Complicated cellular behaviors emerge from networks of simple molecular and/or force- based interactions, and they underlie all of biology. Our failure to understand such emergent phenomena¡ limits progress today in many areas of research. We therefore urgently need new methods for dealing with such complexity.

One very promising tool is so-called agent-based computer modeling, which involves the explicit simulation of small-scale local interactions, following the trajectory through time of thousands to millions of states. The construction and application of these models integrates classical mechanics with biophysics and computer science, and it requires a great deal of data from experimental cell biology. Detailed agent-based simulations give us a way to explore, through a blizzard of arithmetic on fast memory-laden computers, the complex emergent behaviors that characterize all interesting cellular behaviors. This mimicking of biological systems in silico does not generate an elegant mathematical encapsulation of a system. But it has the great advantage of avoiding any need to intuit the outcome of myriad biochemical and force feedback loops, a task at which human intelligence is demonstrably frail.

In this talk I will discuss further the philosophy and nature of agent-based modeling in cell biology, present the results of published works on the actin-based motility of L. monocytogenes that demonstrate advantages of this methodology, and share preliminary work on the modeling of actomyosin contractile networks (i.e. collections of actin filaments, crosslinkers, and motor proteins).

• 1. Wikipedia page on agent-based modeling
• 2. Rafelski SM, Alberts JB, and Odell GM (2009) An Experimental and Computational Study of the Effect of ActA Polarity on the Speed of Listeria monocytogenes Actin-based Motility. PLoS Comp. Bio. 5(7): e1000434.
• 3. Drake T and Vavylonis D (2010) Cytoskeletal dynamics in fission yeast: a review of models for polarization and division. HFSP J. 4(3-4):122-30.

• 1. Ching, S., Cimenser,A., Purdon, P. L., Brown E. N., and Kopell, N. J. (2010). "Thalamocortical model for a propofol-induced alpha-rhythm associated with loss of consciousness", Proceedings of the National Academy of Sciences, 107, 52, 22665-22670.
• 2. Brown, E. N., Lydic, R. and Schiff, N. D. (2010). "Mechanisms of disease", New England Journal of Medicine, 363, 2638-50.

Growth cone guidance and neuronal polarization are essential processes in the establishment of neural connections in the developing nervous system. Using Xenopus spinal neurons as a model system both in vitro and in vivo, we seek to understand the cellular and molecular mechanisms underlying growth cone guidance and neuronal polarization induced by diffusible guidance molecules, such as semaphorin 3A (Sema3A). Our studies have demonstrated the concentration-dependent Sema3A induction of bi-directional growth cone turning. A low concentration of Sema3A induces growth cone repulsion mediated by cGMP-dependent membrane hyperpolarization and Ca2+ influx through cyclic nucleotide-gated channels (CNGCs). In contrast, a high concentration of Sema3A attracts growth cones, and requires PKG-dependent membrane depolarization and Ca2+ influx through R-type Ca2+ channels. Moreover, the high, but not the low concentration of Sema3A converts axons to dendrites by the induction of functional R-type Ca2+ channels. Our studies revealed further that Sema3A-induced cGMP triggers de novo synthesis of R-type Ca2+ channels and their targeting to the plasma membrane. Therefore, both functional R-type Ca2+ channels and PKG activity are required for the conversion of axons to dendrites. I will discuss the multiple activities of a diffusible guidance molecule Sema3A that act through specific classes of ion channels in conjunction with cyclic nucleotide signaling.

• 1. Hong K, Nishiyama M. (2010). From guidance signals to movement: signaling molecules governing growth cone turning. Neuroscientist 16(1):65-78.
• 2. Nishiyama M, von Schimmelmann MJ, Togashi K, Findley WM, Hong K. (2008). Membrane potential shifts caused by diffusible guidance signals direct growth-cone turning. Nat. Neurosci. 11(7):762-71.
• 3. Togashi K, von Schimmelmann MJ, Nishiyama M, Lim CS, Yoshida N, Yun B, Molday RS, Goshima Y, Hong K. (2008). Cyclic GMP-gated CNG channels function in Sema3A-induced growth cone repulsion. Neuron 58(5):694-707.
• 4. Barnes AP, Polleux F. (2009). Establishment of axon-dendrite polarity in developing neurons. Annu. Rev. Neurosci. 32:347-81.
• 5. Arimura N, Kaibuchi K. (2007). Neuronal polarity: from extracellular signals to intracellular mechanisms. Nat. Rev. Neurosci. 8(3):194-205.

