Quantitative Biology Seminar
This seminar will discuss challenges and experiences in the design and construction of pathway representation models, as well as tools and strategies for using these models for visualization, data integration, and hypotheses generation.
Biomedical researchers use models of biological systems to integrate diverse types of information. This ranges from multiple high-throughput datasets, functional annotations and orthology data to expert knowledge about biochemical reactions and biological pathways. Such integrative systems are used to develop new hypotheses and answer complex questions such as what factors cause disease; which patients are at high risk; will patients respond to a given treatment; how to rationally select a combination therapy to individual patient, etc.
Precision medicine needs to be data driven and corresponding analyses comprehensive and systematic. We will not find new treatments if only testing known targets and studying characterized pathways. Thousands of potentially important proteins remain poorly characterized. Computational biology methods can help fill this gap with accurate predictions, making disease modeling more comprehensive. Intertwining computational prediction and modeling with biological experiments will lead to more useful findings faster and more economically.
The rapidly growing amount of data and knowledge in scientific articles and in public datasets is increasing the complexity of these models daily, making the work of maintaining them up-to-date ever more challenging, and highlighting the pressing need to develop computational techniques to help with the associated tasks. Among these methods, diverse semantic web technologies, from linked data to ontologies, are increasingly becoming core components of a wide range of tools that are central to systems biology. These integrative tools enable scientists to combine quantitative and qualitative biological data and knowledge represented as formal concepts and rules to generate hypotheses as well as to test if they are sound and consistent. They are also instrumental to answer complex queries and perform advanced tasks, such as combining individual reactions into potential new pathways.
Semantic biological pathway modelling, in particular, has been studied for some time, but it is still at an early stage of development. Specifically, we discuss challenges and experiences in the design and construction of pathway representation models, as well as tools and strategies for using these models for visualization, data integration, and hypotheses generation.
These computational predictions improved human interactome coverage relevant to both basic and cancer biology, and importantly, helped us to identify, validate and characterize prognostic signatures. Combined, these results may lead to unraveling mechanism of action for therapeutics, re-positioning existing drugs for novel use and prioritizing multiple candidates based on predicted toxicity, identifying groups of patients that may benefit from treatment and those where a given drug would be ineffective.
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