Difference between revisions of "Team:Tongji-Software"

What is Alpha Ant?
Already got a brilliant idea in metabolic engineering?
Too much information to search?
Still,
You need Alpha Ant as a guide
Alpha ant is a computational tool for pathway design and reconstruction. With full consideration of metabolic burden and some useful functions, we provide an efficient and powerful pathway design guide. Why Alpha Ant?
Background
Pathway engineering has proven indispensible in synthetic biology for its utility in design of microbes for generating value-added products, which is also the ultimate goal of our project. The core idea is to design and reconstruct pathway for proper use, including introducing heterologous metabolic reaction into a host organism, optimizing genetic processes within cells, modeling for yield prediction, flux balance analysis and so on.
However, it’s quite a challenge to reach high yield and productivity while balancing the metabolic burden in certain organism. For example, it can require sorting through thousands of possible reactions and enzymes. Also, it requires evaluation and simulation of pathway using in silico analysis. Of course, wet-lab experiments are necessary for pathway validation. Inspiration
After investigating into metabolic pathway engineering, we realize that there is much work to do in a certain project. Commonly, we have to do a lot of research before we get started with actual experiment, such as database search, paper reading and so on. So that’s why we come up with this idea that help synthetic biologists do previous work in some ways. In specific, We collect metabolic data from several databases including metabolic reactions, reaction main pairs, enzyme , gene , compound and so on. In this section, we provide all the related information of metabolic pathway. When it comes to ranking criteria, we choose some reliable ones and design novel ones to make our results more convincing. As for pathway search algorithm, we choose DFS (depth first search) because of its great performance in both speed and quality. Origin of Name: Alpha Ant
Alpha Ant stands for an efficient and convenient tool for pathway engineering. Alpha means ‘origin’. In fact, Alpha Ant is the first software equipped with the most comprehensive ranking criteria. Recently, it impressed people by the project “Alpha GO”, which also endowed “Alpha” with intelligence. Alpha Ant means its capacity to find the most efficient metabolic pathway linking two molecules is just like the ant colony’s intelligence of quickly organizing itself to find the most efficient path to a food source once it has been discovered by scouts. So ants are great signal detector and way finder. Data processing
We acquire metabolic reactions, gene information from KEGG. Standard Gibbs Energy are from MetaCyc and eQuilibrator. Furthermore, we obtain compound information from KEGG , ChEBI & KnowledgeBase. Enzyme data are from BRENDA and KEGG. The small molecular drug information is from DRUGBANK. Besides, we use MayaChemTools to calculate physiochemical properties of compounds. Since we use so many databases, we came across some problem during data processing. Most challenging thing is to string all these information together because each database has its unique ID and special data format. We tried our best to integrate all these information and we hope our software can be useful to synthetic biologists. Algorithm
Finding proper metabolic pathway is a typical search problem. Consequently, we turn a biosynthesis problem into a directed graph search problem. Not only do we need to get all of the solutions that satisfy the constraints, but also need to record the search path. Users need to input. We use DFS algorithm here. In theoretical computer science, DFS is typically used to traverse an entire graph, and takes time Θ(|V| + |E|),[2] linear in the size of the graph. The core idea of DFS is simple and elegant, so that it is convenient for us to introduce appropriate pruning algorithms based on the original algorithm. More details can be found in modeling. Ranking criteria
In total, we have three ranking criteria, which are thermodynamic feasibility & competition of heterologous reactions, atom mapping and toxicity of compound. Rights are given to users to decide different weights of different ranking criteria. Many of you may think that length of pathway should be one of the ranking criteria , however, the fact is that the shortest pathway could be the most unrealistic one. So we decide not to use it. 1. Thermodynamic feasibility & competition of heterologous reactions As we all known, thermodynamic feasibility of a certain reaction can decide the probability of reaction. In many occasions , the smaller standard Gibbs is, the more probability of reaction is. And so does competition of heterologous reactions. Enzymes, ribosomes and source compounds are possible things that may trigger We compute the probability of each reaction with △rG through the Boltzmann distribution. According to study of Hiroyuki Kuwahara et.al[3], they derive a mathematical description of the weighting scheme. And in our software, we use this formula to compute and generate a score of each reaction. 2. Atom conservation Given a chemical reaction, an atom mapping rule defines which atom of a substrate compound is transferred to which atom of a product compound [4]. This is helpful for many applications of system biology, in particular in metabolic pathway engineering. Reducing the loss of atoms from the start compound to the target compound is likely to provide good route candidates for pathway design. 3. Toxicity of compound We use the data from Knowledgebase to assess potential toxic effects of chemical compounds on certain organism. Then these effects will be taken into account according to the given weight when we calculate the total score. Additional functions
To improve is to change, to be perfect is to change often. At the beginning of the beginning, we only developed the most ordinary search function. After communicating with some experimenters in Tongji University, we start to know their needs. All we need to do is to try our best to meet their needs. So we add those two functions, which are microbiological recommendation and multi-microbial system. 1. Microbiological recommendation
Don’t know which expression system to use? We offer microbiological recommendation function for experimenters. Based on this purpose, we develop a model to scoring each microorganism (details can be found in model section). After ranking all those score, we provide users with top five organisms to choose. At the same time, related information about organism and pathway are optional to get. 2.FBA Flux balance analysis (FBA) is a mathematical method for simulating metabolism in genome-scale reconstructions of metabolic networks. It can evaluate the metabolic flux distribution, and is one of the most used modeling approaches for metabolic systems. In comparison to traditional methods of modeling, FBA is less intensive in terms of the input data required for constructing the model. Simulations performed using FBA are computationally inexpensive and can calculate steady-state metabolic fluxes for large models (over 2000 reactions) in a few seconds on modern personal computers. Users can select one from pathway search result. Since E.coli is the most frequently used host organism, we will analyze the selected pathway and construct a new model based on classic E.coli core model(from biomodel.com). After simulating this model, our software will provide quantitative predictions of cellular behavior such as metabolic flux patterns by using cobra toolbox which provides insights into the metabolic pathways [5]. 3. SMILES comparison Original thinking about this topic is derived from our visits to WuXi AppTec. Experts of WuXi AppTec proposed an idea to us. They said that sometimes their company got or designed a novel compound which did not exist in current database, and they want to find a possible way to synthesize it. So it came to our mind that what if we could compare the similarity between different compounds and select the most similar compound as a trigger to help us design new synthetic pathway, which can be very useful in small molecular drug discovery and synthesis. First of all, we convert user’s input SMILES into molecular fingerprints by using RDkit toolbox. Then we compute similarity score between input compound and compound in databases by comparing their fingerprints. At last, output is similarity score and a ranking list. The best thing is that we can search not only novel compound, but also existing compound in database. So if you get a compound with structure information and you don’t know what it is, you will find its compound ID and name by using SMILES comparison. Validation