138 research outputs found
JCell : a Java framework for inferring genetic networks
JCell is a framework for reconstructing and simulating genetic networks in the field of molecular biology. It is completely implemented in Java. The main goal of JCell is to gain deep insights of molecular processes within a cell or tissue under various conditions such as drug concentrations or pathogenic mutations. This question has recently become a major area of research in the field of bioinformatics, because understanding the regulating dependencies enables new therapies of diseases like cancer or Alzheimer. To address the mentioned inference problem, several mathematical models and algorithms have been developed and implemented, which try to infer genetic relationships from genomic experiment data. The program consists of a modular structure, which enables users to utilize the framework also in other research areas such as metabolic pathway reconstruction, signalling cascade analysis or general biochemical processes. Further on, JCell can also be used in other contexts to identify dynamic systems from time series data such as financial applications or engineering problems.
Usability was always the primary focus during development, so that even users without a strong computer science background are able to use the program. Another focus was the ability of JCell to natively import as much file formats as possible to be compatible with the most commonly used analysis tools. Due to the usage of the programming language Java, the framework is platform independent and thus able to work on most hardware/software systems. This is especially important for those research facilities where no expensive hardware can purchased and where no restrictions for the used operating systems can be implied. Further more, the framework is open to public development and new modules can be easily implemented.JCell ist ein komplett in Java realisiertes Framework zur Rekonstruktion und Simulation von genetischen Netzwerken in verschiedenen Bereichen der Molekularbiologie. Ziel ist die eingehende Untersuchung von Abläufen innerhalb einer Zelle oder eines Gewebetyps bei gleichzeitiger Zugabe von Wirkstoffen oder im Falle von krankhafter Entartung. Diese Fragestellung ist zur Zeit eines der wichtigsten Themengebiete der Bioinformatik, da das Verständnis von genetischer Regulation tiefgreifende Möglichkeiten der Diagnostik und Therapie von Krankheiten wie Krebs oder Alzheimer eröffnet. Zur Lösung des so genannten Netzwerk-Inferenzproblems wurden verschiedene Algorithmen und mathematische Modelle implementiert, die aus gegebenen genomischen Experimentdaten versuchen, regulatorische Interaktionen zu rekonstruieren.
Da die gewählte Programmstruktur modular aufgebaut ist, wurden im Laufe der Entwicklung weitere Einsatzgebiete erschlossen. So kann JCell nun auch in anderen Gebieten der Systembiologie, wie zum Beispiel der Forschung im Bereich metabolischer Systeme und der Rekonstruktion von biochemischen Signalwegen innerhalb einer Zelle, eingesetzt werden. Des Weiteren liegen Anfragen von Biotech-Firmen vor, die dynamische Prozesse in biotechnologischen Anlagen besser verstehen wollen.
Bei der Entwicklung war stets die einfache Benutzbarkeit der Applikation das primäre Ziel, damit auch Computer-Laien in der Lage sind, das Programm zu bedienen. Ein weiteres Augenmerk lag auf der Implementierung von Methoden zum Einlesen verschiedenster Dateiformate, sodass die gängigsten Analysetools für Genomexperimente unterstützt werden.
