Natural biomolecular systems process information in a radically different manner than programmable machines. Conformational interactions, the basis of specificity and self-assembly, are of key importance. A gedanken device is presented that illustrates how the fusion of information through conformational self-organization can serve to enhance pattern processing at the cellular level. The device is used to highlight general features of biomolecular information processing. We briefly outline a simulation system designed to address the manner in which conformational processing interacts with kinetic and higher level structural dynamics in complex biochemical networks. Virtual models that capture features of biomolecular information processing can in some instances have artificial intelligence value in their own right and should serve as design tools for future computers built from real molecules

Conrad, Michael

Zauner, Klaus-Peter

English

Southampton (e-Prints Soton)

which macromolecules and polymacromolecular aggregates function. Dynamics refers  to configurations and reconfigurations of the structural organization of a reaction system resulting from the binding interactions between macromolecules. dynamics I  14  structure  formation  =1  ' ~' Macrocomponents  I~  ,,,  ¢J  Macrocomponents !-  influence  -I  ~k~  conformation  '/ \influences  influences  / catalytic effect "-~  Milieu  l~  catalytic effect kinetics Fig. 2. Overview of the CKSD simulator. Each macromoleeule in the CKSD system is represented by a dodecahedron. Each side of the dodecahedron represents a domain (or subunit) of the molecule. The latter could be a protein, a catalytically active RNA or even a membrane region. Each domain has a finite automaton description. The different states of these automata represent  different conformational states;  the state transitions represent either conformational changes or chemical modifications, including ion- ization. Catalytic activity affecting metabolites and also attractive or repulsive interactions with other macrocomponents are  assigned to  the subunit  states. The state transitions of the subunits can depend on the states of neighboring subunits in the same macromolecule and in adjacent macromolecules. They can also depend on the presence  of surrounding microcomponents. A  simple specification language facilitates the  communication of complex models to the simulation engine. Virtual measurement instruments are used to monitor the time development of a  simulation. Active virtual devices are  also provided  and  make it  possible to intervene  during  a  running  simulation,  e.g., to change the  local concentration  of selected molecular species. The simulator is implemented in C++  and  can run on any UNIX workstation with sufficient RAM. The  CKSD simulator is well suited for representing  the main features con- nected with conformation-based computing. The functionality of macromolecules depends on the  pattern  of surrounding  milieu features.  Self-assembly of poly- macromolecular structures can occur. Adaptive self-stabilization is possible, since the diffusion and structure  formation properties of both micro and macrocom- ponents is influenced by local milieu, including charge interactions. An adaptive feedback loop between structural  changes and kinetic behavior is therefore pos- sible. Y  o~ Z  Z Fig. 3. Macrocomponent  distribution  (left) and concentration  gradient  of one micro- component form the milieu (right).  For clarity  only one of the macrocomponents  is shown on the right. Selected outputs from a sample run are shown in Figs. 3 and 4. The macro- component distribution at a single point in time is illustrated in Fig. 3 (left hand side). The macrocomponents are embedded in a milieu of microcomponents. The right  hand  cube in Fig. 3 illustrates  the concentration  gradient  of a  single mi- crocomponent species together with one of the macrocomponents from the left hand cube. Actually the simulation in this instance accommodated 30 microcom- ponent types (a parameter limited only by the computing resources available). Thus a full picture would include the distribution of all macrocomponents (15 in this instance) and an overlay of 30 microcomponent concentration gradients. The temporal development of one of these spatial concentration  gradients is shown in Figure 4. Each  cube  shows  projections of the  local concentration in  the simulation space  along the  three  axes.  The lines in a  projection plane connect  points at which the projected concentrations have equal values. The environment of the maerocomponents is a pattern resulting from a superposition of numerous such gradients and also adjacent macromolecular states. We could choose to represent the time development of any one of the other microcomponents, the distribution of M1 of them at any one instant of time, and also the distribution of states of the macromolecules that respond to and act on the milieu pattern. Fig. 4. Spatiotemporal development of a microcomponent gradient. The time evolution runs  from left  to right.  