Dynamic Behavior of p53 Driven by Delay and a Microrna-34a-Mediated Feedback Loop

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

MicroRNAs in doxorubicin-induced cardiotoxicity: The DNA damage response

Doxorubicin (DOX) is a chemotherapeutic drug widely used for cancer treatment, but its use is limited by cardiotoxicity. Although free radicals from redox cycling and free cellular iron have been predominant as the suggested primary pathogenic mechanism, novel evidence has pointed to topoisomerase II inhibition and resultant genotoxic stress as the more fundamental mechanism. Recently, a growing list of microRNAs (miRNAs) has been implicated in DOX-induced cardiotoxicity (DIC). This review summarizes miRNAs reported in the recent literature in the context of DIC. A particular focus is given to miRNAs that regulate cellular responses downstream to DOX-induced DNA damage, especially p53 activation, pro-survival signaling pathway inhibition (e.g., AMPK, AKT, GATA-4, and sirtuin pathways), mitochondrial dysfunction, and ferroptosis. Since these pathways are potential targets for cardioprotection against DOX, an understanding of how miRNAs participate is necessary for developing future therapies.

Dual roles of SIRT1 in the BAX switch through the P53 module: A mathematical modeling study

SIRT1 is a multifunctional deacetylase that participates in a variety of cellular physiological processes to cope with stress. The anticancer protein P53 is an important target of SIRT1. It has been found that SIRT1 is involved in apoptosis by regulating the activity and intracellular location of P53. Moreover, P53-dependent apoptosis is inseparable from the BCL-2 protein family. Among the members of this family, BAX’s switching dynamics may play a key role in apoptosis. Therefore, a challenging question arises: what effect does SIRT1 have on the BAX switch? To answer this question, we built a small-scale protein network model. Through computer simulation, the properties of SIRT1 that on the one hand promote and on the other inhibit apoptosis are revealed. We found that the opening time of the BAX switch will be delayed in the case of either SIRT1 excess or deficiency. Similarly, the stimulus threshold required for apoptosis will also increase in the above two scenarios. Thereby, we proposed that SIRT1 has an optimal content at which the probability of apoptosis is greatest. In addition, P53 oscillation requires the concentration of SIRT1 to be higher than the optimal value. This work may be helpful both experimentally and clinically.