Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_2796_Библиотеки_им_академика_М_И_Перельмана
.pdf
viii ◾ Content s
https://t.me/medicina_free
AUTOIMMUNE T CELLS ARE THOUGHT TO BE ERRORS 93
WE EXPLORE THE IDEA THAT T CELLS CAN HELP TO REMOVE
HYPER-SECRETING MUTANTS
T CELLS CAN TELL THE DIFFERENCE IN ANTIGEN BETWEEN
NEIGHBORING CELLS
AUTOIMMUNE SURVEILLANCE CAN ELIMINATE ANY MUTANT, AND
CAN DO SO WITH A LOW KILLING RATE
SURVEILLANCE CAN DESCEND TO AUTOIMMUNE DISEASE IN SEVERAL
98
WAYS
ENDOCRINE TISSUES THAT RARELY GET AUTOIMMUNE DISEASE ARE
PRONE TO DISEASES OF MUTANT EXPANSION
EXERCISES
NOTE 109
REFERENCES 109
103
96
93
97
99
chaPter 5 ◾ Inammation and Fibrosis as a Bistable System 111
INTRODUCTION 111
INJURY LEADS TO INFLAMMATION, WHICH GOES TO EITHER HEALING
OR FIBROSIS
INFLAMMATION INCLUDES A MASSIVE INFLUX OF IMMUNE CELLS AND
ACTIVATION OF MYOFIBROBLASTS
MATHEMATICAL MODEL FOR MYOFIBROBLASTS SHOWS BISTABILITY 114
THE MACROPHAGE-MYOFIBROBLAST CIRCUIT PROVIDES TWO
FIBROSIS STATES AND A HEALING STATE
INJURY AND INFLAMMATION CAN BE MODELED BY A TRANSIENT
INFLUX OF MACROPHAGES
THE TIME WINDOW FOR STOPPING INFLAMMATION IS DUE TO
BISTABILITY 122
THE LONG TIMESCALE FOR SCAR MATURATION AND HEALING IS DUE
TO THE SLOWDOWN NEAR AN UNSTABLE FIXED POINT 125
STRATEGIES FOR PREVENTING AND REVERSING FIBROSIS 125
EXERCISES 127
NOTE 133
REFERENCES 133
111
112
118
121
Contents ◾ ix
https://t.me/medicina_free
Part III Aging and Age-Related Diseases
chaPter 6 ◾ Basic Facts of Aging 137
AGING IS DEFINED BY RISK OF DEATH AND DISEASES THAT RISES
WITHAGE 137
AGING HAS NEARLY UNIVERSAL FEATURES 139
GENETICALLY IDENTICAL ORGANISMS DIE AT DIFFERENT TIMES 143
LIFESPAN CAN BE EXTENDED IN MODEL ORGANISMS 144
LIFESPAN IS TUNED IN EVOLUTION ACCORDING TO DIFFERENT
LIFESTRATEGIES 145
MOLECULAR THEORIES OF AGING FOCUS ON CELLULAR DAMAGE 148
DNA ALTERATIONS IN STEM CELLS CAN ACCUMULATE FOR DECADES 148
DAMAGED AND SENESCENT CELLS BRIDGE BETWEEN MOLECULAR
DAMAGE AND TISSUE-LEVEL DAMAGE 152
REMOVING SENESCENT CELLS IN MICE SLOWS AGE-RELATED
DISEASESAND INCREASES AVERAGE LIFESPAN 154
EXERCISES 155
REFERENCES 158
chaPter 7 ◾ Aging and Saturated Repair 160
A THEORY FOR AGING BASED ON SATURATING REMOVAL OF DAMAGE 160
SENESCENT-CELL DYNAMICS IN MICE CAN TEST THE MODEL 163
THE SATURATING REMOVAL MODEL CAN EXPLAIN SENESCENT CELL
DYNAMICS 166
ADDING NOISE TO THE MODEL EXPLAINS THE VARIATION BETWEEN
INDIVIDUALS IN SENESCENT-CELL LEVELS 169
THE SATURATING REMOVAL MODEL CAPTURES THE VARIATION
BETWEEN INDIVIDUALS 169
GOMPERTZ MORTALITY WITH SLOWDOWN IS FOUND IN THE MODEL 172
THE HUMAN GOMPERTZ LAW IS CAPTURED AS WELL 174
