TY  - JOUR
AB  - BACKGROUND: In quantitative biology, mathematical models are used to describe and analyze biological processes. The parameters of these models are usually unknown and need to be estimated from experimental data using statistical methods. In particular, Markov chain Monte Carlo (MCMC) methods have become increasingly popular as they allow for a rigorous analysis of parameter and prediction uncertainties without the need for assuming parameter identifiability or removing non-identifiable parameters. A broad spectrum of MCMC algorithms have been proposed, including single- and multi-chain approaches. However, selecting and tuning sampling algorithms suited for a given problem remains challenging and a comprehensive comparison of different methods is so far not available. RESULTS: We present the results of a thorough benchmarking of state-of-the-art single- and multi-chain sampling methods, including Adaptive Metropolis, Delayed Rejection Adaptive Metropolis, Metropolis adjusted Langevin algorithm, Parallel Tempering and Parallel Hierarchical Sampling. Different initialization and adaptation schemes are considered. To ensure a comprehensive and fair comparison, we consider problems with a range of features such as bifurcations, periodical orbits, multistability of steady-state solutions and chaotic regimes. These problem properties give rise to various posterior distributions including uni- and multi-modal distributions and non-normally distributed mode tails. For an objective comparison, we developed a pipeline for the semi-automatic comparison of sampling results. CONCLUSION: The comparison of MCMC algorithms, initialization and adaptation schemes revealed that overall multi-chain algorithms perform better than single-chain algorithms. In some cases this performance can be further increased by using a preceding multi-start local optimization scheme. These results can inform the selection of sampling methods and the benchmark collection can serve for the evaluation of new algorithms. Furthermore, our results confirm the need to address exploration quality of MCMC chains before applying the commonly used quality measure of effective sample size to prevent false analysis conclusions.
AU  - Ballnus, B.
AU  - Hug, S.
AU  - Hatz, K.*
AU  - Görlitz, L.*
AU  - Hasenauer, J.
AU  - Theis, F.J.
C1  - 51402
C2  - 43041
CY  - London
TI  - Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems.
JO  - BMC Syst. Biol.
VL  - 11
IS  - 1
PB  - Biomed Central Ltd
PY  - 2017
ER  - 

TY  - JOUR
AB  - Background: Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. Results: In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the analysis of the dataset for Raf/MEK/ERK signaling provides novel biological insights regarding the existence of feedback regulation. Conclusion: Many optimization problems considered in systems and computational biology are subject to steady-state constraints. While most optimization methods have convergence problems if these steady-state constraints are highly nonlinear, the methods presented recover the convergence properties of optimizers which can exploit an analytical expression for the parameter-dependent steady state. This renders them an excellent alternative to methods which are currently employed in systems and computational biology.
AU  - Fiedler, A.
AU  - Raeth, S.*
AU  - Theis, F.J.
AU  - Hausser, A.*
AU  - Hasenauer, J.
C1  - 49348
C2  - 41801
CY  - London
TI  - Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.
JO  - BMC Syst. Biol.
VL  - 10
IS  - 1
PB  - Biomed Central Ltd
PY  - 2016
ER  - 

TY  - JOUR
AB  - Background: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion. Results: Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80 %, and lipid yield was improved more than four-fold, compared to standard conditions. Conclusions: Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed.
AU  - Kavšček, M.*
AU  - Bhutada, G.*
AU  - Madl, T.
AU  - Natter, K.*
C1  - 47168
C2  - 40520
TI  - Optimization of lipid production with a genome-scale model of Yarrowia lipolytica.
JO  - BMC Syst. Biol.
VL  - 9
PY  - 2015
ER  - 

TY  - JOUR
AB  - Background Time-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. However, to infer the correlations between the spatiotemporal context and cell state transitions, statistical analysis with an appropriately large number of samples is required. Results Here, we present a method to infer spatiotemporal features predictive of the state transition events observed in time-lapse microscopy data. We first formulate a generative model, simulate different scenarios, such as time-dependent or local cell density-dependent transitions, and illustrate how to estimate univariate transition rates. Second, we formulate the problem in a machine-learning language using regularized linear models. This allows for a multivariate analysis and to disentangle indirect dependencies via feature selection. We find that our method can accurately recover the relevant features and reconstruct the underlying interaction kernels if a critical number of samples is available. Finally, we explicitly use the tree structure of the data to validate if the estimated model is sufficient to explain correlated transition events of sister cells. Conclusions Using synthetic cellular genealogies, we prove that our method is able to correctly identify features predictive of state transitions and we moreover validate the chosen model. Our approach allows to estimate the number of cellular genealogies required for the proposed spatiotemporal statistical analysis, and we thus provide an important tool for the experimental design of challenging single cell time-lapse microscopy assays.  
AU  - Strasser, M.
AU  - Feigelman, J.
AU  - Theis, F.J.
AU  - Marr, C.
C1  - 46815
C2  - 37853
TI  - Inference of spatiotemporal effects on cellular state transitions from time-lapse microscopy.
JO  - BMC Syst. Biol.
VL  - 9
IS  - 1
PY  - 2015
ER  - 

