@phdthesis{Koseska2007, author = {Koseska, Aneta}, title = {Modeling and control of synthetic gene regulatory networks}, address = {Potsdam}, pages = {viii, 82 S.}, year = {2007}, language = {en} } @misc{KoseskaZaikinKurthsetal.2009, author = {Koseska, Aneta and Zaikin, Alexey and Kurths, J{\"u}rgen and Garc{\´i}a-Ojalvo, Jordi}, title = {Timing cellular decision making under noise via cell-cell communication}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45260}, year = {2009}, abstract = {Many cellular processes require decision making mechanisms, which must act reliably even in the unavoidable presence of substantial amounts of noise. However, the multistable genetic switches that underlie most decision-making processes are dominated by fluctuations that can induce random jumps between alternative cellular states. Here we show, via theoretical modeling of a population of noise-driven bistable genetic switches, that reliable timing of decision-making processes can be accomplished for large enough population sizes, as long as cells are globally coupled by chemical means. In the light of these results, we conjecture that cell proliferation, in the presence of cell-cell communication, could provide a mechanism for reliable decision making in the presence of noise, by triggering cellular transitions only when the whole cell population reaches a certain size. In other words , the summation performed by the cell population would average out the noise and reduce its detrimental impact.}, language = {en} } @article{ZakharovaNikoloskiKoseska2013, author = {Zakharova, A. and Nikoloski, Zoran and Koseska, Aneta}, title = {Dimensionality reduction of bistable biological systems}, series = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology}, volume = {75}, journal = {Bulletin of mathematical biology : official journal of the Society for Mathematical Biology}, number = {3}, publisher = {Springer}, address = {New York}, issn = {0092-8240}, doi = {10.1007/s11538-013-9807-8}, pages = {373 -- 392}, year = {2013}, abstract = {Time hierarchies, arising as a result of interactions between system's components, represent a ubiquitous property of dynamical biological systems. In addition, biological systems have been attributed switch-like properties modulating the response to various stimuli across different organisms and environmental conditions. Therefore, establishing the interplay between these features of system dynamics renders itself a challenging question of practical interest in biology. Existing methods are suitable for systems with one stable steady state employed as a well-defined reference. In such systems, the characterization of the time hierarchies has already been used for determining the components that contribute to the dynamics of biological systems. However, the application of these methods to bistable nonlinear systems is impeded due to their inherent dependence on the reference state, which in this case is no longer unique. Here, we extend the applicability of the reference-state analysis by proposing, analyzing, and applying a novel method, which allows investigation of the time hierarchies in systems exhibiting bistability. The proposed method is in turn used in identifying the components, other than reactions, which determine the systemic dynamical properties. We demonstrate that in biological systems of varying levels of complexity and spanning different biological levels, the method can be effectively employed for model simplification while ensuring preservation of qualitative dynamical properties (i.e., bistability). Finally, by establishing a connection between techniques from nonlinear dynamics and multivariate statistics, the proposed approach provides the basis for extending reference-based analysis to bistable systems.}, language = {en} } @article{HempelKoseskaNikoloski2013, author = {Hempel, Sabrina and Koseska, Aneta and Nikoloski, Zoran}, title = {Data-driven reconstruction of directed networks}, series = {The European physical journal : B, Condensed matter and complex systems}, volume = {86}, journal = {The European physical journal : B, Condensed matter and complex systems}, number = {6}, publisher = {Springer}, address = {New York}, issn = {1434-6028}, doi = {10.1140/epjb/e2013-31111-8}, pages = {17}, year = {2013}, abstract = {We investigate the properties of a recently introduced asymmetric association measure, called inner composition alignment (IOTA), aimed at inferring regulatory links (couplings). We show that the measure can be used to determine the direction of coupling, detect superfluous links, and to account for autoregulation. In addition, the measure can be extended to infer the type of regulation (positive or negative). The capabilities of IOTA to correctly infer couplings together with their directionality are compared against Kendall's rank correlation for time series of different lengths, particularly focussing on biological examples. We demonstrate that an extended version of the measure, bidirectional inner composition alignment (biIOTA), increases the accuracy of the network reconstruction for short time series. Finally, we discuss the applicability of the measure to infer couplings in chaotic systems.