Evolutionary Game Theory: Darwinian Dynamics and the G Function Approach

Human societies are organized and developed through collective cooperative behaviours. Based on the information in their environment, individuals can form collective cooperation by strategically changing unfavourable surroundings and imitating superior behaviours. However, facing the rampant proliferation and spreading of misinformation, we still lack systematic investigations into the impact of misinformation on the evolution of collective cooperation. Here, we study this problem by classical evolutionary game theory. We find that the existence of misinformation generally impedes the emergence of collective cooperation on networks, although the level of cooperation is slightly higher for weak social cooperative dilemma below a proven threshold. We further show that this possible advantage diminishes as social connections become denser, suggesting that the detrimental effect of misinformation further increases when 'social viscosity' is low. Our results uncover the quantitative effect of misinformation on suppressing collective cooperation, and pave the way for designing possible mechanisms to improve collective cooperation.

We study a natural extension of classical evolutionary game theory to a setting in which pairwise interactions are restricted to the edges of an undirected graph or network. We generalize the definition of an evolutionary stable strategy (ESS), and show a pair of complementary results that exhibit the power of randomization in our setting: subject to degree or edge density conditions, the classical ESS of any game are preserved when the graph is chosen randomly and the mutation set is chosen adversarially, or when the graph is chosen adversarially and the mutation set is chosen randomly. We examine natural strengthenings of our generalized ESS definition, and show that similarly strong resultsnare not possible for them.

NEKs [NIMA (never in mitosis gene A)-related expressed kinase] are involved in ovarian cancer development and progression, while their association with drug resistance is limited, especially NEK11, and its relationship with drug resistance has never been reported. In this study, on the basis of comprehensive bioinformatic analyses, including mRNA expression according to microarray data, protein/gene interaction, protein-small molecule interaction, annotation of biological process and microRNA-mRNA interaction analysis, we revealed that the NEK11 mRNA was significantly downregulated in 586 cases of ovarian serous cystadenocarcinomas and cisplatin-resistant A2780 ovarian cancer cells, and interacted with 22 proteins and 4 small molecules which all were contributed to drug resistance in ovarian cancer. Furthermore, seven cell cycle-related biological processes were annotated with NEK11, drug resistance and ovarian cancer, suggesting that NEK11 potentially was involved in the drug resistance in ovarian cancer via its regulatory roles in the cell cycle. In addition, among the eight microRNAs predicted to be most strongly targeting NEK11, the majority were involved in drug resistance in ovarian and other cancers. All those results provide a very strong possibility that the notable downregulation of NEK11 in cisplatin-resistant ovarian cancer cells was involved in drug resistance, via its interactions with drug resistance-related genes, proteins, small molecules, microRNAs and biological processes, particularly the cell cycle-related processes. To our knowledge, this is the first report of the association of NEK11 with drug resistance in cancer, and it would pave the way for further investigation of the drug resistance-related functions of this gene.

Drug resistance in cancer cells significantly diminishes treatment efficacy, leading to recurrence and metastasis. A critical factor contributing to this resistance is the epigenetic alteration of gene expression via RNA modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I) editing. These modifications are pivotal in regulating RNA splicing, translation, transport, degradation, and stability. Governed by “writers,” “readers,” and “erasers,” RNA modifications impact numerous biological processes and cancer progression, including cell proliferation, stemness, autophagy, invasion, and apoptosis. Aberrant RNA modifications can lead to drug resistance and adverse outcomes in various cancers. Thus, targeting RNA modification regulators offers a promising strategy for overcoming drug resistance and enhancing treatment efficacy. This review consolidates recent research on the role of prevalent RNA modifications in cancer drug resistance, with a focus on m6A, m1A, m5C, m7G, Ψ, and A-to-I editing. Additionally, it examines the regulatory mechanisms of RNA modifications linked to drug resistance in cancer and underscores the existing limitations in this field.

Microbial communities display complex population dynamics, both in frequency and absolute density. Evolutionary game theory provides a natural approach to analyse and model this complexity by studying the detailed interactions among players, including competition and conflict, cooperation and coexistence. Classic evolutionary game theory models typically assume constant population size, which often does not hold for microbial populations. Here, we explicitly take into account population growth with frequency-dependent growth parameters, as observed in our experimental system. We study the in vitro population dynamics of the two commensal bacteria (Curvibacter sp. (AEP1.3) and Duganella sp. (C1.2)) that synergistically protect the metazoan host Hydra vulgaris (AEP) from fungal infection. The frequency-dependent, nonlinear growth rates observed in our experiments indicate that the interactions among bacteria in co-culture are beyond the simple case of direct competition or, equivalently, pairwise games. This is in agreement with the synergistic effect of anti-fungal activity observed in vivo. Our analysis provides new insight into the minimal degree of complexity needed to appropriately understand and predict coexistence or extinction events in this kind of microbial community dynamics. Our approach extends the understanding of microbial communities and points to novel experiments.

