Synthetic Biology...   a new area of research that combines science and engineering
                                               in order to design and build novel biological functions and systems
            - the design & construction of new biological parts, devices, or systems,
            - the redesign of existing, natural biological systems (cells) for useful purposes,
            - modeling biological systems for lab study.  
    
     This discipline may have begun when Werner Arber, Daniel Nathans and Hamilton O. Smith   discovered restriction enzymes. Restriction endonucleases not only permits one to easily construct a recombinant DNA molecule, but led us into the new era of synthetic biology where new gene arrangements can be constructed and evaluated. 
  
           Some current research models include:
                            replicase (self-replicating molecular) systems ( Szostak Lab @ Harvard),
                         molecules that mimic DNA (Kool Group @ Stanford),
                         complex gene networks and how they work (vanOudenaarden lab @ MIT),
                         George Church's synthetic genome work @ Harvard,
                         Pawson's Lab for modular proteins @ Mt. Sinai,
                         Andrea Belcher's work on bio-circuits @ MIT,
                            and experiments on artificial life.  
         isal

 

 

 


  Artificial Life Experimentation...       
   
       What is Life and What defines the Living State ?
                   Life & Living are terms easily understood by the lay person, but not the biologist... 
   
       Artificial experimental systems may us help define some life (cell) properties...
       Artificial life:  artificial systems that exhibit behavior characteristic of natural living systems.
  
     AL is study of life through the use of human-made analogs of living systems, evolving software that is more alive than a computer virus. Computer scientist Christopher Langton coined the term in the late 1980s when he held the first "International Conference on the Synthesis and Simulation of Living Systems" (otherwise known as Artificial Life I) at the Los Alamos National Laboratory.
  
     The field is characterized by the extensive use of computer programs and computer simulations
     which include evolutionary computation (evolutionary algorithms (EA), genetic algorithms (GA),
     genetic programming (GP), swarm intelligence (SI), ant colony optimization (ACO),
     artificial chemistries (AC), agent-based models, and cellular automata (CA).

  





 
   much Artificial Evolution Software is based on book Blind Watchmaker
                                                                            by Richard Dawkins of Oxford Univ.
           Natural Selection, the unconscious, automatic, blind yet essentially non-random process
                      that Darwin discovered, has no purpose in mind,
                      yet can build.... great COMPLICATED SYSTEMS...
  
       some computer applet examples:
 
                     1)  Weasel uses Scorer & Breeder subroutines to evolve a solution complex problem
                                     Breeder creates possible solutions until it matches a supplied 'phrase'
                                     Scorer only tells the Breeder how close each guess is to the target phrase
                                                 Breeder makes lots of random guesses; highest-scoring guess is
                                                 then bred with other guesses (by combining parts of each) with
                                                 some random mutation; Breeder continues until it finds original
                                                 phrase.
                         Systematically trying all possible guesses (1051-- would virtually never complete;
                         but a simple evolution-like program can find a solution after only 60,000 tries.
 
  
 

             
            
 

             


        2) Biomorphs - This applet draws biomorphs, plausible lifelike forms
                                                  (morphological shapes of stick figures)
            The applet defines 8 genes comprising the biomorph's genome. Each gene
            draws a smooth line independently, and the biomorphs are redrawn.
            Each gene produces a line in a set direction at a constant speed until it
            reaches its maximum (or minimum) value, then it bounces back the other
            way. This is why the biomorphs are oriented up, then down, then up again;
            one of the genes controls up-ness or down-ness, and it is bouncing back
            and forth along with the other genes.

            Biomorphs shows what a wide variety of forms is possible even with a
            very limited genome...  one may see forms that look like insects, trees,
            etc... Eight genes can make - 1,071,794,405 different biomorphs!

