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The influence of selected transition elements on cell metabolism

Terpiłowska Sylwia, Rafińska Katarzyna, Gołębiowski Adrian, Kowalkowski Tomasz, Buszewski Bogusław

Ecological Chemistry and Engineering. S

2023

Vol. 30, nr 4

471--488

The elements are present in the environment. Moreover, they are used in pharmacy and the production of new materials used in medical applications. They are often as environmental pollutants. They can accumulate in organisms and induce toxic effects on the cellular level. HepG2, L929 and Caco-2 cell lines were exposed to known concentrations of chromium chloride, iron chloride, nickel chloride, molybdenum trioxide and cobalt chloride (200 or 1000 μ M used alone and in combinations). Concentrations of chromium, iron, nickel, molybdenum and cobalt in the cell lysate and the culture medium were determined by ICP-MS. Moreover, sodium, potassium, calcium and magnesium concentrations were also measured. What is more, cells were observed under light and scanning electron microscope. The dose-dependent increase in the concentration of chromium, iron, nickel, molybdenum and cobalt in all cell lines after incubation with elements was observed. Potassium concentration decreases while sodium calcium and magnesium increase after incubation of cells with of mentioned elements. The incubation of cells with microelements induces cell morphology changes. The presented study shows the crucial role of tested microelements in the induction of cell death as a result of an imbalance of sodium, potassium, calcium and magnesium concentration inside the cell.

Verification of Dynamic Behaviour in Qualitative Molecular Networks Describing Gene Regulation, Signalling and Whole-cell Metabolism

Grabowski M., Bokota G., Sroka J., Kierzek A.

Fundamenta Informaticae

2018

Vol. 160, nr 1/2

199--219

We present a tool for the verification of qualitative biological models. These models formalise observed behaviours and interrelations of molecular and cellular mechanisms. During its development a model is continuously verified. Predicted behaviours are compared with behaviours observed in experimental data. Moreover, the model must not exhibit behaviours which contradict existing knowledge about capabilities of the biological system under investigation. Model development is an iterative process involving many rounds of prediction, verification and refinement. Due to the complexity of biological systems this process is laborious and error prone, which motivates the development of “model debugging” tools. The qualitative models we investigate represent large-scale molecular interaction networks describing gene regulation, signalling and whole-cell metabolism. We integrate a steady state model of whole-cell metabolism with a dynamic model of gene regulation and signalling represented as a Petri net. This Quasi-Steady State Petri Net (QSSPN) representation allows the generation of dynamic sequences of molecular events satisfying substrate, activator, inhibitor and metabolic flux requirements at every state transition. The reachability graph of the dynamic part of the model is examined and for every transition in this graph the satisfaction of metabolic flux requirements is verified by well-established linear programming techniques. Our approach is based on network connectivity alone and does not require any kinetic parameters. We demonstrate the applicability of our method by analysing a large-scale model of a nuclear receptor network regulating bile acid homeostasis in human hepatocyte. To date, simulation and verification of QSSPN models have been performed exclusively by Monte Carlo simulation. Random walks through the state space were used to find examples of behaviour satisfying properties of interest. Here, we provide for the first time for QSSPN models an exhaustive analysis of the state space up to a finite depth, which is possible due to several effective optimisations. Contrary to the Monte Carlo approach, we can prove that certain behaviour cannot be realised by the model within a given number of steps. This allows rejection of models which are not capable to reproduce experimentally observed behaviours, as well as verification that biologically unrealistic behaviours cannot occur in the simulation. We show an example of how these features improve identification of problems in large scale network models.