TY  - JOUR
AB  - Background Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range of structural adaptations that determine their binding affinity, the number of interaction sites, and the lifetime. In terms of stability it has become customary to distinguish between obligate and non-obligate interactions dependent on whether or not the protomers can exist independently. In terms of spatio-temporal control protein interactions can be either simultaneously possible (SP) or mutually exclusive (ME). In the former case a network hub interacts with several proteins at the same time, offering each of them a separate interface, while in the latter case the hub interacts with its partners one at a time via the same binding site. So far different types of interactions were distinguished based on the properties of the corresponding binding interfaces derived from known three-dimensional structures of protein complexes. Results Here we present PiType, an accurate 3D structure-independent computational method for classifying protein interactions into simultaneously possible (SP) and mutually exclusive (ME) as well as into obligate and non-obligate. Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the constituents of non-obligate complexes possess a higher degree of structural disorder, more short linear motifs, and lower functional similarity compared to obligate interaction partners while SP and ME interactions are characterized by significant differences in network topology. Each interaction type is associated with a distinct set of biological functions. Moreover, interactions within multi-protein complexes tend to be enriched in one type of interactions. Conclusion PiType does not rely on atomic structures and is thus suitable for characterizing proteome-wide interaction datasets. It can also be used to identify sub-modules within protein complexes. PiType is available for download as a self-installing package from http://webclu.bio.wzw.tum.de/PiType/PiType.zip
AU  - Goebels, F.*
AU  - Frishman, D.
C1  - 28942
C2  - 33587
CY  - London
TI  - Prediction of protein interaction types based on sequence and network features.
JO  - BMC Syst. Biol.
VL  - 7
PB  - Biomed Central Ltd
PY  - 2013
ER  - 

TY  - JOUR
AB  - BACKGROUND: The establishment of the mid-hindbrain region in vertebrates is mediated by the isthmic organizer, an embryonic secondary organizer characterized by a well-defined pattern of locally restricted gene expression domains with sharply delimited boundaries. While the function of the isthmic organizer at the mid-hindbrain boundary has been subject to extensive experimental studies, it remains unclear how this well-defined spatial gene expression pattern, which is essential for proper isthmic organizer function, is established during vertebrate development. Because the secreted Wnt1 protein plays a prominent role in isthmic organizer function, we focused in particular on the refinement of Wnt1 gene expression in this context. RESULTS: We analyzed the dynamics of the corresponding murine gene regulatory network and the related, diffusive signaling proteins using a macroscopic model for the biological two-scale signaling process. Despite the discontinuity arising from the sharp gene expression domain boundaries, we proved the existence of unique, positive solutions for the partial differential equation system. This enabled the numerically and analytically analysis of the formation and stability of the expression pattern. Notably, the calculated expression domain of Wnt1 has no sharp boundary in contrast to experimental evidence. We subsequently propose a post-transcriptional regulatory mechanism for Wnt1 miRNAs which yields the observed sharp expression domain boundaries. We established a list of candidate miRNAs and confirmed their expression pattern by radioactive in situ hybridization. The miRNA miR-709 was identified as a potential regulator of Wnt1 mRNA, which was validated by luciferase sensor assays. CONCLUSION: In summary, our theoretical analysis of the gene expression pattern induction at the mid-hindbrain boundary revealed the need to extend the model by an additional Wnt1 regulation. The developed macroscopic model of a two-scale process facilitate the stringent analysis of other morphogen-based patterning processes.
AU  - Hock, S.
AU  - Ng, Y.K.
AU  - Hasenauer, J.
AU  - Wittmann, D.M.*
AU  - Lutter, D.
AU  - Trümbach, D.
AU  - Wurst, W.
AU  - Prakash, N.
AU  - Theis, F.J.
C1  - 26305
C2  - 32162
TI  - Sharpening of expression domains induced by transcription and microRNA regulation within a spatio-temporal model of mid-hindbrain boundary formation.
JO  - BMC Syst. Biol.
VL  - 7
PB  - Biomed Central
PY  - 2013
ER  - 