}, language = {en} } @article{KoseskaVolkovKurths2009, author = {Koseska, Aneta and Volkov, Evgenij and Kurths, J{\"u}rgen}, title = {Detuning-dependent dominance of oscillation death in globally coupled synthetic genetic oscillators}, issn = {0295-5075}, doi = {10.1209/0295-5075/85/28002}, year = {2009}, abstract = {We study dynamical regimes of globally coupled genetic relaxation oscillators in the presence of small detuning. Using bifurcation analysis, we find that under strong coupling via the slow variable, the detuning can eliminate standard oscillatory solutions in a large region of the parameter space, providing the dominance of oscillation death. This result is substantially different from previous results on oscillation quenching, where for homogeneous populations, the coexistence of oscillation death and limit cycle oscillations is always present. We propose further that this effect of detuning-dependent dominance could be a powerful regulator of genetic network's dynamics.}, language = {en} } @phdthesis{Koseska2011, author = {Koseska, Aneta}, title = {Dynamics of biological networks : data analysis, modeling and bifurcations}, address = {Potsdam}, year = {2011}, language = {en} } @article{ZakharovaVadivasovaAnishchenkoetal.2010, author = {Zakharova, Anna and Vadivasova, Tatjana and Anishchenko, Vadim S. and Koseska, Aneta and Kurths, J{\"u}rgen}, title = {Stochastic bifurcations and coherencelike resonance in a self-sustained bistable noisy oscillator}, issn = {1539-3755}, doi = {10.1103/Physreve.81.011106}, year = {2010}, abstract = {We investigate the influence of additive Gaussian white noise on two different bistable self-sustained oscillators: Duffing-Van der Pol oscillator with hard excitation and a model of a synthetic genetic oscillator. In the deterministic case, both oscillators are characterized with a coexistence of a stable limit cycle and a stable equilibrium state. We find that under the influence of noise, their dynamics can be well characterized through the concept of stochastic bifurcation, consisting in a qualitative change of the stationary amplitude distribution. For the Duffing-Van der Pol oscillator analytical results, obtained for a quasiharmonic approach, are compared with the result of direct computer simulations. In particular, we show that the dynamics is different for isochronous and anisochronous systems. Moreover, we find that the increase of noise intensity in the isochronous regime leads to a narrowing of the spectral line. This effect is similar to coherence resonance. However, in the case of anisochronous systems, this effect breaks down and a new phenomenon, anisochronous-based stochastic bifurcation occurs.}, language = {en} } @article{ZakharovaKurthsVadivasovaetal.2011, author = {Zakharova, Anna and Kurths, J{\"u}rgen and Vadivasova, Tatyana and Koseska, Aneta}, title = {Analysing dynamical behavior of cellular networks via stochastic bifurcations}, series = {PLoS one}, volume = {6}, journal = {PLoS one}, number = {5}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0019696}, pages = {12}, year = {2011}, abstract = {The dynamical structure of genetic networks determines the occurrence of various biological mechanisms, such as cellular differentiation. However, the question of how cellular diversity evolves in relation to the inherent stochasticity and intercellular communication remains still to be understood. Here, we define a concept of stochastic bifurcations suitable to investigate the dynamical structure of genetic networks, and show that under stochastic influence, the expression of given proteins of interest is defined via the probability distribution of the phase variable, representing one of the genes constituting the system. Moreover, we show that under changing stochastic conditions, the probabilities of expressing certain concentration values are different, leading to different functionality of the cells, and thus to differentiation of the cells in the various types.}, language = {en} } @article{HempelKoseskaKurthsetal.2011, author = {Hempel, Stefan and Koseska, Aneta and Kurths, J{\"u}rgen and Nikoloski, Zoran}, title = {Inner composition alignment for inferring directed networks from short time series}, series = {Physical review letters}, volume = {107}, journal = {Physical review letters}, number = {5}, publisher = {American Physical Society}, address = {College Park}, issn = {0031-9007}, doi = {10.1103/PhysRevLett.107.054101}, pages = {4}, year = {2011}, abstract = {Identifying causal links (couplings) is a fundamental problem that facilitates the understanding of emerging structures in complex networks. We propose and analyze inner composition alignment-a novel, permutation-based asymmetric association measure to detect regulatory links from very short time series, currently applied to gene expression. The measure can be used to infer the direction of couplings, detect indirect (superfluous) links, and account for autoregulation. Applications to the gene regulatory network of E. coli are presented.}, language = {en} } @article{HempelKoseskaNikoloskietal.2011, author = {Hempel, Sabrina and Koseska, Aneta and Nikoloski, Zoran and Kurths, J{\"u}rgen}, title = {Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study}, series = {BMC bioinformatics}, volume = {12}, journal = {BMC bioinformatics}, number = {1}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {10.1186/1471-2105-12-292}, pages = {26}, year = {2011}, abstract = {Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications. Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study. Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.}, language = {en} }