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De-blurring signal network dynamics Dominic Kamps, and Leif Dehmelt ACS Chem. Biol., Just Accepted Manuscript • DOI: 10.1021/acschembio.7b00451 • Publication Date (Web): 14 Aug 2017 Downloaded from http://pubs.acs.org on August 16, 2017

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ACS Chemical Biology

De-blurring signal network dynamics Dominic Kamps and Leif Dehmelt Department for Systemic Cell Biology, Max Planck Institute of Molecular Physiology and Fakultät für Chemie und Chemische Biologie, Technische Universität Dortmund, Dortmund, Germany

Abstract: To orchestrate the function and development of multicellular organisms, cells integrate intraand extracellular information. This information is processed via signal networks in space and time, steering dynamic changes in cellular structure and function. Defects in those signal networks can lead to developmental disorders or cancer. However, experimental analysis of signal networks is challenging as their state changes dynamically and differs between individual cells. Thus, causal relationships between network components are blurred if lysates from large cell populations are analyzed. To directly study causal relationships, perturbations that target specific components have to be combined with measurements of cellular responses within individual cells. However, using standard single-cell techniques, the number of signal activities that can be monitored simultaneously is limited. Furthermore, diffusion of signal network components limits the spatial precision of perturbations, which blurs the analysis of spatio-temporal processing in signal networks. Hybrid strategies based on optogenetics, surface patterning, chemical tools and protein design can overcome those limitations and thereby sharpen our view into the dynamic spatio-temporal state of signal networks and enable unique insights into the mechanisms that control cellular function in space and time.

Spatio-temporal organization of signal networks Cells have to integrate external signals and their internal state to generate, control and adapt their function. This integration encompasses numerous processes that occur simultaneously inside an individual cell. To generate specific cellular behaviors, those processes have to be organized in space and time. Such organization occurs on a wide range of spatial and temporal scales, ranging from ångströms to meters and picoseconds to years1-4. Box 1: Spatio-tempeoral information processing in signal networks. Top panels: Network topology diagrams with two signal network components X and Y. Each component represents a specific protein that can exist in an active or inactive state. Arrows indicate activation and bar-headed lines indicate inhibition. C represents a constant, constitutive inhibition mechanism that is independent of the network components (i.e., a constitutively active phosphatase or GTPase activating protein, GAP). To study such networks, the activity of component X can be acutely perturbed and monitored over time. Middle panels: Computational simulation of typical temporal responses to a short perturbation pulse (orange). By itself, a negative feedback generates homeostasis and quickly shuts down a change in activity. In combination with positive feedback, the activity of component X can self-amplify. Within certain parameter ranges and initial conditions, for example if component Y is inactivated very slowly, such systems can generate a single transient activity maximum following a small perturbation and thus exhibit excitable behavior. Under different conditions, for example if component Y is inhibited more rapidly, self-amplification and self-inhibition of X can alternate over time. Thus, such systems can also ACS Paragon Plus Environment

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generate self-sustained, recurring activity maxima, i.e. oscillations. Bottom panels: Computational simulation of spatio-temporal responses in 2 dimensions (e.g. a biological membrane) to a short, local perturbation pulse. Snapshots show a single time point following the local perturbation and kymographs show the development of the activity pattern over time. If only a negative feedback is present, the activity quickly dissipates close to the local perturbation via diffusion and the homeostatic response. Depending on parameters and the initial state, additional positive feedback can enable single or recurring, global wave propagation patterns that can reach distant regions. All simulations were performed using a cellular automata framework that was developed earlier5.

