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
➢ 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
➢Complementarity, base-pairing: ● Deoxyribonucleic acid: ➢A-T – Nucleic acid ➢C-G – Genetic information – Two strands of polymers
● Polymer: – Sequence of nucleotides
● Nucleotide: – has one of four bases:
● adenine (A)
● cytosine (C)
● guanine (G)
● thymine (T) Adleman’s Experiment, DNA Computing • Solves an instance of Hamiltonian Path Problem HPP problem: • given: directed graph • by DNA manipulation • find: a path coming through all the T A T G A C T vertices exactly once A T A C T G A T A T G A C T • DNA computing operations: A T A C T G A A C T • cloning (PCR) α1 α2 α1 T G A α2 • splicing 4 β1 A C T β2 • lengthening/shortening T G A 1β1 β2 • separating/fusing 5 • cutting • sequencing 2 3 • other! Adleman’s Experiment, DNA Computing 1. Encoding: • Vertex single strand short DNA 1 1 • Edge single strand short DNA complementary to adjacent vertices-DNA 1 2 1-2 1 1-2 1 2. Cloning 1-2 1 1-2 1 1-2 1 3. Generating all pathes (hybridization) 1-2 1 1-2 1 1-2 Adleman’s Experiment, DNA Computing 1. Encoding: • Vertex short DNA • Edge short DNA complementary to adjacent vertices-DNA
4 2. Cloning 2 1 3. Generating all pathes (hybridization) 3 4. Filtering out non-hamiltonian1-2 pathes 3-4 2-3 Adleman’s Experiment, DNA Computing
1. Encoding: • Vertex short DNA • Edge short DNA complementary to adjacent vertices-DNA
2. Cloning 3. Generating all pathes (hybridization) 4. Filtering out non-hamiltonian pathes
5. Result: • Remaining molecules DNA computing
➢ Initiated by Leonard Adleman from University of Southern California in 1994
➢ Proof-of-concept: solved 7-point HPP
➢ Turing universal computational devices could be built
➢ Milestones: ➢ 1994: Adleman's experiment
➢ 1997: theoretical implementation of Boolean circuits
➢ 2002: programmable molecular computing machine
➢ 2004: attempts to build DNA computer to diagnose cellular cancer activity and to release anti-cancer drug
➢ 2000's: increased interest in DNA nanotechnology
➢ 2013, January: stored JPEG photograph, Shakespearean sonnets and an audio file of Martin Luther King, Jr.speech “I Have a Dream” on DNA
➢ 2013, March: implemented transcriptor – a biological transistor Cellular computing
Advantages:
• Massive parallelism
• Replication
• Filtering
• No supervision
Gene assembly in ciliates during sexual reproduction Ciliates
- ∃for >billion years
- 1000s of species
- Most complex 1-cell organisms known
- Some >4mm large Micrographia Gallery Enlargement Stylonychia
Micrographia Gallery Enlargement Coleps Macronuclei - Unicellular eukariotes - They have 2 types of nuclei
Macronuclei : 1 DNA - 1 gene , gene not fragmented .
GENE
MAC Micronuclei - Unicellular eukariotes - They have 2 types of nuclei
Micronuclei : - DNA organized on chromosomes, - One DNA molecule - many genes , - genes are fragmented , - fragments (MDS's) are shuffled, - some fragments are inverted ,
- MDSs separated by IESs MDS3 IES MDS1 IESMDS4 IES MDS2
MIC
Gene Assembly MDS3 MDS1 MDS4 MDS2
MIC
-Remove IESs Gene assembly -Unscramble MDSs -Ligate MDSs
GENE
MAC Pointers
• Pointers : • short nucleotide sequences • on MDS edges
p MDS q Pointers
• Pointers : • short nucleotide sequences • on MDS edges An MDS “points” to the next MDS by means of common pointer
p MDS1 q q MDS2 r Assembled gene • First MDS of a gene • Pointers : • begins with marker b, • short nucleotide sequences • last MDS of a gene • on MDS edges • ends with marker e;
• Assembled gene : • MDSs spliced on common pointers, in orthodox order
b MDS1 p2 MDS2 p3 pn MDS n e Cellular computing
➢ Studying or implementing computations in living cells
➢ Examples:
➢ Gene assembly in ciliates – permutations and inversions of DNA fragments. Turing universal.
➢ In vivo programmable and autonomous finite-state automation with E. coli
➢ In vivo cellular logic gates and genetic circuits that alter the cell's existing biochemical processes Molecular computing
➢ Emergent interdisciplinary field concerned with programming molecules ➢ To perform a desired computation, or
➢ Fabricate a desired object, or
➢ Control functioning of specific molecular system
➢ Central principle: ➢ The data can be encoded within bio-molecules
➢ Tools of molecular science could be used to manipulate and process the data
➢ Program: ➢ collection of molecules, when placed in a suitable substrate,
➢ Will perform a specific function (could be interpreted as program execution) Molecular computing – an overview course ➢ During this course we will learn about: ➢ Computations with DNA and other bio-molecules in vitro
➢ DNA Computing and self-assembly of nano-structures
➢ Computation with bio-molecules in vivo
➢ Cellular Computing
➢ Biologically inspired model of distributed parallel computations:
➢ Membrane computing – abstracting from multi-compartmental membranar structure of living organic systems
➢ We will overview: ➢ Basics of DNA structure and manipulation
➢ Formal models for DNA Computing and DNA-based computational devices
➢ Biology and computational models of gene assembly in ciliates
➢ Theory of membrane computing Course info
➢ No strict requirements towards students. All background will be provided on-demand
➢ 7 weeks, twice per week st ➢ First lecture – today, 31 of August, 2015
th ➢ Last lecture – Thursday, 15 of October, 2015
➢ Mondays – 15:15 – 16:45 Cobol, ICT
➢ Thursdays – 10:15 – 11:45 Algol, ICT
➢ Exams: ➢ TBD
➢ Lecturer: ➢ Vladimir Rogojin, B5050, ICT [email protected]