8 research outputs found
Agile Modelling of Cellular Signalling (Invited Paper)
AbstractWe illustrate with a simple example how using a rule-based approach to the modelling of protein interaction networks allows for quickly putting together models (ease of expression), and quickly modifying them (ease of variation)
Compact Representation of Photosynthesis Dynamics by Rule-based Models (Full Version)
Traditional mathematical models of photosynthesis are based on mass action
kinetics of light reactions. This approach requires the modeller to enumerate
all the possible state combinations of the modelled chemical species. This
leads to combinatorial explosion in the number of reactions although the
structure of the model could be expressed more compactly. We explore the use of
rule-based modelling, in particular, a simplified variant of Kappa, to
compactly capture and automatically reduce existing mathematical models of
photosynthesis. Finally, the reduction procedure is implemented in BioNetGen
language and demonstrated on several ODE models of photosynthesis processes.
This is an extended version of the paper published in proceedings of 5th
International Workshop on Static Analysis and Systems Biology (SASB) 2014.Comment: SASB 2014 full pape
(Mathematical) Logic for Systems Biology (Invited Paper)
International audienceWe advocates here the use of (mathematical) logic for systems biology, as a unified framework well suited for both modeling the dynamic behaviour of biological systems, expressing properties of them, and verifying these properties. The potential candidate logics should have a traditional proof theoretic pedigree (including a sequent calculus presentation enjoying cut-elimination and focusing), and should come with (certified) proof tools. Beyond providing a reliable framework, this allows the adequate encodings of our biological systems. We present two candidate logics (two modal extensions of linear logic, called HyLL and SELL), along with biological examples. The examples we have considered so far are very simple ones-coming with completely formal (interactive) proofs in Coq. Future works includes using automatic provers, which would extend existing automatic provers for linear logic. This should enable us to specify and study more realistic examples in systems biology, biomedicine (diagnosis and prognosis), and eventually neuroscience
Rule-based epidemic models
Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. An important class of system that would be intractable if expressed as reactions or ordinary differential equations can be efficiently simulated when expressed as rules. In this paper we demonstrate the utility of the rule-based approach for epidemiological modelling presenting a suite of seven models illustrating the spread of infectious disease under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. Rule-based models allow to combine transparent modelling approach with scalability and compositionality and therefore can facilitate the study of aspects of infectious disease propagation in a richer context than would otherwise be feasible
Rule-based epidemic models
Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. An important class of system that would be intractable if expressed as reactions or ordinary differential equations can be efficiently simulated when expressed as rules. In this paper we demonstrate the utility of the rule-based approach for epidemiological modelling presenting a suite of seven models illustrating the spread of infectious disease under different scenarios: wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. Rule-based models allow to combine transparent modelling approach with scalability and compositionality and therefore can facilitate the study of aspects of infectious disease propagation in a richer context than would otherwise be feasible
Toward Accessible Multilevel Modeling in Systems Biology: A Rule-based Language Concept
Promoted by advanced experimental techniques for obtaining high-quality data and the steadily accumulating knowledge about the complexity of life, modeling biological systems at multiple interrelated levels of organization attracts more and more attention recently. Current approaches for modeling multilevel systems typically lack an accessible formal modeling language or have major limitations with respect to expressiveness. The aim of this thesis is to provide a comprehensive discussion on associated problems and needs and to propose a concrete solution addressing them
Defining complex rule-based models in space and over time
Computational biology seeks to understand complex spatio-temporal phenomena across multiple
levels of structural and functional organisation. However, questions raised in this context
are difficult to answer without modelling methodologies that are intuitive and approachable for
non-expert users. Stochastic rule-based modelling languages such as Kappa have been the focus
of recent attention in developing complex biological models that are nevertheless concise,
comprehensible, and easily extensible. We look at further developing Kappa, in terms of how
we might define complex models in both the spatial and the temporal axes.
In defining complex models in space, we address the assumption that the reaction mixture
of a Kappa model is homogeneous and well-mixed. We propose evolutions of the current iteration
of Spatial Kappa to streamline the process of defining spatial structures for different
modelling purposes. We also verify the existing implementation against established results in
diffusion and narrow escape, thus laying the foundations for querying a wider range of spatial
systems with greater confidence in the accuracy of the results.
In defining complex models over time, we draw attention to how non-modelling specialists
might define, verify, and analyse rules throughout a rigorous model development process. We
propose structured visual methodologies for developing and maintaining knowledge base data
structures, incorporating the information needed to construct a Kappa rule-based model. We
further extend these methodologies to deal with biological systems defined by the activity of
synthetic genetic parts, with the hope of providing tractable operations that allow multiple users
to contribute to their development over time according to their area of expertise.
Throughout the thesis we pursue the aim of bridging the divide between information sources
such as literature and bioinformatics databases and the abstracting decisions inherent in a
model. We consider methodologies for automating the construction of spatial models, providing
traceable links from source to model element, and updating a model via an iterative
and collaborative development process. By providing frameworks for modellers from multiple
domains of expertise to work with the language, we reduce the entry barrier and open the field
to further questions and new research