3. SYSTEMS BIOLOGY

1. 3. SYSTEMS BIOLOGY • Genomics • Opportunities in post-genomics • Challenges – methods, analysis • Systems Engineering + Genomics = Systems Biology • Synthesis and integration of data types • Systems analysis • Etc.etc.

2. Bio-medical Science + Engineering • Background (www.dbi.tju.edu) • New Program at the interface • Biomedical option • Nascent core curriculum: biomed courses TJU tech courses UD and new course(s) • Work across-between campuses (SEPTA, I95) joint advisorship • Dan Zak and Rishi Khan zak@che.udel.edurishi@capsl.udel.edu

3. Opportunities • Two current joint UD-TJU funding sources : BISTI and DARPA • Interface of systems engineering and genomics data acquisition and data analysis • Experimental opportunities • Modeling opportunities

4. Objectives • Understanding, prediction and control of human functional processes • Disease models and relevancy • Reverse engineering opportunities

5. Multi-scale Model Problem • Organizational Structure in Biology • Single Cells (Building blocks) • Tissues (organization of multiple cells) • Organism • Fundamental Cellular Processes • Inputs (Signal Transduction) • Transcription • Translation • Outputs – adaptive processes

6. Characteristics • Consists of verycomplex interconnections of large number of “processes” • Structure and function encoded in DNA sequences in the genome. • Similar to Chemical Processes but on a significantly more elaborate scale

7. Engineer’s Viewpoint of Central Issue • Biological “system” consists of “processes” • “Process flowsheet” encoded within genome; not overt and explicit. (Can we decode and exploit?) • Ultimate objective: quantitative prediction of cellular function; (for system-wide analysis and novel synthesis and design). • Similarity to chemical processes can be exploited !?

8. SYSTEMS BIOLOGY: Projects • Multi-scale modeling and analysis of adaptive cellular and system processes • Mammalian systems – neurons, brains, brain functions and diseases • CAKE – a model benchmark tool, and a matrix of solutions for Genetic Regulatory Circuits. • Development and Analysis of kinetic signaling models • Link of signaling models to Genetic Regulatory Circuits • Genetic Regulatory Circuits to neuronal electrical behavior

11. SYSTEMS BIOLOGY: Projects Experimental • Development of useful mammalian microarray, promoter activity and proteomic data – e.g. • New methods and applications - e.g. polynies Analysis • CAKE – combining global datasets, experimental design, prediction - e.g. genetic regulatory circuit, gene network prediction from combined data sets • Development and analysis of kinetic signaling models and e.g. link of signaling models to genetic regulatory circuit • Genetic regulatory circuits to output – e.g. neuronal electrical behavior and to biological neural network function Tools: • clone updater, PAINT, masliner II, qualitative modeling approaches, Bayes networks • The IT world – MDL BioSPICE scope, EWG ontologies

12. Relevance • Biological understanding and prediction: e.g. principles of gene regulation; relating neuron dynamics to neural network function • Disease: e.g. addiction; homeostasis; mental illness and mood; liver disease; heart disease • Reverse engineering: following slides

13. Path Forward • Talk to us, talk to Dan/Rishi, visit TJU • Explore opportunities in the Systems Biology problem space and define a project direction • Projects can extend ongoing work • Projects can initiate new work