In 1997 D.L. Taylor and colleagues introduced the concept of high content screening (HCS). Since that time there has been an explosion in the use of automated microscopes combined with automated image analysis programs to analyze scores of features from millions of cells treated with tens of thousands of perturbagens. Although this approach was originally envisioned as a way to speed the drug discovery process it is being adapted to study diverse biological problems ranging from functional genomics to the analysis of development in living organisms using so-called high throughput phenotyping (HTP). Because the cost of the instrumentation for high throughput phenotyping is relatively low, many academic institutions around the world are acquiring it. However, developing the surrounding infrastructure is not easy. Additionally, analyzing HTP data is challenging because of the high dimensionality of the data and the requirement that data be normalized using extensive controls. In the course we will describe the workflows, data cleansing, image analysis, data analysis, statistical analysis and data visualization tools needed to conduct a HTP campaign. Examples of successful campaigns will be presented, focusing on neurons and how molecular pathways responsive for specific types of differentiation and regeneration can be identified.

Students will be encouraged to analyze publically available data sets using commercial and open source software packages.

• Recommended Read:
• • Altschuler, S.J., and L.F. Wu. (2010). Cellular heterogeneity: do differences make a difference? Cell. 141:559-563.
  • Buchser, W.J., T.I. Slepak, O. Gutierrez-Arenas, J.L. Bixby, and V.P. Lemmon (2010). Kinase/phosphatase overexpression reveals pathways regulating hippocampal neuron morphology. Mol Syst Biol. 6:391.
  • Samara, C., C.B. Rohde, C.L. Gilleland, S. Norton, S.J. Haggarty, and M.F. Yanik (2010). Large-scale in vivo femtosecond laser neurosurgery screen reveals small-molecule enhancer of regeneration. Proc Natl Acad Sci U S A. 107:18342-18347.
• Optional Read:
• • Collinet, C., M. Stoter, C.R. Bradshaw, N. Samusik, J.C. Rink, D. Kenski, B. Habermann, F. Buchholz, R. Henschel, M.S. Mueller, W.E. Nagel, E. Fava, Y. Kalaidzidis, and M. Zerial. 2010. Systems survey of endocytosis by multiparametric image analysis. Nature. 464:243-249.
  • Jones, T.R., A.E. Carpenter, M.R. Lamprecht, J. Moffat, S.J. Silver, J.K. Grenier, A.B. Castoreno, U.S. Eggert, D.E. Root, P. Golland, and D.M. Sabatini (2009). Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning. Proc Natl Acad Sci U S A. 106:1826-1831.
  • Moore, D.L., M.G. Blackmore, Y. Hu, K.H. Kaestner, J.L. Bixby, V.P. Lemmon, and J.L. Goldberg (2009). KLF family members regulate intrinsic axon regeneration ability. Science. 326:298-301.
  • Sacher, R., L. Stergiou, and L. Pelkman (2008). Lessons from genetics: interpreting complex phenotypes in RNAi screens. Curr Opin Cell Biol. 20:483-489.

Dendrites allow neurons to integrate information from sensory or synaptic inputs, and dendritic geometry is highly variable from one neuronal type to another. It has been suggested that diverse morphologies of dendritic arbors contribute to the differential processing of synaptic or sensory inputs that each neuron can receive. The spatial disposition and local density of branches within the arbor limit the number and type of inputs, but the genetic programs underlying such diverse geometry are not well understood.

It should be also noted that the nervous system is reorganized during animal life at multiple structural levels to strengthen, elaborate, and/or modify already acquired functions, and even add on novel ones. One way to achieve this reorganization is local remodelling of the hardwiring of the circuitry, such as that of stage-specific dendrite patterns. Once neurons undergo the remodelling phase, the dendritic arbors persist throughout adult life, which is months or even years.