TY  - JOUR
AB  - Background: Predicting a system's behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity in combination with the large number of parameters hampers the calculation of prediction confidence intervals and renders classical approaches as hardly feasible. Results: In this article reliable confidence intervals are calculated based on the prediction profile likelihood. Such prediction confidence intervals of the dynamic states can be utilized for a data-based observability analysis. The method is also applicable if there are non-identifiable parameters yielding to some insufficiently specified model predictions that can be interpreted as non-observability. Moreover, a validation profile likelihood is introduced that should be applied when noisy validation experiments are to be interpreted. Conclusions: The presented methodology allows the propagation of uncertainty from experimental to model predictions. Although presented in the context of ordinary differential equations, the concept is general and also applicable to other types of models. Matlab code which can be used as a template to implement the method is provided at http://www.fdmold.uni-freiburg.de/(similar to)ckreutz/PPL.
AU  - Kreutz, C.*
AU  - Raue, A.
AU  - Timmer, J.*
C1  - 11453
C2  - 30665
TI  - Likelihood based observability analysis and confidence intervals for predictions of dynamic models.
JO  - BMC Syst. Biol.
VL  - 6
PB  - Biomed Central Ltd.
PY  - 2012
ER  - 

TY  - JOUR
AB  - ABSTRACT: BACKGROUND: In radiation protection, biokinetic models for zirconium processing are of crucial importance in dose estimation and further risk analysis for humans exposed to this radioactive substance. They provide limiting values of detrimental effects and build the basis for applications in internal dosimetry, the prediction for radioactive zirconium retention in various organs as well as retrospective dosimetry. Multi-compartmental models are the tool of choice for simulating the processing of zirconium. Although easily interpretable, determining the exact compartment structure and interaction mechanisms is generally daunting. In the context of observing the dynamics of multiple compartments, Bayesian methods provide efficient tools for model inference and selection. RESULTS: We are the first to apply a Markov chain Monte Carlo approach to compute Bayes factors for the evaluation of two competing models for zirconium processing in the human body after ingestion. Based on in vivo measurements of human plasma and urine levels we were able to show that a recently published model is superior to the standard model of the International Commission on Radiological Protection. The Bayes factors were estimated by means of the numerically stable thermodynamic integration in combination with a recently developed copula based Metropolis-Hastings sampler. CONCLUSIONS: In contrast to the standard model the novel model predicts lower accretion of zirconium in bones. This results in lower levels of noxious doses for exposed individuals. Moreover, the Bayesian approach allows for retrospective dose assessment, including credible intervals for the initially ingested zirconium, in a significantly more reliable fashion than previously possible. All methods presented here are readily applicable to many modeling tasks in systems biology.
AU  - Schmidl, D.
AU  - Hug, S.
AU  - Li, W.B.
AU  - Greiter, M.
AU  - Theis, F.J.
C1  - 10479
C2  - 30219
TI  - Bayesian model selection validates a biokinetic model for zirconium processing in humans.
JO  - BMC Syst. Biol.
VL  - 6
PB  - BioMed Central
PY  - 2012
ER  - 

TY  - JOUR
AB  - BACKGROUND: With the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions. RESULTS: In our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination. CONCLUSIONS: In summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.
AU  - Krumsiek, J.
AU  - Suhre, K.
AU  - Illig, T.
AU  - Adamski, J.
AU  - Theis, F.J.
C1  - 4681
C2  - 28253
TI  - Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data.
JO  - BMC Syst. Biol.
VL  - 5
IS  - 1
PB  - BioMed Central Ltd
PY  - 2011
ER  - 

