# BIO-103

### From CYPHYNETS

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+ | These notes were prepared for the mathematical biology module for Bio-103, Spring 2009. | ||

+ | |||

+ | '''Instructor in-charge:''' Shahid Khan | ||

+ | |||

+ | '''Guest lecturers''': Muhammad Sabieh Anwar, Abubakr Muhammad | ||

+ | |||

+ | '''Note taker:''' Mohammad Adil, BS-2012 | ||

+ | |||

+ | |||

= Autoregulation in Transcription Networks = | = Autoregulation in Transcription Networks = | ||

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===Moral of this analysis=== | ===Moral of this analysis=== | ||

+ | |||

+ | <math> X(t) \rightarrow X_{s.s} = K (Steady State) </math> | ||

Q. What is the response time? | Q. What is the response time? | ||

- | Q. What is the time taken to | + | Q. What is the time taken to reaches <math> X_{s.s/2}</math>? |

- | Using linear approximation, | + | Using linear approximation, |

+ | <math> \frac{K}{2} = \beta {T_{1/2}}^{a} </math>, | ||

+ | Where a <math>\rightarrow </math> autoregulation. | ||

- | Stronger the value of | + | <math>{T_{1/2}}^{a} = \frac{k}{2 \beta} </math> |

+ | <math> \rightarrow </math> | ||

+ | Response time for Negative autoregulation. | ||

+ | |||

+ | Stronger the value of <math> \beta </math>, shorter the time. | ||

- | Using autoregulation : Use a stronger promoter | + | Using autoregulation : Use a stronger promoter <math> \beta </math>, to give an initial fast production. |

Nature can tune the following parameters, | Nature can tune the following parameters, | ||

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1) K : By mutations in the binding site of the X in the promoter. | 1) K : By mutations in the binding site of the X in the promoter. | ||

- | 2) | + | 2) <math> \beta </math> : By mutations in the binding site of the RNA in the promoter. |

Thus tuning the steady state and response time independently. | Thus tuning the steady state and response time independently. | ||

====Comparing with an unregulated/simply regulated Transcription Network==== | ====Comparing with an unregulated/simply regulated Transcription Network==== | ||

+ | |||

+ | <math> Z \rightarrow X \ </math> and | ||

+ | <math> \frac{dX}{dt} = \beta_{u} - \alpha_{u} X </math> | ||

+ | where <math> u \rightarrow unregulated</math> | ||

Again objective is to produce, | Again objective is to produce, | ||

- | + | <math> X_{s.s} = K </math> | |

+ | but now triggered by Z. | ||

- | + | For a fair comparison let us pick <math> \beta_{u}, a_{u} </math> such that | |

+ | |||

+ | <math> X_{s.s} = \frac{\beta_{u}}{\alpha_{u}} = K </math> | ||

+ | |||

+ | What is the response time? | ||

From the last lecture, | From the last lecture, | ||

+ | |||

+ | <math> {T_{1/2}}^{u} = \frac{log 2}{\alpha_{u}} </math> | ||

+ | |||

+ | <math> {T_{1/2}}^{a} = \frac{K}{\beta 2} </math> | ||

Comparing the two, | Comparing the two, | ||

+ | |||

+ | <math> \frac{{T_{1/2}}^{u}}{{T_{1/2}}^{a}} = \frac{K \alpha_{u}}{2 log2 \beta} </math> | ||

By tuning (maths) (i.e making it big), response of autoregulator can be made much faster than simple control by Z. | By tuning (maths) (i.e making it big), response of autoregulator can be made much faster than simple control by Z. | ||

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==== Comparison II==== | ==== Comparison II==== | ||

- | + | <math> {X_{s.s}}^{u} = K </math> , <math> {X_{s.s}}^{a} = \frac{\beta_{u}}{\alpha_{u}} </math> | |

- | + | 1) <math> \beta , \alpha </math> (Fluctuations in the metabolic capacity) vary from cell to cell. | |

+ | |||

+ | 2) <math> K </math> (Strength of chemical bonds between X and RNA) vary little. | ||

Thus we can conclude that autoregulation is much more robust. | Thus we can conclude that autoregulation is much more robust. | ||

== Summary == | == Summary == | ||

- | |||

- | |||

- |

## Current revision

These notes were prepared for the mathematical biology module for Bio-103, Spring 2009.

**Instructor in-charge:** Shahid Khan

**Guest lecturers**: Muhammad Sabieh Anwar, Abubakr Muhammad

**Note taker:** Mohammad Adil, BS-2012

## Contents |

# Autoregulation in Transcription Networks

## Gene transcription and translation

Talk about basic biology. I will fill this later.

## Activation Dynamics

Please start here. Basic activation differential equations. Column 1 of Pages 1, 2

Suppose that gene X regulates gene Y via activation. This is represented symbolically as

Let the concentration of mRNA be given by *Y*_{m}

Let dynamics of the mRNA concentration is governed by the following simple differential equation.

