Mathematical Modelling of Analytical Biosensor in Detection of Cancer
Keywords:
Amperometric biosensor, Cancer, Mathematical modelling.Abstract
An amperometric biosensor is an analytical device which is highly used in disease detection.
It contains a ligand which is biological device known as glucose oxidase. It also contains a
transducer which converts biological signal to electrical signal. The transducer is one which
converts one form of energy to another form of energy. In this paper the work is presented in
the form of 3D amperometric biosensor to detect cancer at what stage it is. The analytical
device is 3D label free in detection of cancer. Cancer is a burning problem all around the
world in which research on cancer detection is going on very fast to detect a device and
also a drug to detect and eradicate this dangerous disease. Cancer is defined as abnormal,
unregulated and uncontrolled growth of cells or tissues. It is NOT a pathogen.it is simply
uncontrolled growth of living cells. Initially, cancer cells appear as healthy cells, slowly, they
lose grip with human body, become flexible and separate from regular living cell array. The
cancer cell has less content of oxygen and become hypoxia cells. Cancer is found more in
USA and China. India stands third in cancer, in INDIA, the mostly found cancer is JAW
cancer and breast cancer. They are due to chewing tobacco, drinking alcohol and also due to
genetic factors respectively. Very recently, IITB have developed a RAMAN spectrogram to
detect lung cancer at early stage on Nano scale phenomenon. In this Paper I would like to
present the mathematical modelling of amperometric biosensors in early detection of cancer.
The main motive in this paper goes with initial conditions of cancer cell, the equations are
developed, slowly the equations are modified with the growth of cancer cell as per initial
conditions to final conditions i.e. meta stage to advanced stage. The Numerical simulation of
mathematical equations were developed in MATLAB 2018 software and results are produced,
compared with previous results.