            Biomorphs also demonstrates how an accumulation of changes can turn one
            form into a quite different form, even when each individual change is
            very small (wow! Natural Selection is powerful).                   
        

another
 simulation
 of evolution
of biomorphs
(in color)
a viewer
   
more Biomorphs
  
aLife Lab
 

 

 

 
 
     a number of AL programs don't believe in the possibility of generating a "living process" outside of a carbon-based chemical solution [weak alife position], but rather attempt to simulate Evolution, by trying instead to mimic life processes to understand the appearance of single phenomena.
    
 one ex: SimEvol -  a world consisting of bugs (small, colorful, moving squares) and their food,
             randomly placed stationary bacteria (the purple dots). Bacteria are added to the world at
             a slow, steady pace.  the bugs eat their food by moving onto it, and they move strictly
             according to their limited genome (they have no sense organs to help them detect food).
             The "best genome" is one that keeps a bug moving mostly in straight lines, turning only
             occasionally; this keeps a bug constantly moving to new areas. rather than exhausting the
             local food supply and then slowly starving. Initially random, bugs wandered in circles &
             died for lack of food.  One or two survive, for their DNA programmed them to move a
             a little straighter; they bear offspring. With abundant food, they breed explosively;
             soon, these bugs outstrip the food supply and begin to die off in great numbers. The
             population continues in repeated boom-and-bust cycles of various sizes, each cycle
             favoring straighter-moving offspring. Now, the populations size is more of less constant.
  The bugs colors have meaning:      Red bugs are strong enough to reproduce, but are not yet old enough.
                                    Green bugs are old enough to reproduce, but are not yet strong enough.
                                    Yellow bugs are both old enough and strong enough to reproduce.
                          Grey bugs are neither old enough nor strong enough to reproduce.

 

 2nd ex:

    boids - Craig Reynolds computer simulations of animal motion as birds flocking and fish schooling.
   
     program exhibits emergent behavior. the complexity of Boids arises from the interaction of
     individual agents (the boids) adhering to a set of simple rules:
 
           separation: steer to avoid crowding local flockmates
           alignment: steer towards the average heading of local flockmates
           cohesion: steer to move toward the average position of local flockmates 

  Boids produced life-like solutions to evading obstacles placed in their path (see boids flight )
 

  

 

 


   3rd Ex:
   Tierra* by Tom Ray  - a simulated Darwinian evolution computer program
                                     TIERRA Site     &      source code
  Tierra C source code creates a virtual computer, with a Darwinian operating system, that allows
   executable machine codes to be evolvable (mutatable - random flipping of bits or allow
   recombination swapping of code segments), with code remaining functional for selection to improve
   the code over time.

   Other program evolved from the 1st and are illustrate the power of natural selection. The
   program keeps track of factors that affect the course of code - keeps a record of births and
   deaths, sequences the code of every creature, and maintains a gene-bank of successful genomes.
 
   Tierra creates  synthetic organisms where CPU time is the 'energy' resource and memory is the
  'material' resource.  CPU time is for self-replication. Mutation generates new forms, and evolution
   proceeds by natural selection as different genotypes compete for CPU time and memory space.

  

 

       ]
 
 Tierra are small computer programs of assembly language code designed to self copy & evolve.
    
   like a computer virus,  compete for cpu time & memory space...
                         a faster replicator    =    greater  survivability.
    Primordial program  (the ancestor) had 80 instructions,
             was stored in cpu  (lived),    consumed cpu cycles  (metabolized),
            copied itself  (reproduced),  moved up in que (locomotion),  was removed (died).  
         
   New programs (the descendents) emerged...  Have They Mutated & Evolved?
                     first "mutant" one had 79  instructions
                      one version had 36  instruction… but replicated 6x faster
                      some had only   45  lines  &   lost ability to replicate,
                                but borrowed instruction from other = parasites
                     some programs became defensive = immunized against the parasitic borrowers
                     some became extinct   and disappeared

   Some proponents of AI [strong alife - Strong AI]  believe that "life is a process which can be
   abstracted away from any particular medium". Notably, Tom Ray, who declared that his program
   Tierra was not simulating life in a computer, but was synthesizing it.
                                       Mallery's analogy of the Star Trek cloud exhibiting  living attributes.
       Are TIERRIANs really ALIVE ? or at the least do they have properties akin to life?






 

      
        Christopher Langton, who coined the term Synthetic Biology, in the late 1980s pursued the idea that the computer could emulate living creatures... He succeeded in creating the first self-replicating computer organism... VANTS  neither alive nor dead, but that can exhibit some attributes of life.

