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THE "HGP"
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The human genome project has revolutionized the practice of biology and the future potential of medicine. It has given us a genetics parts list of the components of life-what I term discovery science (e.g., large databases of information)-and a series of paradigm changes in our view of biology that led to the conclusion that systems biology will dominate the 21st century. These include the perspective that biology is an informational science, the revolution high-throughput tools are effecting in discovery science, and the central role of computation, mathematics, and statistics. I will illustrate what I mean by systems biology-and the challenges it presents to academia and industry.
* DO IN SILICO MODELS OF METABOLISM REPRESENT THEIR IN VIVO COUNTERPARTS WELL ?
Methods have been developed that enable the reconstruction of metabolic maps on a genomewide basis. Further, mathematical modeling approaches now exist to analyze the capabilities of reconstructed metabolic networks. The question that we address in this talk is how well do these in silico models represent actual cell behavior.
* THE USE OF WHOLE CELL MODELLING IN A PHARMACEUTICAL ENVIRONMENT :
A unified representation for cellular networks is being designed to facilitate the modeling of cellular behavior. This approach allows us to simulate three types of the cellular networks (metabolic, genetic, and protein interactions) simultaneously and/or independently. These include connectivity analysis, concentration balance analysis, FBA (Flux Balance Analysis), MCA (Metabolic Control Analysis), transient behavior, steady state simulations, bifurcation analysis, and pathway optimization.
* ALZHEMIER'S IN SILICO :
An interactive simulation environment that is being developed to investigate some of the neuroinflammatory phenomena associated with Alzheimer's disease will be presented. The simulation depicts a cascade of inflammatory interactions between glial cells (microglia and astrocytes) and neurons, culminating in the local death of neuronal tissue. Numerous biologically based parameters can be manipulated interactively to investigate possible outcomes of interventions.
* A SYSTEMS APPROACH TO SELECTION OF THERAPEUTIC STRATEGIES IN PHARMACEUTICAL RESEARCH THROUGH THE USE OF COMPUTIONAL TOOLS :
As medical science uncovers more and more layers of biological complexity in human health and disease, it becomes increasingly difficult to predict the therapeutic effect of intervening pharmacologically at a certain point in a pathophysiologic process. Only an approach that deals with biological systems (not just the components of these systems), using computational methods to understand system complexity through modeling and simulation, will enable us to make sense of the rapidly increasing knowledge we are gaining. We will present examples of using a systems approach to explore therapeutic strategies. While the focus will be on "top-down" modeling of physiological processes and biological structures, we will also discuss the future for "bottom-up" modeling based on genomic/proteomic information and why ultimately both top-down and bottom-up approaches are needed.
* USE OF IN SILICO MODELLING TO ACCELERATE CANCER DRUG DEVELOPMENT :
The high and growing cost of developing a new drug is well documented, and the high rate of failures throughout the development phases from discovery through clinical phases contributes significantly to this cost.
* INTEGRATING GENETIC, IN VITRO AND CLINICAL DATA THROUGH BIOSIMULATION FOR DECISION SUPPORT :
Already used in every other research-and-development intensive industry, is beginning to be applied in the pharmaceutical industry. Appropriate use of biosimulation provides pharmaceutical companies with effective ways of exploring the likely clinical impact of manipulating molecular targets in specific patient types. Genetic, in vitro, and clinical data can all be used to refine the biosimulation process, developing novel biological insights and supporting effective decision making. Case studies illustrating this process and applications of biosimulation will be presented.
* CLINICAL TRIAL SIMULATION :
Computer-Assisted Trial Design (CATD) can be used to test a variety of "what if" scenarios related to specific trial design issues and drug characteristics. By using CATD, the drug development team can determine the range of plausible results a trial would produce prior to actually conducting the trial. A model of drug action and disease response that integrates current knowledge and assumptions is required to explore the different "what if" scenarios. The incorporation of CATD in the development process can result in better decision making on drug dosing and trial design because of better use of available knowledge and ability to explore the importance of a large amount of drug and trial design variables.
* HIGH-THROUGHPUT MODELLING AND SIMULATION :
Clinical Trial Simulation has the potential to significantly improve design of clinical trials and interactions with regulatory agencies. However, current technologies do not permit this technology to be used to its full potential in making decisions. There is neither sufficient time nor personnel to fully implement modeling and simulation without dramatically impacting timelines. A solution under development incorporates large-scale distributed computing with machine learning technology to deliver the results on the same development timelines as traditional methods.
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The deluge of data and related technologies generated by the Human Genome Project (HGP) and other genomic research presents a broad array of commercial opportunities. Seemingly limitless applications cross boundaries from medicine and food to energy and environmental resources, and predictions are that life sciences may become the largest sector in the U.S. economy.
Established companies are scrambling to retool, and many new ventures are seeking a role in the information revolution with DNA at its core. IBM, Compaq, DuPont, and major pharmaceutical companies are among those interested in the potential for targeting and applying genome data.
In the genomics corner alone, dozens of small companies have sprung up to sell information, technologies, and services to facilitate basic research into genes and their functions. These new entrepreneurs also offer an abundance of genomic services and applications, including additional databases with DNA sequences from humans, animals, plants, and microbes.
Other applications include gene fragments to use for drug development and target identification and evaluation, identification of candidate genes, and RNA expression information revealing gene activity. Products include protein profiles; particular genotypes associated with such specific medically important phenotypes as disease susceptibility and drug responsiveness; hardware, software, and reagents for DNA sequencing and other DNA-based tests; microarrays (DNA chips) containing tens of thousands of known DNA and RNA fragments for research or clinical use; and DNA analysis software.
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SOME OF THE BIG PLAYERS IN THE MARKET - WHERE DO YOU WANT TO BE ?
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