"
Team:Dundee/Modeling/introduction
From 2014.igem.org
| (12 intermediate revisions not shown) | |||
| Line 18: | Line 18: | ||
<div class="jumbotron"> | <div class="jumbotron"> | ||
<h1>Introduction</h1> | <h1>Introduction</h1> | ||
| - | <p id="lead">Maths is Fun | + | <p id="lead">Maths is Fun..and Useful Too!</p> |
</div> | </div> | ||
<div class="row"> | <div class="row"> | ||
| Line 27: | Line 27: | ||
<div class="row"> | <div class="row"> | ||
<div class="col-xs-12"> | <div class="col-xs-12"> | ||
| + | <h2>Aims</h2> | ||
<p> | <p> | ||
| - | In order to help analyze, construct and optimise the biochemical pathways in The Lung Ranger, we used a variety of mathematical tools to create algorithms and simulations. This allowed us to accelerate the development and testing of various hypotheses. | + | In order to help analyze, construct and optimise the biochemical pathways in The Lung Ranger, we used a variety of mathematical tools to create algorithms and simulations. This allowed us to accelerate the development and testing of various project-driven hypotheses. |
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | |||
| + | <div class="row"> | ||
| + | <div class="col-xs-12"> | ||
| + | <h2>Methodology</h2> | ||
| + | <p> | ||
| + | Mathematical modelling has played a significant role in the development of synthetic biology. As an investigative tool, modelling is capable of abstracting complex systems and reducing them to their core components. Thus a quantitative understanding of the interaction between these core components can be generated, leading to optimal system design and control. | ||
<br/> | <br/> | ||
<br/> | <br/> | ||
| - | + | In our project, we used a variety of mathematical tools with which to design and study our engineered signal transduction pathways. As illustrated below, each of the approaches, ordinary differential equations (ODEs); stochastic simulation algorithm (SSA) and NetLogo, provided a different, but complementary understanding of the system. | |
| - | + | ||
| - | + | ||
| - | + | ||
| - | + | ||
</p> | </p> | ||
| Line 42: | Line 48: | ||
</div> | </div> | ||
<div class="row"> | <div class="row"> | ||
| - | <div class="col-xs- | + | <div class="col-xs-2"> |
| + | </div> | ||
| + | <div class="col-xs-8"> | ||
| - | <img class= " | + | <img class= "img-responsive" src="https://static.igem.org/mediawiki/2014/8/82/Screen_Shot_2014-10-07_at_22.24.08.png" /> |
| + | </div> | ||
| + | <div class="col-xs-2"> | ||
</div> | </div> | ||
</div> | </div> | ||
| Line 52: | Line 62: | ||
<div class="col-xs-12"> | <div class="col-xs-12"> | ||
<p> | <p> | ||
| - | Each modelling technique | + | Each modelling technique allows for the investigation of processes at different levels of observation. ODE allows for prediction as the population level. SSA takes account of low molecule numbers (intrinsic noise) and allows for single cell comparison and a better understanding of sub-population behaviour. The NetLogo simulation tool affords single, intra-cellular resolution and provides the most visually accessible representation of the biochemistry under study. All codes are available - just click on the appropriate links in the "References" section at the end of each modelling page. |
| + | <br/> | ||
| + | <br/> | ||
| + | We applied the same methodology to the study of each of the systems: PQS, BDSF and DSF. Our approach was to first build models for the core components within the engineered system. In this way, we could generate predictions regarding the expected outcome - qualitative and quantitative promoter expression behaviour in response to varying signal input levels. Having built this theoretical framework, we could then more easily address questions and technical problems generated by the wet science. Most importantly, we were able to use our modelling framework to suggest improvements and solutions to the technical issues that arose and thus steer project design in a positive feedback loop. | ||
</p> | </p> | ||
| - | |||
</div> | </div> | ||
</div> | </div> | ||
| Line 95: | Line 107: | ||
$('.submenu li').addClass('hide'); | $('.submenu li').addClass('hide'); | ||
} | } | ||
| - | |||
| - | |||
}); | }); | ||
| + | |||
| + | $( "#info" ).click( function() { | ||
| + | $('#welcomeNote').appendTo("body"); | ||
| + | $("#welcomeNote").modal(); | ||
| + | }); | ||
| + | |||
</script> | </script> | ||
</body> | </body> | ||
</html> | </html> | ||
| + | {{:Team:Dundee/Template/welcomeNoteSchool}} | ||
Latest revision as of 23:19, 17 October 2014
Introduction
Maths is Fun..and Useful Too!
Aims
In order to help analyze, construct and optimise the biochemical pathways in The Lung Ranger, we used a variety of mathematical tools to create algorithms and simulations. This allowed us to accelerate the development and testing of various project-driven hypotheses.
Methodology
Mathematical modelling has played a significant role in the development of synthetic biology. As an investigative tool, modelling is capable of abstracting complex systems and reducing them to their core components. Thus a quantitative understanding of the interaction between these core components can be generated, leading to optimal system design and control.
