Special course in Computer Science: Molecular Computing Lecture 1: Introduction Vladimir Rogojin Department of CS, Abo Akademi http://combio.abo.fi/teaching/special-course-in-computer-science-molecular-computing/ Fall 2015 Space that is computing ➢ Universe as information and information processing ➢ World as a computer or being computed ➢ Konrad Zuse, 1969 – the Universe is being computed by cellular automata or other discrete computing machinery World science festival ➢ Digital physics – theories based on premise that “the Universe is, at heart, describable by information, and is therefore computable ” Rh izome | Rechnender Raum 3D Virtual Creature Evolution ➢ Artificial evolution simulation by Lee Graham ➢ In an artificially simulated environment ➢ Artificial organisms are generated ➢ Purpose: visualize and research body shapes and strategies to achieve fitness function 3D Virtual Creature Evolution ➢ Evolution simulation: artificial organisms evolve to achieve highest fitness ➢ Fitness criteria: body size, maximum height, average height, contact with ground, catching flying spheres, etc. www.snipview.com ➢ Artificial environment: landscape, gravity, water pools, etc. ➢ Artificial organisms: consist of blocks-joints- motors, can reproduce sexually/asexually Can mutate www.youtube.com ➢ There were reported 220 artificial species so far Synthetic bacteria ➢ Can one ➢ design on a computer an artificial genome, ➢ synthesize the respective DNA sequences in wet-lab, and ➢ Basing purely on this artificial genome grow living organisms ➢ THE ANSWER: YES, WE CAN! www.mit.edu Mycoplasma laboratorium ➢ Minimal Genome Project – find minimal set of genes able to sustain life ➢ J. Craig Venter Institute (JCVI) – non-profit genomics research institute founded by J. Craig Venter ➢ Experiments with M. genitalium – reduced to 382 genes ➢ Artificial genome with 382 genes – Mycoplasma laboratorium www.synthetic-bestiary.com ➢ Plan – generate synthetic genome of M.laboratorium and inject it into a proper cell, to use its translation and replication biochemical machinery and environment Synthia ➢ Synthesized M.mycoides genome of 1,078,809 bp from a computer record from scratch ➢ Transplanted the synthetic genome into DNA-free M.capricolum cell ➢ The new genome took over, the new organism multiplied ➢ Craig Venter: “the first species... to have its parents be a computer” holistichealthinsider.com ➢ Technologies: DNA sequencing, long DNA synthesis, genome transplantation twin-cities.umn.edu Self-assembled nanostructures ➢ Programmable matter - “any bulk substance which can be programmed to change its physical properties” ➢ Principle: coupling computation to its material properties ➢ Goal: creating nano-scale stable structures, like: ➢ Crystal latices, nanotubes, arbitrary shapes ➢ Functional: molecular machines, and DNA computers ➢ Implementations: ➢ DNA walkers – nanoparticles transport and direct chemical synthesis ➢ Molecular wires – molecular-scale electronics ➢ Smart drugs – targeted drug delivery Nanobots ➢ Molecule-size robots www.explainingthefuture.com ➢ Nanomedicine: ➢ Targeted drug delivery (detect and kill cancer cells) ➢ Surgery, ➢ Monitoring of diabetes, ➢ Biomedical instrumentation, ➢ Etc. thevine.com.au ➢ Design issues: ➢ Sensing, power communication, navigation, manipulation, locomotion, onboard computation Molecular machines ➢ Molecular car: ➢ Molecular-sized “4-wheel” devices capable for moving (rolling) on the surface: ➢ Engine-less car: ➢ Non-controlable ➢ undirected movement ➢ on hot metallic surfaces ➢ Electric-driven car: ➢ Electrically powered ➢ wheels drive car in ➢ the desired direction ➢ Motor nanocar: ➢ Nanocar with the synthetic molecular motor Molecular-scale electronics ➢ Branch of nanotechnology ➢ Single molecules as electronic components: ➢ Wires ➢ Transistors ➢ Rectifiers ➢ Contrary to conventional electronics: ➢ Bottom up approach rather than top down approach ➢ An integrated circuit is self-assembled from properly designed molecules ➢ Problems with traditional bulk approach: precision limitations pubs.rsc.org Transcriptors ➢ Analogy: semiconducting material-based transistor ➢ Transcriptor : DNA/RNA/enzyme -based logic device ➢ A computer needs: ➢ Store information ➢ Transmit information ➢ Logic operations www.kurzweilai.net ➢ In biochemistry all the three functions were finally implemented ➢ The invention of biological counterpart of a transistor – transcriptor was finally announsed on March 2013 in Stanford University preethisiribhat.wix.com BIL gates ➢ Transcriptor: ➢ Device composed of a complex of biological materials: DNA/RNA/proteins ➢ Three-terminal device with a logic control system ➢ On the physical level the device controls the flow of RNA www.kurzweilai.net polymerase across a strand