Carmen Del Vecchio
RicercatoreS.S.D.: ING-INF/04 - Automatica
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Genesworks is a Matlab code to simulate populations' genrtics. This code simulates the spread in apopulation of a diallelic gene placed on X-Chrosome according to the mathematical model in  and .
For Matlab users the code is available at the following link
NOTE: the download will start immediately by clicking on the link. You need either to install Matlab or download the Windows 64-bit version of the MCR for R2014a from the MathWorks Web site by navigating to http://www.mathworks.com/products/compiler/mcr/index.html
 F. Verrilli, H. Kebriaei, L. Glielmo, M. Corless, and C. Del Vecchio. 2017. “Effects of selection and mutation on epidemiology of X-linked genetic diseases.” Mathematical biosciences and engineering 14 (3): 755.
 C. Del Vecchio, F. Verrilli, L. Glielmo, and M. Corless. 2017. “A discrete time population genetic model for X-linked recessive diseases.” International Journal of Biology and Biomedical Engineering 11: 7–15.
Epidemiologic modelling of genetic diseases
The aims of this research project is to develop mathematical models to describe the distribution of genetic diseases among a population; that is for a given disease, a suitable model should allow to predict at each time, the number of healthy, carrier - individuals who carry a defective gene but show no symptoms of the disease - and affected subjects. The major reason for devoting attention to this topic is represented by the inadequacy of the currently used mathematical instrument to describe the transmission of genetic disease within a predefined population. Indeed the Hardy-Weinberg (HW) principle, first described in 1908, and currently adopted in genetic studies [Chen 2010], can be exploited to infer the probabilistic frequency of a genetic disease among a population. However the HW principle relies on very stringent assumptions, such as the absence of de novo mutations, immigrations or natural selection, that are not verified in nature. This implies that there is no reliable tool that at present can be used to predict the spread of genetic disorders. This lack also limits epidemiological studies aiming at quantify the effectiveness of treatments on disease cure, the effects of selecting actions (i.e. natural selection or selection due to prenatal diagnosis) on the disease diffusion, as well as the influence of gene mutations on the spread of the diseases.
The first objective of this research project is to develop mathematical models to reproduce the inheritance mechanism of genetic diseases in a population grouped by age, sex, and health condition with respect to the disease. Relying on population dynamic models we have developed dynamic model for each type of genetic disease differentiating among autosomal or sex chromosome gene location, and dominant or recessive gene trait. Different assumptions can also be made regarding population size and mating rules among groups. To allow mathematical tractability we have firstly supposed that the population is of constant finite size; in a second step we considered selecting factors and migrations. The most frequently adopted rule for mating is that individuals in the population mix randomly, i.e. individuals mate according to the product rule of probability; this is more realistic in large populations and implies that the studied trait does not influence reproduction. Models developed models allow including de novo mutations (i.e. affected sibling born to unaffected parents), fitness factors (i.e. different reproductive rates among population’s group) and, possibly, control actions on the disease (such as pharmacological therapies or voluntary fertility control).
The second objective of the research is to gain insight on model’s properties. Exploiting system’s analysis instruments we defined for most of the cases equilibrium point of the system and the stability properties of the system. This has permited to gain insight on the distribution of the individual within the population among healthy, carrier and affected.
Finaly we developed a code in Matlab that simulate the dnamic systems modeling diseases epidemiology.
The code is available at the following link
you will be asked for a passowrd please contact me at the following email address firstname.lastname@example.org