Difference between revisions of "Team:NCTU Formosa/Modeling"

 
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Single chain variable fragment as probe
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<p>In the modeling part, we discover <font color="#AC1F4A">optimum protein expression time</font>.</p>
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<p>We use <font color="#AC1F4A">Hill-function based model</font> : </p>
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<div class="image">
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<img src="https://static.igem.org/mediawiki/2015/e/eb/Nctu_formosa_model_equation.png" height="300px">
 
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<p>In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the <font color="#AC1F4A">genetic algorithm (GA)</font> in MATLAB.</p>
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    Single chain variable fragment (scFv) Abs are one of the <font color=#b51c48> recombinant antibody(rAb)</font> fragments, which are popular therapeutic alternatives to full length of monoclonal Abs. Compared to generating whole Abs from animal cell culture, scFv are smaller and can be expressed rapidly, economically and in large quantities in a bacterial host, such as<font color=#b51c48> E. coli</font>. A scFv <font color=#b51c48>possesses the complete antigen binding site</font>, which contains the variable heavy (VH) and variable light domain of an antibody. The VH domain is linked to a VL domain by an introduced flexible polypeptide linker. A scFv is capable of binding its target antigens with an affinity similar to that of the parent mAb. Due to containing the specific antigen binding unit, scFv fragments show tremendous versatility and importance in<font color=#b51c48> human therapeutics and diagnostics</font>. [1] In addition, scFv fragments can be envisaged to be applied in the non-pharmaceutical sector, such as in the food, cosmetic or environmental industries. The unique and highly specific antigen-binding ability might, for example, be exploited to block specific enzymes (e.g. enzymes that cause food spoilage), bacteria (e.g. in toothpaste or mouthwashes) or to detect environmental factors present in very low concentrations (as biosensors).[2]
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<p>To achieve this, we needed to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offered focus of the present difficulty, also known as the <font color="#AC1F4A">Estimation of Model Parameters Challenge</font>.</p>
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<p>By using the differential function which was derived from these optimum parameters which were calculated by GA can help us to simulate the optimum protein expression. </p>
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<p>The graph of simulated protein expression versus time were drawn Figure 1, 2 and 3. Thus, we can find the <font color="#AC1F4A">optimum protein expression time</font>.
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However, the simulated protein expression curve is slower than the experimental curve by one hour. Thus, to find the most exact optimum protein expression time, we infer that subtracting one hour of the optimum protein expression time would be correct.
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</p>
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<img src="https://static.igem.org/mediawiki/2015/1/15/NCTU_Formosa_modeling_2.png" height="300px"><br><br>
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Figure 1.
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From this graph, the protein expression reaches peak after growing about 18 hours.
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
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<img src="https://static.igem.org/mediawiki/2015/5/55/NCTU_Formosa_modeling_3.png" height="300px"><br><br>
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Figure 2.
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From this graph, the protein expression reaches peak after growing about 16 hours.
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
  
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<img src="https://static.igem.org/mediawiki/2015/3/3f/NCTU_FORMOSA_SCFV_BC.PNG" height="300px"><br><br>
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Figure 3.
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From this graph, the protein expression reaches peak after growing about 11 hours.
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This means that the E.Cotector can have maximum efficiency at this point.<br><br>
 
