ICS280 Homework 1

– Due Thursday Jan 16

• Read 2 of the 4 review articles

• Get software

DNA(+ boundproteins)

Transcription to mRNA; Translation to proteinProtein (MyoD)

Regulation of DNA transcriptionby proteins (transcription factors)

mRNA

Structures, motors, sensors, effectors,feedback circuitry, ...

DNA RNA Protein Gene Regulation (DNA)

Gene Expression Data: Immunofluorescence

hunchback andKruppelwith nuclear mask(Kosman, Reinitz, SharpPSB 1998)

Gene Expression Data:Immunofluorescence

Drosophila gap and pair-rule gene expression as protein.Green: Kruppel. Blue: giant. Red: even-skipped.Courtesy John Reinitz.

Gene

Gene Expression Clusters - C. Elegans

Transcriptional Gene Regulation Networks

• Is oversimplified by mass-action kinetics

• Gene Regulation Network (GRN) model

Drosophila eve stripe expression in model (right) and data (left). Green: eve expression, red: kni expression. From [Reinitz and Sharp, Mech. of Devel., 49:133-

158, 1995 ]. [Mjolsness et al. J. Theor. Biol. 152: 429-453, 1991]

T

v

Extracellularcommunication

i i ij j i i ijv g T v h v

CLV3CLV1

?

Fletcher et al., Science v. 283, 1999 Brand et. al., Science 289, 617-619, (2000)

WUS

Meristem Simulation

Repressilator

X

Y

Z

PZ

PX

PY

Repressilator

Eukaryotic Cell Cycle - Schematic

J. Tyson and B. Novak, J. theor. Biol. (2001) 210, 249}263

Cell CycleProtein

Interactions

J. Tyson and B. Novak, J. theor. Biol. (2001) 210, 249}263

Cell Simulation Software

CELLERATOR

interactivebiologicalmodeldescription

Systems BiologyMarkup Languagemodel

SBMLmodel SBML

CELL MODEL READER

SOLUTION/OPTIMIZATION

ENGINECODE WRITER

templates

LA code(C++)

observeddata

annealer state parameters

SOLVER/OPTMIZERFOR THE CELL

MODEL

modelparameters

SBMLmodel

OTHERAPPLICATIONS

FOR SBMLMODELS

. . .

DIMENSIONREDUCTION

BiologicalHypotheses

Datasets

Regulations and reactions

Mathematical model generation Simulation

Mining Simulation results

Followup experiments

Optimization

Biology User

MAP Kinase Pathways in Solution

INPUT

OUTPUT

Cellerator Demo

Cellerator: Automatic Model Generation

Reaction Syntax ODE Interpretation

Hill Function:

{{A1 B,hill[]},{A2 B,hill[]}...},

B r0 (r1 vi Ai )n

i1p

K n (r1 vi Ai )ni1p

Neural Network Dynamics (Genetic Regulatory Network):

{{A1 B,GRN[]},{A2 B,GRN[]},}

B R

1 exp Ti Aini hii1

p Non-hierarchical Cooperative Activation (Pseudo-MWC Dynamics):

{{A1 B,NHCA[]},

{A2 B,NHCA[]},}B

1 ( Ti Ai

n i )mi1p

k( Ti Ai

ni )mi1p ( Ti

Ain i )m

i1p

NHCA with Cooperative Binding:

{A1,A2,,Ap B,

NCHA[TPLUS {T1,T2

,},]}B

1 ( Ti Ai

n i )mi1p

k( Ti Ai

ni )mi1p ( Ti

Ain i )m

i1p

Model Generation

and Use

Solver

Output Canonical FormSystem of ODEs

Input Canonical FormBiochemical Notation

Concentrationsvs. Time

Activity(e.g., Cell Division)

A

BC

E.g. MAP Kinase Cascade

K3 K4

K3*

K2 K3

*

K2*

K3*

K2**

K1 K2

**

K1*

K2**

K1**

With A. Levchenko

J. Ferrell model w/o scaffold

Cellerator Arrow Translation

Elementary Reactions

• Bimolecular in solution: – A, B {C}– Example: Yeast Fus3 phosphorylates Far1, arrests cell

cycle.• FUS3, KSS1 also phosphorylate Ste12 TF/Dig1/Dig2, leads to

mating

• Binding/unbinding at a site:– A, S S-A– Example: Swi5p binds to DNA e.g. UTR for HO,

responsible for mating type switch in daughter yeast cells.

SWI5 details

– Dephosphorylation of Swi5 by cdc14p is instrumental in up-regulating Sic1p level which leads to M progression [MIPS, BJW notes]

– Nuclear localization (NLS) sequence is normally phosphorylated in S, G2, and M when Swi5p is located in the cytoplasm, and dephosphorylated during G1 when Swi5p enters the nucleus . Phosphorylation of the NLS is catalyzed by the B cyclin kinase. Three serines phosphorylated by cdc28p in vitro [MIPS]

– Pho2p-Swi5p-DNA ternary complex is significantly more stable (t[1/2] = 20 min) than either Pho2p-DNA (t[1/2] = 2 min) or  Swi5p-DNA (t[1/2] = 15 sec) binary complexes [BJW notes]

Transcription Factor Binding

2

2

homodimerized:

AP

A

Note: n=2 homodimer cooperativity coefficientboth amplifies and suppresses signals.

