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Why this simplicity? Evolution may have converged on such understandable circuits
because they are the only kinds of designs that can work reliably. If there were three or
more dynamic variables essential to the function, and dozens of rate constants with no
separation of timescales, the system would be less predictable and can be sensitive to noise
inherent in biological systems. An example of such sensitivity is chaos. Chaos can never
occur in a two-variable circuit, as guaranteed by a theorem of dynamical systems called
the Poincare-Bendixson theorem. You can depend on a simple circuit to predictably do the
work, as long as parameters don’t cross a bifurcation point.
SIMPLICITY IN ETIOLOGY
In addition to simplicity in structure and in models, there is a third level of simplicity – the
ability to discover core drivers of diseases and explain them in terms of basic physiological laws. Some diseases have clear causes or etiology, such as the pathogens in infectious
diseases or germline mutations in rare genetic diseases. e origin of many other diseases,
however, is complex with multiple interacting genetic and environmental factors. e same
goes for aging, which is driven by numerous types of damage. e surprise is that one can
sometimes untangle this complexity and predict specic ultimate causes of aging and diseases. ese ultimate causes drive the more proximal dysfunctions.
To nd simplicity one must look for it, instead of assuming in advance that things are
irreducibly complex. We may end up failing, but it is a mistake not to try.
e rst conceptual step is therefore to avoid assuming that a disease is an accident
of genetics and environment. Instead, it is useful to assume that the disease is due to an
unavoidable fragility of a circuit motif. Each disease has a physiological counterpart, a process which protects the circuit from fragility. Disease occurs when the protection mechanism fails because a parameter crosses a threshold, taking the circuit beyond its design
specications. e behavior of the circuit then changes dramatically. Oen the change is
from stability of cell populations to instability – cell numbers shrink (degeneration, progressive brosis) or grow (tumors).
ese fundamental causes of many diseases can be deduced from the three physiological laws we discussed. Law 1, all cells come from cells, leads to exponential cell growth that
requires size control circuits. Size control circuits, however, are fragile to saturation eects
– law 2. When the circuit attempts to compensate for a parameter change, cell mass or
other mitigating factor can hit a carrying capacity, abrogating compensation. Homeostasis
is lost, as in prediabetes and hypothyroidism.
Size control circuits are also fragile to mutations that inevitably arise according to law
3 – cells mutate. Certain mutations cause a cell to mis-sense its regulatory signal. Such
mutant cells mis-interpret a normal level of signal as a high level. ese deluded mutant
cells behave like normal cells would if the signal was high, and therefore hyper secrete and
also outgrow their neighboring cells, threatening to take over the tissue and disrupt homeostasis. As a result, circuits must have protection mechanisms against mutant takeover.
One such mutant-resistance mechanism, biphasic control, programs the cells to kill
themselves if their input signal is too high, with the logic that mutant cells that mis-sense
the signal are thus eliminated. is, however, adds a fragility to naturally occurring high
Epilogue ◾ 247
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signal levels, which then can kill all of the cells. is occurs in late stage type-2 diabetes, in
which elevated glucose levels lead to glucotoxicity in beta cells.
Another proposed anti-mutant protection mechanism is surveillance by self-reactive
T cells. e T cells act as a police force that can weed out hyper-secreting mutant cells. In
certain individuals, this surveillance can tip into self-perpetuating autoimmune disease.
Examples are type-1 diabetes and Hashimoto’s thyroiditis.
e three laws also provide a theory for aging. Law 1, all cells come from cells, applies to
stem cells. Law 3 – cell mutate – also applies, leading to stem cells with altered epigenetics
that can give rise to damaged progeny cells. e damaged progeny become senescent cells
that set o systemic inammation and reduced regeneration. Law 2 – biological processes
saturate – leads to saturation of the removal mechanisms of the damaged and senescent
cells. When removal approaches its limit, senescent cell levels rise sharply. ey cause
systemic inammation and reduced regeneration that can push the parameters of circuit
motifs beyond their operating regime, until a circuit fails – causing the onset of a disease.
