Download SysBio Class - The Computable Plant

SysBio Class
Major papers to cover
ReiSharp
Davidson/Yuh/Bolouri, Endo16
Yeast microarrays: Spellman et al.
Ferrell
Ideker et al (w. Hood, yeast)
Cellerator
Yeast ChIP-chip
Drosophila, DV modeling: Barkai ? von Dassow et al.
Something on scale-free networks
Barabasi 48 species paper – Pierre
New paper in Science – hierarchies
Science, Nature summaries – click through library
Repressilator - Liebler
Sporulation switch in b. subtilus – Hidde de Jong
Qualitative modeling, ODE’s mixed. Java models
D. Holloway
S. J. Gould book : evodevo thought;
also plant evodevo book (reviewed in ~19 Dec 02 Science mag)

papers still needed: A. Levchenko, NFkB; Ferrell MAPK; repressilator; von
Dassow;
Major topics to cover
Classification of bio processes
Networks:
mechanical networks, reaction networks, regulatory networks, dynamic
computational networks, (software transformation networks,) …
data sources: Pathway databases, p:p interactions, microarray, images, …
Arrow translation: intro to mathematical model-building
Problem: obtaining the rate constants
Schema:
reactions, reactants (protein state modifications),
knowledge sources, models.
Scientific inference systems perspective
Observation; hypothesis generation; prediction; experiment
Enzyme kinetics schemata
MWC
Ping-pong reaction schema
Gene Ontology
Markov Chain modeling
Detailed balance
Relation to reaction schema
Bayes nets, Petri nets, other network formalisms & learning algorithms
Developmental modeling
Plant development details
Evo-devo modeling
Major software elements to supply
Graphical/numerical environments
Matlab or Mathematica or PyNumeric or R or …
Cellerator
ML/ANN environment
SIGMOID Pathway database
(OJB or Pymerase or squashed sigmoid or …)
distributed computing backbone : CORBA, Java 2, .net, …
SBML/SBW
Extensions to handle development
Data sources to cover
Microarray / yeast
GRID p:p interactions (K. Lin spreadsheets?)
Downloaded KEGG / yeast
Final projects list
Cellerator I/O
Pathway GUI - connect to Sigmoid pathway modeling db, other sources
Optimization: connect to Lam Delosme- annealer
Translate KEGG into Cellerator and/or Sigmoid
Translate GRID into Cellerator and/or Sigmoid
circuit inference algorithms
(scalable?)
probabilistic circuit inference/clustering project
ChIP/Chip project: yeast network inference
Fly data fitting
Plant data fitting
Particular pathways to investigate
Synapse formation – Kennedy and Sejnowski …
PKC, Ca oscillations, PI3K, AKT/cancer
metabolism
ICS280 Winter 2003 Mjolsness
Lecture 1
What is systems biology?
Network properties
Amplification – sigmoidal I/O curves
Feedback-dependent behaviors – oscillation, adaptation, …
Nonobvious behaviors from circuit diagram alone
Computable models of cell behavior
Multiscale modeling
What are the objects and relationships to be modeled?
DB schema: reactants, processes/reactions, knowledge sources,
models, behaviors (observable, selectable phenotypes)
Reactants
Central dogma: DNA, RNA, protein
cells, organelles, membranes, …
protein/DNA modifications:
phosphorylation, methylation, acetylation, ubiquitination, ..
localization to compartments, membranes and regions
binding sites: DNA, protein, …
Processes and Reactions
Metabolic network steps
Allosteric enzymes - cooperativity
Protein-protein regulatory interactions
Transcriptional regulation, with feedback
Diffusion, transport, and signaling
Processes (we have models and literature)
Basic issues: resources, information, replication
metabolism
Cell cycle – e.g. budding yeast
Signal transduction pathways
e.g. yeast pheromone response, stress response, …
Multicellular development
Gene/Signal Regulatory Network
e.g. plant growth, Drosophila blastoderm,
many others.
Knowledge sources
Gene expression images
Microarray expression data
p:p interactions
sequence data
textual information retrieval on scientific literature
map to: Biology  mathematics  algorithms  software  biology loop,
scientific method automated assistance.
Examples
Cellerator
Demo1
StochCeller
SGD
SWI4 drill down to sequence, p:p interactions, …
STE5
Suitable mathematical modeling formalisms
Stochastic vs. deterministic network models
Dilute solutions vs. binding sites vs. small# compartments vs. machines
Coarse grained:
Artificial neural nets
Bayes nets
Detailed:
ODE systems
diffusion
Particle simulations
Langevin equations
Software architecture to integrate data sources
database  model generator  simulator 
optimizer  hypothesis formulation
e.g. SBML, Cellerator, KEGG, …
Issue: How to span the gap between small- and genomic-scale data?
Database (of reactions and pathway modeling information)
Knowledge representation schema
Populate with best available datasets
SIGMOID and populating it
Model generation: reactions to mathematical models
Connection from database
Algorithms
Simulation
ODE solvers
Stochastic simulation algorithms
Optimization
Stability analysis
Machine learning
Pattern recognition/hypothesis formulation
Possibilities for coarse-grained or multiscale models
and model reduction
Papers and their topic categories
reviews, yeast_cell_cycle, pathways, software, networks, development,
other; evolution, …
Figures used:
Central dogma
Compartments: nucleus, cytosol, membrane
DNA and protein strings with binding sites
Phosphorylation reaction
3-element regulatory loop with – feedback
(autopilot, thermostat, chemotaxing cell)
or + feedback (flip-flop circuit, cell type decision in multicellular org)
central dogma with TF feedback
Even-skipped GRN figures from my review paper:
real, simulated expression patterns
equations of motion with state variables, compared to Newtonian gravity
Yeast cell cycle microarray expression cluster trajectories – shows noise
Plant Shoot Apical Meristem expression imagery – real, and synthetic
Lecture 2: How to do it
Use of Cellerator
Yeast MAPK signaling + feedback
Yeast cell cycle
Drosophila AP axis
Lecture 3: Mathematical models
Mass action
Homodimeric transcriptional regulation example
Michaelis-Menten kinetics
Complexes and explosion of states
Effects of scaffolding
Allosteric enzymes
Small scale vs. large scale network models
Lecture 4: Stochastic Master Equation
0  B example
solve master equation
get Poisson distribution
stochastic dynamics: deterministic, Langevin/F-P,
random # of molecules / compartment, random positions and momenta
Lecture 5
Indexing : IP3 receptor; MAPK cascade; static compartmental models; (dynamic)
Optimization
Bisection search, brents method, etc
via simulated annealing
Newton’s method, conjugate gradients, BFGS, …
Software support for optimization: code generation and SBML approach.
Collect solutions and make them public
Homework 1
Run Cellerator on your example
HW2
Optimize a parameter in your example,
to give “ground truth” trajectory data
analogous to microarray data
create an optimization “engine” for 1 or more params
scoring function
move generator
search algorithm
HW3
Create a GUI for model generation
Read in pathway data
Display and edit it
Output in Cellerator form
Optional:
Run the model
Optimize it
HW4
Large scale pathway data ingestion
HW5???
Cellerator with indices
Molecular scale + aggregation
Developmental models