This article reviews the growing body of scientific work in artificial chemistry. First, common motivations and fundamental concepts are introduced. Second, current research activities are discussed along three application dimensions: modeling, information processing, and optimization. Finally, common phenomena among the different systems are summarized. It is argued here that artificial chemistries are "the right stuff" for the study of prebiotic and biochemical evolution, and they provide a productive framework for questions regarding the origin and evolution of organizations in general. Furthermore, artificial chemistries have a broad application range of practical problems, as shown in this review.

PML nuclear bodies (NBs) are involved in the regulation of key nuclear pathways but their biochemical function in nuclear metabolism is unknown. In this study PML NB assembly dynamics were assessed by live cell imaging and mathematic modeling of its major component parts. We show that all six nuclear PML isoforms exhibit individual exchange rates at NBs and identify PML V as a scaffold subunit. SP100 exchanges at least five times faster at NBs than PML proteins. Turnover dynamics of PML and SP100 at NBs is modulated by SUMOylation. Exchange is not temperature-dependent but depletion of cellular ATP levels induces protein immobilization at NBs. The PML-RARα oncogene exhibits a strong NB retention effect on wild-type PML proteins. HIPK2 requires an active kinase for PML NB targeting and elevated levels of PML IV increase its residence time. DAXX and BLM turn over rapidly and completely at PML NBs within seconds. These findings provide a kinetics model for factor exchange at PML NBs and highlight potential mechanisms to regulate intranuclear trafficking of specific factors at these domains.

Complex dynamical networks consisting of many components that interact and produce each other are difficult to understand, especially, when new components may appear. In this paper we outline a theory to deal with such systems. The theory consists of two parts. The first part introduces the concept of a chemical organization as a closed and mass-maintaining set of components. This concept allows to map a complex (reaction) network to the set of organizations, providing a new view on the system's structure. The second part connects dynamics with the set of organizations, which allows to map a movement of the system in state space to a movement in the set of organizations.Our world is changing, qualitatively and quantitatively. The characteristics of its dynamics can be as simple as in the case of a friction-less swinging pendulum, or as complex as the dynamical process that results in the creative apparition of novel ideas or entities. We might characterize the nature of a dynamical process according to its level of novelty production. For example, the friction-less swinging pendulum implies a process where the novelty is only quantitative. Whereas the process of biological evolution is highly creative and generates qualitative novelties, which then spread in a quantitative way.Fontana and Buss [1] called processes and systems that display the production of novelty, constructive (dynamical) processes and constructive (dynamical) systems, respectively. Constructive systems can be found on all levels of scientific abstraction: in nuclear physics, where the collision of atoms or subatomic particles leads to the creation of new particles; in molecular chemistry, where molecules can react to form new molecules; or in social systems, where communication can lead to new communication [2]. As a result of a combinatorial explosion, it is easy to create something that is new, e.g., a molecule or a poem that is unique in the whole known universe. Nevertheless, it should be noted that novelty is relative. Whether something is considered to be new or not, depends on what is already there. Thence it follows that whether a system 1 Both authors contributed equally.

BackgroundWe suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined.For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided.ResultsOur simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa.ConclusionsWe conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

For successful mitosis, metaphase has to be arrested until all centromeres are properly attached. The onset of anaphase, which is initiated by activating the APC, is controlled by the spindle assembly checkpoint (M)SAC. Mad2, which is a constitutive member of the (M)SAC, is supposed to inhibit the activity of the APC by sequestering away its co-activator Cdc20. Mad1 recruits Mad2 to unattached kinetochores and is compulsory for the establishment of the Mad2 and Cdc20 complexes. Recently, based on results from in vivo and in vitro studies, two biochemical models were proposed: the Template and the Exchange model. Here, we derive a mathematical description to compare the dynamical behaviour of the two models. Our simulation analysis supports the Template model. Using experimentally determined values for the model parameters, the Cdc20 concentration is reduced down to only about half. Thus, although the Template model displays good metaphase-to-anaphase switching behaviour, it is not able to completely describe (M)SAC regulation. This situation is neither improved by amplification nor by p31(comet) inhibition. We speculate that either additional reaction partners are required for total inhibition of Cdc20 or an extended mechanism has to be introduced for (M)SAC regulation.