Chapter 8: Reference

This section is designed to help you with your project work; it has 2 parts:

 

Tips and Gotchas

 

Simulations built by past SimLife students

These may give you some ideas for your own projects.

1) Reaction Rates (Evans Asumadu)
Individual molecules interact in a simulation of kinetics.
2) Hemostasis (Michelle Au)
A simulation of a blood vessel with platelets, red blood cells, etc that models clotting in response to a wound.

Erin McCarthy (362S22) made another version of this with clotting factors and a measure of blood vessel occluion.
3) Oxidative Stress and mutation (Drew Caruso)
The interaction between cells, mutagens, and anti-oxidants and their role in tumor formation.
4) Hardy Pond (Julie Czapla)
A simulation of nutrients, plankton, and fish exploring eutrophication.
5)Biofilm formation (Larissa DeSouza)
Bacterial quorum sensing with bacteria and auto-inducer agents.

Hannah Bechtel (362S22) made a simulation of P. aeruginosa quorum sensing and biofilm formation.
6) HIV and TB (Ahmed Hafizallah)
A simulation of the interaction between HIV and tuberculosis (TB) in a world of people, hospitals, and clinics.
7) Slime mold 'solving' a maze (Stephanie Kalwass)
Slime mold agents navigating a maze to find food.
8) Breast Cancer cell proliferation (Daniel McManus)
A simulation of breast cancer cells including hormones, macrophages, and adipocytes.

Maeva Fourny (Université de Bordeaux Spring 2018) developed a different simulation of tumor growth.
9) Predatory bacteria (Hang Pham)
A simulation where one species of bacteria hunts another using the molecules secreted by the 'prey'.
10) Immunology (Johnny Tran)
A simulation of immunological memory including viruses, Memory T-cells and killer T-cells.
11) Fish schooling behavior (Monica Elias-Orellana)
A simulation of fish schooling in response to their environment.
12) Social Hierarchy in Clownfish (Joel Rosen)
A simulation of clownfish hierarchy - how they become dominant, how this changes over time, and how the other fish learn this.
13) Ebola (Rebeca Cortazio)
A simulation of the effect of quarantine on an ebola outbreak.
14) Predator-Prey (Mohamadrida Al-rekabi)
Rabbits, grass, and snakes where the rabbits can hide from the snakes in warrens.
15) Mother to Child Transmission of HIV During Pregnancy (Raissa Almeida)
Simulation of HIV transmission with different models of clinic placement.
16) Lyme Disease Transmission (Vu Dinh)
A simulation of lyme disease including mice and ticks measuring the effect of tick control on infection rates.
17) Cane Toads and Crested Toads (Kat Govoni)
A simulation of removing an invasive species using either untrained volunteers or professional ecologists.
18) Marine Tapeworm Life Cycle (Nelson Nease)
A simulation of a multiple-host tapeworm life cycle that involves tapeworms, copepods, fish, and birds.
19) Predator-Prey (Grace Oyinlola)
Sharks, sea urchins, sea turtles, clownfish, and anemones where predators have preferences for prey and exploring the effects of land refuges for some prey.
20) H1N1 Influenza (Rumana Papia)
A model of recombination of influenza strains H1N9 and H3N1 in birds, pigs, and humans exploring the effects of different pig populations.
21) The Effect of PGC-1 Alpha on Plaque Formation in Alzheimer's Disease (Jumanaa Shareef)
Simulation of the proteins involved in plaque formation in Alzheimer's disease and the effect of different levels of PGC-1 alpha on the rate of plaque formation.
22) Eutrophication (Mabel Valencia-Yang)
A simulation involving algae, oxygen, fish, grass, boats and excess nutrients exploring the effect of the number of boats on eutrophication rates.
23) Peppered Moths (Mélanie Carriat)
A population genetics simulation where moths mate and pass on color traits that make them more or less visible to predators depending on the color of the environment. You can find Mélanie's code here.

Joyce Price (362S21) made a related simulation; you can find her code here.
24) Malaria Life Cycle and Immune Response (James Poirier)
Simulation of the many stages of the malaria life cycle and the effect of varying T-cell levels on its reproduction. You can find James' code here.
25) Response to Wound Infection (Lucas Porras)
Simulation of bacteria entering the bloodstream and the immune response depending on the size of the initial infection. You can find Lucas' code here.
26) Human Female Fertility Hormone Cycles (Cynthia Saint-Orens)
A simulation of human fertilite hormones including the ovaries and pituitary as well as the LH and estradiol in a female with one ovary, two ovaries, or taking contraceptive pills. You can find Cynthia's code here.

