@article{GrimbsArnoldKoseskaetal.2011,
  author    = {Grimbs, Sergio and Arnold, Anne and Koseska, Aneta and Kurths, J{\"u}rgen and Selbig, Joachim and Nikoloski, Zoran},
  title     = {Spatiotemporal dynamics of the Calvin cycle multistationarity and symmetry breaking instabilities},
  series = {Biosystems : journal of biological and information processing sciences},
  volume    = {103},
  journal   = {Biosystems : journal of biological and information processing sciences},
  number    = {2},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0303-2647},
  doi       = {10.1016/j.biosystems.2010.10.015},
  pages     = {212 -- 223},
  year      = {2011},
  abstract  = {The possibility of controlling the Calvin cycle has paramount implications for increasing the production of biomass. Multistationarity, as a dynamical feature of systems, is the first obvious candidate whose control could find biotechnological applications. Here we set out to resolve the debate on the multistationarity of the Calvin cycle. Unlike the existing simulation-based studies, our approach is based on a sound mathematical framework, chemical reaction network theory and algebraic geometry, which results in provable results for the investigated model of the Calvin cycle in which we embed a hierarchy of realistic kinetic laws. Our theoretical findings demonstrate that there is a possibility for multistationarity resulting from two sources, homogeneous and inhomogeneous instabilities, which partially settle the debate on multistability of the Calvin cycle. In addition, our tractable analytical treatment of the bifurcation parameters can be employed in the design of validation experiments.},
  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}
}
@misc{HempelKoseskaNikoloskietal.2017,
  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},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-400924},
  pages     = {26},
  year      = {2017},
  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}
}
@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}
}
@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}
}
@phdthesis{Koseska2007,
  author    = {Koseska, Aneta},
  title     = {Modeling and control of synthetic gene regulatory networks},
  address   = {Potsdam},
  pages     = {viii, 82 S.},
  year      = {2007},
  language  = {en}
}
@phdthesis{Koseska2011,
  author    = {Koseska, Aneta},
  title     = {Dynamics of biological networks : data analysis, modeling and bifurcations},
  address   = {Potsdam},
  year      = {2011},
  language  = {en}
}
@article{KoseskaVolkovKurths2011,
  author    = {Koseska, Aneta and Volkov, Evgenii and Kurths, J{\"u}rgen},
  title     = {Synthetic multicellular oscillatory systems controlling protein dynamics with genetic circuits},
  series = {Physica scripta : an international journal for experimental and theoretical physics},
  volume    = {84},
  journal   = {Physica scripta : an international journal for experimental and theoretical physics},
  number    = {4},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {0031-8949},
  doi       = {10.1088/0031-8949/84/04/045007},
  pages     = {10},
  year      = {2011},
  abstract  = {Synthetic biology is a relatively new research discipline that combines standard biology approaches with the constructive nature of engineering. Thus, recent efforts in the field of synthetic biology have given a perspective to consider cells as 'programmable matter'. Here, we address the possibility of using synthetic circuits to control protein dynamics. In particular, we show how intercellular communication and stochasticity can be used to manipulate the dynamical behavior of a population of coupled synthetic units and, in this manner, finely tune the expression of specific proteins of interest, e.g. in large bioreactors.},
  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}
}
@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}
}