Durch Verwendung der Programmiersprache Java ist eine weitreichende Plattformunabhängigkeit gewährleistet, sodass JCell auf den meisten Rechnerarchitekturen läuft. Dies hat den Vorteil, dass Anwender keine spezielle Hardware bereitstellen müssen und auch keinerlei Einschränkungen bei der Auswahl eines Betriebssystems haben. Daneben bietet Java noch den Vorteil, dass fremde Entwickler schnell eigene Module in das bestehende Framework einbinden können, was besonders im Hinblick auf die Open-Source-Verfügbarkeit eine wichtige Rolle spielt
Gene regulatory network modelling with evolutionary algorithms -an integrative approach
Building models for gene regulation has been an important aim of Systems Biology over the past years, driven by the large amount of gene expression data that has become available. Models represent regulatory interactions between genes and transcription factors and can provide better understanding of biological processes, and means of simulating both natural and perturbed systems (e.g. those associated with disease). Gene regulatory network
(GRN) quantitative modelling is still limited, however, due to data issues such as noise and restricted length of time series, typically used for GRN reverse engineering. These issues create an under-determination problem, with many models possibly fitting the data. However,
large amounts of other types of biological data and knowledge are available, such as cross-platform measurements, knockout experiments, annotations, binding site affinities for transcription factors and so on. It has been postulated that integration of these can improve
model quality obtained, by facilitating further filtering of possible models. However, integration is not straightforward, as the different types of data can provide contradictory information, and are intrinsically noisy, hence large scale integration has not been fully
explored, to date. Here, we present an integrative parallel framework for GRN modelling, which employs
evolutionary computation and different types of data to enhance model inference. Integration is performed at different levels. (i) An analysis of cross-platform integration of time series microarray data, discussing the effects on the resulting models and exploring crossplatform
normalisation techniques, is presented. This shows that time-course data integration is possible, and results in models more robust to noise and parameter perturbation, as
well as reduced noise over-fitting. (ii) Other types of measurements and knowledge, such as knock-out experiments, annotated transcription factors, binding site affinities and promoter sequences are integrated within the evolutionary framework to obtain more plausible GRN models. This is performed by customising initialisation, mutation and evaluation of candidate model solutions. The different data types are investigated and both qualitative and
quantitative improvements are obtained. Results suggest that caution is needed in order to obtain improved models from combined data, and the case study presented here provides
an example of how this can be achieved. Furthermore, (iii), RNA-seq data is studied in comparison to microarray experiments, to identify overlapping features and possibilities of integration within the framework. The extension of the framework to this data type is
straightforward and qualitative improvements are obtained when combining predicted interactions
from single-channel and RNA-seq datasets
Perturbation biology: inferring signaling networks in cellular systems.
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology
Novel modeling formalisms and simulation tools in computational biosystems
Tese de doutoramento em BioengenhariaThe goal of Systems Biology is to understand the complex behavior that
emerges from the interaction among the cellular components. Industrial
biotechnology is one of the areas of application, where new approaches for
metabolic engineering are developed, through the creation of new models and
tools for simulation and optimization of the microbial metabolism. Although
whole-cell modeling is one of the goals of Systems Biology, so far most models
address only one kind of biological network independently. This work
explores the integration of di erent kinds of biological networks with a focus
on the improvement of simulation of cellular metabolism. The bacterium
Escherichia coli is the most well characterized model organism and is used
as our case-study.
An extensive review of modeling formalisms that have been used in Systems
Biology is presented in this work. It includes several formalisms, including
Boolean networks, Bayesian networks, Petri nets, process algebras,
constraint-based models, di erential equations, rule-based models, interacting
state machines, cellular automata and agent-based models. We compare
the features provided by these formalisms and classify the most suitable ones
for the creation of a common framework for modeling, analysis and simulation
of integrated biological networks.
Currently, there is a separation between dynamic and constraint-based
modeling of metabolism. Dynamic models are based on detailed kinetic reconstructions
of central metabolic pathways, whereas constraint-based models
are based on genome-scale stoichiometric reconstructions. Here, we explore
the gap between both formulations and evaluate how dynamic models
can be used to reduce the solution space of constraint-based models in order to eliminate kinetically infeasible solutions.
The limitations of both kinds of models are leading to new approaches
to build kinetic models at the genome-scale. The generation of kinetic models
from stoichiometric reconstructions can be performed within the same
framework as a transformation from discrete to continuous Petri nets. However,
the size of these networks results in models with a large number of
parameters. In this scope, we develop and implement structural reduction
methods that adjust the level of detail of metabolic networks without loss
of information, which can be applied prior to the kinetic inference to build
dynamic models with a smaller number of parameters.
In order to account for enzymatic regulation, which is not present in
constraint-based models, we propose the utilization of Extended Petri nets.
This results in a better sca old for the kinetic inference process. We evaluate
the impact of accounting for enzymatic regulation in the simulation of
the steady-state phenotype of mutant strains by performing knockouts and
adjustment of enzyme expression levels. It can be observed that in some
cases the impact is signi cant and may reveal new targets for rational strain
design.
