scholarly journals The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer

2021 ◽  
Author(s):  
Benjamin Wölfl ◽  
Hedy te Rietmole ◽  
Monica Salvioli ◽  
Artem Kaznatcheev ◽  
Frank Thuijsman ◽  
...  
Keyword(s):  
Game Theory ◽  
Cancer Progression ◽  
Evolutionary Game Theory ◽  
Cancer Biology ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Great Promise ◽  
Full Potential ◽  
Biological Interactions ◽  
Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with evolutionary game theory has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary competition between different cell types and therefore needs to be viewed as an evolutionary game.

scholarly journals The contribution of evolutionary game theory to understanding and treating cancer

2020 ◽  
Author(s):  
Benjamin Wölfl ◽  
Hedy te Rietmole ◽  
Monica Salvioli ◽  
Frank Thuijsman ◽  
Joel S. Brown ◽  
...  
Keyword(s):  
Game Theory ◽  
Cancer Progression ◽  
Evolutionary Game Theory ◽  
Cancer Biology ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Great Promise ◽  
Full Potential ◽  
Theory Approach ◽  
Game Theoretic

AbstractEvolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather, inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. We discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with an evolutionary game theory approach has medically useful implications that can inform and create a lockstep between empirical findings, and mathematical modeling. We suggest that cancer progression is an evolutionary game and needs to be viewed as such.

scholarly journals Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations

2014 ◽  
Vol 4 (4) ◽  
pp. 20140037 ◽  
Author(s):  
David Liao ◽  
Thea D. Tlsty
Keyword(s):  
Game Theory ◽  
Population Dynamics ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Treatment Strategies ◽  
Analysis Method ◽  
Analysis Techniques ◽  
Game Theoretic ◽  
And Control

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

The philosophical interpretation of language game theory

2021 ◽  
Author(s):  
Nick Zangwill
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Language Game ◽  
Linguistic Meaning ◽  
Theoretic Approach ◽  
Philosophical Interpretation ◽  
Game Theoretic ◽  
Game Theoretic Approach

Abstract I give an informal presentation of the evolutionary game theoretic approach to the conventions that constitute linguistic meaning. The aim is to give a philosophical interpretation of the project, which accounts for the role of game theoretic mathematics in explaining linguistic phenomena. I articulate the main virtue of this sort of account, which is its psychological economy, and I point to the casual mechanisms that are the ground of the application of evolutionary game theory to linguistic phenomena. Lastly, I consider the objection that the account cannot explain predication, logic, and compositionality.

scholarly journals Fisheries management as a Stackelberg Evolutionary Game: Finding an evolutionarily enlightened strategy

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245255
Author(s):  
Monica Salvioli ◽  
Johan Dubbeldam ◽  
Kateřina Staňková ◽  
Joel S. Brown
Keyword(s):  
Game Theory ◽  
Fisheries Management ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Fish Population ◽  
Stackelberg Equilibrium ◽  
Negative Effects ◽  
Evolutionary Changes ◽  
Body Sizes

Fish populations subject to heavy exploitation are expected to evolve over time smaller average body sizes. We introduce Stackelberg evolutionary game theory to show how fisheries management should be adjusted to mitigate the potential negative effects of such evolutionary changes. We present the game of a fisheries manager versus a fish population, where the former adjusts the harvesting rate and the net size to maximize profit, while the latter responds by evolving the size at maturation to maximize the fitness. We analyze three strategies: i) ecologically enlightened (leading to a Nash equilibrium in game-theoretic terms); ii) evolutionarily enlightened (leading to a Stackelberg equilibrium) and iii) domestication (leading to team optimum) and the corresponding outcomes for both the fisheries manager and the fish. Domestication results in the largest size for the fish and the highest profit for the manager. With the Nash approach the manager tends to adopt a high harvesting rate and a small net size that eventually leads to smaller fish. With the Stackelberg approach the manager selects a bigger net size and scales back the harvesting rate, which lead to a bigger fish size and a higher profit. Overall, our results encourage managers to take the fish evolutionary dynamics into account. Moreover, we advocate for the use of Stackelberg evolutionary game theory as a tool for providing insights into the eco-evolutionary consequences of exploiting evolving resources.

Research on Organization Tacit Knowledge Transferring: An Evolutionary Game Theoretic Analysis

2013 ◽  
Vol 756-759 ◽  
pp. 2597-2601
Author(s):  
Dan Li
Keyword(s):  
Game Theory ◽  
Tacit Knowledge ◽  
Evolutionary Game Theory ◽  
Process Model ◽  
Evolutionary Game ◽  
Dynamic Game ◽  
Theoretic Analysis ◽  
Game Theoretic Analysis ◽  
Game Theoretic ◽  
The Stability

