752 research outputs found
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Synthetic biology—putting engineering into biology
Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.
On the emergence and evolution of artificial cell signaling networks
This PhD project is concerned with the evolution of Cell
Signaling Networks (CSNs) in silico. CSNs are complex biochemical networks responsible for the coordination of cellular activities. We are investigating the possibility to build an evolutionary simulation platform that would allow the spontaneous emergence and evolution of Artificial Cell Signaling Networks (ACSNs). From a practical point of view, realizing and evolving ACSNs may provide novel computational paradigms for a variety of application areas. This work may also contribute to the biological understanding of the origins and evolution of real CSNs
Simulation methods in the modelling of bioaffinity assays
Computational model-based simulation methods were developed for the modelling of
bioaffinity assays. Bioaffinity-based methods are widely used to quantify a biological
substance in biological research, development and in routine clinical in vitro diagnostics.
Bioaffinity assays are based on the high affinity and structural specificity between the
binding biomolecules. The simulation methods developed are based on the mechanistic
assay model, which relies on the chemical reaction kinetics and describes the forming
of a bound component as a function of time from the initial binding interaction. The
simulation methods were focused on studying the behaviour and the reliability of
bioaffinity assay and the possibilities the modelling methods of binding reaction kinetics
provide, such as predicting assay results even before the binding reaction has reached
equilibrium. For example, a rapid quantitative result from a clinical bioaffinity assay
sample can be very significant, e.g. even the smallest elevation of a heart muscle marker
reveals a cardiac injury.
The simulation methods were used to identify critical error factors in rapid bioaffinity
assays. A new kinetic calibration method was developed to calibrate a measurement
system by kinetic measurement data utilizing only one standard concentration. A nodebased
method was developed to model multi-component binding reactions, which have
been a challenge to traditional numerical methods. The node-method was also used to
model protein adsorption as an example of nonspecific binding of biomolecules. These
methods have been compared with the experimental data from practice and can be
utilized in in vitro diagnostics, drug discovery and in medical imaging.Siirretty Doriast
Stochastic analysis of nonlinear dynamics and feedback control for gene regulatory networks with applications to synthetic biology
The focus of the thesis is the investigation of the generalized repressilator model
(repressing genes ordered in a ring structure). Using nonlinear bifurcation analysis
stable and quasi-stable periodic orbits in this genetic network are characterized
and a design for a switchable and controllable genetic oscillator is proposed. The
oscillator operates around a quasi-stable periodic orbit using the classical engineering
idea of read-out based control. Previous genetic oscillators have been
designed around stable periodic orbits, however we explore the possibility of
quasi-stable periodic orbit expecting better controllability.
The ring topology of the generalized repressilator model has spatio-temporal
symmetries that can be understood as propagating perturbations in discrete lattices.
Network topology is a universal cross-discipline transferable concept and
based on it analytical conditions for the emergence of stable and quasi-stable
periodic orbits are derived. Also the length and distribution of quasi-stable oscillations
are obtained. The findings suggest that long-lived transient dynamics
due to feedback loops can dominate gene network dynamics.
Taking the stochastic nature of gene expression into account a master equation
for the generalized repressilator is derived. The stochasticity is shown to influence
the onset of bifurcations and quality of oscillations. Internal noise is shown to
have an overall stabilizing effect on the oscillating transients emerging from the
quasi-stable periodic orbits.
The insights from the read-out based control scheme for the genetic oscillator
lead us to the idea to implement an algorithmic controller, which would direct
any genetic circuit to a desired state. The algorithm operates model-free, i.e. in
principle it is applicable to any genetic network and the input information is a
data matrix of measured time series from the network dynamics. The application
areas for readout-based control in genetic networks range from classical tissue
engineering to stem cells specification, whenever a quantitatively and temporarily
targeted intervention is required
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)
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