Introduction A client asked me to build a dynamic engineering model of the thoracic cage with which we can run “what if?” scenarios against. Applying Newton’s laws of motion and Runge-Kutta, I produced the following results. Following the videos presented below, I partially detail my methodology and propose steps for improving the work: Results The […]

# Category: system dynamics

## Emily’s laws of system complexity

First Law For every reduction, there is a greater and opposite clusterfuck. Second Law The first law is a reductionist statement.

## iBioSim: a CAD package for genetic circuits

iBioSim is a CAD package for the design, analysis, and simulation of genetic circuits. It can also be used for modeling metabolic networks, pathways, and other biological/chemical processes [1]. The tool provides a graphical user interface (GUI) for specifying circuit design and parameters, and a GUI for running simulations on the resulting models and viewing […]

## proportional-integral (PI) controller in Vensim

In my last post, I discussed an attempt at designing a PID controller using the Kepler Scientific Workflow system. Here I report on a similar (yet successful) development of a proportional-integral (PI) controller in Vensim PLE. Vensim is a software package for describing and simulating dynamic models, particularly those involving feedback. I’ve often described it […]

## attempted PID controller with Kepler

I wanted to check out the Kepler scientific workflow system (https://kepler-project.org/), and decided to build a PID controller model with it. Here I report on my results. The following schematic, taken from the Wikipedia entry http://en.wikipedia.org/wiki/PID_controller, shows the basic configuration of a PID controller. PID stands for “proportional integral derivative”, reflecting the fact that the […]

## monte-carlo simulation in C++ with MCS-libre

Monte-Carlo simulation is a sometimes elegant (and sometimes crude) method for simulating complex systems. Parameters that affect the system are selected from random distributions and the system response to these values is then calculated. Repeating this process many times produces often useful information about the system. The method is especially useful for examining non-linear systems […]

## simulating a synthetic biology circuit with system dynamics

McAdams and Arkin report the following synthetic biology oscillator circuit in their paper “Gene regulation: Towards a circuit engineering discipline” [1]: The circuit works by having gene R1’s protein inhibit production of R3, who’s protein inhibits production of R2, which in turn inhibits production of R1. Delays in the inhibition processes cause sufficient expression of […]

## system dynamics model of the Oregon Health Plan’s client caseload

Developed this model and wrote this description in 2007 as an analyst for the State of Oregon. We ultimately never used or published this model; I’m posting it here in hopes that someone will find it useful when a Google search delivers it. Introduction The State of Oregon offers medical assistance to low-income individuals […]

## DIY caffeine pharmacokinetics

A night of insomnia last weekend prompted me to build a mathematical model of my caffeine throughput. System dynamics provides the framework: Model description The stock and flow diagram shown above describes the basic system: “Pipes” represent caffeine flow into and out of “reservoirs” (the boxes) that store caffeine. The text labels denote system variables, […]

## data scientist goes coolhunting…

Intuitive coolhunting scales poorly. Here’s some math to help fix that problem: Axioms of cool Five axioms enable us to mathematically model cool: No one is intrinsically cool, individuals simply channel it. Ability to temporarily hold coolness varies by individual. Coolness naturally flows into some individuals more readily than others. Rate of coolness flow into […]