Langton's Virtual Ant's...        an ant starts out on a grid containing black and white cells,
                                               and then follows a specific set of rules:
1. If the ant is on a black square, it turns right 90°
    and moves forward one unit 		 
2. If the ant is on a white square, it turns left 90°
   and moves forward one unit
3. When ant leaves a square, it inverts the color event-
    ually, the vants left a trail behind and built a highway
   When more than one vant was placed on the grid, the result was behavior strikingly similar to that of social insects. The most vivid example was the uncanny parallel to the manner in which certain ants lay pheromone trails for food recruitment - certain vants, after an initial period of meandering, seemed to find each other and interact in order to build a spiraling trail.
                       Chris Langton     &     Swarm Software  
 

 

 

 

    Artificial Life Experimentation is the  study of COMPLEXITY THEORY...
      
  Sante Fe Institute...  are the originators of a theories that deals with the application of computers
          to evolution, which is often called.... theory of Complex Systems or Complexity Theory.
     
  Since 1984 Complexity Theory has been promoted by physicist Murray Gell-Mann, a founder
     of the SFI,  who describes complexity theory by saying that people in this field work from
     the TOP-DOWN, whereas scientists usually work by "reductionism or from the BOTTOM-UP"
    
          A hallmark of complexity theory is the extensive use of computers to model the
          behavior of complex problems such as protein folding, weather, & evolution.
    
          If a computer program, by following the rules (like the rules that govern chemistry),
          spontaneously organizes entities that eat, reproduce and evolve, who’s to say these entities
          aren’t alive? How did the order that lies deep within the most complex of systems come about?
  
 


 






  Is Life (& its origins) an Autocatalytic process (?) -       Stuart Kaufman of SFI  
  
        Did the order of life come from non-living chemicals...   how does this spontaneous order arise?
          Some favor the idea that we need a chemical process called...  autocatalysis. 

Kauffman contends that complexity itself triggers self-organization;
if enough different molecules pass a certain threshold of complexity,
they begin to self-organize into a new entity (even a living cell):
        not unlike the phase transition when water abruptly turns to ice-

 

 

 

 
  MODEL  MOLECULAR  REPLICATIVE  SYSTEMS
     
     Evolution of an RNA world...   (which came 1st DNA or RNA)
  
             in 1989  Sidney Altman  &   Tom Cech  - received Nobel Prize
          for demonstrating that RNA molecules (RIBOZYMES) have CATALYTIC ACTIVITY
          i.e., these RNA's catalyze hydrolysis & condensation rxs of phosphodiester bonds,
           RNA molecules capable of catalyzing polymeric cleavage in a sequence-specific way.                                                                                               ribozymes* 
  
      Maybe, if RNA can be a template and also catalyze polymerization of like molecules,
          i.e., replicate itself, then RNA molecules may have been the
                          1st SELF-REPLICATING living entity.          complementary templating*   
  
      No self-replicating RNA molecules exists naturally today, but lab experimentation may
            establish that it was feasible, and that RNA molecules can be selected for
            via Darwinian evolutionary mechanisms (natural selection).   
                

 




 

 
     Replicative Systems are an experimental bridge between molecules & living organisms...
            the origin of stable self-replicating molecules represents a fundamental obstacle
            to our understanding of the events in the origin of life.
   
           Goal is to find molecular structures, simple enough to have formed spontaneously 
           by molecular self-assembly, but complex enough to have evolved into life as we know it.
  

    EXPERIMENTAL SYSTEMS examples include :
           1. Molecular Replication Systems
                     Jack Szostak (Mass. General) - REPLICASE System...
                           a replicase is a molecular complex that has the ability
                           to make a copy of itself and direct other molecules to replicate themselves…
     
                      novel ribozymes and deoxyzymes (ssDNA's) with catalytic activity,
                                       especially, RNA's that can make other RNA's...
                                       may represent the origins of biological catalysis.
  