TY  - JOUR
AB  - BACKGROUND: In animals, microRNAs (miRNAs) regulate the protein synthesis of their target messenger RNAs (mRNAs) by either translational repression or deadenylation. miRNAs are frequently found to be co-expressed in different tissues and cell types, while some form polycistronic clusters on genomes. Interactions between targets of co-expressed miRNAs (including miRNA clusters) have not yet been systematically investigated.RESULTS: Here we integrated information from predicted and experimentally verified miRNA targets to characterize protein complex networks regulated by human miRNAs. We found striking evidence that individual miRNAs or co-expressed miRNAs frequently target several components of protein complexes. We experimentally verified that the miR-141-200c cluster targets different components of the CtBP/ZEB complex, suggesting a potential orchestrated regulation in epithelial to mesenchymal transition. CONCLUSIONS: Our findings indicate a coordinate posttranscriptional regulation of protein complexes by miRNAs. These provide a sound basis for designing experiments to study miRNA function at a systems level.
AU  - Sass, S.
AU  - Dietmann, S.
AU  - Burk, U.C.*
AU  - Brabletz, S.*
AU  - Lutter, D.
AU  - Kowarsch, A.
AU  - Mayer, K.F.X.
AU  - Brabletz, T.*
AU  - Ruepp, A.
AU  - Theis, F.J.
AU  - Wang, Y.
C1  - 5451
C2  - 29074
TI  - MicroRNAs coordinately regulate protein complexes.
JO  - BMC Syst. Biol.
VL  - 5
PB  - BioMedCentral
PY  - 2011
ER  - 

TY  - JOUR
AB  - Background: Genome-scale metabolic reconstructions have been recognised as a valuable tool for a variety of applications ranging from metabolic engineering to evolutionary studies. However, the reconstruction of such networks remains an arduous process requiring a high level of human intervention. This process is further complicated by occurrences of missing or conflicting information and the absence of common annotation standards between different data sources. Results: In this article, we report a semi-automated methodology aimed at streamlining the process of metabolic network reconstruction by enabling the integration of different genome-wide databases of metabolic reactions. We present results obtained by applying this methodology to the metabolic network of the plant Arabidopsis thaliana. A systematic comparison of compounds and reactions between two genome-wide databases allowed us to obtain a high-quality core consensus reconstruction, which was validated for stoichiometric consistency. A lower level of consensus led to a larger reconstruction, which has a lower quality standard but provides a baseline for further manual curation. Conclusion: This semi-automated methodology may be applied to other organisms and help to streamline the process of genome-scale network reconstruction in order to accelerate the transfer of such models to applications.
AU  - Radrich, K.
AU  - Tsuruoka, Y.*
AU  - Dobson, P.*
AU  - Gevorgyan, A.*
AU  - Swainston, N.*
AU  - Baart, G.*
AU  - Schwartz, J.M.*
C1  - 2947
C2  - 28116
CY  - London
TI  - Integration of metabolic databases for the reconstruction of genome-scale metabolic networks.
JO  - BMC Syst. Biol.
VL  - 4
PB  - Biomed Central Ltd.
PY  - 2010
ER  - 

TY  - JOUR
AB  - BACKGROUND: Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of complex and multifactorial psychiatric diseases such as anxiety and mood disorders. About 50-60% of patients with major depression show HPA axis dysfunction, i.e. hyperactivity and impaired negative feedback regulation. The neuropeptide corticotropin-releasing hormone (CRH) and its receptor type 1 (CRHR1) are key regulators of this neuroendocrine stress axis. Therefore, we analyzed CRH/CRHR1-dependent gene expression data obtained from the pituitary corticotrope cell line AtT-20, a well-established in vitro model for CRHR1-mediated signal transduction. To extract significantly regulated genes from a genome-wide microarray data set and to deduce underlying CRHR1-dependent signaling networks, we combined supervised and unsupervised algorithms. RESULTS: We present an efficient variable selection strategy by consecutively applying univariate as well as multivariate methods followed by graphical models. First, feature preselection was used to exclude genes not differentially regulated over time from the dataset. For multivariate variable selection a maximum likelihood (MLHD) discriminant function within GALGO, an R package based on a genetic algorithm (GA), was chosen. The topmost genes representing major nodes in the expression network were ranked to find highly separating candidate genes. By using groups of five genes (chromosome size) in the discriminant function and repeating the genetic algorithm separately four times we found eleven genes occurring at least in three of the top ranked result lists of the four repetitions. In addition, we compared the results of GA/MLHD with the alternative optimization algorithms greedy selection and simulated annealing as well as with the state-of-the-art method random forest. In every case we obtained a clear overlap of the selected genes independently confirming the results of MLHD in combination with a genetic algorithm. With two unsupervised algorithms, principal component analysis and graphical Gaussian models, putative interactions of the candidate genes were determined and reconstructed by literature mining. Differential regulation of six candidate genes was validated by qRT-PCR. CONCLUSIONS: The combination of supervised and unsupervised algorithms in this study allowed extracting a small subset of meaningful candidate genes from the genome-wide expression data set. Thereby, variable selection using different optimization algorithms based on linear classifiers as well as the nonlinear random forest method resulted in congruent candidate genes. The calculated interacting network connecting these new target genes was bioinformatically mapped to known CRHR1-dependent signaling pathways. Additionally, the differential expression of the identified target genes was confirmed experimentally.
AU  - Trümbach, D.
AU  - Graf, C.*
AU  - Pütz, B.*
AU  - Kühne, C.*
AU  - Panhuysen, M.*
AU  - Weber, P.*
AU  - Holsboer, F.*
AU  - Wurst, W.
AU  - Welzl, G.
AU  - Deussing, J.M.*
C1  - 6069
C2  - 27819
TI  - Deducing corticotropin-releasing hormone receptor type 1 signaling networks from gene expression data by usage of genetic algorithms and graphical Gaussian models.
JO  - BMC Syst. Biol.
VL  - 4
PB  - BioMed Central Ltd.
PY  - 2010
ER  - 

