Biomedical subjects

M Abundo

Publications and source records attributed to M Abundo.

3 recordsLinked to original sources

PubMed1991

A stochastic model for predator-prey systems: basic properties, stability and computer simulation.

A simple stochastic description of a model of a predator-prey system is given. The evolution of the system is described by means of Itô's stochastic differential equations (SDEs), which are the natural stochastic generalization of the Lotka-Volterra deterministic differential equations. Since these SDEs do not satisfy the usual conditions for the existence and uniqueness of the solution, we state a theorem of existence; moreover we study the stability of the equilibrium point and perform a computer simulation to study the behaviour of the trajectories of solutions with given initial data and to estimate first and second moments.

Animals↗

PubMed1989

Numerical simulation of a stochastic model for cancerous cells submitted to chemotherapy.

A stochastic model is proposed to study the problem of inherent resistance by cell populations when chemotherapeutic agents are used to control tumor growth. Stochastic differential equations are introduced and numerically integrated to simulate expected response to the chemotherapeutic strategies as a function of different parameters. Satisfactory demonstration runs of the model indicate that it could represent a useful tool in verifying the results of experimental and clinical chemotherapy courses and planning treatment strategies. Some types of behaviour are illustrated graphically.

Drug Resistance↗

PubMed1989

A stochastic model of the acquisition of drug resistance during antineoplastic chemotherapy.

A stochastic model is proposed to simulate the phenomenon of acquisition of genetic resistance by a tumor cell population treated with antineoplastic cytotoxic agents. In the model, stochastic differential equations are numerically integrated to simulate the expected response after treatment with two different agents. The model takes into account the initial number of cells, the sequence of the agents, the time intervals between administrations, the birth and death rates of the untreated cells, the probabilities of mutations to resistance, and the induced cell-killing kinetics. Satisfactory demonstration runs of the model indicate that it could represent a useful tool in verifying the results of experimental and clinical chemotherapy courses and planning treatment strategies.

Antineoplastic Agents↗