Discrete Dynamical Modeling of Cellular Transformation · Discrete Dynamical Modeling of ......
22
Discrete Dynamical Modeling of Inducible Cellular Phenotypes Current Work (2010-2012) and Future Directions Bradly Alicea http://www.msu.edu/~aliceabr/
Transcript of Discrete Dynamical Modeling of Cellular Transformation · Discrete Dynamical Modeling of ......
Discrete Dynamical Modeling of
Inducible Cellular Phenotypes
Current Work (2010-2012) and Future Directions
Bradly Alicea
http://www.msu.edu/~aliceabr/
Dynamics: an Alternative to
Reductionism Reductionism:
1) study a single gene, look for causal relationship. Problem: not scalable to a
whole-genome context, no account of interactions, correlation ≠ causation.
2) look for smallest possible units of analysis. Problem: there are many
mechanisms at many scales (space and time).
* cannot observe “dynamics”, or big picture of a process.
Complexity:
1) networks, interactions: smallest possible units interact and can form networks.
* gene-gene interactions, complex pathways, synthetic effects, futile cycles.
2) chaos: time-series can exhibit highly variable behavior across many time-
scales (intervals). Yet order maintained (intrinsic randomness – aggregation -
order).
* actin remodeling, local field potentials, embryonic patterning.
Comparison with first poster –
Dynamics Days 2010 (Chicago)
Evolution of a project, or
two sides of the same coin?
2010: excitable, sliding
cellular automata (CA).
2012: CAs + genetic
algorithms (GA).
2010: a way to discover
changes related to
reprogramming,
2012: a way to model
potential reprogramming
scenarios.
Cellular Automata (CAs)
Cellular Automata: discrete dynamical simulation.
Cells have properties and interaction rules, behave in parallel.
Properties: internal state.
Interaction rules: if n > 2 neighbors are red, turn red.
Parallelism: all cells use same set of rules, have same
properties.
current pattern 111 110 101 100 011 010 001 000
new state for
center cell 0 0 0 1 1 1 1 0
Example: Wolfram’s Rule 30 (1-D lattice)
Rule 30 - model Rule 30 - nature
1 2 3 4 5 6 7 8
Below: 2-D von Neumann neighborhood, order 1
Genetic Algorithms (GAs) Genetic Algorithm (GA): set of instructions based on what happens in a
biological genome (during evolution, gene expression). Dynamical simulation.
* used to find adaptive, optimal solutions for problems in computer graphics,
product design, robotics.
001 101 110 011 000
011 111 110 011 010
String can be replicated,
mutated, recombined over
several iterations. Produces
offspring, adaptive output.
Properties:
* single binary (or continuous) string
= chromosome.
* population of individuals.
* operators (mutation,
recombination, selection).
* evolution can be optimal, or it can
produce constrained variation.
Far left: fitness landscape for genetic algorithm optimization.
Courtesy: Mathworks.
Left: genetic algorithmically-evolved robot morphology.
Courtesy: Dr. Josh Bongard, New Scientist..
Dynamical Approximation Poster
(DD2010) Spatial heterogeneity:
colonies, cultures not
uniform.
Dynamical Approximation Poster
(DD2010) Spatial heterogeneity:
colonies, cultures not
uniform.
Kinetics among cells
across culture, colonies
not uniform.
Dynamical Approximation Poster
(DD2010) Spatial heterogeneity:
colonies, cultures not
uniform.
Kinetics among cells
across culture, colonies
not uniform.
Two stimulus model of
reprogramming: virus
and feedbacks.
Dynamical Approximation Poster
(DD2010) Spatial heterogeneity:
colonies, cultures not
uniform.
Kinetics among cells
across culture, colonies
not uniform.
Two stimulus model of
reprogramming: virus
and feedbacks.
Contact inhibition and
other topological
features (higher-D).
Dynamical Approximation Poster
(DD2010) Spatial heterogeneity:
colonies, cultures not
uniform.
Kinetics among cells
across culture, colonies
not uniform.
Two stimulus model of
reprogramming: virus
and feedbacks.
Sliding neighborhood:
cells can merge (B) and
change neighborhood.