For example, reduced regeneration in frontline tissues can cause tissue collapse in susceptible individuals, providing the basis for progressive brotic diseases. Rising levels of
damaged cells with age also overloads (law 2) the innate maintenance programs that normally remove damaged cells, repair tissues, and ght cancer and infection. is overload
gives rise to degenerative diseases in permanent tissues and raises the risk of cancer and
infection with age.
ese theories of diseases are based on circuit motifs and are thus mathematically tractable. In this way, they are fundamentally dierent from prose descriptions. e mathematical approach predicts quantitative patterns like the shape of disease incidence curves
and the scaling of survival curves. ey predict the phases of seasonal oscillations and the
months-long trajectories in the aermath of stress and illness in hormone circuits. Because
the theories make quantitative predictions they can be easily falsied. e quantitative
predictions can be tested by new experiments and by large biomedical datasets, as we have
seen. e more falsication tests they pass, the more condence we have in the theories.
anks to their simplicity, these theories help to pinpoint, out of the sea of potential
interactions, those that are likely to be the most promising targets for treatment. For
example, the autocrine loop of myobroblasts stands out as a target for collapsing brosis.
Experiments inspired by this theory reveal the molecular players in the autocrine loop
and inhibit them as described in Chapter 5; as the theory predicts, inhibiting these factors
below a threshold reduces brosis.
Due to the universality of the theory, the same approach can potentially be translated
across organs, with dierent molecules. For example, inhibiting a myobroblast autocrine
loop inhibits brosis in both heart and liver. e same approach might even be translated
across pathologies – for example, from progressive brosis to cancer microenvironments
which also depend on myobroblast-like cells called cancer-associated broblasts.
Finally, simple theories are optimistic. ey suggest ways to sculpt the cell populations
of the cancer microenvironment, brosis, or autoimmunity, and push circuits back across
the threshold from disease to health. ey point to ultimate causes of aging, which can
be targeted to address all age-related diseases at once. Such optimism can help drive new
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experiments. e results are always more complex and fascinating than the original theory, increasing our understanding. is cycle of theory and experiment is also the seed of
novel strategies for treating disease.
REFERENCE
Adler, M. and R. Medzhitov. 2022. “Emergence of Dynamic Properties in Network Hypermotifs.”
Proceedings of the National Academy of Sciences 119(32): e2204967119.
Alon, U. 2003 Biological networks: the tinkerer as an engineer doi: 10.1126/science.1089072
Acknowledgments
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M collaborative, eective, and devoted partner for the gures and
A
computations. Nigel Orme did many of the gures representing anatomy. Ruslan
Medzhitov provided inspiration, ideas, and help, and commented on the dra.
I’m grateful to my former graduate students whose research is part of the backbone of this
book. Omer Karin, when did his PhD with me, was a pioneer in our transition from systems
biology to systems medicine. Many of the principles in this book arose in our joint work,
mainly biphasic mutant resistance and dynamic compensation of Chapters 2 and 3 and the
saturating removal model of aging in Chapters 6 and 7. Miri Adler and Shoval Miyara worked
on brosis as bistability in Chapter 5, Alon Bar and Avichai Tendler on seasonality in Chapter
3, Tomer Milo and Lior Maimon on bipolar disorder in Chapter 3, Yael Korem Kohanim on
the thyroid axis in Chapter 3 and the surveillance theory of autoimmune in Chapter 4, Itay
Katzir on the incidence of age-related diseases in Chapter 8, Pablo Szkeley on the mass longevity triangle in Chapter 6, and Yifan Yang on the saturating removal model in Chapter 7. Avi
Mayo participated in all of these. Additional research input came from Aurore Woller, Yuval
Tamir, Moria Raz, Michal Shilo, Hila Sheel, Tomer Landsberger, and David Glass.
