10,364 research outputs found
Using in silico models to simulate dual perturbation experiments: procedure development and interpretation of outcomes.
BackgroundA growing number of realistic in silico models of metabolic functions are being formulated and can serve as 'dry lab' platforms to prototype and simulate experiments before they are performed. For example, dual perturbation experiments that vary both genetic and environmental parameters can readily be simulated in silico. Genetic and environmental perturbations were applied to a cell-scale model of the human erythrocyte and subsequently investigated.ResultsThe resulting steady state fluxes and concentrations, as well as dynamic responses to the perturbations were analyzed, yielding two important conclusions: 1) that transporters are informative about the internal states (fluxes and concentrations) of a cell and, 2) that genetic variations can disrupt the natural sequence of dynamic interactions between network components. The former arises from adjustments in energy and redox states, while the latter is a result of shifting time scales in aggregate pool formation of metabolites. These two concepts are illustrated for glucose-6 phosphate dehydrogenase (G6PD) and pyruvate kinase (PK) in the human red blood cell.ConclusionDual perturbation experiments in silico are much more informative for the characterization of functional states than single perturbations. Predictions from an experimentally validated cellular model of metabolism indicate that the measurement of cofactor precursor transport rates can inform the internal state of the cell when the external demands are altered or a causal genetic variation is introduced. Finally, genetic mutations that alter the clinical phenotype may also disrupt the 'natural' time scale hierarchy of interactions in the network
Systems biology in animal sciences
Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits
Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations
Delay is an important and ubiquitous aspect of many biochemical processes.
For example, delay plays a central role in the dynamics of genetic regulatory
networks as it stems from the sequential assembly of first mRNA and then
protein. Genetic regulatory networks are therefore frequently modeled as
stochastic birth-death processes with delay. Here we examine the relationship
between delay birth-death processes and their appropriate approximating delay
chemical Langevin equations. We prove that the distance between these two
descriptions, as measured by expectations of functionals of the processes,
converges to zero with increasing system size. Further, we prove that the delay
birth-death process converges to the thermodynamic limit as system size tends
to infinity. Our results hold for both fixed delay and distributed delay.
Simulations demonstrate that the delay chemical Langevin approximation is
accurate even at moderate system sizes. It captures dynamical features such as
the spatial and temporal distributions of transition pathways in metastable
systems, oscillatory behavior in negative feedback circuits, and
cross-correlations between nodes in a network. Overall, these results provide a
foundation for using delay stochastic differential equations to approximate the
dynamics of birth-death processes with delay
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
The Nondeterministic Waiting Time Algorithm: A Review
We present briefly the Nondeterministic Waiting Time algorithm. Our technique
for the simulation of biochemical reaction networks has the ability to mimic
the Gillespie Algorithm for some networks and solutions to ordinary
differential equations for other networks, depending on the rules of the
system, the kinetic rates and numbers of molecules. We provide a full
description of the algorithm as well as specifics on its implementation. Some
results for two well-known models are reported. We have used the algorithm to
explore Fas-mediated apoptosis models in cancerous and HIV-1 infected T cells
How enzyme economy shapes metabolic fluxes
Metabolic fluxes are governed by physical and economic principles.
Stationarity constrains them to a subspace in flux space and thermodynamics
makes them lead from higher to lower chemical potentials. At the same time,
fluxes in cells represent a compromise between metabolic performance and enzyme
cost. To capture this, some flux prediction methods penalise larger fluxes by
heuristic cost terms. Economic flux analysis, in contrast, postulates a balance
between enzyme costs and metabolic benefits as a necessary condition for fluxes
to be realised by kinetic models with optimal enzyme levels. The constraints
are formulated using economic potentials, state variables that capture the
enzyme labour embodied in metabolites. Generally, fluxes must lead from lower
to higher economic potentials. This principle, which resembles thermodynamic
constraints, can complement stationarity and thermodynamic constraints in flux
analysis. Futile modes, which would be incompatible with economic potentials,
are defined algebraically and can be systematically removed from flux
distributions. Enzymes that participate in potential futile modes are likely
targets of regulation. Economic flux analysis can predict high-yield and
low-yield strategies, and captures preemptive expression, multi-objective
optimisation, and flux distributions across several cells living in symbiosis.
Inspired by labour value theories in economics, it justifies and extends the
principle of minimal fluxes and provides an intuitive framework to model the
complex interplay of fluxes, metabolic control, and enzyme costs in cells
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