Heterodimers increase specificity.

TF Activation before Binding

Mass Action Kinetics

• Law of mass action for dilute solution in equilibrium:

• Applied to bimolecular “prereaction” interactions:

in equilibrium, impliesji

i j

i i j ji j

mn

i j

n A m B

A C B

Cellerator Arrows: Catalytic ReactionsR e a c t i o n S y n t a x O D E i n t e r p r e t a t i o n

{ S PE

, a, d, k }S a E S d S

P k ( S E )

E a E S ( d k ) ( S E ) ( S E )

{ S PF

E, a, d, k,

a1, d1, k1}

S k 1 ( PF ) a E S d ( S E )

P a 1 F P d 1 ( PF ) k ( S E )

E a E S ( d k ) ( S E ) ( S E )

F a 1 F P ( d 1 k 1 ) ( PF ) ( PF )

{ S PE

, k } S k E S P

{ S PE

} S ( k vE ) S n

K n S n P

Cellerator Arrows: Transcriptional RegulationR e a c t i o n S y n t a x O D E I n t e r p r e t a t i o n

H i l l F u n c t i o n :

{ {A 1 B, hill[ ]},

{A 2 B, hill[ ]} . . . } ,B r 0

( r 1 v i A i ) ni 1p

K n ( r 1 v i A i ) ni 1p

N e u r a l N e t w o r k D y n a m i c s ( G e n e t i c R e g u l a t o r y N e t w o r k ) :

{ {A 1 B, GRN[ ]},

{A 2 B, GRN[ ]}, }B

R

1 exp T i A in i h ii 1

p N o n - h i e r a r c h i c a l C o o p e r a t i v e A c t i v a t i o n ( P s e u d o - M W C D y n a m i c s ) :

{ {A 1 B, NHCA[ ]},

{A 2 B, NHCA[ ]}, }B

1 ( T i A i

n i ) mi 1

p

k ( T i A i

n i ) mi 1p ( T i

A in i ) m

i 1p

N H C A w i t h C o o p e r a t i v e B i n d i n g :

{ A 1 , A 2 , , A p B,

NCHA[TPLUS {T 1 , T 2

, }, ] }B

1 ( T i A i

n i ) mi 1

p

k ( T i A i

n i ) mi 1p ( T i

A in i ) m

i 1p

MAPK Pathways in Saccharomyces cerevisiae

http://www.genome.ad.jp/kegg/

MAPK cascades

Madhani, HD. Fink, GR. THE RIDDLE OF MAP KINASE SIGNALING SPECIFICITY [Review]. Trends in Genetics. 14(4):151-155, 1998 Apr.

KEGG yeast cell cycle

GO hierarchy for Molecular Function:Transcription Factor

Source: SGD

SigmoidReaction Schema

in UML

Sigmoid Reactant Schema in UML

Sigmoid Knowledge Source,Model Schemata

Reactions DB

Bioinformatics Software Architecture

MLXAnalyses - Clustering - Classification - Cross-validation - Scoring - Gene list tools

Regulatory Cell ModelsCellerator, SBML

Expression DB - MAGE-OM + mods - Genex - FGDB

conversionconversion

Python/Java/CORBA

Image DB - MLX image classes - Diamond Eye (JPL)

Sequence DB

GUI - Genespring - Mimir - (Genetrix, others?)

Current Cellerator Library

• Myogenesis (Chris Hart)• CMX Mitotic Oscillator (Goldbeter)• Repressilator (Elowitz & Leibler)• IP3 Calcium Channel (DeYoung & Keizer)• MAPK on Scaffold• Cell Cycle (Novak & Tyson)• Glycolysis (Sel’kov)• Ring Oscillator (enzymatic or transcriptional)• Meristem (in progress)• Hematopoietic Differentiation (in progress; includes C/EPB;

PU.1; GATA-1; AML1; CBF;NFKB; CSFR)

Cellerator Canonical Forms in Everyday Language

• Input: Arrows + IC + rates (Palette Driven)– Mass action– Enzymatic– Transcriptional– Cascades – Modules (e.g., MAPK)

• Intermediate Output– simple chemical reactions (where appropriate)

• Output - ODES– Mathematica equations, SBML, C, FORTRAN, HTML,

MATHML, XML– Optional Numerical Solution + Plots

Cellerator Arrows: Law of Mass Action

R e a c t i o n S y n t a x O D E I n t e r p r e t a t i o n

{S P, k} S P k S {A B C, k } A B C k AB

{A B n C, k } A B C k AB n

{A B, kf, kr } A B k f A k r B

{A B F C, kf, kr } A B C k f AB k r C

{ A, k } A k {B , k } B k B