Work with colleagues informed the book as well, with Ruslan Medzhitov in Chapters4
and 5, Johannes Dietrich Chapters 2, 3, and 4, Dan Jarosz, Nan Hao, Lev Tsimring Je
hasty Chapters 6 and 7. Amos Tanay and Neta mendelssohn Cohen provided Clalit data
for Chapters 3, 8, and 9. Valery Krizhanovsky and Amit Agrawal for senescence cell work
in Chapter 7. Stefan Kallenberger, Scott Friedman, Shuang Wang, Eldad Tzahor, Shimrit
Mayer, and Ruth Scherz-Shouval for brosis experiments in Chapter 5.
I wrote much of this book during a sabbatical at Stanford and the CZI biohub. Steve
Quake was the perfect host. Steve and Liquon Liu took the course based on the book dra at
Stanford bioengineering and provided great insights. Other colleagues who read the dra or
otherwise helped include Sisi Chen, Bo Wang, James Ferrell, Eytan Yae, Mike Grecius, Ed
Marti, Greg Huber, ea Tlsty, David Schnieder, Reviel Netz, and Wendell Lim.
My brother Gidi Alon was a superb partner to cra the narrative of early Systems
Medicine lectures, and my niece Roni Stok was an active participant in two courses,
Systems Medicine and Hormone circuits. Gefen Alon and Tamar Alon helped with song
lyrics. Galia Moran was always encouraging and supportive.
I thank the Weizmann Institute students in Systems Medicine 2019, 2020 and in
Hormone Circuits 2021, and Stanford students in BE333 2021, whose questions helped me
to be more clear. ank you to CRC press and Elliot Morrisa who kept me oriented.
249
250 ◾ Acknowledgments
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Michael Elowitz helped to design the graphics for the periodic table in Chapter 9. Hehosted
me, with Barbara Wold, to give a four lecture nanocourse at Caltech to test the book out,
which gave me a boost of motivation. Michael also read the entire book dra and edited it.
Some of his comments were hilarious.
Index
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Note: Bold page numbers refer to tables and italic page numbers refer to gures.
aberrant dynamics 8
acute response 50
Addison’s disease 95, 101
adenomas, toxic 225, 225–226
age of onset 236–238, 236–238
age-related disease 141, 144, 192, 193, 210–214,
213,214
incidence curves of 192–194, 193–194
incidence of 213–214, 213, 214
and lifespan 154–155, 154, 155
slow down aging and 181–183, 182
age-related mortality, infectious diseases
201–202,202
aging 137–143, 138–143
dynamics of 160
evolutionary theories of 145
molecular theories of 148, 149
population statistics of 148
saturating removal model for 160–163, 161, 162
slow down aging and aging-related diseases
181–183, 182
theory of 160–163
alpha cells, pancreatic 240
alveoli 202–203, 203–204
Alzheimer’s disease 229
amplifying cells, transit 227
antibodies 69, 81, 92, 98
antigens 92
from proteins 95
anti-interferon autoantibodies 239
anti-mutant protection mechanism 243
apoptosis 114
ascertainment bias 62
atherosclerosis 234
atopic triad 230
autoantibodies, anti-interferon 239
autoantigens 95
in cell-type-specic autoimmune diseases 96
autocrine loop 113, 114
autoimmune disease 90, 223, 226
cell types with 91
vs. mutant expansion disease 102
of non-endocrine cells 228–229, 228
organ-specic 91, 91
prone to diseases of mutant expansion 99–103
surveillance 98–99, 99
Autoimmune Surveillance of Hyper-secreting
Mutants (ASHM) 94, 94
autoimmune surveillance theory 94–95, 94, 95, 99,238
autoimmune T cells 89, 93
autoreactive T cells 94, 95
barrier tissues 225
exhibit immune hypersensitivity diseases
230–231, 231
infectious diseases occur in 233–235, 233