We and other groups have been addressing all of the above mechanisms by using Drosophila dendritic arborization (da) neurons. We are interested in roles of cell surface molecules, such as the 7-pass transmembrane cadherin Flamingo, and other molecules that are localized in various subcellular compartments. We have been addressing how those molecular machineries operate by combining the power of genetics, in vivo imaging, and computational approaches.

• Recommended Read:
• • Jan YN, Jan LY. Branching out: mechanisms of dendritic arborization. Nat Rev Neurosci. 11: 316-28 (2010).
• Optional Read:
• • Computational Modeling of Dendritic Tiling By Diffusible Extracellular Suppressor. Kohei Shimono, Kaoru Sugimura, Mineko Kengaku, Tadashi Uemura, and Atsushi Mochizuki. Genes to Cells, 15: 137–149 (2010).
  • Spatial control of branching within dendritic arbors by dynein-dependent transport of Rab5-endosomes. Satoh D, Sato D, Tsuyama T, Saito M, Ohkura H, Rolls MM, Ishikawa F, Uemura T. Nat Cell Biol. 10: 1164-71 (2008).
  • Development of morphological diversity of dendrites in Drosophila by the BTB-zinc finger protein abrupt. Sugimura K, Satoh D, Estes P, Crews S, Uemura T. Neuron, 43: 809-22 (2004).

On pyramidal neurons in the cerebral cortex, excitatory synapses terminate at spines, short protrusions joined to the main dendrite by a thin neck. Recent studies show that dendritic spines are dynamic structures. Their rapid creation, destruction and shape-changing are essential for short- and long-term plasticity at excitatory synapses. The onset of long-term potentiation (LTP), spine-volume growth, and an increase in receptor trafficking are coincident, enabling a ‘functional readout’ of spine structure that links the age, size, strength and lifetime of a synapse. The rapid, activity-triggered plasticity is highly characteristic to each spine, and may relate to cognitive processes. Long-term spine dynamics are implicated in long-term memory: intrinsic fluctuations in volume can explain synapse maintenance over long periods, and involved in the decay of memory. Rewiring of neuronal networks by spines makes the learning processes combinatorial and rich, unlike classical neuronal network models. Furthermore, impaired spine dynamics may cause psychiatric and neurodevelopmental disorders. Thus, spine dynamics are the wealthy cellular phenomena which underlie cognition and memory.

• Recommended Read:
• • Matsuzaki, M., Honkura, N., Ellis-Davies, G.C.R., and Kasai, H. (2004). Structural basis of long-term potentiation in single dendritic spines. Nature 429, 761-766.
  • Tanaka, J., Horiike, Y., Matsuzaki, M., Miyazaki, T., Ellis-Davies, G.C.R., and Kasai, H. (2008). Protein synthesis and neurotrophin-dependent structural plasticity of single dendritic spines. Science 319, 1683-1687.
  • Yasumatsu, N., Matsuzaki, M., Miyazaki, T., Noguchi, J., and Kasai, H. (2008). Principles of long-term dynamics of dendritic spines. J. Neurosci. 28: 13592-13608.
• Optional Read:
• • Noguchi, J., Matsuzaki, M., Ellis-Davies, G.C.R., and Kasai, H. (2005). Spine-neck geometry determines NMDA receptor–dependent Ca2+ signaling in dendrites. Neuron 46, 609-622.
  • Honkura, N., Matsuzaki, M., Noguchi, J., Ellis-Davies,G.C.R., and Kasai, H. (2008). The subspine organization of actin fibers regulates the structure and plasticity of dendritic spines. Neuron 57, 719-729.
  • Morita, S., Yasumatsu, N. Noguchi, J., and Kasai, H. (2009). Generation, elimination and weight fluctuations of synapses in the cerebral cortex. Communicative and Integrative Biology 2, 1-4.
  • Kasai, H., Fukuda, M, Watanabe, S., Hayashi-Takagi, A., and Noguchi, J. (2010). Structural dynamics of dendritic spines in memory and cognition. Trends Neurosci. 33, 121-129.
  • Kasai, H., Hayama, T., Ishikawa, M., Watanabe, S., and Yagishita, S. (2010). Learning rules and persistence of dendritic spines. Eur. J. Neurosci. 32, 241-249.