In summary, we have created a solid framework with a common formalism
and methods for metabolic modeling. This will facilitate the integration with
gene regulatory networks, as we have already addressed many issues also
associated with these networks, such as the trade-o between size and detail,
and the representation of regulatory interactions.O objectivo da Biologia de Sistemas é compreender os comportamentos que
resultam das complexas interacções entre todos os componentes celulares.
A biotecnologia industrial é uma das áreas de aplicação, onde novas abordagens
para a engenharia metabólica são desenvolvidas através da criação
de novos modelos e ferramentas de simulação e optimização do metabolismo
microbiano. Apesar de um dos principais objectivos da Biologia de Sistemas
ser a criação de um modelo completo de uma célula, até ao momento
a maioria dos modelos desenvolvidos incorpora de forma separada cada tipo
de rede biológica. Este trabalho explora a integração de diferentes tipos de
redes biológicas, focando melhorar a simulação do metabolismo celular. A
bactéria Escherichia coli é o organismo modelo que estáa melhor caracterizado
e é usado como caso de estudo.
Neste trabalho é elaborada uma extensa revisão dos formalismos de modela
ção que têm sido utilizados em Biologia de Sistemas. São considerados
vários formalismos tais como, redes Booleanas, redes Bayesianas, redes de
Petri, álgebras de processos, modelos baseados em restrições, equações diferenciais,
modelos baseados em regras, máquinas de interacção de estados,
autómatos celulares e modelos baseados em agentes. As funcionalidades inerentes
a estes formalismos são analisadas de forma a classificar os mesmos
pelo seu potencial em servir de base à criação de uma plataforma para modela
ção, análise e simulação de redes biológicas integradas.
Actualmente, existe uma separação entre modelação dinâmica e modelação
baseada em restrições para o metabolismo celular. Os modelos dinâmicos
consistem em reconstruções cinéticas detalhadas de vias centrais do metabolismo,
enquanto que os modelos baseados em restrições são construídos à escala genómica com base apenas na estequiometria das reacçõoes. Neste trabalho
explora-se a separação entre os dois tipos de formulação e é avaliada a
forma como os modelos dinâmicos podem ser utilizados para reduzir o espaço
de soluções de modelos baseados em restrições de forma a eliminar soluções
inalcançáveis. As limitações impostas por ambos os tipos de modelos estão a conduzir
à criação de novas abordagens para a construção de modelos cinéticos à
escala genómica. A geração de modelos cinéticos a partir de reconstruções
estequiométricas pode ser feita dentro de um mesmo formalismo através da
transformação de redes de Petri discretas em redes de Petri contínuas. No
entanto, devido ao tamanho destas redes, os modelos resultantes incluem
um número extremamente grande de parâmetros. Neste trabalho são implementados
métodos para a redução estrutural de redes metabólicas sem
perda de informação, que permitem ajustar o nível de detalhe das redes. Estes
métodos podem ser aplicados à inferência de cinéticas, de forma a gerar
modelos dinâmicos com um menor número de parâmetros.
De forma a considerar efeitos de regulação enzimática, os quais não são representados em modelos baseados em restrições, propõe-se a utilização de
redes de Petri complementadas com arcos regulatórios. Este formalismo é
utilizado como base para o processo de inferência cinética. A influência
da regulação enzimática na determinação do estado estacionário de estirpes
mutantes é avaliada através da análise da remoção de reacções e da variação
dos níveis de expressão enzimática. Observa-se que em alguns casos esta
influência é significativa e pode ser utilizada para obter novas estratégias de
manipulação de estirpes.
Em suma, neste trabalho foi criada uma plataforma sólida para modelação
do metabolismo baseada num formalismo comum. Esta plataforma facilitará
a integração com redes de regulação genética, pois foram abordados vários
problemas que também se colocam nestas redes, tais como o ajuste entre
o tamanho da rede e o seu nível de detalhe, bem como a representação de
interacções regulatórias entre componentes da rede
Using systems biology approaches to elucidate gene regulatory networks controlling the plant defence response
Transcriptional regulation controlling pathogen-responsive gene expression in Arabidopsis
is believed to underlie the plant defence response, which confers partial
immunity of Arabidopsis to infection by Botrytis cinerea. In this thesis networks
of transcriptional regulation mediating the defence response are studied in various
ways.
