Instytut Podstawowych Problemów Techniki
Polskiej Akademii Nauk


Anna Gambin

Ostatnie publikacje
1.  Wronowska W., Charzyńska A., Nienałtowski K., Gambin A., Computational modeling of sphingolipid metabolism, BMC SYSTEMS BIOLOGY, ISSN: 1752-0509, DOI: 10.1186/s12918-015-0176-9, Vol.9, pp.47-1-16, 2015

As suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.

The existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.

The model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer’s disease, which are associated with sphingolipid metabolism.

Słowa kluczowe:
Sphingolipid metabolism, Kinetic model, Sensitivity analysis

Afiliacje autorów:
Wronowska W. - inna afiliacja
Charzyńska A. - Uniwersytet Warszawski (PL)
Nienałtowski K. - IPPT PAN
Gambin A. - inna afiliacja
2.  Jetka T., Charzyńska A., Gambin A., Stumpf M.P.H., Komorowski M., StochDecomp—Matlab package for noise decomposition in stochastic biochemical systems, BIOINFORMATICS, ISSN: 1367-4803, DOI: 10.1093/bioinformatics/btt631, Vol.30, No.1, pp.137-138, 2014

Motivation: Stochasticity is an indispensable aspect of biochemical processes at the cellular level. Studies on how the noise enters and propagates in biochemical systems provided us with non-trivial insights into the origins of stochasticity, in total, however, they constitute a patchwork of different theoretical analyses.

Results: Here we present a flexible and widely applicable noise decomposition tool that allows us to calculate contributions of individual reactions to the total variability of a system’s output. With the package it is, therefore, possible to quantify how the noise enters and propagates in biochemical systems. We also demonstrate and exemplify using the JAK-STAT signalling pathway that the noise contributions resulting from individual reactions can be inferred from data experimental data along with Bayesian parameter inference. The method is based on the linear noise approximation, which is assumed to provide a reasonable representation of analyzed systems.

Afiliacje autorów:
Jetka T. - inna afiliacja
Charzyńska A. - Uniwersytet Warszawski (PL)
Gambin A. - inna afiliacja
Stumpf M.P.H. - Imperial College London (GB)
Komorowski M. - IPPT PAN
3.  Dojer N., Gambin A., Mizera A., Wilczyński B., Tiuryn J., Applying dynamic Bayesian networks to perturbed gene expression data, BMC BIOINFORMATICS, ISSN: 1471-2105, DOI: 10.1186/1471-2105-7-249, Vol.7, No.249, pp.1-11, 2006

A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.

We extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.

We show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

Afiliacje autorów:
Dojer N. - Uniwersytet Warszawski (PL)
Gambin A. - inna afiliacja
Mizera A. - IPPT PAN
Wilczyński B. - inna afiliacja
Tiuryn J. - Uniwersytet Warszawski (PL)

Kategoria A Plus


logo ippt            ul. Pawińskiego 5B, 02-106 Warszawa
  +48 22 826 12 81 (centrala)
  +48 22 826 98 15

Znajdź nas

© Instytut Podstawowych Problemów Techniki Polskiej Akademii Nauk 2024