Substituting the hill function form for activation, we get

When the concentration of *X*^{ * } is high to begin with, then the hill function saturates to β_{1}, thus giving us a simpler looking differential equation,

One can write a solution for this simple linear differential equation,

The steady state of this reaction is

This is the fast part of the reaction.

For translation of mRNA into protein Y, we study the slow reaction,

Since the fast reaction, reaches its steady state value very early, we can safely substitute the steady state value compute above.

Let us combine various constants into a new one, β,

Again, the solution is given by

and the steady state concentration of protein is given by

Note again that this is a slow reaction, because the time constant for the translation reaction is much larger than the time constant for the slow transcription reaction.

## Repression Dynamics

Suppose that gene X regulates gene Y via repression. This is represented symbolically as

Let the concentration of mRNA be given by *Y*_{m}

The dynamics of the mRNA concentration is governed by the following simple differential equation

Substituting the Hill function for repression, we get

When the concentration of *X*^{ * } is high to begin with, the Hill function goes to 0, thus giving us a simpler looking differential equation

One can write a solution for this simple linear differential equation

The steady state of this reaction is

This is the fast part of the reaction.

For translation of mRNA into protein, we study the slow reaction,

Since the fast reaction reaches its steady state very early, we can substitute the steady state value,

The solution is given by,

*Y*(*t*) = *Y*(0)*e*^{ − αt}

and the steady state concentration of protein is given by

Note again that this is a slow reaction, because the time constant is much larger for the transcription reaction than the time constant for the translation reaction.

## Autoregulation as a Network Motif

Suppose that gene X regulates itself via activation or repression. This is represented symbolically as for activation and for repression.

Going back to the equation for regulation,

Here we replace Y by X. Also we replace
β(*X*) with
*f*(*X*) because
is a fast reaction.
We get the following differential equation for autoregulation,

where the type of hill function *f*(*X*) depends on whether the reaction is
or
.
We call this an autoregulated or self-regulated circuit/network.

Q. Why is this arrangement important in transcription networks?

For this we introduce the idea of "network motif".

### Network Motif

Take a transcription network .Try to spot a "motif".

By evolutionary processes, different edges are being generated or killed at random.

Q. Which patterns are "significant" or "accidental"?

We can generate a random network ( Edros-renyi) to see what a randomly generated transcription network looks like

#### Recipe/Algorithm for creating random network

Given N nodes ( Here proteins/genes) and E edges ( E will be the # of edges in the network),

1) Pick a node randomly.

2) Pick another node randomly.

3) Put an edge from the 1st to 2nd node.

4) Repeat the process E times.

Consider the probability of having a self-edge,

(A node has N choices to chose between)

Average number of self-nodes of E-edges

Standard Deviation (Assume Poisson Distribution)

Example: For *N* = 424 , μ = 1.2

For *E* = 519 , σ = 1.1

In this case, expected self-edges = 0 , 1 , 2

But let us consider a real E.Coli Network with the same number of nodes and edges,

Here, # of self-edges = 40!

So, there must be a reason nature keeps auto-regulation in transcription networks. Patterns such as auto-regulation that are extremely hard to explain as "evolutionary accidents" are called "Network Motifs". Examples of other network motifs are:

1) Feed Forward Loops.

2) Two node feedback loops.

There are many more network motifs (see ref). Motifs can also be discovered in other types of networks.

## What is feedback control?

I will add this.

## Robustness via Autoregulation

Pages 6,7,8

We know that autoregulation is important but what is it's purpose? Refer back to equation for

where

Suppose that initially *X* is zero or . Hence
and the we get the following equation,

while *X* < *K*

In fact when X is too small α*X* also goes to zero. So we have,

This differential equation has a simple solution,

while

When X crosses K, , so β is turned off. Hence X decays by,

Solving the differential equation we get,

*X*(*t*) = *X*(*T*)*e*^{ − α(t − T)}

If X overshoots K, then X decays back to K.

If X undershoots K, then X again triggers to .

### Moral of this analysis

Q. What is the response time?

Q. What is the time taken to reaches *X*_{s.s / 2}?

Using linear approximation, , Where a autoregulation.

Response time for Negative autoregulation.

Stronger the value of β, shorter the time.

Using autoregulation : Use a stronger promoter β, to give an initial fast production.

Nature can tune the following parameters,

1) K : By mutations in the binding site of the X in the promoter.

2) β : By mutations in the binding site of the RNA in the promoter.

Thus tuning the steady state and response time independently.

#### Comparing with an unregulated/simply regulated Transcription Network

and where

Again objective is to produce,

*X*_{s.s} = *K*
but now triggered by Z.

For a fair comparison let us pick β_{u},*a*_{u} such that

What is the response time?

From the last lecture,

Comparing the two,

By tuning (maths) (i.e making it big), response of autoregulator can be made much faster than simple control by Z.

#### Comparison II

,

1) β,α (Fluctuations in the metabolic capacity) vary from cell to cell.

2) *K* (Strength of chemical bonds between X and RNA) vary little.

Thus we can conclude that autoregulation is much more robust.