The adaptive and evolutionary potential of chemical artificial cells has been explored using a range of theoretical techniques and simulation tools. Novel simulation platforms have been developed that integrate physical self-assembly, chemical reactions and evolution of genetic subsystems. The fundamental evolvabilty and stable integration of simple artificial cell architectures has been established. Efficient simulation platforms have been established for investigating artificial cell functionality with the specific emphasis on controlled self-assembly based on molecular surface recognition (cf programmed self-assembly).
In order to clearly describe and distinguish between various possible paths of development for achieving artificial cells a new graphical language have been developed for representing different kinds of protocells, and different stages along the way to their development. This representational scheme applies equally to experimental achievements, simulations, and hypothetical schemes or goals, and thus provides a common platform for comparing and contrasting protocell accomplishments. The graphical language is illustrated under the presentation of the Chemical Roadmap to Artificial Cells.
Annual workshops have been held at ECLT presenting progress in the research.
Evolutionary aspects of basic cell functionalities: templating, container growth, and metabolism
A variety of models have been developed and investigated for
the study of combined dynamics between container growth and templating.
Evolutionary aspects including resistance against parasites have also been
investigated in this context. More complex models have been developed that
include reaction networks and a minimal metabolism. Several of these models
have been based on the ”Los Alamos Bug”
as a basic model characteristic.
--> Container growth and replicator dynamics in pre-biotic chemistry
--> Synchronization of replicator and aggregate growth
--> Combined lipid/template growth synchronisation and template evolution
--> Generic Darwinian selection in catalytic protocell assemblies
--> Evolutionary aspects of physical self-assembling kinetic models
Assembly into higher-order structure based on cells with
controllable adhesion properties
One important feature of future artificial cells may be achieved in larger aggregates of cells that exhibit specific spatial structure. One of the most basic properties for multi-vesicular self-assembling systems is the adhesion properties of the vesicles. Different mechanisms such as osmotic stress, Coulomb interactions and specific interactions can be used to bind vesicles together. Different adhesion mechanisms will produce differently shaped vesicular structures. Therefore, a software package has been developed to simulate interacting multi-vesicular systems relevant to the self-assembly of complex functional ensembles of artificial cells and their potential as distributed robotic system.
--> Simulation of higher-order self-assembly
Computational functionalities of artificial cell populations
The investigation of molecular computation in protocells has
been done with the development of a maximally simplified string based
artificial chemistry, as described below. An exploration of distributed computation in protocells has also been completed.
--> Evolution of Protocell-embedded Molecular Computation
--> Distributed computation in protocells
Theoretical development supporting simulation at several levels
An information-theoretic framework has been developed for analysis of flows of information in complex pattern formation. The formalism has been applied to the Gray-Scott model exhibiting self-replicating spots formation. An extension of the model so that the self-replicating patterns phenomenon can be supported also in a model of the ”fan reactor” has been successfully demonstrated.
--> Extended Gray-Scott model for the fan reactor
We have developed a novel technique that, as indicated by preliminary results, may be used to find hierarchical dynamics in discrete dynamical systems. The core of our approach is to find the partitions of a systems phase space that result in coarse dynamics that exhibit the Markov property. The method can be used for deriving coarse grained dynamics in discrete models, such as cellular automata and finite state machines.