Developmental Biology, Networks and Amorphous Computing

Developmental Biology, Networks and Amorphous Computing Amorph Workshop, January 2007 David Irons, Nick Monk University of Sheffield

Outline 1. What is amorphous computing? 2. Where does developmental biology fit in? 3. Case studies and properties of interest.

Amorphous Computing Category 1 : Geographically Embedded • Potential Examples • Wireless Sensor Networks • Mobile Ad Hoc Networks • Spray Computing Figure from http://www.ece.ncsu.edu/wireless/wsn.html Smart Dust (Berkeley, from 2001) Wireless thermocouple node from MicroStrain

Amorphous Computing Category 1 : Geographically Embedded • Potential Examples • Wireless Sensor Networks • Mobile Ad Hoc Networks • Spray Computing

Amorphous Computing Category 1 : Geographically Embedded • Potential Examples • Wireless Sensor Networks • Mobile Ad Hoc Networks • Spray Computing • Nodes may be able to enter or leave the system over time • Nodes may be susceptible to faults • Nodes may be mobile

Amorphous Computing Category 2 : Virtual Environment • Every node can (theoretically) communicate with every other node • Any network architecture can be created • Nodes may be able to enter or leave the system over time • Potential Examples • Grid Computing • Peer-to-Peer Networks

Amorphous Computing Category 2 : Virtual Environment • Every node can (theoretically) communicate with every other node • Any network architecture can be created • Nodes may be able to enter or leave the system over time • Potential Examples • Grid Computing • Peer-to-Peer Networks Sub-graph of gnutella peer network http://www.cybergeography.org/

Amorphous Computing Summary • Vast numbers of nodes that must use un-prescribed localinformation to decide • which other nodes to communicate with • what task / function to perform Nodes could be sensors, processors, computers, mobile devices etc The system may be subject to geographical constraints Commonly used terms • Ubiquitous computing • Ad-hoc networks • Amorphous computing • Pervasive computing

Amorphous Computing Challenges Task / function allocation (to the individual nodes) Setting up a spatial coordinate system Robustness Scalability Fault tolerance Adaptability to environmental changes Power management Security

Networks in Developmental Biology Inter-cellular networks Biological cells use local information to receive signals, transmit signals and ‘decide’ their eventual fate Drosophila Wing Jaiswalet al, 2006, Development 133, 925-935

Networks in Developmental Biology Inter-cellular networks Biological cells use local information to receive signals, transmit signals and ‘decide’ their eventual fate

Networks in Developmental Biology Inter-cellular networks Interactions between irregularly placed nodes give rise to an irregular lattice Inter-cellular interactions between cells are analogous to interactions in geographically embedded systems (Category 1) Such irregular lattices could also be created in virtual environments (Category 2)

Networks in Developmental Biology Intra-cellular networks Extra-cellular proteins can signal to neighbouring cells

Networks in Developmental Biology Links to Amorphous Computing Inter-cellular interactions between cells are analogous to inter-node interactions in some amorphous systems • Cellular events could correspond to an individual node’s processing and signalling capabilities • Protein excretion Sending a signal to neighbouring nodes • Signal transduction  Receiving a signal from neighbouring nodes • Intra-cellular interactions  Processing a signal from neighbouring nodes • (Deciding what action to take next)

Patterning during Development Drosophila Wing Jaiswal et al, 2006, Development 133, 925-935 Drosophila Embryo Figure courtesy of Johannes Jaeger Each colour corresponds to a different gene expression profile. These profiles determine the eventual fate of each cell. Arabidopsis Root Nawy et al, 2005, Plant Cell,17, pg1908-1925 Zebrafish neural plate Ashe and Briscoe 2006, Development 133, 385-394

Patterning during Development Delta-Notch White = high expression of notch Black = high expression of delta

Morphogen gradients (French flag model) • Morphogens are proteins that can diffuse over an array of cells and affect them in a concentration dependent manner. • Morphogens are produced from signalling centres

Morphogen gradients (French flag model) • Morphogens are proteins that can diffuse over an array of cells and affect them in a concentration dependent manner. • Morphogens are produced from signalling centres (which are often boundaries) • This process often produces boundaries that partition an array of cells

Boundary Formation and Signalling Centres Cells of different types often have different binding affinities. This causes cells to be pulled towards other cells of the same type

Boundary Formation and Signalling Centres Boundary cells receiving contrasting signals may express new morphogens

1: Zebrafish Neural Tube Figure from Shier and Talbot. 2005. Ann Rev Genet. 39 : 561-613 Movie from Karlstrom and Kane. 1996. Development. 123 • Several morphogens are involved in compartmentalisation and boundary formation. • These morphogens act in a hierarchical manner.

