BCB 444544

1
BCB 444/544
Lecture 40 Systems Biology 40 Dec 3
2
Projects

• Presentations are next week!
• ALL 444 and 544 students are required to attend
  ALL presentations!!
• HW 6 attend presentations and fill out
  evaluation forms for each group
• Written reports are due Friday, December 12th by
  midnight

3
Final Exam

• The entire exam will take place in the computer
  lab
• Open book, notes, computer, internet, anything
  else you want to bring but you must work alone
• Comprehensive
• Mostly lab practical style questions

4
(No Transcript)
5
(No Transcript)
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
(No Transcript)
16
(No Transcript)
17

• Jonathan Wren
• University of Oklahoma
• Systems Biology

18
Lecture overview

• Overview
• The ultimate goal of biology bioinformatics is
  to tie it all together and understand the system
• In the meantime, forced to live in the real
  world, we focus on tying a few things together

19
Systems Biology backers attackers
20
What is Systems Biology?
Is this just another name for physiology?

• The study of the mechanisms underlying complex
  biological processes as integrated systems of
  many interacting components. Systems biology
  involves (1) collection of large sets of
  experimental data (2) proposal of mathematical
  models that might account for at least some
  significant aspects of this data set, (3)
  accurate computer solution of the mathematical
  equations to obtain numerical predictions, and
  (4) assessment of the quality of the model by
  comparing numerical simulations with the
  experimental data.
• -(Leroy Hood, 1999)

21
Why Systems Biology?

• On the technology side (PUSH) Capabilities for
  high-throughput data gathering that have made us
  aware that biological networks have many more
  components than we previously surmised.
• On the biology side (PULL) The realization that
  to the extent that we dont characterize
  biological systems quantitatively in their full
  complexity, the scope and accuracy of our
  understanding of those systems will be
  compromised. (in classical experimental terms,
  the uncontrolled variables in the system will
  undermine our confidence in the conclusions we
  draw from our experiments and observations)

22
Systems Biology vs. traditional cell and
molecular biology

• Experimental techniques in systems biology are
  high throughput.
• Intensive computation is involved from the start
  in systems biology, in order to organize the data
  into usable computable databases.
• Exploration in traditional biology proceeds by
  successive cycles of hypothesis formation and
  testing data accumulates during these cycles.
• Systems biology initially gathers data without
  prior hypothesis formation hypothesis formation
  and testing comes during post-experiment data
  analysis and modeling.

23
Systems Biology is an integration of data
approaches
24
Technologies to study systems at different levels

• Genomics (HT-DNA sequencing)
• Mutation detection (SNP methods)
• Transcriptomics (Gene/Transcript measurement,
  SAGE, gene chips, microarrays)
• Proteomics (MS, 2D-PAGE, protein chips,
  Yeast-2-hybrid, X-ray, NMR)
• Metabolomics (NMR, X-ray, capillary
  electrophoresis)

25
Each system has methods for modeling
Pi Calculus
Petri Nets
Flux Balance Analysis
Differential Eqs
26
Each system has methods for modeling
Boolean Networks
Electrical Circuit Model
Cellular Automata
27
So how can we meaningfully integrate the data?
28
System heterogeneity in size timescale
Atomic Scale 0.1 - 1.0 nm Coordinate data Dynamic
data 0.1 - 10 ns Molecular dynamics
Molecular Scale 1.0 - 10 nm Interaction data Kon,
Koff, Kd 10 ns - 10 ms Interactions
Cellular Scale 10 - 100 nm Concentrations Diffusio
n rates 10 ms - 1000 s Fluid dynamics
29
System heterogeneity in size timescale
Tissue Scale 0.01m - 1.0 m Metabolic
input Metabolic output 1 s 1 hr Process flow
Organism scale 0.01m 4.0 m Behaviors Habitats 1
hr 100 yrs Mechanics
Ecosystem scale 1 km 1000 km Environmental
impact Nutrient flow 1 yr 1000 yrs Network
Dynamics
30
Each of the scales does not fit together
seamlessly

• If one scale (e.g., protein-protein interactions)
  behaves deterministically and with isolated
  components, then we can use plug-n-play
  approaches
• If it behaves chaotically or stochastically, then
  we cannot
• Most biological systems lie between this
  deterministic order and chaos Complex systems

31
As we begin to connect systems we can engage in
inference

• We move up the chain from data to knowledge by
  questioning, observing and then hypothesizing
• These X genes are upregulated together, but are
  they interacting?
• PPI network data suggests Y are
• Are these Y part of a complex?
• If they are always expressed together, that
  suggests maybe yes
• As more data is integrated and systems linked
  together, this becomes easier

32
Problems?
How is static data interpreted since its a
dynamic system?How do we deal with
low-resolution quality?How do we treat missing
data?How do we deal with heterogeneous data
types?How can we identify and evaluate competing
hypotheses inferred by any system?
33
SB is springing out of existing efforts anyway

• E-cell (Keio University, Japan)
• BioSpice Project (Arkin, Berkeley)
• Metabolic Engineering Working Group (Palsson
  Church, UCSD, Harvard)
• Silicon Cell Project (Netherlands)
• Virtual Cell Project (UConn)
• Gene Network Sciences Inc. (Cornell)
• Project CyberCell (Edmonton/Calgary)