basin of attraction 118
Beta-cell-Insulin-Glucose (BIG) model 31, 31
dynamic compensation in 34, 34, 42–43
beta cells 9, 9, 10
compensation, prediabetes 35–37, 36
immune system kills 90
mutant 40, 40
organ size control in 29–30, 29–31
slow feedback loop on 26–28, 27
steady-state mass 32
bias, ascertainment 62
biological processes saturate law 36–37
biphasic mechanism 94, 94
biphasic responses 41
bipolar disorder
gland size uctuations 64–65
as stress-related disease 65–68, 65–69
timescales of 65
bistability 118, 118
cancer cells 198, 199
cancer
incidence curves 198–201, 199–201
risk 226, 226–227
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cancer-inducing eect 200
carrying capacity 116
cell mass dynamics, slow feedback loop of 30
cell populations, dynamics of 26–27
cells mutate 40
cell types
classication by abundance and turnover
221–223, 222
three-zone pattern 226, 226–227, 227
cell-type-specic diseases 91
autoimmune diseases, autoantigens in 96
chronic inammation 200
chronic stress 50
circuit motifs 32, 32–33, 33, 226, 226–227, 227,
235–236, 236
circuit-to-target approach 69
coecient of variation 142
cold brosis 120, 121, 127
comorbidity 230
congestive heart failure 234
cooperativity mechanism 96
corticotrophs 51
corticotropin-releasing hormone (CRH) 49
cortisol 48, 50, 50, 58, 59, 65, 67
Cushing’s syndrome 100, 101
damage producing units (DPUs) 160, 161, 162,
176,178
damped oscillations, in steroid withdrawal over
months 57–58, 58, 59
degenerative diseases 236
depression 64
diabetes 10–11
gestational 37
type-1 diabetes (T 1D) 10, 89, 90
type-2 diabetes (T 2D) 10, 37–39, 37–39
die, genetically identical organisms 143
disease-threshold model 196, 212
disposable soma theory 145
DNA
alterations in stem cells 148–152, 150, 151
chromosomes 148
repair enzymes 148
DPUs see damage producing units (DPUs)
dynamic compensation (DC) 33
in BIG model 34, 34, 42–43
ECM see extracellular matrix (ECM)
electronic health records (EHR) 62
emotional hyper-reactivity 65
endocrine organs/tissues 24, 99–103
organ-specic autoimmune disease 91, 91
endocytosis 119, 119
epigenetic changes 152
evolutionary stable strategy (ESS) 98, 105, 105
evolutionary theories of aging 145
excessive scarring 111
extracellular matrix (ECM) 121, 153
extrinsic mortality 137
feedback loop, nullcline 15, 15
broblasts (ber-forming cells) 113
brosis 111–112
idiopathic pulmonary (see idiopathic pulmonary
brosis (IPF))
strategies for preventing and reversing 125–127
brotic diseases of old age 233–235, 236
rst-passage time process 172
frailty index 141, 142, 143
front-line stem cells 206
front-line tissues 206
brotic diseases of old age 233–235, 235
genetically identical organisms die 143
Geroscience hypothesis 181, 237–238
gestational diabetes 37
gland size uctuations, bipolar disorder by 64–65
glucagon 10
glucocorticoid steroids 57
glucose
dynamics 34
half-life of 20–21
glucose concentration
controlled by insulin 8–10, 9, 10
and dynamics 7–8, 8
insulin resistance 17–18, 18
to variations in insulin sensitivity 25, 26, 27
glucose-insulin circuit, mathematical model for
11–14, 12, 14
glucose tolerance test (GTT) 8, 8
glucotoxicity 29, 37, 37, 38, 41
glycogen 10
Gompertz aging rate 172, 174
Gompertz law 139–140, 148, 172, 174–176, 175, 176,
193
Gompertz–Makeham law 140
Gompertz mortality, with slowdown in model
172–174, 172–173
growth factors 113
GTT see glucose tolerance test (GTT)
half-life
ACTH 49–50
cortisol 50
CRH 49–50
glucose 20–21
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insulin 13
thyroid hormone 79–80, 83