1: Zebrafish Neural Tube ZLI Boundary forms in response to Wnt gradient MHB Boundary forms in response to Wnt gradient Figure from Rhinn et al. Curr. Opin. Neurobiol., 2006. 16, 5-12 • Several morphogens are involved in compartmentalisation and boundary formation. • These morphogens act in a hierarchical manner.

1: Zebrafish Neural Tube ZLI Boundary forms in response to Wnt gradient DMB Boundary forms in response to Fgf gradient MHB Boundary forms in response to Wnt gradient Figure from Rhinn et al. Curr. Opin. Neurobiol., 2006. 16, 5-12 • Several morphogens are involved in compartmentalisation and boundary formation. • These morphogens act in a hierarchical manner.

2: Drosophila wing Figure from “Atlas of Drosophila Development” (by Volker Hartenstein, Interactive Fly)

Figure from Flyview 2: Drosophila wing Figure from “Atlas of Drosophila Development” (by Volker Hartenstein, Interactive Fly) Figure from The Genome of Drosophila melanogaster (1992). D.L. Lindsley and G.G. Zimm

2: Drosophila wing Setting up a co-ordinate system Extra-cellular proteins can signal to neighbouring cells

2: Drosophila wing Setting up a co-ordinate system Morphogen gradients in the wing pouch (P) WG L2 DPP L3 L4 HH L5

2: Drosophila wing Setting up a co-ordinate system Gene expression in the wing pouch (P) DPP HH

3: Feedback and Robustness Example: Hedgehog gradient • Hedgehog is a morphogen that is active in many stages of development in many different organisms (e.g. Drosophila wing, Vertebrate neural tube) • Boundaries need to be accurately positioned despite the unpredictability of morphogen production rates • Feedback mechanisms are conserved across organisms and control boundary positioning L3 L4 HH

3: Feedback and Robustness High morphogen level (HH)  High receptor production (PTC)  High morphogen degradation  Reduced morphogen spread

3: Feedback and Robustness Hh Ptc

3: Feedback and Robustness No Feedback Feedback x • Solid line corresponds to low morphogen production rate. • Dashed line corresponds to high morphogen production rate • Feedback mechanisms control the morphogen transport and ensure the • morphogen gradient is robust

4. Scalability (Drosophila wing) Figure from Crickmore and Mann (2006), Science,313,63-68 Individual wings are different sizes, but the wing features (such as veins) are correctly proportioned A secondary wing called the haltere is a miniature version of the main wing

4. Scalability (Drosophila wing) P A HH L4 L3 pMad DPP L5 L2 Intercellular feedback controls receptor levels (Tkv) and cellular response (pMad, Omb, Sal)

4. Scalability (Drosophila wing) The cellular response to Dpp can control cell proliferation in the wing. It is believed that cell proliferation occurs where there is high signalling differences between neighbouring cells. i.e. where the pMad gradient is steepest High proliferation rates

4. Scalability (Drosophila wing) Crickmore and Mann (2006), Science,313,63-68 Ubx in the Haltere prevents repression of the receptor Tkv. High receptor levels close to source normalise Dpp signalling (and reduce its range) This process can reduce wing growth by ~30% Ubx

4. Scalability (Drosophila wing) Vestigial (Vg) allows controls cell proliferation. Since Vg is regulated by both Wg and Dpp signalling, growth is controlled in 2 dimensions WG Vg L2 DPP L5

4. Scalability (Drosophila embryo) Bicoid Hunchback Images from FlyEx Database Poustelnikovaet al 2004 Images from Houchmandzadeh et al 2002, Nature, 415, 798-802

4. Scalability (Drosophila embryo) Bicoid Hunchback Images from FlyEx Database Poustelnikovaet al 2004 Images from Houchmandzadeh et al 2002, Nature, 415, 798-802 Hunchback response is scalable despite natural fluctuations in the upstream morphogen Bicoid and varying temperatures.

5. Regeneration Morphogen gradients are also believed to underlie regeneration. Gargioli and Slack 2004. Development.131, 2669-2679

Controlling cell proliferation and growth Incorporating Cell Proliferation into Amorphous Computing Case : Geographically embedded systems • Starting from a few central seed nodes, the system could grow into the full space Case : Virtual environments • New nodes join a waiting list • A ‘proliferating’ node could send out a call to recruit a node from the waiting list

Conclusions In developmental biology, regulation primarily occurs at the cellular level. The regulatory mechanisms from developmental biology can be mirrored by amorphous computing systems (by viewing the computational nodes as biological cells) Some of the challenges for Amorphous computing are faced by, and solved by, biological systems. Where these challenges overlap, it is plausible that developmental biology can help provide a solution or a trick to solve it

Developmental Biology, Networks and Amorphous Computing