TY  - JOUR
AB  - The understanding of regulatory and signaling networks has long been a core objective in Systems Biology. Knowledge about these networks is mainly of qualitative nature, which allows the construction of Boolean models, where the state of a component is either 'off' or 'on'. While often able to capture the essential behavior of a network, these models can never reproduce detailed time courses of concentration levels. Nowadays however, experiments yield more and more quantitative data. An obvious question therefore is how qualitative models can be used to explain and predict the outcome of these experiments. Results: In this contribution we present a canonical way of transforming Boolean into continuous models, where the use of multivariate polynomial interpolation allows transformation of logic operations into a system of ordinary differential equations (ODE). The method is standardized and can readily be applied to large networks. Other, more limited approaches to this task are briefly reviewed and compared. Moreover, we discuss and generalize existing theoretical results on the relation between Boolean and continuous models. As a test case a logical model is transformed into an extensive continuous ODE model describing the activation of T-cells. We discuss how parameters for this model can be determined such that quantitative experimental results are explained and predicted, including time-courses for multiple ligand concentrations and binding affinities of different ligands. This shows that from the continuous model we may obtain biological insights not evident from the discrete one. Conclusion: The presented approach will facilitate the interaction between modeling and experiments. Moreover, it provides a straightforward way to apply quantitative analysis methods to qualitatively described systems.
AU  - Wittmann, D.M.
AU  - Krumsiek, J.
AU  - Saez-Rodriguez, J.*
AU  - Lauffenburg, D.A.*
AU  - Klam, S.*
AU  - Theis, F.J.
C1  - 2105
C2  - 27001
SP  - 98
TI  - Transforming Boolean models to continuous models: Methodology and application to T-cell receptor signaling.
JO  - BMC Syst. Biol.
VL  - 3
PB  - Biomed Central
PY  - 2009
ER  - 

TY  - JOUR
AB  - In the bacterium Escherichia coli the transcriptional regulation of gene expression involves both dedicated regulators binding specific DNA sites with high affinity and also global regulators - abundant DNA architectural proteins of the bacterial nucleoid binding multiple sites with a wide range of affinities and thus modulating the superhelical density of DNA. The first form of transcriptional regulation is predominantly pairwise and specific, representing digitial control, while the second form is (in strength and distribution) continuous, representing analog control. RESULTS: Here we look at the properties of effective networks derived from significant gene expression changes under variation of the two forms of control and find that upon limitations of one type of control (caused e.g. by mutation of a global DNA architectural factor) the other type can compensate for compromised regulation. Mutations of global regulators significantly enhance the digital control, whereas in the presence of global DNA architectural proteins regulation is mostly of the analog type, coupling spatially neighboring genomic loci. Taken together our data suggest that two logically distinct - digital and analog - types of control are balancing each other. CONCLUSION: By revealing two distinct logical types of control, our approach provides basic insights into both the organizational principles of transcriptional regulation and the mechanisms buffering genetic flexibility. We anticipate that the general concept of distinguishing logical types of control will apply to many complex biological networks.
AU  - Marr, C.
AU  - Geertz, M.*
AU  - Hütt, M.-T.*
AU  - Muskhelisvili, G.*
C1  - 3434
C2  - 25528
TI  - Dissecting the logical types of network control in gene expression profiles.
JO  - BMC Syst. Biol.
VL  - 2
PB  - BioMed Central
PY  - 2008
ER  -