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3D Spatial Programming

Problem: How to program 3D spatial structures:

  • Is it possible to discover and utilize the proper hierarchy of organization and dynamics to compile global 3D spatial structures through the use of smaller cellular objects? --> Biology does it. Can we learn from developmental biology?


  • What are the levels of abstraction?
  • What are the interfaces between the levels of abstraction?


  • Agent = individual cell

Hierarchy Level II: Agent Behavioral Primitives

  1. replicate (cell division)
  2. die
  3. change shape/structure
  4. crawl
  5. adhesion to substrates/other cells
  6. emit chemical signals (diffusible or membrane bound)
  7. receive chemical signals via receptors; also electrical and mechanical sensors?
  8. store information (memory)
  9. process signal and current state information

  • NOTE: All of the above primitives are accomplished via a lower level of abstraction: biochemical primitives

Hierarchy Level I: Biochemical Primitives

  1. Gene Regulation
    • Transcription Factors
      • activators
      • inhibitors
    • Chromatin modification ==> global gene accessibility
      • Methylation
      • Acytlation
  2. Protein modifications producing allosteric structural changes influencing protein function (Ex: on/off switch, change functionality)
    • Kinases/Phosphatase (Phosphorylation state)
    • Nucleotide Exchange Factors (GEF's, ATP additions, etc...)
    • Methylation & Acytlation
  3. Polymerization of identical subunits (Ex: tubulin --> Microtubules)
  4. Dimerization (homo/hetero-)
  5. Protein complexes to perform higher level function not capable via single proteins

Hierarchy Diagram

SpatialProgramming Hierarchy.jpg


One salient difference between amorphous computing and bio-cellular computing is that biology has devised an elegant hierarchical differentiation scheme to allow specialization and sub-specialization of cell types to distribute functions to a non-homogenous group of agents. Ultimately we want to meld differentiation into an amorphous-like computing system to utilize the power of hierarchical organization an heterogeneous populations.

A key component in the process of differentiation in biology is the idea I call a decision network. The purpose of a decision network is to perform a computation between communicating cells to determine the fates of the cells involved. A simple example of a decision network is the Delta-Notch ligand/receptor system found in nearly all organisms that performs a lateral inhibition role to prevent all cells within a local environment from differentiating into the same cell type. It is essentially a competition network where one cell wins and then subsequently inhibits the losers. Another decision network example is found in Dictyostelium prespore/prestalk (Psp/Pst) cell differentiation which is composed of a small negative feedback network acting as a homeostat that robustly produces a particular ratio of Psp/Pst cells (~75/25) within a given colony.

The utilization and understanding of decision networks are going to be a key interface in ultimately programming cellular differentiation within colonies of similar cells.

I argue that once a cell has made a decision what fate to take on, it wastes no time to act on that decision. For example, it is generally excepted that once a cell has decided it will commit suicide it will enter a fixed-action pattern (term stolen from behavioral neuroscience) to follow through to completion. What good is a half dead cell? Furthermore in Dictyostelium, once the cells have decided they will enter the social phase of their life, they immediately switch over to a new gene expression state (~25% of gene expression is modulated). This gene expression modulation occurs many times as the cells proceed with different morphogenetic transformations through development, but maintain a relatively stable gene expression pattern while no morphogenetic transformations are occurring. If it is true that significant state transitions occur in a crisp, decisive manner then it gives hope that genetic programming may be possible through the interaction of small decision networks and more global gene expression modules.

If you haven't noticed yet, I have absolutely no idea what I'm talking about and encourage any input/comments via email to:

Some Principles of Developmental Biology

Original List:

  1. Life vs. Death
    • Cell proliferation (replication)
      • Stem Cells - pleuri-potent cell replacers
    • Cell death (apotosis)
    • NOTE: Need both proliferation and death
  2. Differentiation
    • Cells change function and structure in a hierarchical way to build up a complex organism originating from a single cell
  3. Cell morphogenesis
    • Cell shape/structure change to facilitate global morphogenesis
  4. Hierarchical Organization
    • Cell differentiation results in reduction of cell potential
    • Many possible cell fates all stemming from initial zygote (root of tree)
  5. Induction
    • Cell communication that alters/induces cell fates
    • Often times there are inducing centers --> a particular cell or group of cells emitting the induction factor(s)
    • Community effect - if cell placed in certain environment, the cell may be induced to take on same fate as its neighbors
  6. Semi-modularity
    • Sub-division of tasks (tissues and organs), help organize functional units
    • Boundary formation - creates compartments to further order cells
  7. Cell sorting
    • Cells can sort into groups of like cells through selective adherence to each other and to the extracellular matrix
  8. Strong Attractor Systems
    • Allow fate determination to be relatively stable once arrived
  9. Competency
    • Differential cell abilities to respond to signals due to its current state
  10. Epigenetics
    • Chromatin structure and regulation of gene expression --> mechanism for changing competency and differentiation
  11. Combinatorial code of gene expression
    • With limited number of genes/signals cells reuse same signals to mean different things given different competencies of cells listening
  12. Maternal factors
    • Inherited directly from mother directly from mother cell
    • Inherited regulatory molecules can effect cell fate autonomously without induction (determinants)
  13. Lateral inhibition
    • Neighbors prevented from taking on same fate
  14. Analog to digital conversion through nonlinear thresholding
    • Cells respond differently to the same signal when it is presented at different concentrations (morphogen).
    • Continuous information contained within a concentration gradient is converted to a discrete outcome through thresholds of activation.
  15. Cell migration (motility)
    • Often regulated by signaling molecules
  16. Cell adhesion
    • Cells capable of adhering/pulling on other cells, surfaces, and extracellular matrices through surface proteins
  17. Cell sensing of self and environment
    • Senses and interprets chemical, electrical and mechanical signals from other cells and environment
  18. Cellular information processing
  19. Cells process information based self state and environmental state
  20. Morphogenesis
    • Taking on 3D form
    • Typically embryos form 3D structures by first forming 2D sheets and subsequently folding
    • 3D structure is often required for organ functionality
  21. Genetic pattern formation
    • Spatially and temporally regulated differential gene expression
    • Stimulatory vs. Inhibitory signaling (positive & neg feedback)
  22. Evolution
    • Conservation of certain gene functions through evolution (homologs, orthologs)
  23. Cell memory
    • Cell has ways of remembering past events by changing its current state based on experience