Virtual Seminars on Genomics and Bioinformatics

1
Virtual Seminars on Genomics and
Bioinformatics Sharing Knowledge with the
World www.VirtualGenomics.Org
Presenting Val Bykoski Boston University and
Virtek, Inc.
2
(No Transcript)
3
H. Bolouri E. Davidson
4

• Dr. Val Bykoski
• MS in Bioorganic Chemistry from Moscow Technology
  University
• PhD in Physics and Applied Math from Russian
  Academy of Sciences.
• Institute for Chemical Physics, Moscow
• National Institute for Control Problems, Moscow,
• Royal Society, London, Guest Professor lecturing
  at six UK Universities
• He was awarded the International Norbert Wiener
  Prize
• Boston University as a Faculty Research Professor
  
• University of Massachusetts at Lowell (CS and EE
  Departments)
• University of New Hampshire
• Consultant for Raytheon, Xerox, and EMC,
• Published over 100 papers and has 7 patents.

5
Understanding Cell Dynamics via Data Analysis

• Val Bykoski
• valb_at_cns.bu.edu
• Boston University and Virtek, Inc.
• Boston University Grid Access Facility
• December 18, 2003

6
Plan

• Motivation and Goal
• Existing Approaches to Cell Modeling
• Data Used to Build Models
• How Data Build a Model
• Next-Generation Data-Driven Models
• Cell Infrastructure
• Results
• Future Directions, Conclusion

7
Dynamic Genome

• "In the future attention undoubtedly will be
  centered on genome, and with greater appreciation
  of its significance as a highly sensitive organ
  of the cell, monitoring genomic activities and
  correcting common errors, sensing the unusual and
  unexpected events, and responding to them, often
  by restructuring the genome.
• Barbara McClintock, Significance of the Genome
  Responses to Challenges, Nobel Lecture, December
  8, 1983

8
Knowledge-gain-and-use Genome

• A goal for the future would be to determine the
  extent of knowledge the cell has of itself, and
  how it utilizes this knowledge in a 'thoughtful'
  manner when challenged.
• Barbara McClintock, ibid.

9
Motivation

• Current models have drawbacks
• sequence-based
• no realistic substrate, no temporal organization
• So, current understanding is limited
• Thus a bottleneck in
• understanding diseases, their dynamics, their
  diagnostic and curing
• in drug discovery technologies

10
Goal Understanding Cell Dynamics in Env

• How a cell recognizes stimuli and responds
• How response structures are integrated into
  genome
• How stress-induced response develops
• How unanticipated challenges are handled
• Next-gen models should provide
• - structured cell substrate
• - mechanisms for spatial and temporal control
• - knowledge-gain-and-use mechanism

11
Existing Cell Models Pros and Cons

• Kinetic models (such as eCell, MCell)
• can handle many 100s metabolites
• But cell infrastructure is missing
• realistic cell substrate
• spatial context where things occur
• temporal context when things occur
• interrelation context how things are related

12
Existing Models (2)

• Advanced approaches
• - Modules - integrated model units
• - Synthetic biol. systems (phiX174, 5386 bp)
• Existing models are unrealistically
  simple/limited
• - Critical bottleneck in drug discovery efforts
• - Need refinement substrate and processes
• Next step building more realistic models using
  data and model-building automation

13
What is Data? Biology is Data
Taken in Context

• To talk, data needs to be related to biomedical
  context
• Then, it is piece of logic, a unit to build
  models
• Datapairs need to be representative
• - to cover a range of contexts, to be biol.
  reasonable
• Context is the quality of d-model, its resolving
  power
• - sample prep, staining/fixing methods,
  conditions, etc.
• To predict, datapairs need to be generalized

14
How Data Build a Model?

• How physics builds its models
• - takes data in their context - datapairs
• - generalizes datapairs - common features
• - as analytical model yMod(x,T) or
• - as a table with interpolation yInt(x,T)
• Model and Data are equivalent, if taken entirely

15
How Data Build a Model (2)

• Biology cannot afford analytical models, only
  data-driven
• Start a generic cell/framework/model
• Generic model gets trained by datapairs
• a highly specific model with more data
• a less specific model with less data
• Cell model gets updated with each new data(!)

