lecture 3: statistical basis for macroscopic...
TRANSCRIPT
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Probability, the three central distributions, and qualitative behavior of dynamical systems. The power of sketching global behaviors
Lecture 3: Statistical Basis for Macroscopic Phenomena Winter 2017
R. Ranganathan Green Center for Systems Biology, ND11.120E
Carl Friederich Gauss 1777 - 1855
Pierre-Simon LaPlace 1749 - 1827
Simeon Denis Poisson 1781 - 1840
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So, today we do two things: consider the statistics of systems of various size, and we learn to think qualitatively about behaviors of linear dynamical systems.
Linear
Nonlinear
n = 1
n = 2 or 3
n >> 1
continuum
exponential growth and decay
single step conformational change
fluorescence emission
pseudo first order kinetics
fixed points
bifurcations, multi stability
irreversible hysteresis
overdamped oscillators
second order reaction kinetics
linear harmonic oscillators
simple feedback control
sequences of conformational change
anharmomic oscillators
relaxation oscillations
predator-prey models
van der Pol systems
Chaotic systems
electrical circuits
molecular dynamics
systems of coupled harmonic oscillators
equilibrium thermodynamics
diffraction, Fourier transforms
systems of non-linear oscillators
non-equilibrium thermodynamics
protein structure/function
neural networks
the cell
ecosystems
Diffusion
Wave propagation
quantum mechanics
viscoelastic systems
Nonlinear wave propagation
Reaction-diffusion in dissipative systems
Turbulent/chaotic flows
adapted from S. Strogatz
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So, today we do two things: consider the statistics of linear systems of various size, and we consider a few examples of such dynamical systems.
The goals will be two-fold:
(1) Review the essentials probability theory….what are probabilities, ways of counting, the three central distributions of primary importance….the binomial, Poisson, and Gaussian.
(2) See how probability theory provides a powerful basis for predicting the behavior of simple systems. And, reinforces the importance of fluctuations in driving reactions.
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Probability theory….starting with the basics.
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Probability theory….starting with the basics.
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Probability theory….starting with the basics.
or stated mathematically…
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Probability theory….starting with the basics.
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Probability theory….starting with the basics.
the concept of joint probability…
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Probability theory….starting with the basics.
…and then the conditional probability…
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Probability theory….starting with the basics.
This leads to the general definition of statistical independence…
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Probability theory….starting with the basics.
This leads to the general definition of statistical independence…
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Probability theory….starting with the basics.
This leads to the general definition of statistical independence…
Also…
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Probability theory….starting with the basics.
This leads to the general definition of statistical independence…
Also…
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Probability theory….starting with the basics.
…and finally the combined probability…
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Counting statistics…permutations and combinations
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Counting statistics…permutations and combinations
We can simplify this….
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Counting statistics…permutations and combinations
This is the number of ways of taking n things r at a time….
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Counting statistics…permutations and combinations
For example, if we have a tetrameric channel, we can ask how many ways are there of taking two subunits at a time….
1 2
3 4
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Counting statistics…permutations and combinations
For example, if we have a tetrameric channel, we can ask how many ways are there of taking two subunits at a time….
1 2
3 4
This is the number of permutations…
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Counting statistics…permutations and combinations
But if we don't care about the order of taking pairs, then this is called the number of combinations of n things taken r at a time…
This is the number of permutations…
This is the number of combinations…
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Counting statistics…permutations and combinations
But if we don't care about the order of taking pairs, then this is called the number of combinations of n things taken r at a time…
This is the number of permutations…
This is the number of combinations…
So for the tetrameric channel….12 permutations but only 6 combinations
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Counting statistics…permutations and combinations
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This gets us to the binomial distribution…
And in general, for getting r heads in n tries….
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This gets us to the binomial distribution…
And in general, for getting r heads in n tries….
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This gets us to the binomial distribution…
And in general, for getting r heads in n tries….
But, what if p(head) is not same as p(tail)?
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This gets us to the binomial distribution…
This is the binomial probability density function….
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This gets us to the binomial distribution…
This is the binomial probability density function….
It gives us the probability of getting r events out of n trials given a fixed probability of the event with each trial.
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This gets us to the binomial distribution…
This is the binomial probability density function….
It gives us the probability of getting r events out of n trials given a fixed probability of the event with each trial.