  
 

 

 

 

 
  2.  Ribozymes - Gerald Joyce & Martin Wright, et al  (@ Scripps)
          complex RNAs also can be assembled from preexisting structural and functional domains  

   
     Joyce & Wright used a test tube of ribozymes that can reproduce indefinitely,  
            starting from a pool of 1016 different RNA molecules, with 3 hypervariable
           regions (of 85 nucleotides), that could give rise to different catalytic motifs
          they selected  some with mutations and improved rate of replication...  Scripps Report
  
               "…with a starting ribozyme molecule, with barely detectable DNA-cleavage
                   activity, after 63 "generations" of in vitro selection for catalysis, 
                        showed a number variants of ribozymes, that cleave single-stranded DNA with
                        high efficiency and specificity. These ribozymes had accumulated an 
                      average of 27 mutations relative to the wild type ribozymes and had
              improved their ability to cleave DNA by 106-fold...".

                  i.e., the ribozymes evolved (???)             Origins of Life Prize - Abiogenesis

 

 

 

 

 
  3)  PROTOBIONTS...   in vitro chemically made artificial cell systems ? 
                      Sidney W. Fox  University of Miami (1912 - 1998)
                              Director of the NASA supported Institute for Molecular Evolution at UM.
                              his laboratory conducted analyses of the first moon rock samples... 

      1st produced  proteinoids  from amino acid solutions... dropped on hot lava rock, sand or clay.
      then  Protobionts  - an aggregate of abiotically produced chemically reactive molecules
             internally, they are chemically different from their environment, & and  are metabolically active

        Some types of Protobionts -
  • Polypeptides, polysaccharides & nucleic acids form  coacervates droplets =  figure*
    • maybe with enzymatically active interior
  • Proteinoids form microspheres, which  
    • are selectively permeable
    • have membrane potentials 
  • liposomes form from phopspholipids,     figure-1*   &    figure2*,   which
    • are microscopic spherical vesicles that form when phospholipids are hydrated
    • can engulf smaller proteinoids making more active ones
 

 

 

 

   

Vesicular reconstitution experiments can hint at how the first natural protobionts might have
become more selectable via molecular evolution for the properties of living systems...

          example involving - bacteriorhodopsin & ATP synthase*
   
                     bacteriorhodopsin - a photosynthetic proton pump in halobacteria
                     ATP synthase - a multisubunit enzyme of mitochondria that synthesizes ATP
                     2-4, dinitorphenol - a metabolic poison that uncouples H-transport in mito

          what this experiment established is that a H+ gradient across membranes can be the
          source of energy for the synthesis of ATP via the enzyme ATP synthase.

but, the net result is that an artificial man-made membrane vesicle can be made
to mimic a major cellular metabolic reaction - the synthesis of ATP.

   

 

 

 

      
   4) Synthetic Biology & Protocell Research  
       a bottom-up approach...  to synthetically build function molecular living systems.
            goals: to assemble all the molecular components and synthetically form life
                     to understand why & how matter can self-organize... and become living.
                     constructing fully functional cells from scratch
                          engineering new genetic circuits, entire genomes, or organisms,
                          altering gene content &  arrangements to make novel designer genes
                          in order to make complex biological machines.
            i.e., artificial creation of DNA, genes, viri, & cells that mimic, or surpass, natural systems.
     
Some Examples:
   
   a. Synthetic Polio Virus - Aug,2002 : Molecular Origin of Life Research or Bioterrorism?  
            E. Wimmer from the University of New York at Stony Brook used the poliovirus' widely known genetic
            sequence to synthesize the virus from shelf chemicals. Following a recipe they downloaded from
            the internet and using gene sequences from a mail-order supplier, he reported a cell free synthesis of
            polio virus . The artificially constructed virus appears identical to its natural counterpart; when injected
            it into mice the animals were paralyzed and died.
   

 

 

 

 more bottom-up examples:
        
  b. Phi X-174 virus is synthesized - November 2003 :  Craig Venter and colleagues created an
        artificial version of Phi X-174 by piecing together synthetic DNA ordered from a biotechnology company.
        They used a technique called polymerase cycle assembly (PCA) to link the strands of DNA together.

