168 research outputs found
Model transformation of metabolic networks using a Petri net based framework
The different modeling approaches in Systems Biology create models with different levels of detail. The transformation techniques in Petri net theory can provide a solid framework for zooming between these different levels of abstraction and refinement. This work presents a Petri net based approach to Metabolic Engineering that implements model reduction methods to reduce the complexity of large-scale metabolic networks.
These methods can be complemented with kinetics inference to build dynamic models with a smaller number of parameters. The central carbon metabolism model of E. coli is used as a test-case to illustrate the application of these concepts. Model transformation is a promising mechanism to facilitate pathway analysis and dynamic modeling at the genome-scale level.(undefined
Modeling formalisms in systems biology
Systems Biology has taken advantage of computational tools and high-throughput experimental data to model several biological processes. These include signaling, gene regulatory, and metabolic networks. However, most of these models are specific to each kind of network. Their interconnection demands a whole-cell modeling framework for a complete understanding of cellular systems. We describe the features required by an integrated framework for modeling, analyzing and simulating biological processes, and review several modeling formalisms that have been used in Systems Biology including Boolean networks, Bayesian networks, Petri nets, process algebras, constraint-based models, differential equations, rule-based models, interacting state machines, cellular automata, and agent-based models. We compare the features provided by different formalisms, and discuss recent approaches in the integration of these formalisms, as well as possible directions for the future.Research supported by grants SFRH/BD/35215/2007 and SFRH/BD/25506/2005 from the Fundacao para a Ciencia e a Tecnologia (FCT) and the MIT-Portugal Program through the project "Bridging Systems and Synthetic Biology for the development of improved microbial cell factories" (MIT-Pt/BS-BB/0082/2008)
Biomodelkit - a framework for modular biomodel-engineering
Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Dissertation, 2017von Dipl.-Ing. Mary-Ann BlätkeLiteraturverzeichnis: Seite [177]-18
A collective intelligence framework for in silico representations of biomolecules and their activities.
The novel framework proposed in this thesis offers great potential for modelling the multi scale adaptive dynamics from molecules to cell at the physiological timescale. Most approaches for modelling biological phenomena focus on studies based on a specific instance of life, where specific biological problems are analysed. Mechanistic models based on universal principles will facilitate in developing general models for wider application in systems biology. The aim of the thesis is to investigate best approaches in representing biological complexity from molecules to cells and developing computational approaches to bring abstract theories to practical use by: (i) Identifying the major biomolecular self organising mechanism. (ii) Using a bottom-up integrative approach to model the internal organisation of the biological cell. (iii) Develop a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies. This thesis argues that a system theoretic approach based on Collective Intelligence where the concepts of self organisation and emergence underlie the approach is ideal to represent the multi scale and multi objective nature of the biological cell from the bottom up. This thesis proposes a Collective Intelligence based cell modelling and simulation environment to conduct analysis studies on the collective behaviour of biomolecules
BioAmbients: an abstraction for biological compartments
AbstractBiomolecular systems, composed of networks of proteins, underlie the major functions of living cells. Compartments are key to the organization of such systems. We have previously developed an abstraction for biomolecular systems using the π-calculus process algebra, which successfully handled their molecular and biochemical aspects, but provided only a limited solution for representing compartments. In this work, we extend this abstraction to handle compartments. We are motivated by the ambient calculus, a process algebra for the specification of process location and movement through computational domains. We present the BioAmbients calculus, which is suitable for representing various aspects of molecular localization and compartmentalization, including the movement of molecules between compartments, the dynamic rearrangement of cellular compartments, and the interaction between molecules in a compartmentalized setting. Guided by the calculus, we adapt the BioSpi simulation system, to provide an extended modular framework for molecular and cellular compartmentalization, and we use it to model and study a complex multi-cellular system
Recent advances in petri nets and concurrency
CEUR Workshop Proceeding
Rule-based multi-level modeling of cell biological systems
<p>Abstract</p> <p>Background</p> <p>Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax.</p> <p>Results</p> <p>Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II.</p> <p>Conclusions</p> <p>Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.</p
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
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