guest blog post by Rolie Barth
Philip Ball has published an impressive study searching for the most
important processes controlling the long way from genes and cells to
genetic networks, tissues, organs and bodies. This implies he is talking
about eukaryotic cells and their development and evolution. I start this
blog with a short summary of the general picture he presents in his book.
Then I discuss the concept of circular causality in more detail using a
diagram to explain the multilayered nature of developmental processes.
Finally, I will give an example of such a process.
If I understand Ball correctly, he is arguing for a view of life in which –
in my own words – circular causality plays the leading role.
Unfortunately, he himself did not mention this concept explicitly. But
anyway, he frequently writes about top-down and bottom-up causality between
the different layers of organization in an organism.
Genes play an
important role in a multitude of complex processes, characterized not only
by linear causality (from bottom to top, from genes to tissue) but also by
feedback loops between the different levels of life: genes, networks, cells,
tissues, bodies and groups of organisms in ecological systems. All these
levels of organization are complex systems that are mainly characterized by
(my words) the laws of biophysical phenomena whose dynamical behavior can be
understood using the chaos theory. Even though Ball does not mention this
theory (well, he does mention the catastrophe theory once), he often uses
terminology from chaos theory, in particular: attractors. He describes
attractors as valleys or basins in a landscape of possibilities. They are
called attractors because the final state of a dynamical system is, as it
were, drawn to one of those valleys (one of the possible final states).
To
be honest, I might not have recognized this all-round scheme if I had not
written about it in part II of my book
De kosmos en het leven, een Meesterwerk, 2021 (The Cosmos and Life, a Masterpiece).
Thinking about complex processes from the perspective of circular
causality, the question of what is the most important part of the system
actually becomes irrelevant. If we want to find an organizing center, I
would talk about cell-centered life.
Considering the importance of the
concept of circular causality one could speak about the third secret of life
next to the DNA-code (first) and allostery (second, according to Jacques
Monod).
What is circular causality?
Many, many biological processes, as well as physical processes, are
characterized by feedback loops between parts of a system. To clarify my
point, think of the temperature control system of your home heating
system. The room temperature is measured and when it is lower than the
desired temperature the control unit sends a signal to the boiler to start
burning. Thus heating the water in the radiators, resulting in an increase
of the room temperature until it is higher than the desired temp. Then the
control unit switches off your boiler, and so on. So, information about
the room temperature is used by the control unit to regulate the boiler
and thereby the room temperature – and thus the causal loop is closed.
Because this feedback system is trying to minimize the difference
between actual and desired temperature we call this a
negative feedback. You find this kind of controlling systems on
many levels of your body, from gene regulation to homeostasis (all kind of
processes like controlling body temperature and glucose concentration in
the blood). Glucose concentration is maintained within some variation on a
certain level by two feedback systems, one controlled by insulin to
prevent glucose concentration getting too high and the second to watch
that it doesn’t get too low, controlled by glucagon.
Negative feedback is stabilizing all kinds of biological processes. But
organisms also use positive feedback for example to generate neural
impulses. Positive feedback means that a closed loop enhances an incoming
signal up to a maximum level. The best known example is the singing around
(buzzing) of a microphone signal, when some sound or even noise from the
speakers is picked up by the microphone. Global warming due to increase of
CO2-concentrations is a process with positive feedback too. More CO2 in the
atmosphere increases the global temperature resulting in permafrost to melt
partially. This leads to a release of greenhouse gasses CO2 and methane.
Causally this is a self-amplifying process and therefore a positive feedback
loop.
Combinations of feedback loops and moving particles
Both electronic systems and biological networks may have
combinations of negative and positive feedback loops. The most simple
version creates electrical signals or molecule concentrations that vary in
a waveform during time. One of them is our internal biological clock. More
complex combinations of positive and negative feedback loops may result in
a kind of memory, switching the output level of the system to a permanent
high level after an initial, short input signal.
Much more
fascinating things happen when positive and negative feedback loops are
part of systems with a large collection of moving particles, for example
biomolecules in a growing tissue. In physical, chemical, biological and
ecological systems interactions between ‘particles’ can give rise to all kind of patterns or structures.
Examples are: sand ripples, mackerel clouds (physical), the
Belousov–Zhabotinsky reaction (chemical), skin patterns, limb structures
(biological) and large scale stripes and spots in semi-arid areas
(ecological) etcetera. To illustrate what is meant we will take a look the
way skin patterns can be described by mathematical models. These were
first described by Alan Turing in 1952 and applied to biological processes
by Hans Meinhardt and Alfred Gierer since 1974. Philip Ball discusses
Turing patterns in chapter 8 (p. 307-328, printed version) which shows
many parallels to my own book chapters 13 (p. 197-201) and 15 (p.
264-273).
Turing patterns like zebra stripes are examples of
self-organization. At the start of such processes two or more
species of molecules are randomly distributed in the fluid between cells
of a developing tissue. So the initial situation is one without any
pattern. Specific interactions between the two types of molecules, called
morphogens, ánd differences in diffusion velocities may lead to all kinds
of patterns (see p. 198 De kosmos en het leven).
In these
systems, one kind of morphogen is working as an activator: it promotes the
production of its own type of morphogens. The activator therefore has a
self-amplifying effect. In addition, the activator promotes also the
production of inhibitor morphogens. The activator therefore acts as a
catalyst for production of new activators and inhibitor molecules. On the
other hand, the inhibitor molecules de-activate the production of new
activator molecules. So we see two kinds of feedback: (1) positive
feedback of the activator (green, figure 2 below) and (2) negative
feedback of the inhibitor (red).