Other students have built on this simulation (the links will take you to their code):
27) Complex predator-prey with habitats and behavior.(Kevin Connors 362S21) There are 3 types of agents in this model which are pursuit predators (cyan), ambush predators (black), and bait fish (orange) which is the prey. The 4 types of terrain are shallow waters (light blue), open water (darker blue), light grassy areas (light green), and dense grassy areas (darker green). Code for Kevin's project can be found here.
28) Sea Turtle Hatchlings (Johnny Feng 362S21). For this predator and prey simulation, baby sea turtles hatch from their eggs with an objective to crawl toward the ocean to make it out alive. For the hatchlings to survive, they must avoid obstacles such as predators, trash, and wave agents. When the simulation runs, a couple of things will happen. The nests are spawned randomly throughout the world with each having a different amount of eggs. This makes it so that there is no fixed population of sea turtles and so that it mimics a realistic scenario of predator and prey. From this, I can track the total population of turtles, the number of surviving turtles, and the number of turtles that are eaten/dead. Code for Johnny's project can be found here.
29) Wound Healing in Axolotl (Shauna Kelly 362S21). Hello future Bio 362 students! This simulation was meant to simulate wound epithelium formation in an amputated axolotl limb. Axolotls are a type of salamander that are widely studied in many labs for their regenerative capacities. They can heal scarlessly and regenerate entire limbs, some organs, and even parts of their brain! This simulation mostly simulates cell-cell interations featuring blood clotting, skin healing, and a little bit of thinking outside of the box. For example, I knew cells cannot 'see' perse however I used an agent called 'pressure' to represent a physical force acting on the blood vessel to make it stop bleeding. To get this project to work press set-up twice or set-up then toggle forever. Code for Shauna's Project can be found here.
30) Electron Transport Chain (Theresa Mora 362S21). My project was about the electron transport chain which occurs in the mitochondria of eukaryotes. It is a very simple model as I wanted to make it easier for the audience to understand (as oxidative phosphorylation is difficult to understand as it is). Each agent is colored seperately to differentiate between each component of my model. There are two types of agents in my model: static and dynamic in which static agents stay in place while dynamic agents interact with their environment. My static agents include four different complexes, and two substrates: Coenzyme Q and Cytochrome C. These agents sit on a solid line indicating the membrane which separates the intermembrane space from the mitchondrial matrix. My dynamic agents include two redox-active coenzymes, NADH and FADH2 as well as ATP/ADP and water molecules. These dynamic agents have a specific behavior which allows them to interact with the static agents. When the proper collisions between static and dynamic agents occur, the reaction takes place and the movement of electrons is shown. Code for Therersa's project can be found here.
31) Macrophages in wound infection (Estefania Reyes-Sepulveda 362S21). This is a cross section view of a deep wound (purple cut). The red area represents the bloodstream where B-cells(pink circles) are located and produce antibodies(white circles) as well as some macrophages (green squares), in the pink area we have the skin where the bacteria (blue circles) enters to infect the tissue, but as the bacteria move they collide with antibodies that tag the bacteria (black circles) and both bacteria release a signal (aquamarine triangles) to indicate they are present. My goal was that after a macrophage ate any bacteria it would release a yellow stain to indicate other macrophages where the bacteria are located. Code for Estefania's project can be found here.
32) Epidemic with masking and stay at home mandate (Lubna Shaikh 362S21). The goal of this simulation was to observe the spread of COVID-19 infection and to note the effects of mask and stay at home advisory on the number of people infected and the length of the pandemic. The simulation starts with humans roaming around freely and one infected individual (red). The red human coughs and spread the viral stinks which causes other healthy individuals (black) to become infected (red). After a period of time, some infected individual dies and some recover (blue), as is seen in real life cases of COVID. Upon implementing mask mandate, healthy masked individuals’ (white) chances of getting infected decreases and masked infected individuals (purple) spread less viral stinks. By following ‘stay at home advisory’, all the humans go back to their home and thus, only spread the infection to fellow house members. Code for Lubna's project can be found here.
33) Maternal health & the built environment (Ciyana Smith 362S21). I created a human ecosystem scale simulation of the daily interactions of pregnant women with their natural environment. Through my simulation one can observe the effect of built environment such as, access to hospitals, food types, community health care, and more on the health of birthing women and their children. Pregnant women agents are able to interact with their environment in a “natural” way using traits for hunger, health, and money. Varying access and opportunities to interact with health promoting aspects of the environment can be observed in relation to the overall health of mothers and thus infant outcomes. As a control the behavior of the pregnant women agents remains the same, while the environment changes. Maternal health is monitored throughout the pregnancy up to birth to observe the effect of built environment on overall maternal health. Code for Ciyana's Project can be found here.