THE SATURATING REMOVAL MODEL EXPLAINS AGING PATTERNS ALSO
IN ORGANISMS THAT LACK SENESCENT CELLS 176
RAPID SHIFTS BETWEEN HAZARD CURVES 177
SCALING OF SURVIVAL CURVES 178
APPROACHES TO SLOW DOWN AGING AND AGING-RELATED DISEASES 181
EXERCISES 183
NOTES 189
FURTHER READING 190
REFERENCES 190
x ◾ Contents
https://t.me/medicina_free
chaPter 8 ◾ Age-Related Diseases 192
DISEASES CAUSED BY THRESHOLD CROSSING OF SENESCENT CELLS
HAVE AN EXPONENTIAL INCIDENCE CURVE
DECLINE OF INCIDENCE AT VERY OLD AGES IS DUE TO POPULATION
HETEROGENEITY
THE MODEL DESCRIBES WELL THE INCIDENCE CURVES OF
AGE-RELATED DISEASES
CANCER INCIDENCE CURVES CAN BE EXPLAINED BY THRESHOLD
CROSSING OF TUMOR GROWTH AND REMOVAL RATES
MANY INFECTIOUS DISEASES HAVE AGE-RELATED MORTALITY
A THEORY FOR IPF, A DISEASE OF UNKNOWN ORIGIN 202
STEM CELLS MUST SELF-RENEW AND SUPPLY DIFFERENTIATED CELLS 205
INCIDENCE OF IDIOPATHIC PULMONARY FIBROSIS CAN BE EXPLAINED
BY STEM-CELL REMOVAL EXCEEDING PROLIFERATION
SUSCEPTIBILITY TO IPF INVOLVES GENETIC AND ENVIRONMENTAL
FACTORS THAT INCREASE STEM-CELL DEATH
IPF IS MATHEMATICALLY ANALOGOUS TO ANOTHER AGE-RELATED
DISEASE, OSTEOARTHRITIS
REMOVING SENESCENT CELLS CAN REJUVENATE THE INCIDENCE OF
AGE-RELATED DISEASES BY DECADES
EXERCISES 214
FURTHER READING 219
REFERENCES 219
194
195
210
213
192
198
201
206
209
chaPter 9 ◾ Periodic Table of Diseases 220
PERIODIC TABLE OF DISEASES 220
CELL TYPES CAN BE CLASSIFIED BY ABUNDANCE AND TURNOVER 221
THE TABLE SHOWS BROAD PATTERNS OF DISEASES 223
CANCER RISK RISES ALONG THE DIAGONAL OF THE TABLE 224
SECRETORY CELLS SHOW THREE ZONES: TOXIC ADENOMAS,
AUTOIMMUNE DISEASES, AND CANCER
THE THREE-ZONE PATTERN CAN BE EXPLAINED BY CIRCUIT MOTIFS 226
THE PERIODIC TABLE EXPLAINS AUTOIMMUNE DISEASES OF
NON-ENDOCRINE CELLS 228
PERMANENT TISSUES HAVE DEGENERATIVE DISEASES OF FAILED
MAINTENANCE 229
BARRIER TISSUES EXHIBIT IMMUNE HYPERSENSITIVITY DISEASES 230
225
Contents ◾ xi
https://t.me/medicina_free
INFECTIOUS DISEASES OCCUR MAINLY IN BARRIER TISSUES 231
FRONT-LINE TISSUES GET PROGRESSIVE FIBROTIC DISEASES OF
OLDAGE
CIRCUIT MOTIFS UNDERLIE THE DISEASE PATTERNS IN THE TABLE 235
AGE OF ONSET AND LIFETIME RISK SHOW PATTERNS IN THE TABLE 235
PREDICTED DISEASES IN THE TABLE 238
EXERCISES 241
REFERENCES 242
EPILOGUE: SIMPLICITY IN SYSTEMS MEDICINE, 244
ACKNOWLEDGMENTS, 249
INDEX, 251
233
https://t.me/medicina_free
DOI: 10.1201/9781003356929-1
Introduction
https://t.me/medicina_free
, I’ U A, from the Weizmann Institute in Israel. In my PhD in
H
physics I looked for patterns in turbulent mixing; what I loved about physics were the
moments of seeing, in complex systems, suddenly an angle where things looked simple.
