Evaluation of Colony Formation Dataset of Simulated Cell Cultures

In vitro biological experiments and in silico individual-based computational models are widely used to understand the low-level behavior of cells and cellular functions. Many of these functions can not be directly observed, however, may be deduced from other properties that can be well measured and modeled. In this paper, we present a procedure to evaluate synthetic cell colony formation generated by an off-lattice individual-based model. The calculated shape features of the artificial cell aggregates can be related to the parameter values of the simulated agents, therefore this data can be used to quantify properties of real-life cells such as motility or binding affinity that can not be easily determined otherwise. Our experiments showed that only a few of these parameters are responsible for the difference in shape features of the colonies

Design, synthesis and biological evaluation of thiosemicarbazones, hydrazinobenzothiazoles and arylhydrazones as anticancer agents with a potential to overcome multidrug resistance

There is a constant need for new therapies against multidrug resistant (MDR) cancer. An attractive strategy is to develop chelators that display significant antitumor activity in multidrug resistant cancer cell lines overexpressing the drug efflux pump P-glycoprotein. In this study we used a panel of sensitive and MDR cancer cell lines to evaluate the toxicity of picolinylidene and salicylidene thiosemicarbazone, arylhydrazone, as well as picolinylidene and salicylidene hydrazino-benzothiazole derivatives. Our results confirm the collateral sensitivity of MDR cells to isatin-β-thiosemicarbazones, and identify several chelator scaffolds with a potential to overcome multidrug resistance. Analysis of structure-activity-relationships within the investigated compound library indicates that NNS and NNN donor chelators show superior toxicity as compared to ONS derivatives regardless of the resistance status of the cells. © 2016 Elsevier Masson SAS

Two main mutational processes operate in the absence of DNA mismatch repair

The analysis of tumour genome sequences has demonstrated high rates of base substitution mutagenesis upon the inactivation of DNA mismatch repair (MMR), and the resulting somatic mutations in MMR deficient tumours appear to significantly enhance the response to immune therapy. A handful of different algorithmically derived base substitution mutation signatures have been attributed to MMR deficiency in tumour somatic mutation datasets. In contrast, mutation data obtained from whole genome sequences of isogenic wild type and MMR deficient cell lines in this study, as well as from published sources, show a more uniform experimental mutation spectrum of MMR deficiency. In order to resolve this discrepancy, we reanalysed mutation data from MMR deficient tumour whole exome and whole genome sequences. We derived two base substitution signatures using non-negative matrix factorisation, which together adequately describe mutagenesis in all tumour and cell line samples. The two new signatures broadly resemble COSMIC signatures 6 and 20, but perform better than existing COSMIC signatures at identifying MMR deficient tumours in mutation signature deconstruction. We show that the contribution of the two identified signatures, one of which is dominated by C to T mutations at CpG sites, is biased by the different sequence composition of the exome and the whole genome. We further show that the identity of the inactivated MMR gene, the tissue type, the mutational burden or the patient's age does not influence the mutation spectrum, but that a tendency for a greater contribution by the CpG mutational process is observed in tumours as compared to cultured cells. Our analysis suggest that two separable mutational processes operate in the genomes of MMR deficient cells. © 2020 The Author(s

Time scale and dimension analysis of a budding yeast cell cycle model

BACKGROUND: The progress through the eukaryotic cell division cycle is driven by an underlying molecular regulatory network. Cell cycle progression can be considered as a series of irreversible transitions from one steady state to another in the correct order. Although this view has been put forward some time ago, it has not been quantitatively proven yet. Bifurcation analysis of a model for the budding yeast cell cycle has identified only two different steady states (one for G1 and one for mitosis) using cell mass as a bifurcation parameter. By analyzing the same model, using different methods of dynamical systems theory, we provide evidence for transitions among several different steady states during the budding yeast cell cycle. RESULTS: By calculating the eigenvalues of the Jacobian of kinetic differential equations we have determined the stability of the cell cycle trajectories of the Chen model. Based on the sign of the real part of the eigenvalues, the cell cycle can be divided into excitation and relaxation periods. During an excitation period, the cell cycle control system leaves a formerly stable steady state and, accordingly, excitation periods can be associated with irreversible cell cycle transitions like START, entry into mitosis and exit from mitosis. During relaxation periods, the control system asymptotically approaches the new steady state. We also show that the dynamical dimension of the Chen's model fluctuates by increasing during excitation periods followed by decrease during relaxation periods. In each relaxation period the dynamical dimension of the model drops to one, indicating a period where kinetic processes are in steady state and all concentration changes are driven by the increase of cytoplasmic growth. CONCLUSION: We apply two numerical methods, which have not been used to analyze biological control systems. These methods are more sensitive than the bifurcation analysis used before because they identify those transitions between steady states that are not controlled by a bifurcation parameter (e.g. cell mass). Therefore by applying these tools for a cell cycle control model, we provide a deeper understanding of the dynamical transitions in the underlying molecular network

