Dynamics in Decision Making: How Cellular Networks Encode And Decode Temporal Information

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5. PROJECT 1. DYNAMICS IN DECISION MAKING: HOW CELLULAR NETWORKS ENCODE AND DECODE TEMPORAL INFORMATION SUMMARY There is growing evidence that the dynamics of signaling – how the activity of specific pathways changes as a function of time – may play a central role in the specificity of cellular information transmission. One general hypothesis is that distinct external inputs (different growth factors, stresses, etc.) can encode information in the dynamics of how central signaling nodes are activated (i.e. sustained vs transient activation; different frequency activation). In turn, these distinct dynamic properties could be decoded by downstream networks in order to yield distinct cellular response programs. Nonetheless, this dynamic encoding hypothesis has been difficult to test, because we have lacked the tools to systematically perturb signaling dynamics. We have recently developed a suite of cellular optogenetic switches that allow us to activate key intracellular regulatory nodes with light (e.g. Ras, MAPK, cAMP, transcription). Because we can use light to activate these nodes with arbitrary temporal patterns, they are powerful tools to systematically interrogate how cells encode and decode dynamical information. We propose to combine systematic optogenetic stimulation with quantitative response profiling to study a number of canonical cellular decision making systems (mammalian cell proliferation, yeast stress responses, and stem cell differentiation). These studies will give us a deeper quantitative understanding of how cellular information can be encoded in signaling dynamics. In addition, they should provide a basis for a deeper understanding of how changes in dynamics play a role in diseases such as cancer and how dynamic stimulation might also provide new modalities to modulate and control cellular behavior, especially in engineered therapeutic cells (e.g. PROJECT 3 includes engineering dynamic control of stem cell differentiation). We also hope to learn how to engineer signaling networks that can act as specific dynamic filters. LEAD Investigator: EL-SAMAD Investigators: EL-SAMAD, LIM, THOMSON, KROGAN, LI