Primarily, cells organize the cytoplasmic space by generating subcellular structures, including membrane compartments, chromatin or protein assemblies such as kinetochores. Those subcellular structures act as anchors and templates that can facilitate, direct and organize reactions inside cells. Cellular reactions primarily include protein conformational changes, modifications, interactions and enzymatic conversions. Multiple, interlinked reactions can generate higher order functions in reaction networks such as homeostasis, excitation or oscillations (Box 1, top and middle panels)6. Coupling of those reactions with transport processes enables transmission of information inside cells and thereby plays a central role in signal communication and integration. Such systems can also generate patterns and gradients at the scale of cells that can control local states of signal network activity, for example to locally control force generation during cell migration or filament polymerization during the formation of the mitotic spindle (Box 1, bottom panels)7-9. Figure 1: Double logarithmic spatio-temporal map of cellular processes (red) and experimental perturbation methods (blue). Shades of red represent elementary reactions and higher order processes (dark red) or material and information transport processes (light red). The ranges highlighted by the respective areas are based on quantitative data from the bionumbers repository1-3. Shades of blue represent general ranges for the spatio-temporal precision of experimental perturbation strategies. White, dashed arrows represent the spatio-temporal relation of transport processes. Distinct slopes of dashed arrows originate from relationships that scale linearly (nerve conduction, transport and signal propagation) or with the square root of time (diffusion). Specific examples are highlighted at the corresponding coordinates of the double logarithmic map based on the following sources: 1: Carbon monoxide-dependent myoglobin conformational changes10; 2: Action potential back propagation11; 3: Action potential propagation in myelinated axons12; 4: Phototransduction in the visual system13; 5: Diffusion of green fluorescent protein (GFP) in mammalian cytoplasm; 6: Ca2+ wave propagation14; 7: Cytoskeletal reorganization (i.e. actin turnover in the lamellipodium15); 8: Maturation of GFP16, 17; 9: Fast axonal transport18; 10: Cell cycle19; 11: Lightinduced channelrhodopsin-2 opening20; 12: Molecular activity painting21; 13: CRISPR/Cas9 induced gene inactivation: The rate-limiting step in typical gene inactivation methods that directly target genes or transcripts such as CRISPR/Cas9 or RNA interference (RNAi) is the half-life of the gene product (CRISPR: Clustered Regularly Interspaced Short Palindromic Repeats; Cas9: CRISPR-associated). The median protein half-life in non-dividing mammalian cells was determined to be ~36h22.

Spatial and temporal scales of cellular signal network processing At the molecular level, intracellular reactions occur in a wide range of time regimes spanning from picoseconds to years (Figure 1)1-4. Higher-order cellular processes that are based on those reactions require exchange of reactants or information over larger spatial dimensions (Figure 1)1-4. This exchange can be facilitated by diffusion, which scales with the square of the distance. At the spatial scales that are relevant for typical mammalian cells (1–10µm), ACS Paragon Plus Environment

