4,882 research outputs found
An Abstraction Theory for Qualitative Models of Biological Systems
Multi-valued network models are an important qualitative modelling approach
used widely by the biological community. In this paper we consider developing
an abstraction theory for multi-valued network models that allows the state
space of a model to be reduced while preserving key properties of the model.
This is important as it aids the analysis and comparison of multi-valued
networks and in particular, helps address the well-known problem of state space
explosion associated with such analysis. We also consider developing techniques
for efficiently identifying abstractions and so provide a basis for the
automation of this task. We illustrate the theory and techniques developed by
investigating the identification of abstractions for two published MVN models
of the lysis-lysogeny switch in the bacteriophage lambda.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005
Applications of Biological Cell Models in Robotics
In this paper I present some of the most representative biological models
applied to robotics. In particular, this work represents a survey of some
models inspired, or making use of concepts, by gene regulatory networks (GRNs):
these networks describe the complex interactions that affect gene expression
and, consequently, cell behaviour
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
Abstracting Asynchronous Multi-Valued Networks: An Initial Investigation
Multi-valued networks provide a simple yet expressive qualitative state based
modelling approach for biological systems. In this paper we develop an
abstraction theory for asynchronous multi-valued network models that allows the
state space of a model to be reduced while preserving key properties of the
model. The abstraction theory therefore provides a mechanism for coping with
the state space explosion problem and supports the analysis and comparison of
multi-valued networks. We take as our starting point the abstraction theory for
synchronous multi-valued networks which is based on the finite set of traces
that represent the behaviour of such a model. The problem with extending this
approach to the asynchronous case is that we can now have an infinite set of
traces associated with a model making a simple trace inclusion test infeasible.
To address this we develop a decision procedure for checking asynchronous
abstractions based on using the finite state graph of an asynchronous
multi-valued network to reason about its trace semantics. We illustrate the
abstraction techniques developed by considering a detailed case study based on
a multi-valued network model of the regulation of tryptophan biosynthesis in
Escherichia coli.Comment: Presented at MeCBIC 201
Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms
Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability
Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes
Target identification, one of the steps of drug discovery, aims at
identifying biomolecules whose function should be therapeutically altered in
order to cure the considered pathology. This work proposes an algorithm for in
silico target identification using Boolean network attractors. It assumes that
attractors of dynamical systems, such as Boolean networks, correspond to
phenotypes produced by the modeled biological system. Under this assumption,
and given a Boolean network modeling a pathophysiology, the algorithm
identifies target combinations able to remove attractors associated with
pathological phenotypes. It is tested on a Boolean model of the mammalian cell
cycle bearing a constitutive inactivation of the retinoblastoma protein, as
seen in cancers, and its applications are illustrated on a Boolean model of
Fanconi anemia. The results show that the algorithm returns target combinations
able to remove attractors associated with pathological phenotypes and then
succeeds in performing the proposed in silico target identification. However,
as with any in silico evidence, there is a bridge to cross between theory and
practice, thus requiring it to be used in combination with wet lab experiments.
Nevertheless, it is expected that the algorithm is of interest for target
identification, notably by exploiting the inexpensiveness and predictive power
of computational approaches to optimize the efficiency of costly wet lab
experiments.Comment: Since the publication of this article and among the possible
improvements mentioned in the Conclusion, two improvements have been done:
extending the algorithm for multivalued logic and considering the basins of
attraction of the pathological attractors for selecting the therapeutic
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