Computational Modelling and Analysis of a Biological Network

1. Objective:

   Learn to create and simulate computational models of biological networks to gain a comprehensive understanding of their underlying mechanisms. Understand mathematical modeling of biological interactions. Solve ordinary differential equations (ODEs) in MATLAB. Model and analyze the dynamics of cell signaling pathways through simulations.

2. Background:

2.1 Systematic Study of Biological Systems:

Cellular behavior is determined by interactions among many molecules forming a complex network.
The phenotype of a cellular system results from these intricate molecular interactions.
To predict biological properties, constructing, simulating, and analyzing mathematical models of these interactions is essential.

2.2 Mathematical Modeling of Biological Interactions:

2.2.1 Dynamics of Simple Regulation:

Dynamics of a basic interaction in a biological network, referred to as ‘simple regulation’, is described.
For example, molecule X is produced by Y at a rate ββ and degraded by itself at a rate αα.
The concentration of X changes over time, converging to a steady-state value where production and degradation rates balance.

2.2.2 Positive and Negative Autoregulation:

Biological networks often include feedback loops.
Negative autoregulation: a transcription factor represses its own transcription, described by a decreasing Hill function.
Positive autoregulation: a transcription factor enhances its own transcription, described by an increasing Hill function.

2.3 Background of MAPK Signaling Pathway:

MAPK (Mitogen-Activated Protein Kinase) cascades are involved in eukaryotic signal transduction, conserved from yeast to mammals.
They relay extracellular signals to intracellular targets, affecting cell growth, differentiation, and stress responses.
The pathway involves phosphorylation cascades, where MAPKs are activated by MAPK kinases (MKKs), which are activated by MAPK kinase kinases (MKKKs).