34) Neural Networks and Classical Conditioning (Adriana Voci 362S22). This simulation shows a simplified pathway of classical conditioning in the Aplysia (sea slug). In classical conditioning, an animal learns to associate two stimuli: the conditioned stimulus (CS) and the unconditioned stimulus (US). Repeated presentation of a CS followed by a US generates a conditioned response and can enhance certain reflexes. As a defense reflex, Aplysia withdraws its gill in response to noxious stimuli, such as an electric shock to the tail. In Aplysia, the gill withdrawal can be classically conditioned so that the animal learns to associate a weak touch to siphon (CS) with a strong tail shock (US). When this reflex is classically conditioned, the gill withdraws in response to siphon stimulation alone.
My simulation attempts to demonstrate this process between the sensory neurons of the tail and siphon and the motor neuron of the gill. The tail and siphon sensory neurons produce serotonin. The auxiliary agent, the interneuron, produces serotonin when the pathway from the tail sensory neuron is activated. The serotonin converges on the motor neuron, and, if there is enough serotonin, the excitatory neurotransmitter, glutamate, is produced. This results in the gill contracting. You can use the slider values to adjust the time when the siphon is touched, and the tail is shocked to see if the Aplysia was able to "learn"! Code for Adriana's Project can be found here.
35) Prokaryotic Gene Regulation. These two simulations deal with repressors, operons, and ligands to model transcriptional regulation in prokaryotes. They deal with the complex issues of reversible binding and inhibition. Two students have taken different approaches to this problem:
  • Eilish Dillon (362S22): The purpose of this project was to create a simulation displaying the tryptophan operon, a set of genes encoding for trp synthesis in E. coli. Tryptophan is an essential amino acid that E. coli can either get from their environment or make themselves via the trp operon. The trp operon frequently taught to biology students as an example of transcriptional regulation. This project includes polymerases that bind to and travel across the DNA to produce RNA that is ultimately translated into trp synthase. Transcription is repressed when the repressor binds to a trp and then binds to the DNA.
  • Sheyla Manon (362S22) made a This simulation is about the lac operon in E. coli bacteria. The genes in the operon encode proteins that allow the bacteria to use lactose as an energy source. Beta-galactosidase is one of them. This simulation exhibits when the protein beta-galactosidase is made and regulated by the addition of lactose. The agents involved are DNA, the promoter, the lac operon, lac repressors, RNA polymerase, lactose, ribosomes, mRNA, beta-galactosidase. When lactose is low, the repressor is bound to the promoter stopping transcription of beta-galactosidase. Beta-galactosidase degrades lactose. When lactose is added it has a chance of hitting the repressor that is bound to the promoter. When this happens, the repressor unbinds from the promoter and allows transcription to be active by allowing RNA polymerase to bind to the promoter. When transcription is active, mRNA is produced from the lac operon. If the floating mRNA encounters a ribosome, beta-galactosidase is made degrades any lactose it encounters.
36) "Bush Dog Hunt": Complex Hunting Behavior. Kirsten Ward (362S22). My code is supposed to show the different methods in which bush dogs hunt their prey. The main agents are the predator, bush dogs, and the prey, lowland paca and snakes. The bushdogs leave their territory once their energy drops below 80, they then begin to hunt for their target. The black bush dogs will follow the brown bush dogs to simulate a group hunting method to hunt the larger prey, lowland paca. The brown bush dogs will hunt lowland paca with the brown bush dogs. The red bush dogs will simulate the individual hunting method to hunt the smaller prey, snakes. Once close enough they chase the prey item until catching and “eating” them. The bush dogs are able to “sense” that a prey item is within 50 steps of them, and then respond by setting their trait “target” to the closest prey item within that 50 steps. The prey species lowland paca leave their territory to look for fallen mangos which they eat when coming in contact. This increases their energy and serves as a reason for them to leave their territory, however they have a 5% chance of returning to their territory when out to simulate a more realistic foraging behavior. The prey species snakes do not have a territory as they would not colonize or live in groups like the other species in this simulation. They instead roam the entire map looking for frogs to eat which will increase their energy. Additional agents are mangos which serve as a stationary “fallen” food source, mango trees which “supply” the mangos, and frogs which serve as a stationary food source for the snakes. These agents do not sense or respond to anything for they just serve the purpose of the environment.
37) Glucose homeostasis and Diabetes. Sara Rau (362S22). The simulation simulates blood sugar level depending on many conditions. The pancreas (blue box) creates insulin (red dots) and glucagon (white dots) when the pancreas ‘glucose hit count’ is above or below certain levels. When the liver cells are ‘off’ they are brown but when insulin hits them they turn orange and then when they are hit with glucose (red dots) they turn purple. This simulates real life because insulin must bind with the cells before the cells can absorb glucose. When a purple liver cell is hit with glucagon then the liver cells can release the stored glucose. The red circle is a muscle cell. It also turns on to a pink color when insulin and glucose hits it but it does not re-release as muscle cells use the glucose for energy rather than storing it. The sliders are used to change the conditions so you can choose a normal (0) type 1 diabetes (1) or type diabetes (2) and exercise (1) or no exercise (0). There are two versions one with 3 meals a day and one with 2 meals a day to look at the effect of glucose being added to the simulation has.