1,721,032 research outputs found
How to record a million synaptic weights in a hippocampal slice.
No full textA key step toward understanding the function of a brain circuit is to find its wiring diagram. New methods for optical stimulation and optical recording of neurons make it possible to map circuit connectivity on a very large scale. However, single synapses produce small responses that are difficult to measure on a large scale. Here I analyze how single synaptic responses may be detectable using relatively coarse readouts such as optical recording of somatic calcium. I model a network consisting of 10,000 input axons and 100 CA1 pyramidal neurons, each represented using 19 compartments with voltage-gated channels and calcium dynamics. As single synaptic inputs cannot produce a measurable somatic calcium response, I stimulate many inputs as a baseline to elicit somatic action potentials leading to a strong calcium signal. I compare statistics of responses with or without a single axonal input riding on this baseline. Through simulations I show that a single additional input shifts the distribution of the number of output action potentials. Stochastic resonance due to probabilistic synaptic release makes this shift easier to detect. With approximately 80 stimulus repetitions this approach can resolve up to 35% of individual activated synapses even in the presence of 20% recording noise. While the technique is applicable using conventional electrical stimulation and extracellular recording, optical methods promise much greater scaling, since the number of synapses scales as the product of the number of inputs and outputs. I extrapolate from current high-speed optical stimulation and recording methods, and show that this approach may scale up to the order of a million synapses in a single two-hour slice-recording experiment
Molecular computation in neurons: a modeling perspective
No full textNeurons perform far more computations than the conventional framework of summation and propagation of electrical signals from dendrite to soma to axon. There is an enormous and largely hidden layer of molecular computation, and many aspects of neuronal plasticity have been modeled in chemical terms. Memorable events impinge on a neuron as special input patterns, and the neuron has to decide if it should ‘remember’ this event. This pattern-decoding decision is mediated by kinase cascades and signaling networks over millisecond to hour-long timescales. The process of cellular memory itself is rooted in molecular changes that give rise to life-long, stable physiological changes. Modeling studies show how cascades of synaptic molecular switches can achieve this, despite stochasticity and molecular turnover. Such biochemically detailed models form a valuable conceptual framework to assimilate the complexities of chemical signaling in neuronal computation
Biochemical signaling networks decode temporal patterns of synaptic input
No full textSynapses exhibit a wide repertoire of responses to different temporal patterns of synaptic input. Many of these responses are expressed as short and long-term changes in synaptic strength. Electrical properties of channels and calcium buildup can account for rapid aspects of pattern decoding, but it is not clear how more complex input patterns, especially those lasting over many minutes, could be discriminated. This paper shows that a network of signaling pathways can discriminate between complex input patterns lasting tens of minutes, and can give rise to distinct combinatorial patterns of biochemical signaling activity in pathways involved in synaptic change. Regulatory signaling input can alter and even reverse the strengths of responses to input patterns. Thus the synaptic signaling network may function as a temporal decoder that transforms patterns from the time domain into the domain of chemical signaling. This may underlie different synaptic responses to different stimulus patterns
Temporal computation by synaptic signaling pathways
Get PDFSynaptic signaling comprises a complex molecular network. Such networks carry out diverse operations such as molecular logic, signal amplification, memory and other aspects of cellular decision-making ([Bray, 1995]). The synapse in particular encounters complex input patterns that have different temporal sequences. Different input patterns to the synapse are known to give rise to a range of synaptic responses, including facilitation, depression and various forms of short and long-term potentiation. In many cases the stimuli that generate these disparate responses are tens of seconds or more in length, much greater than the typical time-courses of calcium dynamics. In this paper I propose that the synaptic signaling network can perform temporal computation operations such as tuning for stimulus duration or interval. Using simulation methods I show that the simple time-courses of individual signaling pathways combine in the network to give rise to different temporally selective responses. Downstream pathways that exhibit temporal integration or amplitude thresholding select different input patterns and thus perform temporal computation
Managing models of signaling networks
No full textSignaling pathways participate in complex information processing networks. These networks handle housekeeping functions of the cell as well as specialized functions such as synaptic plasticity. I report two developments in managing such networks: a compilation of mass-action kinetic models of signaling pathways, and shared motifs in the chemistry of interactions between signaling pathways. These motifs may prove useful in abstracting signaling networks, without compromising chemical reaction details. The combination of a library of signaling pathway models, and high-level rules to connect these pathways, may simplify development of complex signaling network models
The chemical organization of signaling interactions
Get PDFMotivation: Cellular chemical signaling pathways form complex networks that are beginning to be studied at the level of chemical kinetics and databases of reactions. Chemical reaction details are traditionally represented as lists of reactions and rates. This does not map readily to the block diagram representation familiar to biologists, and obscures the functional organization of signaling networks. This study examines motifs in signaling chemistry and reports common features that may help to formalize such a mapping between pathway block diagrams and the chemistry. The same motifs may facilitate data representation and provide functional abstraction of the chemistry. Results: I classified 74 interactions between 25 signaling pathways in terms of shared chemical motifs. All interactions in this dataset consist of a few communicating molecules from one set of pathways, and a replicating set of reactions and molecules from another. Each unique combination of interacting pathways duplicates the chemical reaction scheme of this replicating set, but involves different rate constants. Signaling pathways can therefore be described in an object-oriented manner as sets of core reactions with well-defined interfaces between pathways. This generalization lends itself to designing simulators and databases for signaling networks
