Virtual Cell Software Repository
From the webpage:
Developing large volume multi-scale systems dynamics interpretation technology is very important source for making virtual cell application systems. Also, this technology is focused on the core research topics in a post-genome era in order to maintain the national competitive power. It is new analysis technology which can analyze multi-scale from nano level to physiological level in system level. Therefore, if using excellent information technology and super computing power in our nation, we can hold a dominant position in the large volume multi-scale systems dynamics interpretation technology. In order to take independent technology, we need to research a field of study which have been not well known in the bio system informatics technology like the large volume multi-scale systems dynamics interpretation technology.
The purpose of virtual cell application systems is developing the analysis technology and service which can model bio application circuits based on super computing technology. For success of virtual cell application systems based on super computing power, we have researched large volume multi-scale systems dynamics technology as a core sub technology.
- Developing analysis and modeling technology of multi-scale convergence information from nano level to physiological level
- Developing protein structure modeling algorithm using multi-scale bio information
- Developing quality and quantity character analysis technology of multi-scale networks
- Developing protein modification search algorithm
- Developing large volume multi-scale systems dynamics interpretation technology interpreting possible circumstances in complex parameter spaces
Amazing set of resources available here:
PSExplorer: Parameter Space Explorer
Mathematical models of biological systems often have a large number of parameters whose combinational variations can yield distinct qualitative behaviors. Since it is intractable to examine all possible combinations of parameters for nontrivial biological pathways, it is required to have a systematic way to explore the parameter space in a computational way so that distinct dynamic behaviors of a given pathway are estimated.
We present PSExplorer, an efficient computational tool to explore high dimensional parameter space of computational models for identifying qualitative behaviors and key parameters. The software supports input models in SBML format. It provides a friendly graphical user interface allowing users to vary model parameters and perform time-course simulations at ease. Various graphical plotting features helps users analyze the model dynamics conveniently. Its output is a tree structure that encapsulates the parameter space partitioning results in a form that is easy to visualize and provide users with additional information about important parameters and sub-regions with robust behaviors.
MONET: MOdularized NETwork learning
Although gene expression data has been continuously accumulated and meta-analysis approaches have been developed to integrate independent expression profiles into larger datasets, the amount of information is still insufficient to infer large scale genetic networks. In addition, global optimization such as Bayesian network inference, one of the most representative techniques for genetic network inference, requires tremendous computational load far beyond the capacity of moderate workstations.
MONET is a Cytoscape plugin to infer genome-scale networks from gene expression profiles. It alleviates the shortage of information by incorporating pre-existing annotations. The current version of MONET utilizes thousands of parallel computational cores in the supercomputing center in KISTI, Korea, to cope with the computational requirement for large scale genetic network inference.
RBS Designer was developed to computationally design synthetic ribosome binding sites (RBS) to control gene expression levels. Generally transcription processes are the major target for gene expression control, however, without considering translation processes the control could lead to unexpected expression results since translation efficiency is highly affected by nucleotide sequences nearby RBS such as coding sequences leading to distortion of RBS secondary structure. Such problems obscure the intuitive design of RBS nucleotides with a desired level of protein expression. We developed RBSDesigner based on a mathematical model on translation initiation to design synthetic ribosome binding sites that yield a desired level of expression of user-specified coding sequences.
SBN simulator: Switching Boolean Networks Simulator
Switching Boolean Networks Simulator(SBNsimulator) was developed to simulate large-scale signaling network. Boolean Networks is widely used in modeling signaling networks because of its straightforwardness, robustness, and compatibility with qualitative data. Signaling networks are not completely known yet in Biology. Because of this, there are gaps between biological reality and modeling such as inhibitor-only or activator-only in signaling networks. Synchronous update algorithm in threshold Boolean network has limitation which cannot sample differences in the speed of signal propagation. To overcome these limitation which are modeling anomaly and Limitation of synchronous update algorithm, we developed SBNsimulator. It can simulate how each node effect to target node. Therefore, It can say which node is important for signaling network.
MKEM: Multi-level Knowledge Emergence Model
Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.
The system constitutes of two parts, tagger and the extractor (may require compilation)
A sentence of interest is given to the tagger which then proceeds to the creation of rule sets. The tagger stores this in a folder by the name of “ruleList”. These rule sets are then given by copying this folder to the extractor directory.
I blogged about an article on this project at: MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge.