Cells and Their Functions:

Cell Theory: All living organisms are composed of cells. The cell is the basic unit of life, and all cells arise from pre-existing cells.
Cell Types:
- Prokaryotic Cells: Lack a nucleus and membrane-bound organelles (e.g., bacteria).
- Eukaryotic Cells: Have a nucleus and membrane-bound organelles (e.g., animal and plant cells).
Cell Organelles and Their Functions:
- Nucleus: Contains DNA, controls cellular activities.
- Mitochondria: Powerhouse of the cell, site of ATP production.
- Ribosomes: Protein synthesis.
- Endoplasmic Reticulum (ER): Protein and lipid synthesis (Rough ER has ribosomes, Smooth ER does not).
- Golgi Apparatus: Modifies, sorts, and packages proteins and lipids.
- Lysosomes: Digestive enzymes for breakdown of waste materials.
- Cell Membrane: Semi-permeable membrane that controls movement of substances in and out of the cell.

Basic Cellular Processes:

DNA Replication: The process by which a cell duplicates its DNA before division.
Transcription and Translation: The processes of synthesizing RNA from DNA and proteins from RNA.
Cell Division:
- Mitosis: Division of a eukaryotic cell’s nucleus into two genetically identical nuclei.
- Meiosis: Division that reduces the chromosome number by half, creating four haploid cells.

Introduction to Stochastic Processes:

Definition: Processes that involve randomness and can be analyzed statistically but not precisely predicted.
Examples in Biology: Gene expression, molecular interactions, population dynamics.

Applications in Biology:

Gene Expression: Random fluctuations in the number of RNA and protein molecules produced by a gene.
Enzyme Kinetics: Variability in the interaction rates between enzymes and substrates.
Population Dynamics: Random events influencing birth, death, and migration rates in populations.

Example of Stochastic Simulation: Gillespie Algorithm

Purpose: Simulate the time evolution of a set of chemical reactions in a well-mixed system.
Algorithm Steps:
1. Initialization: Set initial conditions and parameters.
2. Calculate Propensities: Determine the likelihood of each reaction occurring in a small time interval.
3. Time Step Selection: Choose the time until the next reaction using an exponential distribution.
4. Reaction Selection: Select which reaction will occur based on their propensities.
5. Update: Update the state of the system.
6. Iteration: Repeat steps 2-5 until the desired simulation time is reached.

Introduction to Agent-Based Modeling (ABM):

Definition: A class of computational models for simulating the interactions of agents (individual entities) to assess their effects on the system as a whole.
Agents: Autonomous entities with defined behavior and state, which interact with each other and their environment.

Applications in Biology:

Key Components of ABM:

Introduction to NetLogo:

Purpose: A programmable modeling environment for simulating natural and social phenomena.
Features: Simple syntax, built-in functions, and an interactive interface.

The button below will take you to the code for the NetLogo model.

An image of the NetLogo Model.

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globals [ticks]

globals [ticks]: Defines a global variable ticks which keeps track of the simulation time steps.

turtles-own [energy] patches-own [food]

turtles-own [energy]: Each turtle (agent) will have its own energy attribute.
patches-own [food]: Each patch (environment unit) will have its own food attribute.

to setup clear-all setup-patches setup-turtles reset-ticks end

to setup ... end: Defines the setup procedure.
- clear-all: Resets the simulation environment.
- setup-patches: Calls the procedure to initialize patches.
- setup-turtles: Calls the procedure to initialize turtles.
- reset-ticks: Resets the global ticks variable to zero.

to setup-patches ask patches [ set food random 10 set pcolor scale-color green food 0 10 ] end

to setup-patches ... end: Defines the setup-patches procedure.
- ask patches [...]: Iterates over all patches.
  - set food random 10: Assigns a random amount of food (0-9) to each patch.
  - set pcolor scale-color green food 0 10: Colors the patch based on the food amount, with a gradient from no food (color=0) to maximum food (color=10).

to setup-turtles create-turtles 100 [ setxy random-xcor random-ycor set energy random 10 set color blue ] end

to setup-turtles ... end: Defines the setup-turtles procedure.
- create-turtles 100 [...]: Creates 100 turtles.
  - setxy random-xcor random-ycor: Places each turtle at a random position.
  - set energy random 10: Assigns a random energy value (0-9) to each turtle.
  - set color blue: Sets the turtle's color to blue.

to go ask turtles [ move eat reproduce death ] tick end

to go ... end: Defines the go procedure, which runs each simulation step.
- ask turtles [...]: Commands all turtles to perform their behaviors in each step.
  - move, eat, reproduce, death: Calls the respective procedures for each turtle.
- tick: Advances the simulation time by one step.

to move rt random 360 fd 1 set energy energy - 1 end

to move ... end: Defines the move procedure.
- rt random 360: Rotates the turtle a random amount (0-359 degrees).
- fd 1: Moves the turtle forward by one step.
- set energy energy - 1: Decreases the turtle's energy by one.

to eat ask patch-here [ if food > 0 [ set food food - 1 set pcolor scale-color green food 0 10 ask myself [ set energy energy + 5 ] ] ] end

to reproduce if energy > 20 [ set energy energy / 2 hatch 1 [ set energy energy / 2 ] ] end

to reproduce ... end: Defines the reproduce procedure.
- if energy > 20 [...]: Checks if the turtle's energy is greater than 20.
  - set energy energy / 2: Halves the turtle's energy.
  - hatch 1 [ set energy energy / 2 ]: Creates a new turtle with half the parent's energy.

to death if energy <= 0 [ die ] end

to death ... end: Defines the death procedure.
- if energy <= 0 [ die ]: Removes the turtle if its energy is zero or less.