16
Building Genome to Balance Inverted Pendulum (how
data can build a model genome)

• Initially a structureless substrate, no model
• Data source
• - a permanently falling down pendulum
• Datapairs ltpState, Ctrgt or ltS, Rgt
• S and R get associated into a SR-pattern
• Multiple SRs overlap/form genome G ?SiRiT
• Which recognizes new S and build a new R

17
Genome SR-Dynamics Quality of Control
A
c
c
?
B
c
?
Angle (S) and control (R) time diagrams for
inverted pendulum
18
Building a Genome to Balance Pendulum
Genome
G ?Sp Rp
S
?
Env
R
RGS (?SR)S
19
So What?

• Online model building
• Datapairs train aCell to balance
• Trial-and-error process, errors ignored
• Only successful SRs overlapped/generalized
• Response structure, a genome, emerges
• Space-variance gets created by data

20
Next-Generation Data-Driven Cell Models

• Complex models can not be hand-built David
  Waltz
• Biol models too complex to be built by hand
• The only choice is a model driven/built by data
• Computer is able to generalize zillions datapairs
  
• So, no complexity limit for data-driven models
• And so model can be built using more realistic
  cell architecture

21
A Generic Framework to Build Models

• Is a generic cell (like stem cells)
• Its substrate is aperiodic structure, aCell
  (Fig.)
• aCell is built from space-variant BIOuniTs, biots
• Biot Ctr-MGLNAP, CtrH-bond Control,
• MGLNAP
• MetalGlycoLipoNucleoAminoacidPhosphate,
• Model is a generalized/overlapped set of SRs
• After training, it is a customized model, theCell

22
Simple examples of aperiodic structures
Aperiodic structures obtained by interference
23
Env Builds and Updates a Cell

• Env is the driving force
• (in phylogenetic timeframe )
• aCell is a container for distributed SR-patterns
  
• It gets updated incrementally by Env
• - nothing gets built from scratch(!)
• It has capability to reason, i.e. to smooth
  responses over whole genome
• (B. McClintock)

24
Cell Reasoning/Interpolation Capability

• Schematics of interpolation (smoothing) in
  S-space

y (responses)
new output
y2
Y Int(x,T), T xi,yi
yN
y1
X (stimuli)
x
xN
x2
x1
new
Stimuli (x) and responses (y) spaces
25
Genome Response Dynamics
Genome G, or knowledge base KB

• If no cross talk

If S?Sk is arbitrary stimulus, as, e.g.,
heat-shock
26
Chain Response to a Stimulus
A Chain training/folding 1?1/2?2/3?3/4? A?A/B?B
/C?C/D? B Response/unfolding a?b?c? I? 56I?2
3 D?
A
B
Genome may generate chain responses
27
Cell Infrastructure Observations

• structured cell substrate
• dynamic knowledge-driven genome (B.McClintock)
• senses events, gains knowledge, restructures
  itself
• uses knowledge to build responses -
• "in a truly remarkable way"
• Figs. follow

28
Cilia cross-section
29
Microtubules of various origin
30
Cilium architecture
31
Cilia cross-section
32
What is Generic Cell Substrate

• Aperiodic crystal, aCell 3D sequence of units
• biot Ctr-MGLNAP or
• Ctr-MetalGlycoLipoNucleoAminoacidPhosphate
• Ctr is H bonds 2(in AT) or 3(in GC)
• A generic cell architecture, aCellpolybiot,
• has a rigid 3D core poly-MGLNAP
• has a 3D plasticity substrate, H-bond lattice
• DNA, RNAs, proteins, etc. are just poly-X, X

33
aCell Radial Structure

• Control matrix Mxyz (b) generates space-var units
• Mxyz (b) const equifunctional surfaces
  (membranes)
• ?Mxyz (b) const orthogonal to equifunctional
  ones
• Radial polyB Mr(b) generates a radial sequence

DNA xRNAy Proteins
...
b1
b2
b3
bN
Nucleuslt--gtCytoplasm-Cell Wall//Env
34
Mutation Control via H-bonds
Eel
N
H
H
N
O
O
Tautomeric transition in H-bond in AT and GC base
pairs (WC-1953), which control complementarity,
and thus replication
35
Guanin-uracil bp with H-bonds
36
Processes Involved in Cell-Env Interaction

• Cell is a phylogenetic history of its Env
• It senses stimuli S, builds SR-patterns and
  responses R
• It accumulates the set of SR-patterns, a genome
  
• It generalizes SR patterns by spatial overlapping
  
• It builds responses by smoothing the gained SR
  knowledge, the genome knowledge