  
  c. The 1918 Spanish Flu Virus is Reconstructed - October 2005 : Jeffery K. Taubenberger,
       a molecular pathologist at the Armed Forces Institute of Pathology and his colleagues were able to piece
       together the virus's genes from two unusual sources:
              1) lung tissue removed at autopsy from  a 21-year-old soldier &
               2) frozen body of Inuit woman who died of influenza in Nov 1918 & buried in the Alaskan permafrost.
       These sources provided intact pieces of viral RNA that could be analysed and sequenced. The
       virus has eight "RNA gene segments" & by gene sequencing & PCR, they reassembled the virus. Two
       of the 8 genes: Hemagglutinin-A type 5 [H5] and Neuraminidase type 1 [N1] are surface coat proteins.
       There are at least 16 different HA antigens, which binds the virus to the host cell. Hemagglutinin-A is a
       surface glycoprotein that bind virus to host cell.  Neuraminidase is an surface antigenic glycoprotein enzyme
       Nine neuraminidase subtypes are known, which aid in the efficiency of virus release from infected cells.
    
Since his original publication, Taubenberger's team has successfully created a genetic sequencing of the 1918 virus, resurrected the virus itself to study its effects on lung tissue, and this fall announced a striking similarity between the 1918 virus and today's H5N1 avian flu virus. Their findings indicate that the 1918 virus originated as a bird flu, confirming the legitimacy of concerns about avian flu. The updated episode includes new material and interviews with Taubenberger that reflect these new findings
 

 

 

 

top down
 
   a top-down-up approach:    looks for minimalist essential genome required to make a cell...
       
       J. Craig Venter, a principle investigator (P.I.) of the Human Genome Project , is attempting to make
a synthetic new type of bacterium using DNA manufactured in the lab. He's using the sequenced genes
of a bacterium Mycoplasma genitalium, a gram-positive parasitic bacterium, whose primary infection site may be the human urogenital tract and causes non-gonococcal urethritis.  It's circular chromosome
has  580,073  base pairs, the smallest known genome of any free-living organism determined. M.g.
has a total of only 525 genes (482 encoding for proteins; & 43 RNA genes).
      
        > How many genes does it take to make an organism? What is the minimum genes a cell needs?
          The scientists at The Institute for Genomic Research (TIGR -Venter's group) who determined
           the Mycoplasma genitalium sequence followed this work by systematically destroying its genes
          [so called knock-out cells - done by mutating them with insertions) to see which ones are
           essential to life and which are dispensable. Of the 482 protein-encoding genes, they conclude
           that only 265–350 of them are essential to life.

 > The next step is to artificially assemble these 300+ genes to create a synthetic cell





  

 




If we get more theoretical, we could use [synthetic microbes] to take CO2 [from the atmosphere] for the synthesis of pharmaceuticals, textiles, and other products. We could have synthetic cells that take energy from sunlight and convert it to clean fuels such as hydrogen. That will have an impact on everything from politics to the world economy.

Are these innovations likely to happen?

How likely they are depends on how much investment we as a society are going to make. Right now there is very little, except on the health side. But if we don't do something fairly substantial soon about the CO2 we are adding to the atmosphere, then maybe curing cancer won't be of any value.

Synthetic Genomics, Inc. seeks to lead the world in its ability to design, synthesize and assemble specifically engineered cell level bio-factories.  Synthetic Genomic, Inc.   Synthetically produced organisms with reduced or reoriented metabolic needs will enable new, powerful, and more direct methods of bio-engineered industrial production.


& Digital Artificial Life Lab*
                    ex: Avida  &   [ digital life systems  &  virtual stick/block figures + Biota.org

Viruses   - obligate parasites requiring a host to replicate with some life properties.

 synthetic cells...    or biological factories?
   let's take the genes from the yew tree [for drug Taxol] and put it in a minimalist cell
   and have it do the complex chemistry to manufacture Taxol, which chemists can't do.
        use [synthetic microbes]      - to take CO2 [from atmosphere]
                                                 - for the synthesis of pharmaceuticals, textiles, & other products.
                                                 - take sunlight and convert it to clean fuels such as hydrogen.
   1st step... develop a microbe with a minimal genome [see mycoplasma above]...
                 i.e., create an operating system for biologically-based software
                            Synthetic Genomics, Inc. (Venter Institute) has begun project to remove genes
                            from simple organisms and identify the minimum set of genes necessary for an
                            organism to survive in a controlled environment.
   next...  add the desired biological capabilities (Taxol genes) to minimal genome,
              insert it into an environment that allows metabolic activity and replication
                            – the creation of a synthetic cell.
 