In general, it is used in cases where the total number of trials is not large, and the probability of the event is relatively high
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General shape of the binomial distribution…
So…the probability of getting k events out of n trials given a mean probability of events of p
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General shape of the binomial distribution…
So…the probability of getting k events out of n trials given a mean probability of events of p
Quite reasonably, the most likely outcome is the case of k = 5. In general…the mean is np.
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
Now what happens if n approaches infinity and p is small?
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
This is probably not so obvious….
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
And….
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
Putting it all together….
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There are two interesting limits to the Binomial distribution….first the Poisson distribution
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The nearly universal importance of the Poisson distribution
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The nearly universal importance of the Poisson distribution…example 1
Single-step molecular conformational change. An example of a first order process…
Consider its microscopic characteristics….
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Single-step molecular conformational change. An example of a first order process…
(1) the number of trials is large (and not directly observed)
(2) the probability of barrier crossing is even unknown….all we know is the mean number of events per time (the rate constant).
The nearly universal importance of the Poisson distribution…example 1
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Single-step molecular conformational change. An example of a first order process…
So…
The nearly universal importance of the Poisson distribution…example 1
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Single-step molecular conformational change. An example of a first order process…
So…
The nearly universal importance of the Poisson distribution…example 1
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Two solutions to the first order process!
The deterministic solution….
with initial conditions and specified range…
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Two solutions to the first order process!
The deterministic solution….
with initial conditions and specified range…
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Two solutions to the first order process!
The deterministic solution….
with initial conditions and specified range…
The stochastic solution….
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Two solutions to the first order process!
The deterministic solution….
with initial conditions and specified range…
The stochastic solution….
the microscopic basis for all single-exponential processes…. a large number of random, independent trials with a single characteristic wait time.
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Example 2: Ion channel gating
The stochastic opening and closing of single ion channels….
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With single channel recording…
assay
data
Example 2: Ion channel gating
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assay
data
Example 2: Ion channel gating
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assay
data
Example 2: Ion channel gating
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assay
data
fitting to theory
Example 2: Ion channel gating
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assay
data
fitting to theory
Another example of our process of modeling….
Example 2: Ion channel gating
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Example 2: Ion channel gating
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Example 2: Ion channel gating
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Example 2: Ion channel gating
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Why?
Example 2: Ion channel gating
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So, the sum of many Poisson processes is itself a Poisson process with a mean rate equal to the sum of rates
Example 2: Ion channel gating
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Example 3: fluorescence emission….
The GFP chromophore The Jabolinski diagram….
fluorescence
Fluorescence emission comes out of the thermally equilibrated “singlet” state. The relaxation of molecules is a Poisson process. Thus….
where
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And fluorescence resonance energy transfer (FRET)….
What is the effect of FRET for the fluorescence lifetime of the donor?
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And fluorescence resonance energy transfer (FRET)….
Donor emission is still a single exponential but with a faster rate (shorter lifetime) due to FRET…
where
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The other interesting limit of the Binomial distribution….first the Gaussian distribution
The binomial density function for p = 0.5 and n = 10. Now what happens if n approaches infinity but p is NOT small?
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Then the binomial distribution is well approximated by the Gaussian (or normal) distribution….the bell-shaped curve
The other interesting limit of the Binomial distribution….first the Gaussian distribution
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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What’s so special about the Gaussian distribution?
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Next time…an analysis of n >> 1 linear systems…diffusion and the thermodynamic limit
Linear
Nonlinear
n = 1
n = 2 or 3
n >> 1
continuum
exponential growth and decay
single step conformational change
fluorescence emission
pseudo first order kinetics
fixed points
bifurcations, multi stability
irreversible hysteresis
overdamped oscillators
second order reaction kinetics
linear harmonic oscillators
simple feedback control
sequences of conformational change
anharmomic oscillators
relaxation oscillations
predator-prey models
van der Pol systems
Chaotic systems
electrical circuits
molecular dynamics
systems of coupled harmonic oscillators
equilibrium thermodynamics
diffraction, Fourier transforms
systems of non-linear oscillators
non-equilibrium thermodynamics
protein structure/function
neural networks
the cell
ecosystems
Diffusion
Wave propagation
quantum mechanics
viscoelastic systems
Nonlinear wave propagation
Reaction-diffusion in dissipative systems
Turbulent/chaotic flows
adapted from S. Strogatz