37
Env Control Inbound and Outbound Signals

• for aCell substrate, the signals are wavefronts
• They are coupled with cells electronic state
• Inbound (S) and Outbound (R) signals interfere to
  form SR-patterns
• which are distributed spatially over the cell
• SR-pattern maxima move Hs in the H-bond lattice
• H-lattice controls mutations (WC-53) and so
  expression

38
Genome gets built-up incrementally

• Env changes drive incremental genome update via
  cell electronic state
• It is the first (fastest) line of response
• Cell electronic state, its red-ox metabolic
  electrons, is a spatial organizer
• It controls the mol. conformation, including the
  H-bond lattice (Hellman-Feynman theorem)

39
Examples of Simple Electronic States
Benzene Molecule C6 H6
Ground (top) and few excited electronic states
John Bernal "Life is an expression of
potentialities of atomic electronic
states", Origin of Life, 1967
40
Novelty-Driven Genome Update

• Spatial overlap of SR-patterns reinforce common
  features
• Permanent in time SR-patterns reinforce a central
  core, a nucleus it is NOT a junk
• Novelty elements get into peripheral layers
• This is novelty-driven reinforcement
• new cell space for new stimuli only
• space-division multiplexing
• old stimuli just reinforce existing SR-patterns

41
Signal Dynamics

• Signal dynamic emerges by reinforcing repeated
  signal patterns
• from amorphous wavefront dynamics to
• focused signal paths to developed nervous system

42
Summary of Results SR-Dynamics

• Cell dynamics is driven by Env via genome
• Stimulus S triggers the associated response R via
  SR-pattern
• Each SR-pattern is a result of genome
  restructuring to match S-challenge
• Each SR-pattern is distributed over the cell
• Each SR-pattern integrates many genes
• Each SR-pattern provides a fully-loaded response
  R to S

43
Summary (2) aCell

• Cell is an aperiodic crystal, aCell
• aCell is generic framework to build models
• aCell is based on space-variant BIOuniT, biot
• Datapairs customize the framework into theCell
• Signals are wavefronts and may interfere
• Signal paths emerge in genome building

44
Summary (3) Genome Dynamics

• Genome is a spatially overlapped set of
  SR-patterns, a knowledge base
• Each stimulus S incrementally updates genome
• Genome can be sliced associatively by changing
  S

45
Summary (4) Hi-speed vs Low-speed Env

• Env controls flexibility/specificity ratio
• In hi-speed, novelty-enriched Env
• - loss of protein components
• - enrichment by hi-speed invasive crystal-like
  component
• - stress-induced crystallization
• In low-speed, highly specific Env
• - loss of nucleic components
• - enrichment by hi-specific protein components
• - env-driven protein diversification

46
Future Directions Next-Gen Methods to Control
Cell Dynamics

• Computer-engineered stimuli and structures
• generic X-shock-like stimuli (dX)
• physical nanosubstrates, optical patterns
• species-sensitive structures
• as drugs and modifiers

47
Hg-sensitive optical element (lens)
Multi-layer Hg-sensitive structure computed using
Hg spectrum (US EPA database)
48
Response of the Hg element
Selective response peaks match the original Hg
lines
49
Hg-specific focusing function
Hg-specific lens focuses only light from Hg lamp
50
Open Source Project

• The open source project aCell (aCell.sf.net) is
  setup.
• The contributors are welcome to join the project
• email valb_at_cns.bu.edu
• Recent publications
• 1. A Control and Expression Framework for Data
  Analysis in Bioinformatics
• http//www.omg.org/lsr/oibc/abstracts/bykoski.doc
  
• 2. Emergence of Genetic Code and Cell
  Infrastructure
• http//midas-10.cs.ndsu.nodak.edu/bio/papers/Gene
  tic_Code_Corr.doc

51
Acknowledgments

• This research is work in progress, and was
  performed during a long period of time.
• Discussions with
• Prof. H.C.Longuet-Higgins, FRS,
• Prof. Herbert Froelich, FRS,
• Prof. Simon Shnol of Moscow University
• Prof. Evgeny Nikitin of Technion University,
  Israel,
• Prof. Simon Berkovich of Georgetown
  University,
• Prof. Lev Levitin of Boston University
• are thankfully acknowledged.
• Computer-engineered species-sensitive optical
  structures have been
• developed jointly with Prof. Mike Fiddy,
  UMass/Lowell.
• NSF support for this research is thankfully
  acknowledged