Development of drug resistance in cancer has major implications for patients’ outcome. It is related to processes involved in the decrease of drug efficacy, which are strongly influenced by intratumor heterogeneity and changes in the microenvironment. Heterogeneity arises, to a large extent, from genetic mutations analogously to Darwinian evolution, when selection of tumor cells results from the adaptation to the microenvironment, but could also emerge as a consequence of epigenetic mutations driven by stochastic events. An important exogenous source of alterations is the action of chemotherapeutic agents, which not only affects the signalling pathways but also the interactions among cells. In this work we provide experimental evidence from in vitro assays and put forward a mathematical kinetic transport model to describe the dynamics displayed by a system of non-small-cell lung carcinoma cells (NCI-H460) which, depending on the effect of a chemotherapeutic agent (doxorubicin), exhibits a complex interplay between Darwinian selection, Lamarckian induction and the nonlocal transfer of extracellular microvesicles. The role played by all of these processes to multidrug resistance in cancer is elucidated and quantified.

We study the fixation probability of a mutant type when introduced into a resident population. We implement a stochastic competitive Lotka–Volterra model with two types and intra- and interspecific competition. The model further allows for stochastically varying population sizes. The competition coefficients are interpreted in terms of inverse payoffs emerging from an evolutionary game. Since our study focuses on the impact of the competition values, we assume the same net growth rate for both types. In this general framework, we derive a formula for the fixation probability varphi of the mutant type under weak selection. We find that the most important parameter deciding over the invasion success of the mutant is its death rate due to competition with the resident. Furthermore, we compare our approximation to results obtained by implementing population size changes deterministically in order to explore the parameter regime of validity of our method. Finally, we put our formula in the context of classical evolutionary game theory and observe similarities and differences to the results obtained in that constant population size setting.

Multi drug resistance in Colorectal Cancer- approaches to overcome, advancements and future success

Altered expression of cellular redox genes and proteins contributes to invasion, metastasis, and drug resistance in cancer. NADPH oxidase (NOX) isoforms are the pro-oxidant enzymes that generate ROS as a primary product. Dysregulation of NOX activity and expression alters ROS generation, which either directly or indirectly modulates cell death and survival signaling during the progression of cancer. Nuclear factor erythroid 2-related factor 2 (Nrf-2) is an inducible transcription factor, which transcribes an array of antioxidant genes and protects cancer cells from the oxidative stress. Both NOXs and Nrf-2 participate in the regulation of cellular redox homeostasis; but their dysregulation promotes oxidative stress, which contributes to the progression of different types of cancer. Indeed, the role of NOX isoforms and Nrf-2 in developing the drug resistance in cancer is largely unknown. In the present study, we have explored the association of NOX isoforms and Nrf-2 signaling with the MDR1 gene expression in colon carcinoma cells (HCT-116/R). The MDR1 gene was overexpressed to develop resistant HCT-116/R cells and the NOX activation and ROS generation were monitored. We also assessed the role of NOX isoforms and Nrf-2 in the 5-fluorouracil (5-FU) mediated apoptotic cell death of HCT-116/R cells. The HCT-116/R cells demonstrated higher expression of HIF-1α, Nrf-2, and HO-1 and were highly resistant to 5-FU; they also displayed upregulated expression and activity of NOX-2, as well as elevated ROS levels. Interestingly, the treatment with HDC, a specific NOX-2 inhibitor, reduced the ROS levels in HCT-116/R cells. The treatment with HDC and ML-385 (specific inhibitor of Nrf-2) augmented the 5-FU-mediated apoptotic cell death of HCT-116/R cells, which suggests that NOX-2 and Nrf-2 are involved in the development of the chemoresistant phenotype of these cells. Taken together, NOX-2 and Nrf-2 are associated with developing drug resistance of colorectal cancer cells and might be potential targets to overcome drug resistance during cancer therapy.