Contact inhibition and
other topological
features (higher-D).
Dynamical Approximation Poster
(DD2010) Series of constraints (infectability, contact
inhibition, substrate). Determine initial,
subsequent state.
Dynamical Approximation Poster
(DD2010) Series of constraints (infectability, contact
inhibition, substrate). Determine initial,
subsequent state.
Infectability: excitable media approach.
Excitability in neurons and slime molds
(physical, electrical potentials).
Infection creates a “potential” in each cell
(cells interact, potential of magnitude can
convert cell).
Dynamical Approximation Poster
(DD2010) Series of constraints (infectability, contact
inhibition, substrate). Determine initial,
subsequent state.
Autonomous factors: infectability +
feedback = internal state, then external state.
* what is secondary stimulus? Intercellular
signaling?
Infectability: excitable media approach.
Excitability in neurons and slime molds
(physical, electrical potentials).
Infection creates a “potential” in each cell
(cells interact, potential of magnitude can
convert cell).
Dynamical Cellular Encodings
(DD2012)
Encoding: turn cell and
cell population behavior
into a computational
model.
Dynamical Cellular Encodings
(DD2012)
Hybrid Model: CA and
GA work in tandem.
Mapped to infectibility,
reprogramming process.
Encoding: turn cell and
cell population behavior
into a computational
model.
Dynamical Cellular Encodings
(DD2012)
Hybrid Model: CA and
GA work in tandem.
Mapped to infectability,
reprogramming process.
Encoding: turn cell and
cell population behavior
into a computational
model.
Population of cells are
infected. Fraction of
cells become carriers.
Trigger for additional
changes (focus on
intercellular factors).
Dynamical Cellular Encodings
(DD2012)
Design of each cell’s genome (basic
functional units). Initial switch (epigenetic
state), segments are expressed in
combination, at different intensities.
Dynamical Cellular Encodings
(DD2012)
Design of each cell’s genome (basic
functional units). Initial switch (epigenetic
state), segments are expressed in
combination, at different intensities.
More complex ruleset than 2010
poster. Rules for influencing
conversion of neighbors based on rate
of leaderless protein exchange, etc.
Leaderless Proteins – Role in
Inducing Cellular Phenotype Intercellular signaling is done by leaderless proteins:
In leaderless mRNAs, 3’ end is cleaved,
modifies sites of action (Cell, 147(1),
147-157 – 2011).
Interleukin 1-β – secretory protein lacking a signal
peptide (special route to transport). Mol Bio. Cell,
10(5), 1463-1475 (1999).
Figure 6 Figure 1
Leaderless Proteins – Role in
Inducing Cellular Phenotype Intercellular signaling is done by leaderless proteins:
Occupy x,y,z,t? Shoaling fish?
What does it mean to be leaderless? In leaderless mRNAs, 3’ end is cleaved,
modifies sites of action (Cell, 147(1),
147-157 – 2011).
Interleukin 1-β – secretory protein lacking a signal
peptide (special route to transport). Mol Bio. Cell,
10(5), 1463-1475 (1999).
Figure 6 Figure 1
Leaderless: not embedded in a hierarchy.
Leaderless molecules do not follow the leader (but
not necessarily random behavior).
* under certain conditions, leaderless activity
(shoaling fish?) may lead to order, patterned
behavior.
See also: Intracellular signaling proteins as
"smart" agents in parallel distributed processes.
BioSystems, 50, 159-171 (1999).
Future Work and Directions
Reconcile models presented
in 2010 (top) and 2012
(bottom).
Systems process model vs.
algorithmic model.
More explicit kinetics, stochastic
components?
Future Work and Directions
An excitable model focused on the activities of leaderless proteins:
Leaderless
proteins
Epigenetic
state Infected
Cell
Gene
regulation
Combinatorial
action
Inducement to a pluripotent state is still a two-stage process (positive for
virus, positive for pluripotency.
Needs more explicit stochastic mechanism within each cell (CA is
stochastic at the spatial (macro) scale.
Determines local cellular state
Collectively determine
neighborhood state
Focus on “alternative” intercellular
signaling molecules (take an alternate
pathway to action, more likely to
have a collective effect).
