This course will introduce the student to contemporary Systems Biology focused on mammalian cells, their constituents and their functions. Biology is moving from molecular to modular. As our knowledge of our genome and gene expression deepens and we develop lists of molecules (proteins, lipids, ions) involved in cellular processes, we need to understand how these molecules interact with each other to form modules that act as discrete functional systems. These systems underlie core subcellular processes such as signal transduction, transcription, motility and electrical excitability. In turn these processes come together to exhibit cellular behaviors such as secretion, proliferation and action potentials. What are the properties of such subcellular and cellular systems? What are the mechanisms by which emergent behaviors of systems arise? What types of experiments inform systems-level thinking? Why do we need computation and simulations to understand these systems? The course will develop multiple lines of reasoning to answer the questions listed above. Two major reasoning threads are: the design, execution and interpretation of multivariable experiments that produce large data sets; quantitative reasoning, models and simulations. Examples will be discussed to demonstrate “how” cell- level functions arise and “why” mechanistic knowledge allows us to predict cellular behaviors leading to disease states and drug responses.
Identifying Emergent Properties: Bistability I
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From the course by Icahn School of Medicine at Mount Sinai
Introduction to Systems Biology
236 оценки
Icahn School of Medicine at Mount Sinai
236 оценки
Course 1 of 6 in the Specialization Systems Biology and Biotechnology
From the lesson
Strengths and Limitations of Different Types of Models | Identifying Emergent Properties
Module description goes here.
Meet the Instructors
Ravi Iyengar, PhDDorothy H. and Lewis Rosenstiel Professor
Department of Pharmacology and Systems Therapeutics, Systems Biology Center New York (SBCNY)
[MUSIC]. In this lecture I'm going to talk to you
about integrated reasoning, where one can merge bottom up and top down lines of
thinking and identify emergent properties.
And the property that I'm going to talk about the most and which I actually know
the best about is Bistability. Okay, let's start off with what is an
emergent property? This is sort of a key question in systems
biology because. [UNKNOWN] The word systems says we want
to understand what happens at a systems level.
And emergent properties are properties that a system possesses as a whole, but
cannot be attributed to any individual component of the system.
Emergent properties of cell biological systems include bistability,
ultrasensitivity robustness and probably many others that are waiting to be
discovered. So what is bistability?
Bistability is the ability of a system to exist in two stable states.
That is, a system can switch between one state and another.
Often switching a stimulus-driven, and hence enables change in the state of the
cell, so the cell can receive a stimulus chan, turn on.
a set of networks that enable it to become bistable, and switch to another
state and respond differently. So, you might ask why is this integrated
reasoning. The reason this is integrated reasoning
is because we use the ability of ODE based, in this case ODE based modeling to
understand bistability as a property of the positive feedback loop.
And this, and as I told you positive feedback loops are a regulatory motifs
with, sub-graphs. Within a large network.
And this allows us to enumerate the capability of a network identified by
topological analysis. And so you will combine both, dynamical,
reasoning from dynamical modelling. And reasoning from network modeling.
And hence, I'd like to call this sort of integrated.
Okay let's think about an example of a dynamical analysis of a bistable system.
I'm going to use the example from my paper that [UNKNOWN] worked on back in
1999, because this is sort of any easy one and, uses very classical cell
signaling pathways. And I can put it in the context of known
biological functions now. So, you've seen this diagram of this
positive feedback loop here now many times, so you should feel comfortable
with it. You can get a signal from EGF4 that
activates either phosphate-base c or the RAS pathway that turns on protein kinase
c. Up here on MAP Kinase and together with
PLA2 they form a positive feedback loop that allows a signal to propagate leading
to MAP Kinase. all of the reactions in this network have
been well-studied and these reactions are all sort of given here, and these schemes
are taken from that paper. So to set up a, ODE model what one needs
to do is to identify the reactions involved in the model, and these are
shown below here [SOUND]. You have to parameterize the reaction,
that is find the reaction rates and concentrations.
As I told you these are classical signalling pathways and most of these
proteins. Well, actually I think all of the
proteins in this pathway have been purified.
And so, and there has been a study with respect to their biochemical property so
we had been able to at some work, both find and or estimate the parameter.