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Figure 2
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Pattern formation occurs because morphogens A and B diffuse through the
fluid between cells, with different speeds. A random, local increase of
concentration activator A, communicates via cellular mechanism to gene A
to raise its production, ultimately leading to more activator molecules
and more inhibitors in the intercellular space. That would make no
difference except when the burst of inhibitors B spreads much faster than
the activators A. The result of this is a slowing down of the production
of gene A in cells further away from the initial fluctuation (see figure
3). This leads to a decrease in activator concentration in these areas. So
the initial random fluctuation leads to the formation of a wave pattern in
the intercellular space, finally resulting in a fixed and stable pattern,
stripes, dots or more complicated shapes.
Looking at these
processes two remarkable things show up. The two populations of many
morphogens manifest a collective behavior in the form of waves, extending
across a whole tissue. And these waves are changing the behavior of genes,
far down in the cell nucleus. So there is a top-down causation from
pattern to genes. Just like the traffic jam as a whole restricts your
behavior as an individual car driver. And of course it also works the
other way around – individual genes are influencing the wave form. And
furthermore those waves are phenomena resulting from physical mechanisms.
So, in these processes, gene expression is controlled by physics! In my
book I suggested to call this extra-genetic regulation.
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Fig. 3 Concentrations of activator (green) and inhibitor (red)
versus position at different times.
A random fluctuation
in the activator concentration
starts up a process of wave
formation, resulting in a stable pattern.
|
Mathematical models can be used to simulate the processes of limb and
finger pattern-formation. Finger patterns correspond to a wave of a
morphogen called SOX9. At positions where the concentration of SOX9 rises
above a genetically defined threshold this morphogen induces cell
differentiation: mesenchymal stem cells transform into chondrocytes
(cartilage cells). So the wave pattern in combination with gene activity
induces a condensation of this pattern.
What does it all mean?
This kind of morphological processes has been recognized in many different
stages of embryonic and fetal development. In combination with physical
mechanisms, positive and negative feedback loops between genes and their
products can lead to pattern formation starting from a structureless
situation. One could wonder what is the central feature leading to pattern
formation? The genes, the morphogens, the laws of motion, the feedback
loops, the cells? The answer may be strange: none of them is thé central
part of the system. All of them are indispensable, be it that positive
feedback is the generating ‘force’ of the process.
One of the most
important conclusions is that genes neither program for a pixel like
description of biological patterns nor for a kind of
positional information
– where genes are coding at which position a stripe of a pattern should
start and end (as theorized by Lewis Wolpert). No, genes are coding for
processes of pattern formation. Genes are not creating patterns, they
are providing suitable parameters of pattern formation processes, which are
physical of origin and therefore universal. Mutations may tune gene
interactions in such a way that patterns and structures in the course of
generations are optimally robust and flexible.
In short: genes don’t
code for pattern pixels, nor for pattern global positions but for a pattern
process. This means Ball is right in saying that genes don’t code for a
blueprint. But genes located on DNA strings, transfer much of the
information needed for pattern formation to next generations. So genes are
indispensable as the memory of life and as tools for the variation of life
forms.
In many chapters of his book, Philip Ball demonstrates that these kind of
processes can be identified at all levels of biological organization:
genetic networks, cell fate decision, tissue formation, pattern and
structure formation.
Final conclusion: Philip Ball has written a very illuminating book about
How Life Works. I read a lot of new interesting things about known
biological processes. I agree with his frequently repeated statement that
genes don’t code for a blueprint of structures and shapes. The analysis of
complex systems driven by feedback loops shows that genes work in
combination with physical mechanisms for the formation of biological
structures. Ball is right to emphasize that there is no linear route from
genes to pattern. Causal interactions are running up and down, from
pattern to genes and the other way around. But these multilevel
interactions do not deny the importance of genes but emphasize them.
Unfortunately, Ball does not give much attention to this fact, sometimes
actually denying this. Since the causality of these formation processes is
circular in nature it is impossible and unnecessary to discuss which of
the components is most important. A gene centered view is ruled out by
this view of life. And because feedback loops are essential for so many
biological processes, circular causality may be called the third secret of
life.
In a future blog I hope to discuss some chapters of
Ball’s book demonstrating the importance of circular causality during
embryonic development.
References
Allostery: Jacob, Francois en Jacques Monod, ‘Genetic regulatory mechanisms in the synthesis of proteins’, J Mol Biol. 3, 1961, p.
318-56.
Feedback loops: Xiao-Jun Tian et al., ‘Interlinking positive and negative feedback loops creates a tunable motif in gene regulatory networks,’ Physical Review E, 80, 2009, 011926-1-8.
Feedback
loops: Dong-Eun Chang et al., ‘Building biological memory by linking positive feedback loops,’ PNAS 107-1, 2010, p. 175–180.
Morphogenesis: Allen Turing, “The chemical basis of morphogenesis”,
Philosophical Transactions of the Royal Society of London. Series B,
Biological Sciences, 237, 1952, p. 37-72.
Morphogenesis: Hans Meinhardt and
Alfred Gierer, ‘Applications of a theory of biological pattern formation based on lateral inhibition’, Journal of Cell Science 15, 1974, p.
321-346.