Signaling in Small Subcellular Volumes. I. Stochastic and Diffusion Effects on Individual Pathways
Get PDFAbstractMany cellular signaling events occur in small subcellular volumes and involve low-abundance molecular species. This context introduces two major differences from mass-action analyses of nondiffusive signaling. First, reactions involving small numbers of molecules occur in a probabilistic manner which introduces scatter in chemical activities. Second, the timescale of diffusion of molecules between subcellular compartments and the rest of the cell is comparable to the timescale of many chemical reactions, altering the dynamics and outcomes of signaling reactions. This study examines both these effects on information flow through four protein kinase regulatory pathways. The analysis uses Monte Carlo simulations in a subcellular volume diffusively coupled to a bulk cellular volume. Diffusion constants and the volume of the subcellular compartment are systematically varied to account for a range of cellular conditions. Each pathway is characterized in terms of the probabilistic scatter in active kinase levels as a measure of “noise” on the pathway output. Under the conditions reported here, most signaling outcomes in a volume below one femtoliter are severely degraded. Diffusion and subcellular compartmentalization influence the signaling chemistry to give a diversity of signaling outcomes. These outcomes may include washout of the signal, reinforcement of signals, and conversion of steady responses to transients
Signaling in Small Subcellular Volumes. II. Stochastic and Diffusion Effects on Synaptic Network Properties
Get PDFAbstractThe synaptic signaling network is capable of sophisticated cellular computations. These include the ability to respond selectively to different patterns of input, and to sustain changes in response over long periods. The small volume of the synapse complicates the analysis of signaling because the chemical environment is strongly affected by diffusion and stochasticity. This study is based on an updated version of a previously proposed synaptic signaling circuit (Bhalla and Iyengar, 1999) and analyzes three network computation properties in small volumes: bistability, thresholding, and pattern selectivity. Simulations show that although there are diffusive regimes in which bistability may persist, chemical noise at small volumes overwhelms bistability. In the deterministic situation, the network exhibits a sharp threshold for transition between lower and upper stable states. This transition is broadened and individual runs partition between lower and upper states, when stochasticity is considered. The third network property, pattern selectivity, is severely degraded at synaptic volumes. However, there are regimes in which a process similar to stochastic resonance operates and amplifies pattern selectivity. These results imply that simple scaling of signaling conditions to femtoliter volumes is unlikely, and microenvironments, such as reaction complex formation, may be essential for reliable small-volume signaling
Understanding complex signaling networks through models and metaphors
No full textSignaling networks are complex both in terms of the chemical and biophysical events that underlie them, and in the sheer number of interactions. Computer models are powerful tools to deal with both aspects of complexity, but their utility goes beyond simply replicating signaling events in silicon. Their great advantage is as a tool to understanding. The completeness of the description demanded by computer models highlights gaps in knowledge. The quantitative description in models facilitates a mapping between different kinds of analysis methods for complex systems. Systems analysis methods can highlight stable states of signaling networks and describe the transitions between them. Modeling also reveals functional similarities between signaling network properties and other well-understood systems such as electronic devices and neural networks. These suggest various metaphors as a tool to understanding. Based on such descriptions, it is possible to regard signaling networks as systems that decode complex inputs in time, space and chemistry into combinatorial output patterns of signaling activity. This would provide a natural interface to the combinatorial input patterns required by genetic circuits. Thus, a combination of computer modeling methods to capture the complexity and details, and useful abstractions revealed by these models, is necessary to achieve both rigorous description as well as human understanding
The chemical organization of signaling interactions
No full textMotivation: Cellular chemical signaling pathways form complex networks that are beginning to be studied at the level of chemical kinetics and databases of reactions. Chemical reaction details are traditionally represented as lists of reactions and rates. This does not map readily to the block diagram representation familiar to biologists, and obscures the functional organization of signaling networks. This study examines motifs in signaling chemistry and reports common features that may help to formalize such a mapping between pathway block diagrams and the chemistry. The same motifs may facilitate data representation and provide functional abstraction of the chemistry. Results: I classified 74 interactions between 25 signaling pathways in terms of shared chemical motifs. All interactions in this dataset consist of a few communicating molecules from one set of pathways, and a replicating set of reactions and molecules from another. Each unique combination of interacting pathways duplicates the chemical reaction scheme of this replicating set, but involves different rate constants. Signaling pathways can therefore be described in an object-oriented manner as sets of core reactions with well-defined interfaces between pathways. This generalization lends itself to designing simulators and databases for signaling networks
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