 


                 David Deamer of U.C. Santa Cruz, a synthetic biology researcher, suggests
                               that there are 12 steps required to build an artificial cell.
  
 1. membrane enclosure (done) - in 1965 Alec Bangham (Cambridge) shows amphiphilic molecules can
            self-assemble into microscopic vesicles like 'membranes'
 2. energy capture via membranes (done) - Efraim Racker (Rockerfeller) incorporated bacterial
            bacteriorhodopsin & ATPase into liposomes & generated ATP via light
 3. ion gradients across membranes (done) - in 2004 Irene Chen & Jack Szostak (Harvard)
            membrane growth generates proton gradients that last over 3 hours
 4. macromolecules encapsulated into compartments (done) - in 1985 Deamer shows macromolecules
            are captured into lipid vesicles by mixing both together
 5. macromolecules grow via polymerization (done) - Deamer synthesizes RNA inside liposomes
            using RNA polymerase and ADP
 6. macromolecular catalysts evolve that speed the growth process (done, but not in liposomes)
            in vitro molecular evolution system are made, where successive generations of RNA's
            evolve more efficient reactions at ligating other RNA's (Szostak's lab)
 

     Deamer's  12 steps required to build an artificial cell (cont.)...
  
 7. information capture in a polymeric sequence (done) - DNA's within liposomes have directed
           protein synthesis including GFP, channel proteins, and polymerases. 
 8. sequence information directs growth of catalytic polymers (done) - in 2004 Tetsuya Yomo
           (Osaka U.) use liposomes vesicles with bacterial plasmid DNA to make RNA polymease
           that is required to make GFP.
 9. membrane vesicles divide and grow (done) - in 2003 Szostak's group report that
           FA-vesicles grow by absorbing available FA's & divide when extruded through pores.
 10. mutations are made during replication (done) - in 2001 David Bartel (MIT) make Ribozymes
           that catalyze replication or RNA template molecule that contains errors.
  
 11. membrane system containing genes & enzymes that can be replicated (not done)
 12. genes & enzymes once replicated (#11) are shown among the daughter cells (not done)
 
 

 
 Some (unexplained) Events in Chemical Evolution of Eukaryotes 
                                   the evolution of the eucarya was single most important step in evolution 
                                  of mutlicellular life forms & was a key step that lead to plant & animal life. 
        1. cell membrane encapsulates genetic DNA...   development of nucleus
                    greatest evolutionary invention - it internalized the genome
        2. loss of a rigid cell wall...
                    cells developed ability of phagocytosis - allowed engulfing of foods
                         also allowed cells to clump together -->   multi-cellularity  -->   tissues
        3. evolve a selectively permeable membrane...
                    protects cell, allows uptake gases & nutrients & exchange with environment
        4. evolve a cytoskeleton...
                    provides framework- allowed cell to grow larger, move, & permitted
                           metabolism;    eucarya are 10x larger that bacteria
        5. evolve aerobic respiration...   more efficient energy transformation
        6. develop various organelles... (maybe by endosymbiosis)...
                   a sub-cell part that catalyzes a specific metabolic function
        7. development of sexual cell cycles... (transposons - moveable genes)...
           a method to shuffle genes along chromosomes favored cellular evolution

   Self-Organization,  Darwinian Natural Selection, &  Chance  are thus the engines of the biosphere
        Life may have originated when the mix of different molecules in the primordial soup passed
        a certain level of complexity and self-organized into living entities;
                (if so, then life is not a highly improbable chance event, but almost inevitable).
  
                             Value of this concept of autocatalysis is that it suggests that
                     chemical evolutionary systems may be experimentally testable. 

 

 

Computational biomodeling refers to a type of artificial life research concerned with building computer simulations of biochemical systems. It combines research from the fields of molecular biology, computer science, chemistry, and physics. The immediate goal is to understand how cells develop, work collectively, and survive in changing environments using a purely computational model.

Computational biomodeling is in its infancy. One of the major stumbling blocks is understanding protein folding which is currently being researched by the BlueGene and Folding@home projects.