In our project, we used a variety of mathematical tools with which to design and study our engineered signal transduction pathways. As illustrated below, each of the approaches, ordinary differential equations (ODEs); stochastic simulation algorithm (SSA) and NetLogo, provided a different, but complementary understanding of the system.
Each modelling technique allows for the investigation of processes at different levels of observation. ODE allows for prediction as the population level. SSA takes account of low molecule numbers (intrinsic noise) and allows for single cell comparison and a better understanding of sub-population behaviour. The NetLogo simulation tool affords single, intra-cellular resolution and provides the most visually accessible representation of the biochemistry under study. All codes are available - just click on the appropriate links in the "References" section at the end of each modelling page.
We applied the same methodology to the study of each of the systems: PQS, BDSF and DSF. Our approach was to first build models for the core components within the engineered system. In this way, we could generate predictions regarding the expected outcome - qualitative and quantitative promoter expression behaviour in response to varying signal input levels. Having built this theoretical framework, we could then more easily address questions and technical problems generated by the wet science. Most importantly, we were able to use our modelling framework to suggest improvements and solutions to the technical issues that arose and thus steer project design in a positive feedback loop.
Welcome to Dundee's 2014 Wiki (Site Information)
Cystic Fibrosis is a genetic disease that results in the accumulation of thick mucus in the epithelial linings of the entire body, particularly the respiratory tract. Over the course of a patient’s life, the mucus-lined lung epithelium becomes repeatedly infected with pathogenic bacteria that stimulate an immune response; leading to inflammation, tissue damage, and ultimately, respiratory failure.
The microflora of the Cystic Fibrosis lung changes over time. In childhood, the major coloniser is Staphylococcus aureus, but as the patient matures other bacterial pathogens infect. The later-dominating pathogens, Pseudomonas aeruginosa and Burkholderia cepacia, are very difficult to eradicate and are associated with chronic decline in lung function. Burkholderia is so infectious that patients have to be isolated from one another, and can be denied lung transplants due to the persistence of this bacterium.
It is important, therefore, for medics to monitor and identify the levels of bacteria within the respiratory tract of a Cystic Fibrosis patient. Currently, patients must provide sputum samples and identification of bacteria takes between 72 hours and 2 weeks, by which point the bacteria can be in an antibiotic resistant state.
The Dundee 2014 iGEM project is focused on designing and testing a device that will rapidly and non-invasively identify the bacteria colonising a Cystic Fibrosis patient. Three biosensors will be developed that recognise external signal molecules produced by key bacteria, all of which are known to be in sputum samples of Cystic Fibrosis patients. A quinolone signal (PQS) is produced by Pseudomonas aeruginosa, a Diffusible Signal Factor (DSF) is produced by Stenotrophomonas maltophilia, an organism that is also associated with Cystic Fibrosis lung infection in adults, and BDSF is a related, but chemically distinct molecule that is produced by Burkholderia cepacia.
By engineering signal recognition and signal transduction proteins from these biological systems the Dundee iGEM team want to produce a portable electronic device that will identify infection outside of the clinic, so that patients do not have to travel great distances, and can be used to help health professionals make informed and rapid decisions on antibiotic treatments.
Read this again by clicking on Site Information in the navigation bar!
Bulletin Board (Policy & Practice)
Come look at the poster explaining how we involved the local and national Cystic Fibrosis community in our project, how we listened to people on the front line of the disease and how they affected our project.
School (Project)
Come in and learn about all the parts that make The Lung Ranger. The lab work that we carried out over the summer is explained here in detail, along with the modelling and implementation.
Library (Log Book)
Read about the experience the team has had over the summer. A week by week description of our work, how we carried out that work and some photos of our time together.
Saloon (iGEM community)
Come on in, take a seat and learn how we have interacted with the larger iGEM community. Who the team are, who helped them and how we helped others.
College of Life Sciences
With more than 1100 staff and research students and external funding of around £60 million per year, the College of Life Sciences is one of the largest and most productive research institutes in Europe. This reputation is genuinely global and is reflected in the fact that researchers in our laboratories represent no fewer than 62 different nationalities
Division of Mathematics A small friendly Division which offers a lively and modern programme of study at both undergraduate and postgraduate level.We have research groups in Mathematical Biology, Numerical Analysis and Scientific Computing, Magnetohydrodynamics and Applied Analysis.
School of Computing With research areas in Human Centred Computing, Intelligent Systems, Space Technologies and Theory of Computation, the department has an emphasis on development. Furthermore, this department teaches popular undergraduate programs in Applied Computing and Computing Science.
We are a global charitable foundation dedicated to achieving extraordinary improvements in human and animal health. We support the brightest minds in biomedical research and the medical humanities. Our breadth of support includes public engagement, education and the application of research to improve health. We are independent of both political and commercial interests.