The full milieu pattern  would comprise  a  superposition  of gradients for many different types of microcomponents. Implementation of the CKSD system has only recently been completed and experiments are just beginning. The system can be used as an empirical mod- eling tool, drawing on experimental data to set transition tables, or as a theo- retical tool to investigate general properties of complex molecular information processing systems. We anticipate that it will be possible to use the model to experiment on adaptive self-stabilization, as noted above,  and also to address the significance of structure-kinetic interactions for information processing and control. Potential application areas include complex biochemical networks, cel- lular and metabolic engineering, and design of conformation-driven molecular computing/control modules in which the conformational pattern processing is distributed in a massively parallel way. 5  ANM  Framework The second system, called the artificial neuromolecular (ANM) brain, highlights the vertical flow of information across  different levels. The ANM system can be viewed as a  population of networks built up from pattern processing neurons. Signal patterns  impinging on these  neurons are  integrated in space  and time by the cytoskeletal network and associated modulating proteins.  Neuron firing is triggered by readout enzymes that are suitably activated. Variation-selection evolution is used to generate a repertoire of pattern processing neurons. A mem- ory system, basically a synaptic facilitation scheme with hierarchical controls, is used to select cytoskeletal neurons from the repertoire  and to orchestrate them into groupings  adapted  to perception-action  tasks.  The system has  been used for maze navigation tasks involving recognition of a series of 64-bit patterns  [10] and  more recently used for Chinese  character  recognition.  Here again what  is important  are  the  features  represented:  molecular readout,  subcellular  signal integration,  structure-function  plasticity based on redundancy  and modulating interactions,  and multicellular networks built from moldable building blocks. What is the relationship between the ANM architecture and the CKSD sim- ulator discussed in the previous section? The notion of percolation provides the conceptual link. From the natural systems point of view it is important to under- stand how information impinging on the organism filters down to the molecular level and how the molecular level processing percolates up to control the macro- scopic behavior of the organism. This is manifestly important in developmental and immunological processes. The working hypothesis of the ANM framework is that it is also key to the computational capabilities of the brain. From the artifi- cial systems point of view it is important to understand  what kind of computer architectures  could gainfully utilize conformational pattern  recognition. We re- gard the ANM system as a precursor architecture into which conformation-driven pattern recognition could eventually be embedded. The  CKSD  simulator  can  supply  cellular  dynamics for  the  ANM  system from both of the above points of view. The full simulator is too computationally costly to embed in the higher level architecture for real time artificial intelligence applications.  However, it would be quite feasible to use a  simplified version of the simulator to evolve dynamic processes with pattern processing capabilities, and to evolve these dynamics in the context of an ANM type design. 6  From  Silicon  to  Carbon Recent years have seen expansive progress in the technologies required to imple- ment carbon-based information processing devices [5, 6,  11]. Some of the perti- nent technologies include: protein engineering,  recombinant DNA, directed evo- lution, Langmuir-Blodgett films, and optomolecular electronic interfaces. Biosen- sors, optical interfaces to dynamic chemical reactions for image processing, DNA computing, and bacteriorhodopsin (BR) films and sensors are at the prototype stage. OpticM systems that utilize BR are at the product stage. These prototypes, like the simulation systems alluded to in the previous sec- tion, capture the natural biomolecular mode of processing in only a fragmentary way. Yet  one can  discern  an emerging synthesis.  Virtual  molecular computers already have applicative value, as evidenced by the ANM system. The basic idea is  complementary to the  classical  artificial  intelligence  objective of giving  the machine  a  representation  of the external  world.  Give it  instead,  or  as well,  a representation  of the internal  world of biological information processing.  Some essential  aspects of biological information  processing  are too computationally costly for  real  time  applications  and  in  any  event  would be more efficient  if realized in carbon  rather  than  silicon.  Simulations that  push  these limits pro- 

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Molecular Computing: from conformational pattern recognition to complex processing networks

Molecular Computing: from conformational pattern recognition to complex processing networks

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