Once we get a model with these parameters, what one needs to do is to
constrain the model in a modular fashion, and I'll talk about this in the next
slide in a minute. And also ensure that the simulations
obeys basic thermodynamic principles, that is, mass conservation.
Remember that in this system, except for MKP none of the other components are
either synthesized or degraded. So do, mass conservation one needs to
make sure. That the amount of mass that you start
with at the beginning of the simulation, is the same at which you end the
simulation. So, you can't, so the level, the total
concentration of EGF1/g, the beginning and the end and total concentration of
RAS or MAP Kinase the beginning and the end has to be same.
The fractions of the uh, [INAUDIBLE] part which had activated, may be different and
they vary with respect to time. But if you take the inactive and active
together, they ought to be sort of constant throughout simulations.
This is what one means by mass conservation.
Additionally, because these are all coupled reversible reactions the system
has to obey the thermodynamic principle of microscopic reversability.
Which means that all of the forward reactions thus, product of all of the
forward reactions has to equal the product of all of the reverse reactions.
with these two constrains the mo, modelling should proceed say in a pretty
straightforward manner. Okay, constraining models.
This is sort of, actually, a big deal in building realistic models.
Always remember the famous spherical car with a tail and nine par, eight or ten
parameters. And so, one wants to really constrain
these models so that they are as realistic, plausibly realistic from an
experimental perspective. And since kinetic parameters are drawn
from many sources for instance, the different proteins in this network might
have been purified from many different hm, laboratories.
have been purified in laboratories and could be from different cells and tissues
it's hm, and all these kind of variabilities and they may now have been
assayed in exactly the same conditions. So, it's good practice to constrain the
model by running test simulations to see the model behavior is similar to what
have been observed in experiments. Two, two examples are shown here below.
I already think, I think I discussed the hm, the time course example.
The previous lecture in considering limitations of models, and here is a dose
response example of, of [UNKNOWN] gamma being activated by EGFR.
or EGF in the in high and low calcium, and you can see in both cases, there is
reasonably good agreement between the output of the simulations and the
experimental data. My recommendation generally is not to
tweak or change the parameters so the simulations exactly fit the model.
So, for instance here you see that the time course is a little bit offset, this
is because maybe the rates that we are using in the model are not exactly what
is observed in these cell types. But we sort of let them be.
we know the differences and keep track of them, but we run the model with the most
reasonably selected parameters. The appropriate place to change
parameters is during the systematic parameter variation.
This is a meaningful exercise because it can tell us the best set of parameters
for an observed experimental profile. And by doing this sort of parameter
variations, one can identify parameters that may best suit a specific
experimental system. Then of course one has to go back into
the experimental system and test or verify the parameters that we identified
through the simulations are indeed what we what you can measure.
So, these kind of parameter variations systematic parameter variations, are um,
[UNKNOWN] can generate hypothesis to do more accurate experiments.
So, using all these constraints, one can sort of run simulations to determine
unde, the conditions under which one can get transient and sustained responses.
When we first did these experiment simulations, we were not sure what we
were going to find. And so we ran the simulations under the
variety of conditions to ask the question whether a brief, small or short duration
signals could produce long duration output.
So, here is the result of the simulations.
And [INAUDIBLE] ran these in under multiple conditions, he, what he found
was that simula, a stimulus of a sufficient amplitude, 5 nano molar.
And sufficient duration 100 minutes, moves the system from a basil to a fully
activated state. Other stimuli that are less intense, or,
or smaller in aptitude or smaller in duration might produce transient
activity. But the eventually call the sys, cause
the system to come back to baseline when the stimulus is removed.
So, now one can ask the question, how does this happen?
You can see that this, these two lines intersect each other at three different
points. and each of these points actually have a
sort of a physical meaning in terms of what is going on.
A is the continuously active state, which is why we call it A.
B is the inactive, or the basal state. Called B and T is the metastable or
threshold and state and I'll come to T in a minute.
So, A and B together form the two stabled states and that's why the system is
called bistable. Okay.
