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| <div class="passage">Flux Balance Analysis (FBA) is a mathematical method for simulating metabolism in | | <div class="passage">Flux Balance Analysis (FBA) is a mathematical method for simulating metabolism in |
| metabolic networks. It is based on linear programming to calculate fluxes when the model is stable. | | metabolic networks. It is based on linear programming to calculate fluxes when the model is stable. |
− | <br> | + | <br><br> |
− | <br>
| + | |
| Assuming S is a matrix, which contains all reactions in a model. In this matrix, metabolites consumed take | | Assuming S is a matrix, which contains all reactions in a model. In this matrix, metabolites consumed take |
| negative coefficients, and metabolites produced then take positive ones. Furthermore, v is a vector that | | negative coefficients, and metabolites produced then take positive ones. Furthermore, v is a vector that |
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| \textbf{S}\textbf{v}=0 | | \textbf{S}\textbf{v}=0 |
| $$ | | $$ |
− | <div class="text-center psgImg"> | + | And then, FBA tries to maximize or minimize the objective function \(\textbf{c}^\textrm{T} \textbf{v}\), which |
− | <img alt="" src="https://static.igem.org/mediawiki/2019/d/d8/T--USTC-Software--colla_biomaster.png"> | + | c contains the weight of all reactions contributing to the function. Now it is clear that FBA is a linear |
− | <p>Figure 1: UESTC biomaster</p> | + | programming problem, and it just works. |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/e/e4/T--USTC-Software--model_fba_banlanced.png" alt=""> |
| + | <p>The conceptual basis of constraint- based modeling. With no constraints, the fIux |
| + | distribution of a biological network may lie at any point in a solution space. When mass balance |
| + | constraints imposed by the stoichiometric matrix S (labeled 1) and capacity constraints imposed |
| + | by the lower and upper bounds (a;and b;) (labeled 2) are applied to a network, it defines an |
| + | allowable solution space. The network may acquire any flux distribution within this space, but |
| + | points outside this space are denied by the constraints. Through optimization of an objective |
| + | function, FBA can identify a single optimal fIux distribution that lies on the edge of the |
| + | allowable solution space.</p> |
| </div> | | </div> |
− | Our team quickly contacted with the UESTC team to ask if we could make good | + | </div> |
− | use of the data. They satisfied our request without hesitation. After getting their data, we build our | + | <h4>Flux Variability Analysis</h4> |
− | project on it. Through the way of developing the project, we also contributed our biobricks to their project | + | <div class="passage"> |
− | which they might need. Their project was updated as well. During the process of our communication, both of | + | Flux Variability Analysis (FVA) is an extension of FBA. It can show the minimum and maximum range of each |
− | our
| + | reaction flux while they satisfy constraints and have the same optimal objective by solving a double linear |
− | project were upgraded. | + | programming problem. |
− | <div class="text-center psgImg"> | + | <br><br> |
− | <img alt="" src="https://static.igem.org/mediawiki/2019/3/3f/T--USTC-Software--colla_tongji.jpg"> | + | However, when the metabolic network contains internal loops, the result can be too high in absolute to be |
− | <p>Figure 2: Communicating with UESTC</p> | + | realistic. Users can select the "loopless" option to avoid such internal loops, and get a more consistent |
| + | result. |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/f/f3/T--USTC-Software--model_analysis.png" alt="" style="max-height: 800px"> |
| + | <p>Whole process of analysis</p> |
| </div> | | </div> |
| + | </div> |
| + | <h4>Regulatory Flux Balance Analysis</h4> |
| + | <div class="passage"> |
| + | Regulatory Flux Balance Analysis is a brilliant combination of FBA and transcriptional regulation. It integrates regulatory network and adds constraints based on the FBA. Therefore the solution space can be compressed, and our results can be more credible and authentic. |
| + | <br><br> |
| + | The combined metabolic/regulatory model can predict the ability of mutant E.coli strains to grow on defined media as well as time courses of cell growth, substrate uptake, metabolic by-product secretion, and qualitative gene expression in various conditions. |
| + | <br><br> |
| + | By applying this method in our software, we can make it more precisely. |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/5/54/T--USTC-Software--model_rFBA.png" alt="" style="max-height: 800px"> |
| + | <p>rFBA</p> |
| + | </div> |
| + | </div> |
| + | <h3>Computer Models</h3> |
| + | <h4>Object-Relational Mapping</h4> |
| + | <div class="passage"> |
| + | Object-Relational Mapping (ORM) enables us to work with databases more comfortable and safer. It fills the |
| + | gap between object-oriented programming languages and relational databases and avoids the vulnerability of |
| + | SQL Injection. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/c/cb/T--USTC-Software--model_mapping.png" alt=""> |
| + | <p>Object-Relational Mapping</p> |
| + | </div> |
| + | We use ORM to organize our metabolic networks, biobricks data, and user's computational models. It brings us |
