892 research outputs found
Cell fate reprogramming by control of intracellular network dynamics
Identifying control strategies for biological networks is paramount for
practical applications that involve reprogramming a cell's fate, such as
disease therapeutics and stem cell reprogramming. Here we develop a novel
network control framework that integrates the structural and functional
information available for intracellular networks to predict control targets.
Formulated in a logical dynamic scheme, our approach drives any initial state
to the target state with 100% effectiveness and needs to be applied only
transiently for the network to reach and stay in the desired state. We
illustrate our method's potential to find intervention targets for cancer
treatment and cell differentiation by applying it to a leukemia signaling
network and to the network controlling the differentiation of helper T cells.
We find that the predicted control targets are effective in a broad dynamic
framework. Moreover, several of the predicted interventions are supported by
experiments.Comment: 61 pages (main text, 15 pages; supporting information, 46 pages) and
12 figures (main text, 6 figures; supporting information, 6 figures). In
revie
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
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
Functional analysis of High-Throughput data for dynamic modeling in eukaryotic systems
Das Verhalten Biologischer Systeme wird durch eine Vielzahl regulatorischer Prozesse beeinflusst, die sich auf verschiedenen Ebenen abspielen. Die Forschung an diesen Regulationen hat stark von den großen Mengen von Hochdurchsatzdaten profitiert, die in den letzten Jahren verfügbar wurden. Um diese Daten zu interpretieren und neue Erkenntnisse aus ihnen zu gewinnen, hat sich die mathematische Modellierung als hilfreich erwiesen. Allerdings müssen die Daten vor der Integration in Modelle aggregiert und analysiert werden. Wir präsentieren vier Studien auf unterschiedlichen zellulären Ebenen und in verschiedenen Organismen. Zusätzlich beschreiben wir zwei Computerprogramme die den Vergleich zwischen Modell und Experimentellen Daten erleichtern. Wir wenden diese Programme in zwei Studien über die MAP Kinase (MAP, engl. mitogen-acticated-protein) Signalwege in Saccharomyces cerevisiae an, um Modellalternativen zu generieren und unsere Vorstellung des Systems an Daten anzupassen. In den zwei verbleibenden Studien nutzen wir bioinformatische Methoden, um Hochdurchsatz-Zeitreihendaten von Protein und mRNA Expression zu analysieren. Um die Daten interpretieren zu können kombinieren wir sie mit Netzwerken und nutzen Annotationen um Module identifizieren, die ihre Expression im Lauf der Zeit ändern. Im Fall der humanen somatischen Zell Reprogrammierung führte diese Analyse zu einem probabilistischen Boolschen Modell des Systems, welches wir nutzen konnten um neue Hypothesen über seine Funktionsweise aufzustellen. Bei der Infektion von Säugerzellen (Canis familiaris) mit dem Influenza A Virus konnten wir neue Verbindungen zwischen dem Virus und seinem Wirt herausfinden und unsere Zeitreihendaten in bestehende Netzwerke einbinden. Zusammenfassend zeigen viele unserer Ergebnisse die Wichtigkeit von Datenintegration in mathematische Modelle, sowie den hohen Grad der Verschaltung zwischen verschiedenen Regulationssystemen.The behavior of all biological systems is governed by numerous regulatory mechanisms, acting on different levels of time and space. The study of these regulations has greatly benefited from the immense amount of data that has become available from high-throughput experiments in recent years. To interpret this mass of data and gain new knowledge about studied systems, mathematical modeling has proven to be an invaluable method. Nevertheless, before data can be integrated into a model it needs to be aggregated, analyzed, and the most important aspects need to be extracted. We present four Systems Biology studies on different cellular organizational levels and in different organisms. Additionally, we describe two software applications that enable easy comparison of data and model results. We use these in two of our studies on the mitogen-activated-protein (MAP) kinase signaling in Saccharomyces cerevisiae to generate model alternatives and adapt our representation of the system to biological data. In the two remaining studies we apply Bioinformatic methods to analyze two high-throughput time series on proteins and mRNA expression in mammalian cells. We combine the results with network data and use annotations to identify modules and pathways that change in expression over time to be able to interpret the datasets. In case of the human somatic cell reprogramming (SCR) system this analysis leads to the generation of a probabilistic Boolean model which we use to generate new hypotheses about the system. In the last system we examined, the infection of mammalian (Canis familiaris) cells by the influenza A virus, we find new interconnections between host and virus and are able to integrate our data with existing networks. In summary, many of our findings show the importance of data integration into mathematical models and the high degree of connectivity between different levels of regulation
Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis
Boolean networks (BNs) are discrete dynamical systems with applications to
the modeling of cellular behaviors. In this paper, we demonstrate how the
software BoNesis can be employed to exhaustively identify combinations of
perturbations which enforce properties on their fixed points and attractors. We
consider marker properties, which specify that some components are fixed to a
specific value. We study 4 variants of the marker reprogramming problem: the
reprogramming of fixed points, of minimal trap spaces, and of fixed points and
minimal trap spaces reachable from a given initial configuration with the most
permissive update mode. The perturbations consist of fixing a set of components
to a fixed value. They can destroy and create new attractors. In each case, we
give an upper bound on their theoretical computational complexity, and give an
implementation of the resolution using the BoNesis Python framework. Finally,
we lift the reprogramming problems to ensembles of BNs, as supported by
BoNesis, bringing insight on possible and universal reprogramming strategies.
This paper can be executed and modified interactively.Comment: Notebook available at
https://nbviewer.org/github/bnediction/reprogramming-with-bonesis/blob/release/paper.ipyn
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