# 20.109(S11):Wrap-up analysis and modelingb (Day8)

(Difference between revisions)
 Revision as of 10:28, 6 April 2011 (view source) (→Introduction)← Previous diff Revision as of 11:10, 7 April 2011 (view source)Next diff → Line 5: Line 5: ==Introduction== ==Introduction== - a few words on role of modeling... + As alluded to in previous lectures, engineering biology is a relatively new field and there are a number of challenges we have yet to overcome. So far, it’s fair to say that most designs for synthetic circuits have been designed by hand, meaning that the heuristic for circuit design is basically trial and error, which your TA can admit, can be quite painful. - general utility and limitations, incl. role in exp/model/exp workflow + However, as circuit complexity grows, it’s imperative that we develop computational tools that aid in system design. Other engineering disciplines have many tools already; for example, in electrical engineering a common tool that is used is called SPICE that simulates how an electronic circuit will behave. While many tools have been designed for synthetic biology, there are no gold standards quite yet as the mathematical framework for understanding genetic circuits, as well as just raw characterization data for ‘parts’ are largely still unavailable. - main types (deterministic and stochastic, etc.) + Today we want you to begin to understand the modeling process for system design and characterization. We will be using MATLAB, a numerical computing environment commonly used in both industry and academia for various engineering, mathematical, and scientific applications. It is our goal for you that through quantitative modeling, you can begin to appreciate some of the dynamics of the system as well as study how manipulation of parameters can lead to different outputs. - + - our purpose today, how fits into this context + ==Protocols== ==Protocols==

## Revision as of 11:10, 7 April 2011

20.109(S11): Laboratory Fundamentals of Biological Engineering

## Introduction

As alluded to in previous lectures, engineering biology is a relatively new field and there are a number of challenges we have yet to overcome. So far, it’s fair to say that most designs for synthetic circuits have been designed by hand, meaning that the heuristic for circuit design is basically trial and error, which your TA can admit, can be quite painful.

However, as circuit complexity grows, it’s imperative that we develop computational tools that aid in system design. Other engineering disciplines have many tools already; for example, in electrical engineering a common tool that is used is called SPICE that simulates how an electronic circuit will behave. While many tools have been designed for synthetic biology, there are no gold standards quite yet as the mathematical framework for understanding genetic circuits, as well as just raw characterization data for ‘parts’ are largely still unavailable.

Today we want you to begin to understand the modeling process for system design and characterization. We will be using MATLAB, a numerical computing environment commonly used in both industry and academia for various engineering, mathematical, and scientific applications. It is our goal for you that through quantitative modeling, you can begin to appreciate some of the dynamics of the system as well as study how manipulation of parameters can lead to different outputs.

## Protocols

Before you begin today, make sure your β-gal data is correct (see previous FNT). Before you leave, make sure to post the Miller units and % of maximal Miller units for your original and modified truth table on today's Talk page.

### Introduction to model: ideal case

You will begin by running code that already includes idealized data for all assays. That way you can see in principle how we move from one transfer function to the next, and ultimately to a spatiotemporal model of the edge detector.

Answer some Q based on code/figures?

Step through each part here or in commented code.

### Modifying the model: broken and fixed systems

You will now include some of your own data for the original and modified (both IPTG-sensitive) systems. In between the limited data points that you have, you will need to invent additional data; otherwise the code will not be able to run properly.

Given some ranges to make certain parts of code work?

Save these figures for your report.

### Implementing other approaches to fixing the system

Although we fixed the system (at least theoretically!) by altering Plux-λ, one can imagine other ways to fix or at least impact the system output.

Where in the code could you modify the AHL diffusion constant? How does changing diffusion impact the outcomes? How about for the AHL synthesis rate?

## For next time

Your first draft of the research article is due by 11 a.m. on Day 1 of Module 3.