Identifying new topoisomerase II poison scaffolds by combining publicly available toxicity data and 2D/3D-based virtual screening

Molecular descriptor (2D) and three dimensional (3D) shape based similarity methods are widely used in ligand based virtual drug design. In the present study pairwise structure comparisons among a set of 4858 DTP compounds tested in the NCI60 tumor cell line anticancer drug screen were computed using chemical hashed fingerprints and 3D molecule shapes to calculate 2D and 3D similarities, respectively. Additionally, pairwise biological activity similarities were calculated by correlating the 60 element vectors of pGI50 values corresponding to the cytotoxicity of the compounds across the NCI60 panel. Subsequently, we compared the power of 2D and 3D structural similarity metrics to predict the toxicity pattern of compounds. We found that while the positive predictive value and sensitivity of 3D and molecular descriptor based approaches to predict biological activity are similar, a subset of molecule pairs yielded contradictory results. By simultaneously requiring similarity of biological activities and 3D shapes, and dissimilarity of molecular descriptor based comparisons, we identify pairs of scaffold hopping candidates displaying characteristic core structural changes such as heteroatom/heterocycle change and ring closure. Attempts to discover scaffold hopping candidates of mitoxantrone recovered known Topoisomerase II (Top2) inhibitors, and also predicted new, previously unknown chemotypes possessing in vitro Top2 inhibitory activity

Comparison of Michaelis-Menten kinetics modeling alternatives in cancer chemotherapy modeling

Model-based optimization and personalization of tumor therapies require tumor growth models that reliably describe the effect of the drug used during the therapy. A key phenomenon in the drug effect mechanism is the pharmacodynamics of the drugs which limits the maximal effect of the drug. The pharmacodynamics can be modeled with Michaelis-Menten kinetics, that can be realized in the differential equations of the model as a Hill function or bilinear functions with one extra state variable if we consider the quasi steady-state approximation or use triplet motifs, respectively. We use experimental data for a chemotherapeutic drug and carry out parametric identification of our tumor model with both Michealis-Menten kinetics models. The results show that the quasi steady-state approximation has better modeling power and less complexity

Evaluation of colony formation dataset of simulated cell cultures

In vitro biological experiments and in silico individual-based computa- tional models are widely used to understand the low-level behavior of cells and cellular functions. Many of these functions can not be directly observed, however, may be deduced from other properties that can be well measured and modeled. In this paper, we present a procedure to evaluate synthetic cell colony formation generated by an off-lattice individual-based model. The calculated shape features of the artificial cell aggregates can be related to the parameter values of the simulated agents, therefore this data can be used to quantify properties of real-life cells such as motility or binding affinity that can not be easily determined otherwise. Our experiments showed that only a few of these parameters are responsible for the difference in shape features of the colonies

Shotgun Lipidomic Profiling of the NCI60 Cell Line Panel Using Rapid Evaporative Ionization Mass Spectrometry

Rapid evaporative ionization mass spectrometry (REIMS) was used for the rapid mass spectrometric profiling of cancer cell lines. Spectral reproducibility was assessed for three different cell lines, and the extent of interclass differences and intraclass variance was found to allow the identification of these cell lines based on the REIMS data. Subsequently, the NCI60 cell line panel was subjected to REIMS analysis, and the resulting data set was investigated for its distinction of individual cell lines and different tissue types of origin. Information content of REIMS spectral profiles of cell lines were found to be similar to those obtained from mammalian tissues although pronounced differences in relative lipid intensity were observed. Ultimately, REIMS was shown to detect changes in lipid content of cell lines due to mycoplasma infection. The data show that REIMS is an attractive means to study cell lines involving minimal sample preparation and analysis times in the range of seconds. © 2016 American Chemical Society