Frequently Asked Questions

What is Systems Biology?

Systems Biology is the study of a cell, tissue or organism at the system level. It provides an integrated and interacting view of genes, proteins and biochemical reactions. Instead of analyzing individual system components (i.e. genes, proteins and their functions), these biologists consider a system in its entirety including all relevant components and their interactions. Because of the complexity of biological systems, systems biology must incorporate traditional hypothesis-driven research with computational discovery-driven research.

Seralogix analysis incorporates all types of your data including gene expression data, protein data and metabolite profiling data. Your data is analyzed in the context of existing biological knowledge to identify the relevant mechanistic pathways affected and how they change over time.

What are Dynamic Bayesian Networks?

Bayesian networks are graphical models that represent conditional dependencies and independencies among the variables corresponding to biological measurements. These variables are illustrated using nodes that are connected together by lines which represent the relationships between variables. Figure 1a is an example of a Bayesian network describing a gene regulation network. Each gene’s expression is represented by a variable that describes how the genes are regulated by each other. This analysis can become confusing when only a few variables are being analyzed, but the graphical representation illustrates where the regulatory relationships exist between the genes.

Dynamic Bayesian networks are Bayesian networks that are capable of incorporating temporal processes such as time series and feedback loops, essential features of most biological systems, as illustrated in Figure 1b. Thus the ability to incorporate experimental time series measurements is particularly important for modelling biological networks.


Figure 1. Simple example of a static Bayesian network (1a) and the roll out of the static network to form a dynamic Bayesian network (1b) having three time points.

What type of data can Seralogix incorporate into a model?

Seralogix can incorporate most types of biological data into a model; including, but not limited to:

What format does the data need to be in?

Most common data formats are accepted, including:

If your data is in a format not listed above, please contact us to discuss your options.

What information can I receive?

The resulting data you receive depends on the type of service you have selected, and may include:

What will the “output” look like?

The results are posted on Seralogix’s secure client portal, accessible from any computer connected to the internet. On the results page, you will find tabs detailing various aspects of the analysis. Depending on the service chosen, data will include:

Will there be sample results to browse or will that come later? In either case, there should be a link here. To view sample results, go to the Results Interface page.

Can Seralogix analyze data from the new next generation or massively parallel sequencing technologies such as Illumina Genome Analyzer (Solexa), 454 Life Sciences™ System, and Applied Biosystems SOLiD™? Yes. Seralogix can help you understand your next generation (massively parallel) sequencing data, maximizing the return on your investment. Because next generation sequencing technologies utilize gigabase-scale throughput and short read lengths, they are problematic for conventional data analysis techniques. Seralogix’s sequence pipelining service allows clients to view and analyze sequence alignments, nucleotide variants, and splice isoforms, and perform cross-sample comparisons. For more information, see What type of data can Seralogix incorporate into a model.

How does BioSignatureDS differ from bioinformatics software?

Other bioinformatics software allows only a limited view of a biological system; only Seralogix’s proprietary BioSignatureDS™ offers the complete picture. These next generation solutions enable biological discoveries that are otherwise not possible. Highly flexible and easily customized, the BioSignatureDS approach integrates varied experimental data sets including time-course data, with each other and with existing biological knowledge. Unlike other analyses, the BioSignatureDS algorithms use powerful Dynamic Bayesian methods of machine learning and pattern recognition to decode the complexities of systems biology.

What is the workflow process?

See Steps in a Project.

How long will it take?

The turn around time for results is dependent on many factors, including the amount of data to be analyzed, and the condition of the data. Once the data is formatted properly for input into the software, most analyses take 10 days or less.

What prior knowledge is incorporated into the analysis?

Seralogix’s BioSignature Analysis™ incorporates data from public databases including:

How robust are the models?

The robustness of a model is dependent on the quality and quantity of the data as well as certain assumption that may be taken in preprocessing the experimental data for modeling. We can run tests that allow us to measure the robustness and discriminatory power of the models by pathway or other disease model.

How sensitive are the models?

Here again the sensitivity of a model is dependent on the quality and quantity of the data. We can tune models to be for sensitivity but selectivity may be compromised. We can run tests that allow us to measure the sensivitivity and selectivity of the models by pathway or other disease model.

What type of models can be developed?

Models can be developed based on:

What is a mechanistic gene? How is it defined?

Mechanistic genes are genes whose products control key regulatory points in pathways through a variety of methods including altering gene transcription or translation or through post-translation processes such as protein phosphorylation. Traditional analysis only identifies the genes that have changed to most. However, subtle alterations of the expression of mechanistic genes can induce substantial effects on biological processes. In order to identify these mechanistic genes, all genes must be analyzed in context of their parent and neighboring genes.


Figure 1. Example of a MAPK Pathway Analysis. The Blue Concentric Rings Indicate Candidate Mechanistic Genes Discovered Through the Pathway Bayesian Modeling Techniques.

What is a Gene Ontology category?

Gene Ontology (GO) categories are species-independent, qualitative attributes that provide a classification of gene products into molecular functions, biological processes, and cellular components to describe attributes of gene products. Molecular function describes the biochemical function of a gene product; biological process describes a broad biological objective; and cellular component describes the location of a gene product within cellular structures and macromolecular complexes.

I am interested. What do I do next?

If you are interested in our services, you may qualify for a free trial. If you have additional question, please contact us to speak with one of our scientists. Link to FAQ page states: View answers to questions often asked about our services and technology

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