11,537 research outputs found
Dynamical and Structural Modularity of Discrete Regulatory Networks
A biological regulatory network can be modeled as a discrete function that
contains all available information on network component interactions. From this
function we can derive a graph representation of the network structure as well
as of the dynamics of the system. In this paper we introduce a method to
identify modules of the network that allow us to construct the behavior of the
given function from the dynamics of the modules. Here, it proves useful to
distinguish between dynamical and structural modules, and to define network
modules combining aspects of both. As a key concept we establish the notion of
symbolic steady state, which basically represents a set of states where the
behavior of the given function is in some sense predictable, and which gives
rise to suitable network modules. We apply the method to a regulatory network
involved in T helper cell differentiation
Efficient parameter search for qualitative models of regulatory networks using symbolic model checking
Investigating the relation between the structure and behavior of complex
biological networks often involves posing the following two questions: Is a
hypothesized structure of a regulatory network consistent with the observed
behavior? And can a proposed structure generate a desired behavior? Answering
these questions presupposes that we are able to test the compatibility of
network structure and behavior. We cast these questions into a parameter search
problem for qualitative models of regulatory networks, in particular
piecewise-affine differential equation models. We develop a method based on
symbolic model checking that avoids enumerating all possible parametrizations,
and show that this method performs well on real biological problems, using the
IRMA synthetic network and benchmark experimental data sets. We test the
consistency between the IRMA network structure and the time-series data, and
search for parameter modifications that would improve the robustness of the
external control of the system behavior
Discrete time piecewise affine models of genetic regulatory networks
We introduce simple models of genetic regulatory networks and we proceed to
the mathematical analysis of their dynamics. The models are discrete time
dynamical systems generated by piecewise affine contracting mappings whose
variables represent gene expression levels. When compared to other models of
regulatory networks, these models have an additional parameter which is
identified as quantifying interaction delays. In spite of their simplicity,
their dynamics presents a rich variety of behaviours. This phenomenology is not
limited to piecewise affine model but extends to smooth nonlinear discrete time
models of regulatory networks. In a first step, our analysis concerns general
properties of networks on arbitrary graphs (characterisation of the attractor,
symbolic dynamics, Lyapunov stability, structural stability, symmetries, etc).
In a second step, focus is made on simple circuits for which the attractor and
its changes with parameters are described. In the negative circuit of 2 genes,
a thorough study is presented which concern stable (quasi-)periodic
oscillations governed by rotations on the unit circle -- with a rotation number
depending continuously and monotonically on threshold parameters. These regular
oscillations exist in negative circuits with arbitrary number of genes where
they are most likely to be observed in genetic systems with non-negligible
delay effects.Comment: 34 page
Basins of Attraction, Commitment Sets and Phenotypes of Boolean Networks
The attractors of Boolean networks and their basins have been shown to be
highly relevant for model validation and predictive modelling, e.g., in systems
biology. Yet there are currently very few tools available that are able to
compute and visualise not only attractors but also their basins. In the realm
of asynchronous, non-deterministic modeling not only is the repertoire of
software even more limited, but also the formal notions for basins of
attraction are often lacking. In this setting, the difficulty both for theory
and computation arises from the fact that states may be ele- ments of several
distinct basins. In this paper we address this topic by partitioning the state
space into sets that are committed to the same attractors. These commitment
sets can easily be generalised to sets that are equivalent w.r.t. the long-term
behaviours of pre-selected nodes which leads us to the notions of markers and
phenotypes which we illustrate in a case study on bladder tumorigenesis. For
every concept we propose equivalent CTL model checking queries and an extension
of the state of the art model checking software NuSMV is made available that is
capa- ble of computing the respective sets. All notions are fully integrated as
three new modules in our Python package PyBoolNet, including functions for
visualising the basins, commitment sets and phenotypes as quotient graphs and
pie charts
Sparsity-Sensitive Finite Abstraction
Abstraction of a continuous-space model into a finite state and input
dynamical model is a key step in formal controller synthesis tools. To date,
these software tools have been limited to systems of modest size (typically
6 dimensions) because the abstraction procedure suffers from an
exponential runtime with respect to the sum of state and input dimensions. We
present a simple modification to the abstraction algorithm that dramatically
reduces the computation time for systems exhibiting a sparse interconnection
structure. This modified procedure recovers the same abstraction as the one
computed by a brute force algorithm that disregards the sparsity. Examples
highlight speed-ups from existing benchmarks in the literature, synthesis of a
safety supervisory controller for a 12-dimensional and abstraction of a
51-dimensional vehicular traffic network
Nonlinear Models of Neural and Genetic Network Dynamics:\ud \ud Natural Transformations of Ćukasiewicz Logic LM-Algebras in a Ćukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations \ud
A categorical and Ćukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Ćukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Ćukasiewicz Topos with an N-valued Ćukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.\u
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