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Rule-based multi-level modeling of cell biological systems.

Maus C, Rybacki S, Uhrmacher AM - BMC Syst Biol (2011)

Bottom Line: The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness.Rule schemata allow a concise and compact description of complex models.As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Rostock, Institute of Computer Science, Albert-Einstein-Str, 22, 18059 Rostock, Germany. carsten.maus@gmail.com

ABSTRACT

Background: Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.

Results: Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.

Conclusions: Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

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Mating type switching. (A) Switching of mating types in a fission yeast cell lineage. Cells of type M are marked with red color, blue stands for mating type P. The unswitchable and switchable states are denoted by U and S respectively. The figure has been redrawn from [58]. (B) Trajectories of a simulation run with an inital population of 100 unswitchable cells of mating type P. Cells are dying with a rate constant kdeath = 0.006 min-1. Mass-doubling time Td = 116 min.
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Figure 6: Mating type switching. (A) Switching of mating types in a fission yeast cell lineage. Cells of type M are marked with red color, blue stands for mating type P. The unswitchable and switchable states are denoted by U and S respectively. The figure has been redrawn from [58]. (B) Trajectories of a simulation run with an inital population of 100 unswitchable cells of mating type P. Cells are dying with a rate constant kdeath = 0.006 min-1. Mass-doubling time Td = 116 min.

Mentions: The unicellular fission yeast may undergo sexual reproduction when environmental conditions are getting poor, e.g. when cells are starving. Different mating types (P and M) exist enforcing fusion of cells of opposite types only [51]. The product of fusion is a diploid zygote which rapidly enters a sporulation process. Later, when the environmental conditions improve, spores germinate to spawn haploid cells which then undergo normal asexual proliferation again. The mating type of proliferating cells switches sporadically when a cell divides. This phenomenon is regulated by rather complex mechanisms at gene level [52-57]. However, rather stable phenomenological patterns of switching can be observed [58]. One important characteristic is that cells do not only show one of the two different mating types P or M, but can be also categorized into cells that are able to switch their type and those that are not (Figure 6A).


Rule-based multi-level modeling of cell biological systems.

Maus C, Rybacki S, Uhrmacher AM - BMC Syst Biol (2011)

Mating type switching. (A) Switching of mating types in a fission yeast cell lineage. Cells of type M are marked with red color, blue stands for mating type P. The unswitchable and switchable states are denoted by U and S respectively. The figure has been redrawn from [58]. (B) Trajectories of a simulation run with an inital population of 100 unswitchable cells of mating type P. Cells are dying with a rate constant kdeath = 0.006 min-1. Mass-doubling time Td = 116 min.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3306009&req=5

Figure 6: Mating type switching. (A) Switching of mating types in a fission yeast cell lineage. Cells of type M are marked with red color, blue stands for mating type P. The unswitchable and switchable states are denoted by U and S respectively. The figure has been redrawn from [58]. (B) Trajectories of a simulation run with an inital population of 100 unswitchable cells of mating type P. Cells are dying with a rate constant kdeath = 0.006 min-1. Mass-doubling time Td = 116 min.
Mentions: The unicellular fission yeast may undergo sexual reproduction when environmental conditions are getting poor, e.g. when cells are starving. Different mating types (P and M) exist enforcing fusion of cells of opposite types only [51]. The product of fusion is a diploid zygote which rapidly enters a sporulation process. Later, when the environmental conditions improve, spores germinate to spawn haploid cells which then undergo normal asexual proliferation again. The mating type of proliferating cells switches sporadically when a cell divides. This phenomenon is regulated by rather complex mechanisms at gene level [52-57]. However, rather stable phenomenological patterns of switching can be observed [58]. One important characteristic is that cells do not only show one of the two different mating types P or M, but can be also categorized into cells that are able to switch their type and those that are not (Figure 6A).

Bottom Line: The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness.Rule schemata allow a concise and compact description of complex models.As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Rostock, Institute of Computer Science, Albert-Einstein-Str, 22, 18059 Rostock, Germany. carsten.maus@gmail.com

ABSTRACT

Background: Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.

Results: Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.

Conclusions: Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.

Show MeSH