Jorge Carneiro (Gulbenkian Institute of Science, Portugal)
Vassily Hatzimanikatis (École Polytechnique Fédérale de Lausanne, Switzerland)
Oleg Igoshin (Rice University, U.S.A.)
Armindo Salvador (Centre for Neuroscience and Cell Biology, Portugal)
This module will introduce key concepts and tools in the field of Molecular Systems Biology, with an emphasis on theoretic-experimental integration. Main pedagogic objectives are:
1. To highlight the importance of finding generic organizing principles of biological systems, providing some examples in various domains (metabolism, signal transduction, etc.)
2. To familiarize the students with key concepts for the understanding of biological organization at the molecular level. E.g. stability, robustness, molecular noise, design principles.
3. To provide basic background on kinetic and constraints-based modeling, and hands-on training on kinetic modeling, both deterministic and stochastic.
4. To motivate the students for integrative and theory-driven quantitative research.Course program and materialsDay 1 (November 14)
Lecturer: Armindo SalvadorSchedule
9:00-9:30 Course overview
9:30-10:15 Are there laws of Molecular Biology?
10:30-12:00 Design principles of elementary metabolic circuits
14:00-16:00 Introduction to kinetic modeling of biochemical systems: representations of processes and systems
16:15-18:30 Hands-on practice
· Copasi (download from http://copasi.org/tiki-index.php?page=downloadNonCommercial)
Materials for days 1 and 2:
• Alves, R., F. Antunes, et al. (2006). "Tools for kinetic modeling of biochemical networks." Nature Biotechnology 24:667-672.
• Salvador, A. and M. A. Savageau (2006). "Evolution of enzymes in a series is driven by dissimilar functional demands." Proceedings of the National Academy of Sciences of the U. S. A. 103: 2226-2231.
• Coelho, P. M. B. M., A. Salvador, et al. (2009). "Quantifying Global Tolerance of Biochemical Systems: Design Implications for Moiety-Transfer Cycles." PLoS Computational Biology 5(3): e1000319.
• Milo, R., S. Shen-Orr, et al. (2002). "Network Motifs: Simple Building Blocks of Complex Networks." Science 298(5594): 824-827.
• Salvador, A. and M. A. Savageau (2003). "Quantitative evolutionary design of glucose 6-phosphate dehydrogenase expression in human erythrocytes." Proceedings of the National Academy of Sciences of the U. S. A. 100: 14463-14468.
• Savageau, M. A., P. M. B. M. Coelho, et al. (2009). "Phenotypes and tolerances in the design space of biochemical systems." Proceedings of the National Academy of Sciences of the U. S. A. 106: 6435–6440.
• Coelho, P. M. B. M., A. Salvador, and M. A. Savageau (2010). "Relating Mutant Genotype to Phenotype via Quantitative Behavior of the NADPH Redox Cycle in Human Erythrocytes." PLoS ONE 5(9): e13031
• Noor, E., E. Eden, et al. (2010). "Central Carbon Metabolism as a Minimal Biochemical Walk between Precursors for Biomass and Energy." Molecular Cell 39(5): 809-820Day 2
Lecturer: Armindo SalvadorSchedule
9:00-10:15 Introduction to kinetic modeling of biochemical systems: Data sources and approximate representations of kinetics
10:30-12:00 Hands-on practice
14:00-16:00 Introduction to deterministic kinetic modeling of biochemical systems: Steady-states and sensitivity analysis
16:15-18:30 Hands-on practiceDay 3
Lecturer: Jorge CarneiroSchedule
Morning theme: Integrating molecular, cellular and collective dynamics of a multicellular system: the regulatory CD4 T lymphocytes case study
9:00-9:50 The crossregulation model of the dynamics of regulatory CD4 T lymphocytes populations
10:00-10:50 Regulatory gene network controling the differentiation of CD4 T lymphocytes
11:00-12:00 Integrating molecular and cellular networks: from tissue to genes and back.
Afternoon theme: The sensorimotor system of a single cell
14:00-14:50 Swimming and chemotactic behavior of sea urchin spermatozoa
15:00-15:50 Modelling the signalling, morphodynamics and swimming mechanics of a spermatozoon
16:00-16:50 And this is how a sea urchin spermatozoon find its conspecific egg
See here: http://qobweb.igc.gulbenkian.pt/courses/coimbrasysbio2011/
Lecturer: Vassily Hatzimanikatis
9:00-12:00 (with breaks as needed) Constraints-based modeling
14:00-18:30 Paper presentationsMaterials:
For all students:
• Price, N. D., J. L. Reed, et al. (2004). "Genome-scale models of microbial cells: Evaluating the consequences of constraints." Nature Reviews Microbiology 2(11): 886-897.Groups:
Lecturer: Oleg IgoshinSchedule
9:00-9:50 Bistability (lectures interspersed with hands-on practice, breaks as needed)
10:00-11:00 Bistability in the lac operon
11:10-12:30 Stochastic simulations
14:00-15:45 Stochastic switches
16:00-17:00 Friday CNC seminar: TBAMaterials
• Novick A, Wiener M. 1957. Enzyme Induction as an All-or-None Phenomenon PNAS 43: 553-566.
• Elowitz MB, Levine AJ, Siggia ED, Swain PS. 2002. Stochastic gene expression in a single cell. Science297: 1183-6
• Maamar H, Raj A, Dubnau D. Noise in gene expression determines cell fate in Bacillus subtilis. 2007.Science 317: 526-9.
Ozbudak EM, Thattai M, Lim HN, Shraiman BI, Van Oudenaarden A. 2004. Multistability in the lactose utilization network of Escherichia coli. Nature 427: 737-40.Important notes
Students are requested to bring their own laptops with Copasi v4.7 installed (download from http://copasi.org/tiki-index.php?page=downloadNonCommercial).
All reading materials are available for download from here (accessible only to lecturers, PDBEB students and registered participants), except those for Day 3.
Until Monday afternoon, students must organize themselves into groups and indicate their subject preferences for the paper presentations on Thursday afternoon in this spreadsheet.
Stochasticity and ultrasensitivity in bacterial networks
Rice University, Houston, TX
Nonlinearities of rate laws describing biochemical reaction kinetics can often results in ultrasensitive switches in which small change or fluctuation of parameter can lead to large change in network output. Such switches are important for making robust cell decision choices but can be detrimental for networks functioning in homeostasis and desiring noise minimization. In this presentation I’ll discuss biological examples illustrating each of these cases. With combination of mathematical modeling and bioinformatic data analysis, we show that noise minimization and avoidance of ultrasensitive switches explain operon organization of E. coli. These results suggest a central role for gene expression noise in selecting for or against maintaining operons in bacterial chromosomes thereby providing an example of how the architecture of post-translational networks affects bacterial evolution. With combination of mathematical modeling and single-cell microscopy we show the existence and origins of ultrasensitivity in the network responsible for cell-fate decision in sporulating B. subtilis. These results illustrate how unique structure of the sporulation network allows fast and robust population level response despite cellular variability.