A control theoretic three timescale model for analyzing energy management in mammalian cancer cells

Comprehensive review of drug resistance in mammalian cancer stem cells: implications for cancer therapy

Cancer remains a significant global challenge, and despite the numerous strategies developed to advance cancer therapy, an effective cure for metastatic cancer remains elusive. A major hurdle in treatment success is the ability of cancer cells, particularly cancer stem cells (CSCs), to resist therapy. These CSCs possess unique abilities, including self-renewal, differentiation, and repair, which drive tumor progression and chemotherapy resistance. The resilience of CSCs is linked to certain signaling pathways. Tumors with pathway-dependent CSCs often develop genetic resistance, whereas those with pathway-independent CSCs undergo epigenetic changes that affect gene regulation. CSCs can evade cytotoxic drugs, radiation, and apoptosis by increasing drug efflux transporter activity and activating survival mechanisms. Future research should prioritize the identification of new biomarkers and signaling molecules to better understand drug resistance. The use of cutting-edge approaches, such as bioinformatics, genomics, proteomics, and nanotechnology, offers potential solutions to this challenge. Key strategies include developing targeted therapies, employing nanocarriers for precise drug delivery, and focusing on CSC-targeted pathways such as the Wnt, Notch, and Hedgehog pathways. Additionally, investigating multitarget inhibitors, immunotherapy, and nanodrug delivery systems is critical for overcoming drug resistance in cancer cells.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm

Plant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production, physiological studies, and plant transformation/regeneration. However, there are several types of callus with different morphological and developmental characteristics and not all are suitable for all applications. Callogenesis is a multivariable developmental process affected by several intrinsic and extrinsic factors, but the most important driver is plant growth regulator (PGRs) levels and type. Since callogenesis is a non-linear process influenced by many different factors, robust computational methods such as machine learning algorithms have great potential to model, predict, and optimize callus growth and development. The current study was conducted to evaluate the effect of PGRs on callus morphology in drug-type Cannabis sativa to maximize callus growth and promote embryogenic callus production. For this aim, random forest (RF) and support vector machine (SVM) were applied in conjunction with image processing to model and predict callus morphological and physical traits. The results showed that SVM was more accurate than RF. In order to find the optimal level of PGRs for optimizing callus growth and development, the SVM was linked to a genetic algorithm (GA). To confirm the reliability of SVM-GA, the optimized-predicted outcomes were tested in a validation experiment that revealed SVM-GA was able to accurately model and optimize the system. Moreover, our results showed that there is a significant correlation between embryogenic callus production and the true density of callus. KEY POINTS: • The effect of PGRs on callus growth and development of cannabis was studied. • The predictive accuracy of SVM and RF was evaluated and compared. • GA was linked to the SVM for optimizing the callus growth and development.