of DNA ➢ Traditional AND, OR, NOR, NAND, XOR, XNOR gates are replicated by transcriptors and called “Boolean Integrase Logic (BIL) gates” ➢ Likewise transistors, transcriptors can amplify a signal ➢ Group of transcriptors can form a Turing-complete computational device Biocomputers ➢ Not to replace conventional computing silicon-based devices ➢ Meant to be used where electronic systems cannot be implemented and applied: ➢ Reprogramming living cells ➢ Nanobots www.photonics.com ➢ Smart drugs ➢ Etc. ➢ Potential applications: ➢ Fully functional computers at nano-scale, that can sense and manipulate the environment. ➢ Disease warning, diagnostic, control insulin production/consumption, control cell reproduction, detect and suppress cancer cells www.prote.in Biology-based paradigms ➢ Neural nets: ➢ Image/speach/text/pattern recognition ➢ Evolutionary computation, Genetic algorithms, swarm intelligence: ➢ Optimization problems ➢ Cellular automata ➢ Modelling physical and biological processes: ➢ Such as, communication, growth, reproduction, competition, evolution, etc. ➢ Artificial immune systems ➢ Computer security, data analysis, bioinformatics, robotics, etc. ➢ Membrane computing www.doc.ic.ac.uk Natural Computing Computer Natural Science, Nature Computing Mathematics Computations in Nature Molecular computing: •DNA computing www.engineering.com Cellular computing: •Gene assembly in ciliates combio.abo.fi Quantum computing: •Superposition •Entanglement ralphlosey.files.wordpress.com Computations in Nature Molecular computing: •Massive parallelism •Nano-scale www.engineering.com Cellular computing: •Massive parallelism •Nano-scale •Replication •Filtering combio.abo.fi •No supervision Quantum computing: •Exponential speed-up ralphlosey.files.wordpress.com •Information teleportation Natural computing ➢ In general three directions: 1)Nature-inspired paradigms and problem-solving www.onlineinvestingai.com techniques 2)Math and computer-based analysis and simulation of natural phenomena 3)Employing natural components (bio-components and systems) to compute en.wikipedia.org Bioinformatics ➢ Major activity: ➢ Develop software tools to generate useful biological knowledge ➢ Computer science, mathematics and engineering to process bio-data ➢ Databases and information systems: store and organize bio-data www.stsiweb.org www.ocib.ca Bioinformatics and Computational Systems Biology ➢ Two tightly related areas with vague border: ➢ Bioinformatics: ➢ analyzing bio-data to generate bio-knowledge ➢ Computational Systems Biology: ➢ computational modelling of bio-systems and bio- processes to generate bio-knowledge www.bioquicknews.com Synthetic biology qb3.org ➢ Engineering synthetic biological components and systems ➢ Started from genetic engineering techniques based on recombinant DNA technology ➢ Nowadays we can synthesize some bacterial chromosomes: ➢ M.mycoides genome of 1,078,809 bp, grown fully functional cell from the synthetic genome ➢ Other efforts: ➢ Cell reprogramming (for instance to make them produce combustable fuel, novel cancer therapy approaches, etc.) ➢ Designing multi-cellular systems. For instance cell-to-cell communication modules to coordinate living bacterial populations blogs.plos.org www.bio.org Membrane computing ➢ Formalizes membranal cellular structure and intermembranar transport of biochemicals ➢ Terms: strings, multisets, graphs ➢ Membrane system – formal computational device based on multiset rewriting and communication ➢ Basic ingredients: ➢ Membranes – formalize cellular membranes. Membranes determine regions that: github.com ➢ may include other membranes (hierarchical structure) or ➢ can be connected via communication channels (networks) ➢ Multisets of objects – formalize biochemical compounds. ➢ Each membrane has an associated multiset of objects (the membrane's content) ➢ Multiset rewriting/communication rules – formalize biochemical reactions and biochemical cross-membrane transportation: en.wikipedia.org ➢ The rules dictate how membranes' content evolution and inter-membrane communications Membrane computing 1 ab d, in2 1 aaabbc dc d, in4 add 2 4 2 4 aaaaa ad d, out aa d 3 3 a ad, out 1 1 ac acddd 2 4 2 4 aaaaaddd aa 3 3 Membrane computing ➢ Computation: ➢ At each step rules are chosen non-deterministically and in maximal parallel manner (i.e., whatever can evolve – evolves) ➢ The system halts when no rule can be applied. Result – either sequence/multiset of objects expelled into the environment or multiset of objects collected in the “output” membrane webapps2.ucalgary.ca ➢ Applications: ➢ Machine learning, ➢ Modeling of biological systems, ➢ Computer graphics, public-key cryptography, approximation and sorting, ➢ Analysis of computationally hard problems liacs.leidenuniv.nl DNA computing: DNA