| + | the convenience of creating, querying, listing, and connecting models, with acceptable performance. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/f/f0/T--USTC-Software--model_DB.png" alt="" style="width: 600px"> |
| + | <p>Relationship between two types of database</p> |
| + | </div> |
| + | In practice, we use Django ORM with MySQL backend to provide fast, flexible, and reliable service. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/1/19/T--USTC-Software--model_django.png" alt="" style="width: 600px"> |
| + | <p>Django ORM</p> |
| + | </div> |
| + | </div> |
| + | <h4>Message Queues</h4> |
| + | <div class="passage"> |
| + | Our website requires computing large models, and it is quite embarrassing to see users waiting for browsers |
| + | for so long. So we use message queues to maintain computing tasks. |
| + | <br><br> |
| + | We split our programs computing models from our website, and use message queues to send and receive |
| + | information about our computation. A queue is a data structure that stores things waiting to be handled, and |
| + | it obeys the "First Come, First Serve" principle. So, we can store our computing tasks in the queue, and |
| + | response to users instantly about the progress of his tasks. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/3/3f/T--USTC-Software--model_msg.png" alt="" style="max-height: 480px; object-fit: cover;"> |
| + | <p>Message Queues</p> |
| + | </div> |
| + | In Foresyn, we use RabbitMQ and Celery to build our message queues. Here, Celery uses its protocol to |
| + | communicate with web programs and computation programs. And RabbitMQ acts as a broker who accepts and |
| + | forwards messages of Celery. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img src="https://static.igem.org/mediawiki/2019/f/f3/T--USTC-Software--model_rabit.png" alt=""> |
| + | <p>RabbitMQ</p> |
| + | </div> |
| + | </div> |
| + | <h4>Different Searching Engines</h4> |
| + | <div class="passage"> |
| + | When we are developing Foresyn, we discovered that there is a contradiction between speed and accuracy. So |
| + | our strategy is to use slow but accurate algorithm when searching small datasets, and use fast but less |
| + | accurate algorithm when searching large ones. |
| + | <br><br> |
| + | Here, there are only some models like E. coli, but a lot of genes, reactions and metabolites in our |
| + | database. We choose to calculate Levenshtein distances for model searching. Levenshtein distance defines as |
| + | the following recursive function: |
| + | $$ |
| + | \qquad\operatorname{lev}_{a,b}(i,j) = \begin{cases} |
| + | \max(i,j) & \text{if } \min(i,j)=0, \\ |
| + | \min (\operatorname{lev}_{a,b}(i-1,j) + 1,\operatorname{lev}_{a,b}(i,j-1) + 1, |
| + | \operatorname{lev}_{a,b}(i-1,j-1) + 1) & a_i \neq b_i, \\ |
| + | \min (\operatorname{lev}_{a,b}(i-1,j) + 1,\operatorname{lev}_{a,b}(i,j-1) + 1, |
| + | \operatorname{lev}_{a,b}(i-1,j-1)) & \text{otherwise.} \\ |
| + | \end{cases} |
| + | $$ |
| + | which has a time complexity of \(O(nm)\) where n and m are the length of the two strings. |
| + | <br><br> |
| + | When we are handling genes, reactions and metabolites, we use another algorithm. |
| + | </div> |
| + | <h4>Biobricks Recommendation</h4> |
| + | <div class="passage"> |
| + | It is troublesome for Synthesis biologists to decide which kind of biobricks should be used in their |
| + | computing models. To deal with the issue, we designed a biobricks recommending system based on key-words |
| + | extraction and behavior analysis. With our special designed algorithm, our system can give a precise |
| + | prediction of biobricks that users demand. |
| + | <br> |
| + | <div class="psgImg"> |
| + | <img alt="" src="https://static.igem.org/mediawiki/2019/1/1b/T--USTC-Software--model_biobricks.png"> |
| + | <p>Recommendation</p> |
| + | </div> |
| + | </div> |
| + | <h2 class="ref">References</h2> |
| + | <div class="ref-list"> |
| + | <ul> |
| + | <li>1. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis?. *Nat Biotechnol*. |
| + | 2010;28(3):245–248. doi:10.1038/nbt.1614 |
| + | </li> |
| + | <li>2. Gudmundsson S, Thiele I. Computationally efficient flux variability analysis. *BMC Bioinformatics*. |
| + | 2010;11:489. Published 2010 Sep 29. doi:10.1186/1471-2105-11-489 |
| + | </li> |
| + | <li>3. Arne C. Müller, Alexander Bockmayr, Fast thermodynamically constrained flux variability analysis, |
| + | *Bioinformatics*, Volume 29, Issue 7, 1 April 2013, Pages 903–909, |
| + | https://doi.org/10.1093/bioinformatics/btt059 |
| + | </li> |
| + | <li>4. Abdelmoneim Amer Desouki, Florian Jarre, Gabriel Gelius-Dietrich, Martin J. Lercher, CycleFreeFlux: |
| + | efficient removal of thermodynamically infeasible loops from flux distributions, Bioinformatics, |
| + | Volume 31, Issue 13, 1 July 2015, Pages 2159–2165, https://doi.org/10.1093/bioinformatics/btv096 |
| + | </li> |
| + | <li>5. Ambler, Scott W. "Mapping objects to relational databases." On the World Wide Web: http://www. |
| + | AmbySoft. com (2000). |
| + | </li> |
| + | <li>6. Vinoski, Steve. "Advanced message queuing protocol." IEEE Internet Computing 6 (2006): 87-89.</li> |
| + | <li>7. https://medium.com/@jairvercosa/manger-vs-query-sets-in-django-e9af7ed744e0</li> |
| + | <li>8. https://www.nature.com/articles/nbt.1614</li> |
| + | <li>9. https://www.cloudamqp.com/blog/2014-12-03-what-is-message-queuing.html</li> |
| + | <li>10. https://www.rabbitmq.com/</li> |
| + | </ul> |
| </div> | | </div> |
| </div> | | </div> |
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