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diffusion of proteins occurs within several seconds in the cytosol and up to several minutes for transmembrane proteins. This puts a lower limit on intracellular reactions, if reactants are not available locally in sufficient amounts. Cellular structures that locally enrich reactants can overcome those limitations. This enrichment can be mediated by organelles or other supramolecular structures at subcellular scales or by adapter or scaffold proteins at the molecular scale7, 23, 24. To enrich proteins over greater distances, such as in axons, directional protein transport via molecular motors is much more efficient than diffusion, as it scales linearly with the distance. Similarly, spatial transmission of the activity state of molecules is slow and inefficient if it is achieved by diffusion alone. Similar to the direct transport of activated molecules by motors, propagation of information via combined reaction-diffusion systems also scales linearly with distance and can reach very fast speeds25-27, reaching up to 120 m/s for neuronal action potentials. Targeted spatio-temporal perturbation of signal networks Experimental investigations of cellular processes have to account for the wide range of spatial and temporal scales. Slow perturbations based on genetic manipulations that affect the whole cell, such as overexpression of wild-type or mutant proteins, RNA interference or CRISPR/Cas9 mediated gene editing only offer a limited, indirect view on fast subcellular processes (Figure 1). Conditional genetic manipulations, such as temperature-sensitive mutants, enable more acute perturbations28, however, those again usually affect the entire cell. Traditionally, pharmacological perturbations enable very rapid perturbations, but those typically also affect the cell as a whole. Pharmacological compounds can be uncaged by light or applied locally to specifically affect subcellular regions. Such techniques were used to study chemotaxis and axon targeting, but the spatial precision is limited due to diffusion of small pharmacological compounds (Figure 1). Furthermore, the development of compounds that specifically affect selected cellular components is challenging and therefore not generally applicable. Hybrid methods that combine chemical compound development and genetic manipulations enable much more precise spatio-temporal perturbations by targeting proteins to subcellular structures or directly into the vicinity of their reaction partners at the molecular scale (Figure 1)29. Such a hybrid approach was pioneered using derivatives of the compounds FK506 and rapamycin to induce homo- or heterodimerization of fusion proteins30-32. More recently, optogenetic methods were developed that enable light controlled protein dimerization, which offers even more spatial and temporal control over subcellular perturbations (Figure 1)33-40. However, current optogenetic methods also have limitations. The temporal precision of lightcontrolled perturbations is dependent on several factors, including the rate of light-induced switching in the photosensitive protein domain and the activation and deactivation rate of the associated protein of interest41, 42. While light-induced switching usually occurs very fast within milliseconds, spontaneous dark-state reversal is usually much slower. Interestingly, this reversal time can be tuned over a wide range from seconds to minutes via mutagenesis38. Light-based bi-directional switching can be very rapid33, 41, however, such a strategy requires multiple wavelengths for optical control, which is experimentally challenging and further limits the available wavelengths to measure cellular responses to perturbations. It is important to note that the light-based switching of the photosensitive domain is only a means to manipulate an associated protein activity. The activation and deactivation of this associated protein is achieved by the specific design of the perturbation construct. This process can be in ACS Paragon Plus Environment

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the millisecond range if it is mediated by an intracellular protein interaction or conformational change that is directly linked to the photosensitive domain (Figure 2A)43, 44. The Light-oxygen voltage (LOV2) domain is a typical example in which conformational changes occur in the microsecond to millisecond range. This conformational change was successfully used to control the activity of fused proteins, including the small GTPase Rac143 and the potassium channel Kcv45. However, the design of such constructs has to be optimized for each type of perturbation and therefore this strategy is not generally applicable. Protein activity modulation by optogenetically controlled protein targeting can be applied more generally33, 38, 41, but perturbation rates are limited by diffusion and local availability of the dimerization partner (Figure 2B)42. Unhindered diffusion from the cytoplasm to the cytoplasmic surface of an organelle or to the plasma membrane can be very fast (50ms to 1s for typical, small, soluble proteins21). However, if the protein of interest undergoes hindered diffusion, for example due to interactions with larger complexes or organelles, or due to limited mobility through nuclear pores, targeted recruitment is significantly slower. Figure 2: Limits to temporal precision of acute perturbations. (A) Temporal limits of photochemical reactions and associated protein conformational changes. Illumination induces a conformational change of the photosensitive domain (red), which alters the accessibility of effectors to a protein of interest (POI). (B) Temporal limits of protein recruitment kinetics between two interacting domains (D1 and D2) in typical mammalian cells.

The spatial precision of perturbations is primarily limited by diffusion and inactivation rates of the activated molecules (Figure 3A)41, 42. This is especially problematic if chemical dimerizers, such as caged rapamycin, are activated irreversibly via photouncaging46, 47. Due to their fast diffusion, those photouncaged small molecules become evenly distributed within seconds in typical mammalian cells. In addition, the dimerized complex can also diffuse, although slower due to its size. Reversible, light-based activation of photosensitive proteins enables much more precise spatial control at the scale of several micrometers33, 41-43, as activated molecules revert back to the dark state once they diffuse away from the site of illumination. However, in this configuration, patterned continuous or repeated, pulsed illumination is necessary for prolonged perturbation (Figure 3B). Figure 3: Limits to spatial precision of acute perturbations. (A) Spatial limits imposed by free diffusion of activated chemical dimerizers. (B) Spatial limits in reversible photoswitching reactions. The precision is a function of the diffusion coefficient D and the inactivation half-time toff of the photoactivated molecule. (C) Spatial limits for immobilized perturbations, in which diffusion of activated molecules is suppressed. D1 and D2: Dimerization domains; POI: Protein of interest.

