Subjects to study for Ph.D (Biophysics)

Which are common Subjects to study for Ph.D (Biophysics)?????????????

Doctor of Philosophy in Biophysics covers spans all levels of biological organization, from the molecular scale to whole organisms and ecosystems. Commonly this program covers following subjects:

Sem. I
Physical Methods in Biology

Spectroscopy:
Historical background of development of optics
Scattering of Light.
Hydrodynamic Methods
Chromatography
Electrophoresis
Radioactive Methods
Emerging topics in Biophysical methods

Sem. II
Information Processing and the Brain
Electrical behavior of the biological membrane.
Introduction to Nervous System with a special reference to Sensory Receptors and Perception.
. Origin of the concept of Computability, Turing Machines.
Synaptic Transmission: Physicochemical Principles, Resting Potential, Action Potential, Membrane Theory of Action Potential, Hodgkin Huxley Model, Mathematical solutions of H-H equations.
Models of Neurons: Artificial neurons, Physiological Neuronal Network versus Artificial Neural Network.
Mathematical tools to deal with Spiking Neurons.
Neural Basis of Cognition: Principles of Learning & Memory, Cellular Mechanism of Learning & Memory, and Comparison with Machine Learning.
Open discussions on the Interface of Artificial Neural Net and the Brain.

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Answered By StudyChaCha Member

As you are asking about the Subjects to study for Ph.D (Biophysics) so here I would like to provide you the same please have a look on that

The physics of soft materials

Experimental cancer treatment

Tumor physiology

Human electrophysiology and psychophysics

Bio-optics

Photosynthetic systems

Water regulation in plants

Protein folding, - dynamics and – function

Syllabus of Ph.D (Biophysics) are given below please have a look on that

Physical Methods in Biology

BP I

Physical Methods in Biology

Spectroscopy:

1. Historical background of development of optics. Corpuscular theory of light, wave theory of light, Electromagnetic theory of light, Planck’s concept and modern theory of light. Electronic structures of atoms & molecules, theory of chemical bonding.

2. Scattering of Light.
UV & Visible absorption spetrophotometry, Lambert Beer’s Law, molar extinction coefficient and its determination, instrumentation & applications, Fluorescence Spectroscopy: principles and applications, fluorescence polarisation, Polarisation of light, CD and ORD spectroscopy, Fundamentals of X-ray crystallography, instrumentation and biological applications. Principles of magnetic resonance, Nuclear Magnetic Resonance (NMR) & Electron Spin Resonance (ESR) and biological applications, Relaxation studies. Electron Microscopy: Principles of transmission & scanning electron microscope

Hydrodynamic Methods:

Viscosity, Sedimentation equilibrium and velocity centrifugation, Density gradient method, applications.

Chromatography:

Partition and Absorption Chromatography, paper and thin layer chromatography, gel filtration, ion-exchange and affinity chromatography. GLC, HPLC and FPLC. Emerging trends in chromatography

Electrophoresis:

Behaviour of biomacromolecules in electric fields, PAGE, Agarose Gel Electrophoresis, 2D Electrophoresis, Dialectrophoresis.

Radioactive Methods:

Radioactive isotopes, nature of radioactive decay, sample preparation and counting, G.M. and Scintillation counters. Precautions in radio isotope handling. Autoradiography and its biological applications.

Emerging topics in Biophysical methods:

BP II

Information Processing and the Brain

1. Electrical behaviour of the biological membrane.

2. Introduction to Nervous System with a special reference to Sensory Receptors and Perception.

3. Origin of the concept of Computability, Turing Machines.

4. Synaptic Transmission: Physicochemical Principles, Resting Potential, Action Potential, Membrane Theory of Action Potential, Hodgkin Huxley Model, Mathematical solutions of H-H equations.

5. Models of Neurons: Artificial neurons, Physiological Neuronal Network versus Artificial Neural Network.

6. Mathematical tools to deal with Spiking Neurons.

7. Neural Basis of Cognition: Principles of Learning & Memory, Cellular Mechanism of Learning & Memory, and Comparison with Machine Learning.

8. Open discussions on the Interface of Artificial Neural Net and the Brain.
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Answered By StudyChaCha Member