So what is T? T is the metastable state and this
indicates the minimum level to which either PKC or MAP Kinase need to be
activated by stimulus. So, that eventually when the stimulus is
withdrawn, the the system goes to the fully active state.
If the initial stimulus is below that, it will eventually, when the stimulus is
withdrawn, the the system will come back to relax, at the.
And come back to relax at the basal state.
And this is shown here in this time course graphs, there it is, that is
initial stimulation is beyond this threshold, you get this activation, which
you just saw. If it is below this threshold, once the
stimulus is removed it comes back to really, even if you see with a long
duration stimulus, once the stimulus is removed, the system deactivates.
the system deactivates to coming back to the basal state.
This system is actually quite reasonably robust because not only is the activation
sort of require a signal of sufficient amplitude and duration.
Deactivation also needs to be of sufficient amplitude and ma, magnitude.
Of sufficient magnitude and duration from the system.
So you, in these simulations, what [UNKNOWN] was to you a phosphatases
called MAP Kinase phosphatasesg or MKP, that shuts down MAP Kinase.
You did three kinds three levels of simulations.
4 nano molar for 20 minutes, MKP. 8 nano molar for 10 minutes, or 8 nano
molar for 20 minutes. And you can see that only when he did for
8 nano molar for 20 minutes, was the system able to completely deactivate.
Otherwise if you provided a brief stimulus, the system would start to
deactivate. But once hm, the deactivating stimulus
was removed, the system would bounce back.
So, this is because the coupled reactions of MAP kinase phosphatases is sort of
dephosphorylating activated map kinase. But where one [UNKNOWN] is shut down, the
other reactions are still ongoing, and those ongoing reactions now feed back
into the system. And through this loop and so they would
be able to reactivate the deactivated MAP kinase and allow for the system to bounce
back here. This is what you see in this bouncing
back here. So, you can see that the strength of the
system comes from the coupling of the reactions, so even removing any one node
for a brief period will not really break the system.
The system can bounce back, but if you disrupt a node for a very long time, then
the system will completely relax down. Because there is no further signal going
from map kinase to keep the loop operating.
So, why was such bistable behavior in signaling networks surprising?
Well, back in 1994, 96, when these simulations were being run, it was
thought that long-lasting signals were produced in by largely two ways.
One the more common one that was very relevant was activating mutations such as
that in [UNKNOWN] produced persistently activated signaling molecules.
And these activated proteins and in turn produce actuated MAP kinase.
This was the most, sort of, common way in which, cells, go from aquis into a
proliferative state and involved in the development of cancer.
And indeed both [UNKNOWN] are, are oncogenes.
The other more less frequent but actually well known way was for rare biochemical
reactions, such as ADP-ribosylation by bacterial toxins.
by ADP-ribosylation by bacterial toxins that could produce persistently activated
or inhibited molecules. So, for instance, cholera toxin
ADP-ribosylation the G-protein GS, this leads to continuous activation of GS.
Continuous activation of GS causes continuous activation of adenylyl cyclase
and continuous production of cyclic AMP. And indeed, the sort of, the
pathophysiology of diarrhea occurs because of continuously elevated cyclic,
cyclic AMP in the gut allow, blocks water re-absorption causing diarrhea.
So, this is sort of a, these are the kind of pathophysiologies that were associated
with sort of not common biological events.
But we have found is that just simple coupling of wild types of signaling
components because in the loop that I showed you, there were no mutated
proteins, or modified proteins in any way.
These were normal proteins working as normal in their normal fashion within the
cell. But the coupling of these proteins could
produce a feedback loop that leads to switching behavior.
And this switching behavior was, was unanticipated.
How common are such bistable systems? Well they're actually pretty common.
Now, three examples here from different levels of evolution.
The Lac Operon in Ecoli have been shown to be a bistable system,
Back in 1957 and I have that paper listed here.
Jim Farrell and his colleagues have shown that the cell cycle, or the proliferation
response stimulus in Xenopus oocyte, frog oocytes are, function as a bistable
system. And there are many studies in different
brain regions, and I've chosen one In cerebral [UNKNOWN] neuron, where
electrical activity in this brain region behaves as a bistable system.
So, bistable systems are quite common in bi, in biology. [MUSIC]
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