L19. Biocomputing

L19. Biocomputing

1. What is it?

   • used as a catch-all term covering all this area at the intersection of Biology and Computation
     • 1. Computational Biology - solve biological problems with computational tools (such as modeling, algorithms, heuristics)
     • 2. Bioinformatics - management of biological databases, data mining and data modeling, as well as IT-tools for data visualization
     • 3. DNA computing and nano-engineering - use DNA (and other) molecules to perform computations
     • 4. Computations in living organisms - constructing computational components in living cells and in populations, as well as with studying computational processes taking place daily in living organisms

2. Moore's law

   • "principle that the speed and capability of computers can be expected to double every two years, as a result of increases in the number of transistors a microchip can contain"
   • How do we enable moore's law to carry on?
     • limited by the fact that tools to make structure are bigger than the structure itself
       • >SOL= t the structure build itself (self-assembly)
       • At the moment NO DNA transistors exists BUT DNA can be used as a template to grow the wire for transistor
       • Still researching other options
     • Limited by the fact that dry DNA gets denaturated >> introduction to the idea of computers which are not completly solid (no need of hard transistors and starting to use molecules/cells as trns)
     • if using mol ...how do I read?
       • analysis like in the lab
         • Kinetics or production of signal (use enzymes)
         • Specific structures
         • Do gels to know the sides (loops go to diff places) - electropheresis

3. Computing with DNA

   • How do we do in practice?
     • A) Similar to how bacteria do by choosing source of energy
       • particular pathway depending on the [ ] glucose or lactose
       • would chose glucose over lactose if possible! (more efficient)
     • B) Xor gate (true-false)
       • depends on an enzyme mRNA polymerase
       • A terminator sequence prevents transcription of activity by two recombinase enzymes (TP901 & Bxb1) if either both or none are present
       • Complicated: machine check cells individually
       • Consider that in biology everything is analogue (signal 1-0)
   • computing/programming with DNA (and size)
     • Enter programming on DNA to perform tasks as it goes
       • "if" fuction (decision happening)
     • Can deal to shape varying "programmable sensor"
       • Autonomous locomotion "DNA walking"

4. Combining platforms

   • inspiring by the fact that computation in cells occur in diffe places
     • Think about the cell as a serie of components working together
   • use die to view signal in the surface
     • improving by analysing more than one molecule at a time
   • Using DNA origami scaffolds to isolate circuitry (minimizes cross-talk) and bring them closer (maximizes speed – from hours in a tube to minutes)
   • Applications
     • Deliver a payload (drug) : No signal> cache close // signal> open
   • introduce the idea of "MEMBRANE COMPUTING"
     • " area of computer science aiming to abstract computing ideas and models from the structure and the functioning of living cells, as well as from the way the cells are organized in tissues or higher order structures"
     • deals with distributed and parallel computing models, processing multisets of symbol objects in a localized manner (evolution rules and evolving objects are encapsulated into compartments delimited by membranes), with an essential role played by the communication between compartments (and with the environment).
     • take cell as a concept and put it into a computer (artificial intelligence> code how the brain work into a comp)
     • Neuron network
       • predict an outcome based on a set of inputs
       • used in imaging BUT not used in clinics bc outcomes can vary in diff days
       • the more you waint the more accurate algorith (better at prediction as more image are seen, never stop improving "training the network"

5. Conclusion... What do we do this?

   • cam do things that are more difficult with silicon
   • they work independently..we cannot control it
   • example MAYA II (biocomputer play games based on rules you fit in)