The transfer and the management of the tacit knowledge is one of the most important issues in the knowledge transferring context to create organizational competitive advantage in the fast growing competitive world. As we know, organization tacit knowledge transferring is a dynamic game process, this research applies evolutionary game theory to analyze the tacit knowledge transferring activities among organizations and the stability balance issue in organization knowledge transferring. Based on the theory of games, this paper constructs the evolutionary game process model for organization tacit knowledge transferring. And through the equilibrium point of the model, the different knowledge transferring bodies game replicated dynamics and stability are analyzed. With this model, the reason why tacit knowledge is hard to be transferred and the path of tacit knowledge transferring is analyzed.

scholarly journals Game theory in the death galaxy: interaction of cancer and stromal cells in tumour microenvironment

2014 ◽  
Vol 4 (4) ◽  
pp. 20140028 ◽  
Author(s):  
Amy Wu ◽  
David Liao ◽  
Thea D. Tlsty ◽  
James C. Sturm ◽  
Robert H. Austin
Keyword(s):  
Game Theory ◽  
Cancer Cells ◽  
Cancer Progression ◽  
Evolutionary Game Theory ◽  
Stromal Cells ◽  
Evolutionary Game ◽  
Tumour Microenvironment ◽  
Drug Induced ◽  
Galaxy Interaction ◽  
Induced Apoptosis

Preventing relapse is the major challenge to effective therapy in cancer. Within the tumour, stromal (ST) cells play an important role in cancer progression and the emergence of drug resistance. During cancer treatment, the fitness of cancer cells can be enhanced by ST cells because their molecular signalling interaction delays the drug-induced apoptosis of cancer cells. On the other hand, competition among cancer and ST cells for space or resources should not be ignored. We explore the population dynamics of multiple myeloma (MM) versus bone marrow ST cells by using an experimental microecology that we call the death galaxy, with a stable drug gradient and connected microhabitats. Evolutionary game theory is a quantitative way to capture the frequency-dependent nature of interactive populations. Therefore, we use evolutionary game theory to model the populations in the death galaxy with the gradients of pay-offs and successfully predict the future densities of MM and ST cells. We discuss the possible clinical use of such analysis for predicting cancer progression.

scholarly journals Why Darwin would have loved evolutionary game theory

2016 ◽  
Vol 283 (1838) ◽  
pp. 20160847 ◽  
Author(s):  
Joel S. Brown
Keyword(s):  
Game Theory ◽  
Natural Selection ◽  
Evolutionary Game Theory ◽  
Evolutionary Dynamics ◽  
Evolutionary Game ◽  
Scientific Basis ◽  
Matrix Games ◽  
Form And Function ◽  
Heritable Phenotype ◽  
And Function

Humans have marvelled at the fit of form and function, the way organisms' traits seem remarkably suited to their lifestyles and ecologies. While natural selection provides the scientific basis for the fit of form and function, Darwin found certain adaptations vexing or particularly intriguing: sex ratios, sexual selection and altruism. The logic behind these adaptations resides in frequency-dependent selection where the value of a given heritable phenotype (i.e. strategy) to an individual depends upon the strategies of others. Game theory is a branch of mathematics that is uniquely suited to solving such puzzles. While game theoretic thinking enters into Darwin's arguments and those of evolutionists through much of the twentieth century, the tools of evolutionary game theory were not available to Darwin or most evolutionists until the 1970s, and its full scope has only unfolded in the last three decades. As a consequence, game theory is applied and appreciated rather spottily. Game theory not only applies to matrix games and social games, it also applies to speciation, macroevolution and perhaps even to cancer. I assert that life and natural selection are a game, and that game theory is the appropriate logic for framing and understanding adaptations. Its scope can include behaviours within species, state-dependent strategies (such as male, female and so much more), speciation and coevolution, and expands beyond microevolution to macroevolution. Game theory clarifies aspects of ecological and evolutionary stability in ways useful to understanding eco-evolutionary dynamics, niche construction and ecosystem engineering. In short, I would like to think that Darwin would have found game theory uniquely useful for his theory of natural selection. Let us see why this is so.

scholarly journals Evolutionary game theoretic resource allocation simulation for molecular communication

2021 ◽  
Vol 2 (3) ◽  
pp. 111-119
Author(s):  
Caglar Koca ◽  
Meltem Civas ◽  
Ozgur B. Akan
Keyword(s):  
Game Theory ◽  
Resource Allocation ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Evolutionary Process ◽  
Molecular Communication ◽  
Transmission Performance ◽  
Game Theoretic ◽  
Selection Of ◽  
Evolution Approach

Molecular Communication (MC) is an emerging technology using molecules to transfer information between nanomachines. In this paper, we approach the resource allocation problem in Molecular Nano-networks (MCN) from the perspective of evolutionary game theory. In particular, we consider an MCN as an organism having three types of nodes acting as a sensor, relay, and sink, respectively. The resources are distributed among the nodes according to an evolutionary process, which relies on the selection of the most successful organisms followed by creating their offspring iteratively. In this regard, the success of an organism is measured by the total number of dropped messages during its life cycle. To illustrate the evolution procedure, we design a toy problem, and then solve it analytically and using the evolution approach for comparison. We further simulate the performance of the evolution approach on randomly generated organisms. The results reveal the potential of evolutionary game theory tools to improve the transmission performance of MCNs.

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