As discussed above, the achievable spatio-temporal precision of protein activity perturbation is dependent on many parameters that differ widely between distinct experimental strategies, including light/dark state switching kinetics, diffusion and kinetics of recruitment or conformational changes. Useful predictions of spatio-temporal precision therefore have to account for all those specific parameters. A recent study that addressed the spatio-temporal precision of an optogenetic system based on the interaction between CRY2 (Cryptochrome 2) and CIBN (CIB1 N-terminal fragment) illustrates this complexity and shows how mathematical modeling can be used to make reasonable predictions for spatio-temporal perturbations42. ACS Paragon Plus Environment

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An alternative to overcome diffusion-based limitations in the spatial precision of perturbation is the immobilization of activated molecules (Figure 3C). This can be achieved by dimerization-mediated targeting to stable scaffolds21, 48, such as the kinetochore, or by induced clustering of molecules49. However, stable targeting to the plasma membrane is especially difficult, as lateral diffusion leads to rapid blurring of local perturbations. The plasma membrane is a central hub for the integration of external stimuli and internal state and for organizing spatio-temporal signal networks in cell morphogenic signaling. Therefore, precise spatio-temporal perturbations are needed to study signaling at this cellular structure. Although the plasma membrane itself is a very fluid compartment, perturbations can be immobilized on the cell substrate via artificial receptors21, thereby basically abolishing their lateral diffusion (Figure 3C). This strategy was used to sufficiently immobilize photocaged chemical dimerizers to enable irreversible, stable perturbations that were induced with a single pulse of light21. With this strategy, patterned uncaging enabled a new technology that was termed “Molecular Activity Painting”, by which arbitrary, stable perturbation patterns were generated in living cells21. Here, spatial precision is independent of the inactivation half-time and only limited by diffraction of the activation beam or by the precision of surface patterning of the surface-linked immobilization anchor. Table 1 summarizes the specificity, as well as the temporal and spatial precision, that is achievable by various perturbation strategies. Table 1: Summary of selected perturbation methods.

Perturbation method

Specificity

Temporal precision

Spatial Precision

Genetic, chronic

high

hours

entire cell

Genetic, temperaturesensitive mutants28

high

seconds

entire cell

Pharmacological, photocaged50

often unclear

millisecondsminutes

micrometersentire cell

Chemical dimerization, photouncaged47

high

millisecondsminutes

micrometersentire cell

Reversible photoactivation43 (controlled by a single wavelength)

high

millisecondsminutes

micrometers

Reversible photoswitching33

high

millisecondsminutes

micrometers

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Comments Temporal precision dependent on mRNA/protein turnover Temporal precision is limited by speed of temperature switching Stability of local perturbations dependent on diffusion Stability of local perturbations dependent on diffusion Spatial precision dependent on diffusion and activation/deactivat ion kinetics Spatial precision dependent on 5


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(controlled by multiple wavelengths) Immobilized photocaged dimerizer21, 48