Hashimoto’s thyroiditis 89
hazard 137
hazard curves 139–143
rapid shis between 177–178, 178
heart attacks and stroke 234
helicases 148
hepatitis C virus 112
heterogeneity, population 194–195, 195
Hill functions 11–12, 20, 22–23, 96, 172
homeostasis 7
cell mass feedback circuit maintains 31, 31–32
hormone glands 89
hormone seasonality 60–62, 61
with antiphase and spring shi 62–63, 62–64
hormone systems 32, 32–33, 33
HPA axis
classic model for 51–52
circuit, drug targets to lower cortisol 69–70
feedback loop in glands 53, 53–55, 54
glands of 65
with noisy input 66, 66
oscillations 69
responds to physical and psychological stress
48–50, 49, 50
pathway 48
HPA model 69, 70
with white-noise input 67, 68
hyperbolic relation 25
hyper-secreting mutants, T cells remove 93–95,
94,95
hyper-sensing mutant cells 94, 95, 98
hyperthyroidism 226
hypoglycemia 7
IBD see inammatory bowel disease (IBD)
idiopathic pulmonary brosis (IPF) 198, 202–205,
203–204
age-related disease and osteoarthritis 210–212,
211, 212
incidence of 206–209, 207–209
susceptibility to 209–210, 210
immune system, kills beta cells 90
immune thrombocytopenic purpura (ITP) 228
infectious diseases, in barrier tissues 231–233, 232
inammaging 153
inammation 112
chronic 200
injury and 121–122
injury leads to 111–112, 113
time window for stopping 122–124, 124
inammatory bowel disease (IBD) 230
injury and inammation 121–122
innate immune receptor (TLR 7) 238
insulin
glucose concentration controlled by 8–10, 9, 10
half-life of 13
insulin-glucose feedback loop of minimal model 25,
26, 27
insulin resistance 13
glucose levels 17–18, 18
insulin sensitivity 13
glucose levels to variations in 25, 26, 27
between people 16–17, 17
integral feedback loops 31
IPF see idiopathic pulmonary brosis (IPF)
islets of Langerhans 9
ITP see immune thrombocytopenic purpura (ITP)
Kramer’s equation 173
laws of physiology 2, 26, 30, 41, 47, 49, 243, 248, 247
lifespan
age-related diseases and increases average
154–155, 154, 155
evolution 145–148, 146, 147
in model organisms 144–145, 144 –145
live fast die young strategy 146
liver hepatocytes 223
luciferase 163
macrophage-myobroblast 119, 118–121
macrophages 113, 118–124
mania 65, 65
mathematical analogy 211
mathematical model for myobroblasts 114–118
matrix metalloproteinases (MMPs) 121
metformin 181–182
MHC 92, 96
genes 98
variants 98
minimal model 31, 32, 51
insulin-glucose feedback loop of 25, 26, 27
mis-sensing factor 104
MMPs see matrix metalloproteinases (MMPs)
model organisms, lifespan in 144–145, 144–146
molecular theories of aging 148, 148
multi-objective optimality 147
multiple-hit hypothesis 199
multiple sclerosis 228
multistability 112
mutant 39–40, 40
biphasic (u-shaped) response curves protect
against 41, 41–42
cells 93–94
expansion disease vs. autoimmune disease 102
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mutant-resistance mechanism 243
myobroblasts 113
activation of 112–114
mathematical model for 114–118, 115–119
natural selection 39, 47, 90, 102, 148, 162, 163
neighboring cells, T cells dierence in antigen
between 96, 96–98, 98
net growth rate of beta cells 28
neurotoxicity 41
niche, protected 205
NK cells 153, 161, 162
non-alcoholic fatty liver disease (NAFLD) 112
non-endocrine cells, autoimmune diseases of
228–229, 228
nullclines 15, 15, 129
OFF state 118–121, 120, 122
optimality, multi-objective 147
organ-level feedback loops 42
organ size control 28, 52
organ-specic autoimmune disease 91, 91
osteoarthritis 210–212, 210–212
pancreatic alpha cells 240
