Showing papers by "Alfonso Rodríguez-Patón published in 2017"

Probability-based collaborative filtering model for predicting gene-disease associations.

Abstract: Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

A New Improved and Extended Version of the Multicell Bacterial Simulator gro

Abstract: gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell–cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. Cell...

BioBlocks: Programming Protocols in Biology Made Easier

Abstract: The methods to execute biological experiments are evolving. Affordable fluid handling robots and on-demand biology enterprises are making automating entire experiments a reality. Automation offers the benefit of high-throughput experimentation, rapid prototyping, and improved reproducibility of results. However, learning to automate and codify experiments is a difficult task as it requires programming expertise. Here, we present a web-based visual development environment called BioBlocks for describing experimental protocols in biology. It is based on Google’s Blockly and Scratch, and requires little or no experience in computer programming to automate the execution of experiments. The experiments can be specified, saved, modified, and shared between multiple users in an easy manner. BioBlocks is open-source and can be customized to execute protocols on local robotic platforms or remotely, that is, in the cloud. It aims to serve as a de facto open standard for programming protocols in Biology.

Directed evolution of biocircuits using conjugative plasmids and CRISPR-Cas9: design and in silico experiments

Abstract: Recent links between computer science and synthetic biology allow for construction of many kinds of algorithmic processes within cells, obtained either by a direct engineered design or by an evolutionary search. In the latter case, horizontal gene transfer and especially transfer of plasmids by conjugation is generally respected as a crucial source of genetic diversity in bacteria. While some previous studies focused on mutations as the crucial principle to obtain diversity for engineered evolution, here we consider conjugation itself as a tool to generate diversity from a pre-determined library of biocircuits basic components. The recent development of CRISPR-Cas9 and its programmable DNA cutting ability makes it a powerful selection tool able to remove nonfunctional biocircuits from a cell population. In this paper, we describe a framework for controlled bacterial evolution of biocircuits based on conjugation and on CRISPR-Cas9, resulting in a direct biological implementation of an evolutionary algorithm. In silico experiments provide data to estimate the computational/search capability of plasmid-based engineered evolution.

Iteratively collective prediction of disease-gene associations through the incomplete network

Abstract: The prediction of links between genes and disease is still one of the biggest challenges in the field of human health. Almost all state-of-the-art studies on the prediction of gene-disease links focuson a single pair of links, ignoring the associations and interactions among different types of links. Moreover, the biological information networks are usually incomplete. In this paper, we study the similarity measure to be used on two different types of nodes, based on the metapaths between them (Wsrm). Then an iterative self-updating approach for link prediction using heterogeneous information network is proposed to fit the incompletion of the network (ISL), which is a semi-supervised learning formula. Using the biological integrated network constructed from OMIM and HumanNet dataset (30,896 nodes and 1,200,166 edges) we applied our framework. The area under the receiver operating characteristic is 0.941, indicating that our approach significantly outperforms the state-of-the-art gene-disease link prediction approaches. Moreover, the sensitivity analysis signifies that our approach is robust. Consequently, our proposed framework demonstrates an efficient and accurate approach for link prediction between genes and diseases. In addition, during iteration, the accuracy of the result gradually increases. The example dataset and the implementation of our approach is avaliable at https://github.com/xymeng16/ISL.