SBIR-STTR Award

A common challenge in the analysis of genomics data is trying to understand the underlying phenomenon in the context of all complex interactions on various regulatory pathways. Currently, a statistical approach is universally used to identify the most relevant pathways in a given experiment. This approach only considers the set of genes present on each pathway and completely ignores other important biological factors. Here we show that in spite of its general adoption, and independently of the particular model used, this statistical analysis is unsatisfactory, and can often provide incorrect results. Using a systems biology approach, we developed an impact analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. Our preliminary work shows that the classical analysis produces both false positives and false negatives while the impact analysis provides biologically meaningful results. In this Phase I application, we are proposing to develop a prototype that would demonstrate the feasibility of a commercial software analysis package based on this novel approach. Our team has a very strong track record as demonstrated by: a large number of citations to our previous publications, a large user-base for our previously developed software (over 5,000 scientists from all 5 continents), and very strong letters of support. 1

Public Health Relevance:
The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignores other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc.

Public Health Relevance:
The classical statistical approaches, which are universally used to identify the most relevant biological pathways in a given experiment, only consider the number of di(R)erentially expressed genes on each pathway and completely ignores other important biological factors. However, in spite of its general adoption, these statistical approaches are unsatisfactory, and can often provide incorrect results. We propose a novel signaling pathway analysis that includes the classical statistics, but also considers other crucial factors such as the magnitude of each gene's expression change, their type and position in the given pathways, their interactions, etc. 1

Thesaurus Terms:
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Once heralded as the Holy Grail, the capability of obtaining a comprehensive list of genes, proteins or metabolites that are different between disease and normal is routine today. And yet, the holy grail of high-throughput has not delivered so far. Even though such high-throughput comparisons have become relatively easy to perform, understanding the phenomena that cause the disease is as challenging as ever, if not more so. Signaling and metabolic pathways are complex graphs describing genes signaling and biochemical reactions that take place in various subsystems of the organism. The current methods that aim to help us understand the underlying biological phenomena by using the measured differences to identify significantly impacted pathways are rather unsophisticated. Many if not all such methods of- ten treat the pathways as simple sets of genes, and either ignore or under-utilize the very essence of such pathways: the graphs that describe the complex ways in which genes interact with each other. We are proposing the development of a software product for the analysis of gene signaling and metabolic pathways in the context of high throughput data, such as DNA microarrays. The fundamentals of the approach have been tested through the implementation of tools available for free from the PI's academic web site. These tools have an existing user base of 11,266 registered users who already use and trust our software. This user-base constitutes a unique asset for Advaita. The product we envisage will have a number of unique capabilities including the ability to: i) take into consideration the interactions between specific genes;ii) calculate a unique ""impact factor"" that characterizes how impacted a given subsystem of the organism is in the given condition;iii) find the mechanisms of action for diseases and drugs;iv) pinpoint potential points of optimal therapeutic intervention;and v) detect qualitative changes in a living organism by monitoring a large number of parameters (gene expression levels, metabolites, etc.). This product has the potential to: i) shorten the drug development cycle by providing fewer but better drug candidates, ii) help with the understanding of disease and drug mechanisms, iii) detect qualitative changes in biological organisms. The scientific validity of this analysis approach has been verified on real data in a number of applications (several published, others described here as preliminary results). The technical feasibility was demonstrated in over 36,000 benchmark trials. The commercial validity of this product has been verified by implementing a proto- type including the first 2 of the capabilities above. This prototype was already sold to 3 customers in spite of the fact that it only implements a small subset of the final set of capabilities and in spite of the fact that these customers already had access to the software products offered by our competition.

Public Health Relevance:
The benefits of the proposed research are expected to impact a number of research areas spanning from cancer, to obesity, to aging as well as any other life science area in which high-throughput methods (e.g. DNA microarrays, protein microarrays, metabolomics, etc.) are used. The biological user community that can benefit from this research includes any life science researcher using such high-throughput methods in any biological domain and any organism, from yeast, to fruit fly, to mouse, to human.

Thesaurus Terms:
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