Top Banner

Click here to load reader

Discrete Dynamical Modeling of Cellular Transformation · PDF fileDiscrete Dynamical Modeling of ... discrete dynamical simulation. ... fitness landscape for genetic algorithm optimization.

May 26, 2018

ReportDownload

Documents

truongnhu

  • 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: Wolframs 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 cells genome (basic

    functional units). Initial switch (epigenetic

    state), segments are expressed in

    combination, at different intensities.

  • Dynamical Cellular Encodings

    (DD2012)

    Design of each cells 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- 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- 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).

Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.