The key technology concerning the dispatch and control of the cyber-physical-social systems (CPSS) integration and group machine learning (ML) based Web-of-Cells (WoC) is systematically investigated, aiming to develop an intelligent dispatching system that has a high penetration of distributed generations (DG). Based on practical engineering demands and the weakly-centralized WoC, which is characterized by self-organized co-evolution, high independence, high-efficiency synergy, and autonomous learning, a variety of advanced theoretical tools such as complex network theory, group ML, evolutionary game theory, and CPSS-based parallel system theory have been adopted to address the following key issue: How can we achieve overall optimal dispatching and control decision-making in a class of complex systems relying on a large number of group cells with characteristics of limited information, weak controllability, small capacity, and wide distribution? Starting from this, four basic scientific issues are discussed: 1) a modeling method for a self-organized coupled network of the CPSS integration-based WoC; 2) a stability analysis and stability control system of the self-organized evolution of the WoC; 3) a highly autonomous group intelligent decision (GID) method of an independent cell; 4) multi-cell synergetic evolutionary game and GID theory. Hence, an innovative breakthrough on the intersection of complex network game theory and group ML is expected to be obtained, contributing to the emergence of group knowledge in complex circumstances and a significant improvement in the level of GID. Lastly, based on the theoretical investigation in this paper, explorations on the development of the CPSS platform for the WoC as well as its engineering practice in application are conducted. Furthermore, the in-depth development of the WoC, as well as its anticipated technical challenges, are prospected and analyzed with the hope of applying it on practical smart distribution grid demonstration projects in the future.

BackgroundMicroRNAs (miRNAs) are non-coding, single-stranded small RNAs that regulate gene expression negatively, which is involved in fundamental cellular processes. In this study, we investigated the role of miR-490-3P in the development of drug resistance in ovarian cancer cells.MethodsThe human ovarian carcinoma cell line A2780 and A2780/Taxol were exposed to paclitaxel in the presence or absence of microRNA 490-3P transfection, after which cell viability were performed by CCK-8 assay. Reverse transcription polymerase chain reaction (RT-PCR) and western blotting were used to assess the mRNA and protein expression levels of GST-π, MDR1 or P-gp.ResultsOur results showed higher miR-490-3P mRNA expression level in A2780/Taxol cells than in A2780 cells (p < 0.05). Following miR-490-3P transfection, both A2780 and A2780/Taxol cells showed decreased sensitivity to paclitaxel. The mRNA expression levels of MDR1, GST-π (p < 0.05) and protein expression levels of P-gp, GST-π were down-regulated after miR-490-3P transfection in comparison to mock and negative control cancer cells.ConclusionOur results demonstrate for the first time that microRNA 490-3P may be involved in the development of drug resistance in ovarian cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13048-014-0084-4) contains supplementary material, which is available to authorized users.

Rapid development of glutathione- S-transferase-dependent drug resistance in vitro and its prevention by ethacrynic acid

Drug resistance in ovarian cancer: The emerging importance of gene transcription and spatio-temporal regulation of resistance

This paper mainly introduces the basic dynamics model of evolutionary game theory: asymmetric replicator dynamic model and asymmetric replicator dynamic model and its relative conclusions. In order to facilitate understanding, a few simple examples are cited in the text to illustrate the differences between them. On the basis of the above, it also introduces theorists for random dynamic researches and theoretical achievement. Finally, this paper compares the classic game theory and evolutionary game theory in dynamic conceptual difference.

Drug resistance in cancer is shaped not only by evolutionary processes but also by eco-evolutionary interactions between tumor subpopulations. These interactions can support the persistence of resistant cells even in the absence of treatment, undermining standard aggressive therapies and motivating drug holiday-based approaches that leverage ecological dynamics. A key challenge in implementing such strategies is efficiently identifying interaction between drug-sensitive and drug-resistant subpopulations. Evolutionary game theory provides a framework for characterizing these interactions. We investigate whether spatial patterns in single time-point images of cell populations can reveal the underlying game theoretic interactions between sensitive and resistant cells. To achieve this goal, we develop an agent-based model in which cell reproduction is governed by local game-theoretic interactions. We compute a suite of spatial statistics on single time-point images from the agent-based model under a range of games being played between cells. We quantify the informativeness of each spatial statistic and demonstrate that a simple machine learning model can classify the type of game being played. Our findings suggest that spatial structure contains sufficient information to infer ecological interactions. This work represents a step toward clinically viable tools for identifying cell-cell interactions in tumors, supporting the development of ecologically informed cancer therapies.