high

millisecondsminutes

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micrometers

diffusion and switching kinetics Perturbation is irreversible

Monitoring signal network activity The activity of signal network components is proportional to their local concentration and their activity state (Figure 4A). In the most prominent case of signal proteins, the concentration and localization of fluorescent fusion proteins can be measured via microscopy. To also obtain information about the activity state of a signal protein, changes in its conformation or its ability to interact with other proteins can be measured via multiple protein localizations or specifically designed sensor constructs (Figure 4B)7, 51. Overexpressed fluorescent proteins or sensor constructs usually only act as a proxy for the measurement of concentration, localization and activity state, as they differ from the endogenous signal network components. It is thus central to use proper controls to critically evaluate what those proxies actually measure and how they might positively or negatively affect the signal network under study. For example, measurements based on substrate-based sensors only convey a blurred picture of the localization of the corresponding enzymatic activity, if sensor diffusion dominates over sensor reaction kinetics52. Anchoring of sensors to stable scaffolds can reduce this limitation52. In addition, computational modeling can help to integrate information from distinct measurements into a unified model that also can account for experimental limitations7, 42. Figure 4: Activity of signal network components and their experimental investigation. (A) Analysis of cellular protein activity by measuring relative protein concentration and localization via scanning confocal or total internal reflection fluorescence (TIRF) microscopy and by monitoring the protein activity state. (B) Selection of methods to monitor the protein activity state in living cells. The active and inactive state of the protein of interest is shown in grey and orange, respectively. Fluorophores are shown in cyan, yellow or green. Solid arrows illustrate conversions or relocalization. Dotted arrows illustrate diffusive mobility.

Typical sensors that are used to measure protein activity states detect changes in protein conformation or protein interactions using fluorescent resonance energy transfer (FRET)53, 54 or circular-permuted fluorescent proteins (cpFPs)55, 56. Spatio-temporal coincidence (i.e. correlation in space and in time) between two distinct protein species can also be used to characterize protein complex formation in cells57. However, if simple microscopy-based colocalization analysis or fluorescence cross-correlation spectroscopy (FCCS)58 is used to measure coincidence, direct or indirect interactions cannot be distinguished due to the lack of spatio-temporal resolution. Super-resolution, multi-color single molecule tracking can overcome those limitations59, however in this case, only sparse signals can be measured and achieving high temporal resolution that is necessary for such analyses is technically challenging. Furthermore, those strategies listed above usually employ measurements at multiple wavelengths to obtain a single activity measurement (Figure 4B). Due to the broad excitation and emission peaks of typical fluorescent proteins, simultaneous observations of multiple protein activities are very limited. However, protein activity state can also be measured by monitoring a single wavelength. For ACS Paragon Plus Environment

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example, complementation of split fluorescent proteins enables measurements of protein interactions60. Unfortunately, the complementation reaction is irreversible, which often limits the spatial precision and general applicability of this approach. Alternatively, protein activity dynamics can be measured by simple translocation-based sensors 5, 21, 33, 42, 61-66. For example, such sensors can directly bind to the signal molecule of interest, such as the active form of a GTPase5, 21, 33, 42, 61, 62, or a modified signaling lipid64-66 (Figure 4B). In this case, local differences in protein activity can be measured on a subcellular level. However, the affinity of such sensors needs to be optimized to minimize delays in sensor recruitment and dissociation. Alternatively, sensors can be designed to convert a protein activity into a measurable cellular translocation. For example, nuclear import/export signals can be modulated by posttranslational modifications, to report on signal protein activities. Kinase translocation reporters (KTR) are an example for this strategy, which enable measurements of kinase activity at the cellular level63 by monitoring nucleocytoplasmic transport. Thus, with this strategy, a change in protein activity that is not directly associated with a measurable protein translocation can be visualized indirectly. However, translocation could target sensors away from the signal proteins of interest, which might limit their general applicability. Interestingly, these approaches, enable measurements of endogenous protein activity. However, care must be taken, that translocation-based sensors do not interfere with the activity of endogenous proteins67, for example by expressing them at very low levels, combined with sensitive measurements of protein translocation5, 21, 33, 42, 62. Table 2: Summary of selected methods for monitoring protein activity in living cells.

Activity monitoring method

Limitations in spatiotemporal precision

FCCS58

Measurements are only performed at a single or few locations simultaneously.

FRET-based measurement of protein interactions7, 51

Overexpression might limit spatial and temporal precision.