Toward the end of my PhD, I looked for subjects where the physics way of thinking
might help to nd new laws of nature. I didn’t know much about biology – the only thing
I knew about proteins was what I read on the back of a cereal box. en a friend gave me
a textbook on cell biology. Here was matter that was alive! I fell in love with biology and
resolved to see if there are laws to be found.
I wasn’t alone. Luckily, Stanislas Leibler took me on as a postdoc, where I met other
physicists who shared the vision of biological principles, Naama Barkai and Michael
Elowitz. I was encouraged by biologists like Arnold Levine, Yosef Yarden, and Benny
Geiger. Before long, I had my own group back in Israel.
In the rst decade, we focused on understanding the cell, with its networks of interacting proteins. At the time, around 2000, there was massive information on which protein
interacts with whom, but it was hard to make sense of this information: the networks
of interactions looked hopelessly complex. We discovered that these networks are simpler than they appear. ey are made of a handful of recurring basic circuits which we
named network motifs. ese motifs show up again and again in dierent systems and in
all organisms, and each has its own computational function. e basic circuits inside the
cell are described in my book An Introduction to Systems Biology.
en, 10 years ago, I saw a poster in the elevator in my building that changed everything.
e poster announced a talk by Yuval Dor, saying that glucose makes the cells that control
it, called beta cells, both grow and die. is is a paradox – why does glucose do two opposite things to the same cells? It reminded me of a paradox I had studied in bacteria, where
enzymes do a reaction and also its reverse. I felt I could do something in an exciting eld –
human hormone circuits.
is was an opening to a new phase in my research career. I fell in love again with
human medicine and physiology, and how physics-style thinking can help make sense of
our bodies in health and illness.
1
2 ◾ Syste ms Med icine
https://t.me/medicina_free
I’m excited to start this book with you, on systems medicine.
My wish is that some of you will feel the same way – that you can do something in this
eld – and join us.
Our topic is physiological circuits that describe how cells and organs communicate with
each other. Rather than circuits inside a cell, we will discuss circuits of communication
between cells. is level of description is relevant to some of the most common and deadly
diseases that plague humanity.
It’s good to think about the goal of the book. e goal is to start from basic principles
or laws and derive why physiology is built the way it is, why specic diseases happen,
and which new strategies might treat them.
By the end, you will be able to use simple and powerful mathematical models to describe
physiological circuits. e models are powerful because they turn details into useful understanding and new ways to think about medicine. We will understand the fundamental
causes of some of the most mysterious diseases: diabetes, autoimmune diseases, and agerelated diseases such as lung brosis and cancer.
Our trajectory begins with basic principles. From these, we will derive the circuits and
their fragility to disease. We will explore, in three parts, hormone circuits, immune circuits,
and aging and age-related disease. Our story culminates in a periodic table of diseases.
ABOUT MATH AND BIOLOGICAL TERMS
I write this book with a heterogeneous readership in mind. Your background might be
biology, engineering, physics, math, computer science, medicine, or other subjects, as an
undergraduate, graduate, or researcher.
