, Friedrich Frster, Dmitry Korkin, Maya Topf, and Andrej Sali p p p p p p p p 44

From the Determination of Complex Reaction Mechanisms

to Systems Biology

John Ross p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 47

Biochemistry and Physiology of Mammalian SecretedPhospholipases A2Gerard Lambeau and Michael H. Gelb p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p49

Glycosyltransferases: Structures, Functions, and Mechanisms

L.L. Lairson, B. Henrissat, G.J. Davies, and S.G. Withers p p p p p p p p p p p p p p p p p p p p p p p p p p p 52

Structural Biology of the Tumor Suppressor p53

Andreas C. Joerger and Alan R. Fersht p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p55

Toward a Biomechanical Understanding of Whole Bacterial Cells

Dylan M. Morris and Grant J. Jensen p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p58

How Does Synaptotagmin Trigger Neurotransmitter Release?

Edwin R. Chapman p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p61

Protein Translocation Across the Bacterial Cytoplasmic Membrane

Arnold J.M. Driessen and Nico Nouwen p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 64

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Maturation of Iron-Sulfur Proteins in Eukaryotes: Mechanisms,

Connected Processes, and Diseases

Roland Lill and Ulrich Mhlenhoff p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 669

CFTR Function and Prospects for Therapy

John R. Riordan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 701

Aging and Survival: The Genetics of Life Span Extension

by Dietary RestrictionWilliam Mair and Andrew Dillin p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 727

Cellular Defenses against Superoxide and Hydrogen Peroxide

James A. Imlay p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 755

Toward a Control Theory Analysis of Aging

Michael P. Murphy and Linda Partridge p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 777

Indexes

Cumulative Index of Contributing Authors, Volumes 7377p p p p p p p p p p p p p p p p p p p p p p p p

799

Cumulative Index of Chapter Titles, Volumes 7377 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 803

Errata

An online log of corrections to Annual Review of Biochemistry articles may be found

at http://biochem.annualreviews.org/errata.shtml

Envejecimiento Toward a Control Theory

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Transcript of Envejecimiento Toward a Control Theory