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Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact...
We developed a method for analyzing the dynamics of gene regulatory networks in purely qualitative fashion. In our method, constraints for possible behaviors of a network and a biological property of interest are described as Linear Temporal Logic formulas, being automatically analyzed by satisfiability checking. In this way, we can investigate whether there exists some behavior which satisfies a...
Gene regulatory networks capture interactions between genes and other cell substances, resulting in various models for the fundamental biological process of transcription and translation. The expression levels of the genes are typically measured as mRNA concentration in micro-array experiments. In a so-called genetic perturbation experiment, small perturbations are applied to equilibrium states and...
The analysis of cellular behavior and functionality is the most challenging aim of systems biology. The extensive analysis of the interactions between different classes of intra-cellular molecules reacting to genetic/environment changes can elucidate the mechanisms of regulation involved on different cellular processes. We propose a novel framework that enables the integrated analysis of metabolic...
Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of regulatory control. Gene regulation, however, is a process that depends on many different components such as transcription factors (TFs), cis-regulatory motifs and their temporal and...
In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. Evolutionary Algorithms (EAs) have been a successful approach for tackling the underlying in silico optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes...
Application of model quality evaluation to the quasispecies models is presented. These models are useful for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. An estimate of the parameters together with their interval of variability is computed and the quality evaluation is tested on the basis of the model prediction error capability.
The advent of thousands of annotated genomes, detailed metabolic reconstructions and databases within the flourishing field of systems biology necessitates the development of functionally complete computer models of whole cells and cellular systems. Such models would realistically describe fundamental properties of living systems such as growth, division and chromosome replication. This will inevitably...
Estimation of gene networks based on microarray gene expression data is an important problem in systems biology. In this paper we use Bayesian networks as a mathematical model for reverse-engineering gene networks from microarray data. In such a case, structural learning of Bayesian networks is known as an NP-hard problem and we need to use heuristic algorithms to find better network structures. Recently,...
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