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<div class="contentitle">Properties and development of targeted drugs</div>
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This year, we decided to utilize the scFv as probes to detect cancer markers and aid in the prescription of targeted drugs in cancer treatments.
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Targeted drugs therapy utilize compounds that are capable of inhibiting target molecules, the cancer markers which send messages along signaling pathways in cell growth, cell division or cell death. Via specific binding to target molecules, targeted drugs show more accurate attack to cancer cells and less harmful damage to normal tissues. [1] The precision of targeting the cancer cells has enhanced the efficiency of treatment by a large margin. The targeted therapy is a major step forward for many cancers, especially advanced cancers, and physicians and researchers are now focusing on the development of targeted drugs, creating a new era of personalized cancer treatment.[3]Targeted therapy are so-called "personalized medicine" because health care professionals can use clinical test results from a patient to select a specific drug that has a higher likelihood of being effective for that particular person.<br><br>
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According to the statistics, the usage rate of targeted drug therapy has increased within ten years. In Figure 1, in 2003, targeted drug therapy is not commonly used compared with other therapies, accounting for only 11% usage. Over one decade, it is estimated that the usage of targeted drug therapy dramatically increases to<font color=#b51c48> 46%</font>. It indicates targeted drugs therapy is a potential growing field and will become the commonly used therapy in cancer treatments in the near future.
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<div class="contentitle">Pre-diagnosis of targeted drugs treatment</div>
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To create the new era of tailored targeted drugs, doctors must aim at<font color=#b51c48> appropriate target molecules </font>for patients with particular diseases. In 2014,<font color=#b51c48> the U.S. Food and Drug Administration (FDA) </font>issued a guidance to facilitate the development and review of <font color=#b51c48>diagnostics tests</font>. The diagnostics tests are the steps to identify the abnormal cancer biomarkers. Moreover, the purpose of diagnostics tests are to help medical practitioners <font color=#b51c48>determine which patients could benefit from the certain drugs</font>, conversely, those who should not receive the medication. If the treatment decisions is not optimal, it would not only cause the fatal body damage, but also lead to the waste of time, money and medical resources. FDA encourages the joint of targeted drugs therapies and precise diagnostics tests which are essential for the safe and effective use of targeted drugs.[4]
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<div class="contentitle">The concept of combination therapy</div>
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Although targeted drugs treatments can lead to the dramatic regressions of solid tumors, the responses are often short-lived because resistant cancer cells arise after a period of treatment. The major strategy proposed for overcoming the resistance is <font color=#b51c48>combination therapy</font>. The clinical and preclinical researches further indicated that targeted drug therapy combined with another targeted drug therapy or other types of therapies to treat cancers simultaneously may attain greater effects than using only one therapy. With the concept of combination therapy, we can not only improve the treating effect but also reduce the occurrence of cancer cells resistance toward the targeted drugs as there are less probability that a single mutation will cause cross-resistance to both drugs.[2] </div>
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<div class="contentitle">APPOllO E.Cotector</div>
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<div class="content">To enhance the <font color=#b51c48>efficiency of diagnosis </font>and provide reference for<font color=#b51c48> proper usage of targeted drugs</font> and <font color=#b51c48>combination therapy</font>, we come up with the idea of detecting multimarker at the same time and this was how our marvelous E.Cotector is borned. This year, NCTU_Formosa commits to creating a multimarker diagnosis platform via scFv as probes for helping physicians to determine and prescribe the usage of targeted drugs in cancer patients, especially the monoclonal-antibody-targeted drugs.</div>
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Latest revision as of 03:33, 19 September 2015

Modeling

In the modeling part, we discover optimum protein expression time.

We use Hill-function based model :

In order to characterize the actual kinetics of this Hill-function based model that precisely reflects protein expression time, we use the genetic algorithm (GA) in MATLAB.

To achieve this, we needed to focus on the tasks of estimating model parameters from the experimental data in the case of Hill-function based model for parameter inference. These reverse engineering tasks offered focus of the present difficulty, also known as the Estimation of Model Parameters Challenge.

By using the differential function which was derived from these optimum parameters which were calculated by GA can help us to simulate the optimum protein expression.

The graph of simulated protein expression versus time were drawn Figure 1, 2 and 3. Thus, we can find the optimum protein expression time. However, the simulated protein expression curve is slower than the experimental curve by one hour. Thus, to find the most exact optimum protein expression time, we infer that subtracting one hour of the optimum protein expression time would be correct.



Figure 1. From this graph, the protein expression reaches peak after growing about 18 hours. This means that the E.Cotector can have maximum efficiency at this point.



Figure 2. From this graph, the protein expression reaches peak after growing about 16 hours. This means that the E.Cotector can have maximum efficiency at this point.



Figure 3. From this graph, the protein expression reaches peak after growing about 11 hours. This means that the E.Cotector can have maximum efficiency at this point.



Back to Navigation


Go to Safety