parathyroid (PT) gland 99–101
parathyroid hormone (PTH) 32, 32–33, 100, 100
PDC see plasmacytoid dendritic cells (PDC)
periodic table of diseases 220, 220–221, 221
permanent cell types 222–223
permanent tissues, degenerative diseases 229,
229–230
phase portrait 14, 16, 17
photons 163
physical stress, HPA axis responds to 48–50, 49, 50
physiology, laws of 2, 26, 30, 41, 47, 49, 243, 248, 249
pituitary 49
plasmacytoid dendritic cells (PDC) 238
population heterogeneity 194–195, 195
population statistics of aging 148
p 18 promoter 163
prediabetes 7, 35
beta-cell compensation 35–37, 36
predicted diseases in table 238–241, 239, 240
primary sclerosing cholangitis (PSC) 234
programmed cell death 114
progressive brotic disease 234
protein
antigens from 95
translation 152
psoriasis of gut 230
psychological stress, HPA axis responds to 48–50,
49, 50
public T-cell repertoire 95
pulmonary brosis, idiopathic see idiopathic
pulmonary brosis (IPF)
rapid shis between hazard curves 177–178, 178
rate plot 29, 115
reactive oxygen species (ROS) 148
regulatory T cells 93, 97
risk of death 137–138, 138
robust dynamics 33–35, 34, 35
SAD see seasonal component of aective disorder
(SAD)
SASP see senescence-associated secretory phenotype
(SASP)
saturating removal model
aging patterns in organisms 176–177
captures the variation between individuals
163–166, 163–164
senescent cell dynamics 163–166, 163–166
scaling of survival curves 144, 178–181, 179–181
scar maturation, timescale for 125
scarring, excessive 111
sclerosis, multiple 228
seasonal component of aective disorder (SAD) 63
seasonality in hormones 60–62, 61
seasonal timescale, negative feedback loop 55,
55 –57, 56
secrete-and-grow circuit motif 34, 34, 35, 35, 44, 55,
55, 90, 95, 98, 104, 107, 228, 228, 234–236,
234, 238, 244, 246–248
secretory cells 225–226, 225
senescence-associated secretory phenotype (SASP)
152, 153, 212
senescent cells (SnC) 152
accumulation of 154
diseases caused by threshold crossing of 192–194
dynamics in mice 163–166, 163–166
levels, adding noise to model 169
in mice slows age-related diseases 154–155
between molecular damage and tissue-level
damage 152–154
removing 154–155, 154, 155, 213–214, 213
senolytic drugs 154, 182
separatrix 122
single-celled organisms 176
slow down aging and aging-related diseases 181–183
slow life strategy 146
SnC see senescent cells (SnC)
stable xed point 15
steady-state glucose 19–20
steady-state mass, beta-cell 32
stem-cell-based tissues 205
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stem cells
altered 152
DNA alterations in 148–152, 150, 151
front-line 206
removal exceeding proliferation 206–209,
206–209
renewal 150
steroids 48, 65,
glucocorticoid 57
stress
chronic 50
prolonged 58–59, 60
stress-related disease, bipolar disorder as 65–68,
stroke, heart attacks and 234
survival curves 137
scaling of 178–181, 179–181
T cells
autoimmune 89, 93
autoreactive 94, 95
biology 91–92, 92
65
65–69
dierence in antigen between neighboring cells
96, 96–98, 98
regulatory 93, 97
remove hyper-secreting mutants 93–95, 94, 95
T 1D see type-1 diabetes (T 1D)
T 2D see type-2 diabetes (T 2D)
telomerase genes 210
thyroid cells 89
thyroid stimulating hormone (TSH) 33
timescale for scar maturation 125
time window for stopping inammation 122–124
TIMP 1 126
tolerance mechanisms 93
toxic adenomas 225, 225–226
TPO proteins 92
transit amplifying cells 227
type-1 diabetes (T 1D) 10, 89, 90
type-2 diabetes (T 2D) 10, 37–39, 37–39
vicious cycle 38, 99
wild-type cells 104
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