For some of you the following equation is familiar, whereas others need brushing up:
dx
We will use this equation to describe the removal of cells, whose number is x. e rate of
removal is
α
; you can think about this as the probability per unit time that a cell dies. Since
cells are only removed in this equation, and not added, their number declines. In fact, x(t)
declines exponentially with time, t, starting from their initial number x(0), as given by the
solution
You can check this solution by taking the derivative , and since a derivative of an expo-
nential e
−αt
is −αe
−αt
, you get our equation back, =−
at’s the level of the math in the book, ordinary dierential equations that describe
changes over time. You can skip the equations and still enjoy the book or go into them and
learn ways of thinking about modeling.
dt
xt
= xe
()
=−αx
0
()
−αt
dx
dt
dx
dt
α
x.
Introduction ◾ 3
https://t.me/medicina_free
Similarly, for some readers, biological terms like beta cells and hormones are familiar;
for others, they are new. I’ll assume no prior knowledge and use minimal jargon – biologists and physicians will see much fewer gene names than they are used to. I’ll explain
terms when needed. A hormone is a molecule secreted by a set of cells into the blood, where
it reaches and aects cells in distant parts of the body; beta cells are the cells that secrete
the hormone insulin.
I’d also like to say what this book is not. It is not a book on medical bioinformatics,
the gathering and statistical interpretation of biomedical data. is eld, sometimes also
called systems medicine, is described in books listed at the end of this chapter. We will
not discuss applications of machine learning or articial intelligence to medicine. We
will, however, use large medical datasets in this book to test our mechanistic models.
is book is also not exhaustive, and I provide further reading below. e purpose of the
book is to provide a way of thinking, and the examples are chosen to clearly demonstrate
principles.
OUR FIRST FEEDBACK LOOP
is book is also written to be fun. So let’s jump right in! Here is our rst feedback loop
(Figure 0.1). A person can be in a relaxed state of mind. e relaxed state is good for learning and memory. In the relaxed state, our body behaves in specic ways. For example, we
take slow deep breaths.
e wonderful thing is that we can decide to take a deep breath, and this increases the
chances that we enter the relaxed state. Because the relaxed state is good for learning, we
will practice taking nice deep sighs of relief in this book from time to time. Let’s practice
now: you don’t have to, but if you do, I promise you will enjoy it. Let’s all together take a
nice deep sigh of relief.
e next chapter gives a taste of our approach and teaches basic concepts within a fascinating bit of physiology. So let’s take a nice deep sigh of relief – here we go!
FIGURE 0.1 e relaxed state is better for listening, learning, and remembering.
Relaxed
state
better listening
learning, memory
Deep
breathing
4 ◾ Syste ms Med icine
https://t.me/medicina_free
BACKGROUND READING
is book combines several disciplines – Systems Biology, Evolutionary Medicine,
Mathematical Physiology and Dynamical Systems. ese disciplines are exemplied by
the following books which are recommended reading if you want to go deeper.
Stearns, Stephen C., and Ruslan Medzhitov. 2015. Evolutionary Medicine. 1st edition. Sunderland,
Massachussetts: Sinauer Associates is an imprint of Oxford University Press.
Strogatz, Steven H. 2015. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology,
Chemistry, and Engineering, Second Edition. Boulder, CO.
Keener, James, and James Sneyd, eds. Mathematical physiology: II: Systems physiology. New York,
NY: Springer New York, 2009.
Alon, Uri. An introduction to systems biology: design principles of biological circuits. CRC press, 2019.
A dierent approach to systems medicine focuses on statistical analysis of large datasets. It is
described in the following books.
Wolkenhauer, Olaf. Systems Medicine: Integrative, Qualitative and Computational Approaches.
Academic Press, 2020.
Schmitz, Ulf, and Olaf Wolkenhauer, eds. Systems Medicine. Springer, 2016.
Loscalzo, Joseph, ed. Network Medicine. Harvard University Press, 2017.
Yan, Qing. Translational Bioinformatics and Systems Biology Methods for Personalized Medicine.
Academic Press, 2017.
Bai, J.P.F. and J. Hur (Editors). Systems Medicine, 2022.
I
https://t.me/medicina_free
Hormone Circuits
5
Соседние файлы в папке Библиотека им академика М.И. Перельмана