FRET-based sensors, conformation sensing7, 51

Overexpression might limit spatial and temporal precision. Complex design might influence activation kinetics. Diffusion of sensor might blur spatial precision of protein activity measurements. Overexpression might limit precision. Complex design

FRET-based sensors, substrate-based7, 51

cpFP-based sensors, conformation sensing55, 56

Additional comments Cannot distinguish between direct or indirect interactions. Enables direct measurement of complex and component concentrations. Enables quantitative measurements of the interacting fraction in combination with FLIM. Enables quantitative measurements of active/inactive fraction in combination with FLIM. Can be used to measure endogenous protein activity.

Enables quantitative measurements of active/inactive


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Multi-color single particle tracking59

might influence activation kinetics. Sparse measurements can limit both spatial and temporal precision.

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fraction.

Enables direct observation of protein interactions and direct measurements of interaction kinetics. Irreversible activation limits Single wavelength sufficient for Bimolecular fluorescence complementation60 measurements of dynamic activity measurement. processes. Translocation-based Diffusion of sensors delays Can be used to measure sensors, activity measurements. endogenous protein activity. 5, 21, 33, direct translocation Single wavelength sufficient for 42, 61, 62 activity measurement. Kinase translocation Transport of sensors Can be used to measure reporters 63 delays activity endogenous protein activity. measurements. Spatial Single wavelength sufficient for precision limited to the activity measurement. cellular level. Abbreviations: FCCS: fluorescence cross-correlation spectroscopy FRET: fluorescent resonance energy transfer; FLIM: Fluorescence lifetime imaging; cpFPs: circular-permuted fluorescent proteins. Investigation of biological function by correlation and perturbation-response analysis To offer an un-blurred view into signal network dynamics, the method of investigation has to offer sufficient precision to match the relevant spatial and temporal scales of the biological process of interest. Table 2 summarizes the spatial and temporal precision that is typically achieved with various methods to monitor signal network dynamics. Simple monitoring of a single network activity in an unperturbed, resting cell might give some initial insight, however, signal network states are usually controlled by multiple signal activities, that are often interlinked via feedback mechanisms (Box 1)6. Thus, measuring a single activity is often not sufficient to define the state of a dynamic signal network. In particular, translocation-based sensors, that only require a single wavelength to readout signal activity, can be combined to detect spatio-temporal relations between multiple signal network components. This information can be used to define the state of dynamic signal networks more precisely, and thereby sharpen our view on more complex, interlinked, dynamic signal networks. However, the number of activities that can be monitored simultaneously is still limited by the range of fluorescent proteins that can be distinguished via microscopy. Thus, direct investigations of correlative relationships are only feasible for relatively small signal networks. One strategy to limit this problem is spectral unmixing68, which enables independent monitoring of at least 6 fluorescent species in living cells69. Another strategy to overcome this limitation is to spatially segregate distinct protein interactions, for example by constructing a miniaturized protein interaction array inside a single, living cell (Figure 4B)70. This technology is based on bait-presenting artificial receptor constructs (bait-PARCs) that transfer extracellular patterns of immobilized antibodies into corresponding intracellular patterns of bait proteins. Thereby, the identity of distinct bait