First transcriptional regulation was predicted for all genes differentially expressed
during B. cinerea infection by development of a novel clustering approach, Temporal
Clustering by Affinity Propagation (TCAP). This approach finds groups of
genes whose expression profile time series have strong time-delayed correlation, a
measure that is demonstrated to be more predictive of transcriptional regulation
than conventionally used similarity measures. TCAP predicts the known regulation
of GI by LHY, and co-clusters ORA59 and some of its downstream targets.
Predicted novel regulators of pathogen-responsive gene expression were then studied
in a reverse genetics screen, which discovered several novel but weakly altered
susceptibility phenotypes. Comparison of predicted targets to known targets was
complicated by the sparsity of mutant versus wildtype gene expression experiments
performed during B. cinerea infections in the literature.
To explore the context-dependence of transcriptional regulation, evidence of transcriptional
regulation in different contexts was collected. This was compiled to generate
a qualitative model of transcriptional regulation during the defence response.
This model was validated and extended by experimental analysis of transcription
factor-promoter binding in Yeast and transcriptional activation in planta. Comparative
transcriptomics showed that downstream genes of some of these regulators
| TGA3, ARF2, ERF1 and ANAC072 | are over-represented in the list of genes
differentially expressed during B. cinerea infection, which is consistent with these
targets being regulated by them during B. cinerea infection.
Finally this qualitative model was used as prior information and was used along
with gene expression time series to infer quantitative models of the gene regulatory
network mediating the defence response. Some known regulation was predicted,
and additionally ANAC055 was predicted to be a central regulator of pathogenresponsive
gene expression
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Understand Biology Using Single Cell RNA-Sequencing
This dissertation summarizes the development of experimental and analytical tools for single cell RNA sequencing (scRNA-Seq), including 1) scPLATE-Seq, a FACS- and plate-based scRNASeq platform, which is accurate, robust, fully automated and cost-efficient; 2) metaVIPER, an algorithm for transcriptional regulator activity inference based on scRNA-Seq profiles; and 3) iterClust, a statistical framework for iterative clustering analysis, especially suitable for dissecting hierarchy of heterogeneity among single cells. Further this dissertation summarizes biological questions answered by combining these tools, including 1) understanding inter- and intra-tumor heterogeneity of human glioblastoma; 2) elucidating regulators of β-cell de-differentiation in type-2 diabetes; and 3) developing novel therapeutics targeting cell-state regulators of breast cancer stem cells
A framework to identify epigenome and transcription factor crosstalk
While changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. To systematically identify transcription factors (TFs) that can direct chromatin changes during cell fate decisions, we model the genome-wide dynamics of chromatin marks in terms of computationally predicted TF binding sites. By applying this computational approach to a time course of Polycomb-mediated H3K27me3 marks during neuronal differentiation of murine stem cells, we identify several motifs that likely regulate dynamics of this chromatin mark. Among these, the motifs bound by REST and by the SNAIL family of TFs are predicted to transiently recruit H3K27me3 in neuronal progenitors. We validate these predictions experimentally and show that absence of REST indeed causes loss of H3K27me3 at target promoters in trans, specifically at the neuronal progenitor state. Moreover, using targeted transgenic insertion, we show that promoter fragments containing REST or SNAIL binding sites are sufficient to recruit H3K27me3 in cis, while deletion of these sites results in loss of H3K27me3. These findings illustrate that the occurrence of TF binding sites can determine chromatin dynamics. Local determination of Polycomb activity by Rest and Snail motifs exemplifies such TF based regulation of chromatin. Furthermore, our results show that key TFs can be identified ab initio through computational modeling of epigenome datasets using a modeling approach that we make readily accessible
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Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
Publisher Copyright: © 2022We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate cis-regulatory elements (cCREs) in beta cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells, we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation, and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with SOCS1, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta cells during inflammation that modulate T1D risk.Peer reviewe
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