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proteins can be encoded by their spatial arrangement, analogously to macroscopic protein interaction arrays. In principle, immobilization technologies exist that enable patterning at the submicrometer scale71, 72. This would allow generation of very dense protein interaction arrays that could be used to monitor protein interactions via TIRF microscopy, thus enabling parallel measurements of many signal network activities inside an individual, living cell. To push this even further, super resolution microscopy73, 74 could be used to overcome detection limits at array densities that are beyond the diffraction limit. While monitoring multiple signal network activities in resting cells can offer a sharpened view into signal network states, the analysis of correlations can only show that individual signal network components are somehow related to each other. To uncover how those components are linked, cause and effect relationships can be investigated via perturbation-response analyses. The three features of perturbations that are summarized in Table 1 are particularly important to sharpen our view in studying dynamic signal networks: Their specificity, their spatial and their temporal precision. First, analysis of causal relationships is limited if perturbations lack in specificity. Furthermore, a suitable perturbation strategy should achieve the required level of spatio-temporal precision to match the temporal and spatial scales most relevant for the biological process of interest (Figure 1). Perturbations that cannot fulfill those criteria will only offer a blurred picture, and thereby make experimental interpretations of measured cellular responses more difficult. Due to limitations in the range of wavelengths that can be used in microscopy, signal network measurements via translocation-based sensors that only require a single wavelength are particularly well suited for combined perturbation-response analysis. For example, such sensors were combined with acute, optogenetic protein activity perturbations technologies33, 42 or light-controlled chemical/genetic hybrid technologies21, to analyze responses to specific signal network perturbations with high spatio-temporal precision. Conclusion Recent advances in activity sensor development and chemical and optogenetic perturbation techniques opened new possibilities to directly investigate signal networks in living cells. However, it is still challenging to gain a deeper understanding of more complex, higher-order cell functions due to the large number of interconnected components and their reciprocal, dynamic interplay in space and time. Computational modeling can help to integrate experimental knowledge to infer endogenous signal networks in cells. Experimental strategies that combine both light-based perturbation and light-based activity measurements enable direct insight into signal network dynamics. However, their implementation is challenging due to the limited range of wavelengths that is compatible with light microscopy. Combining chemical synthesis with genetic modifications widens the choice of wavelengths and enables new experimental strategies, such as immobilized perturbation and protein interaction arrays in living cells. Those hybrid methods thereby sharpen our view on dynamic signal networks by increasing spatio-temporal precision for perturbation-response analyses and resolving uncertainties in the determination of signal network states that are controlled by multiple interlinked components.

Author information


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Corresponding author Phone ++49-231-755-7057. E-mail: [email protected] ORCID Leif Dehmelt 0000-0002-6559-6496 Acknowledgements We thank P. Bastiaens (MPI Dortmund) for fruitful discussions and M. Schmick (MPI Dortmund), P. Nalbant (University Duisburg-Essen) and M. Gräßl (University Duisburg-Essen) for critical reading of the manuscript. We also thank M. Schmick for providing the framework to perform cellular automata simulations. Keywords: Signal network: A network of multiple components that functionally interact. The causal relationships between those components can be represented graphically by the network topology. Mathematical modeling: Developing and applying theoretical concepts to quantitatively describe cellular processes, including dynamic signal networks. Optogenetics: Designing novel, light-based tools to control cellular function based on genetic manipulations. Photosensitive protein: A protein that changes its functional or structural properties upon irradiation with light. Chemical dimerizer: A small molecule that is able to induce the homo- or heterodimerization of two fusion proteins. Photoactivation: Light-based activation of molecules, for example uncaging of small molecules. Activity sensors: Designed constructs or experimental strategies to directly or indirectly measure activities of cellular components. PARC: A bio-orthogonal artificial receptor, which is immobilized by an extracellular ligand, presenting a functional group in the cytosol, i.e. a bait- or dimerizer-Presenting Artificial Receptor Construct (bait-PARC or dimerizer-PARC).

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Box 1: Spatio-tempeoral information processing in signal networks. 188x253mm (300 x 300 DPI)



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Figure 1: Double logarithmic spatio-temporal map of cellular processes (red) and experimental perturbation methods (blue). 81x47mm (300 x 300 DPI)


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Figure 2: Limits to temporal precision of acute perturbations. 57x23mm (300 x 300 DPI)



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Figure 3: Limits to spatial precision of acute perturbations. 53x20mm (300 x 300 DPI)


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Figure 4: Activity of signal network components and their experimental investigation. 67x32mm (300 x 300 DPI)



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Table of contents graphic 39x19mm (300 x 300 DPI)


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Deblurring Signal Network Dynamics - ACS Chemical Biology (ACS

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