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Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches

Nature Reviews Neuroscience volume 11, pages 239–251 (2010)Cite this article

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Key Points

  • Activity-dependent plasticity of synaptic strength and membrane excitability are important for learning and memory. It is, however, not well understood how activation of the subcellular signalling cascades necessary for neural plasticity is controlled by ongoing neuronal activity; neither is it known how the plasticity in turn shapes neuronal network dynamics. Several computational approaches used in systems biology are of particular relevance for analysing the molecular mechanisms underlying neuronal plasticity, and for predicting which experiments would be most crucial in improving our understanding.

  • One important methodological approach is to simulate biochemical reactions using ordinary differential equations that describe how molecule concentrations change over time. Several parameters in these models correspond to experimentally measurable values such as reaction rates and affinities. Simulations of signalling networks have shown that properties emerge that are not present in individual pathways, such as the generation of distinct outputs depending on input strength and duration, threshold switches and self-sustaining feedback activation.

  • Stochastic effects need to be taken into account when simulating molecular systems in small volumes such as a dendritic spine or the postsynaptic density. Stochastic models have shown that the conclusions reached from deterministic models might be invalid for small volumes or for systems with few molecules.

  • The spatial morphology of neurons needs to be considered in modelling because, for example, diffusion of some molecules combined with local reactions or anchoring of molecules can create microdomains or concentration gradients of these molecules, which can lead to unexpected behaviour.

  • New techniques in live-cell imaging allow high-resolution data in the spatial and temporal domain to be obtained. These imaging data are important for constraining model parameters during the development of both deterministic and stochastic models.

  • In the future, novel multi-scale approaches will move the field forward. Integration of biochemical signalling cascades within multi-compartmental neuronal models of electrical activity can address how activation of signalling pathways are controlled by neuronal activity, and also how activation of subcellular kinases and phosphatases changes membrane excitability or membrane potential through the control of ion channels and receptors.

Abstract

Synaptic plasticity is thought to underlie learning and memory, but the complexity of the interactions between the ion channels, enzymes and genes that are involved in synaptic plasticity impedes a deep understanding of this phenomenon. Computer modelling has been used to investigate the information processing that is performed by the signalling pathways involved in synaptic plasticity in principal neurons of the hippocampus, striatum and cerebellum. In the past few years, new software developments that combine computational neuroscience techniques with systems biology techniques have allowed large-scale, kinetic models of the molecular mechanisms underlying long-term potentiation and long-term depression. We highlight important advancements produced by these quantitative modelling efforts and introduce promising approaches that use advancements in live-cell imaging.

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Figure 1: Signalling pathways underlying synaptic plasticity.
Figure 2: Signalling pathway motifs and computational units.
Figure 3: Spatial representation of a dendrite plus multiple dendritic spines are required to address input specificity and microdomains.
Figure 4: The role of proteins and spatial localization in long-term potentiation.
Figure 5: Computational neuroscience in the future integrates many scales and diverse modelling approaches.

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Acknowledgements

J. H. K. was supported by the Swedish Research Council and the Parkinson's Foundation, and K. T. B. was supported by an HFSP programme grant, the joint NSF-NIH CRCNS programme through NIH grant R01 AA16022 and RO1 AA18060. The authors thank T. Abel and R. Evans for comments on an earlier version of this manuscript.

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Authors and Affiliations

  1. Royal Institute of Technology, School of Computer Science and Communication, AlbaNova University Center, Stockholm, 106 91, Sweden

    Jeanette Hellgren Kotaleski

  2. Molecular Neuroscience Department, Krasnow Institute of Advanced Studies, George Mason University, Fairfax, 22030, Virginia, USA

    Kim T. Blackwell

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  1. Jeanette Hellgren Kotaleski
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Correspondence to Jeanette Hellgren Kotaleski.

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Computational and Experimental Neuroplasticity Laboratory

Glossary

Dynamics

Time-dependent changes in the activity or quantity of a variable.

Kinetics

The rates at which reactions or diffusion occur.

Systems biology

An inter-disciplinary field that focuses on the relationships and interactions between components of complex biological systems.

Current clamp mode

Used for measuring membrane potential in response to current injection.

Markov kinetic model

A description of ion channel gating in which the change in channel conformation from one state to another depends only on the present state and not on previous states.

Cable equation

Describes the flow of electrical current along a wire, dendrite or axon.

Low-pass filter

A filter that reduces the amplitude of high-frequency signals while leaving low-frequency signals unaltered.

Resistive–capacitive models

Modelling a small neuronal compartment as a resistive element in parallel with a capacitor, and modelling dendrites as a connected set of these elements.

Network or graph theory

The study of the interconnectedness of related items. The nodes or vertices of a graph are individual items, and two nodes are linked with an edge if they influence each other.

Power law

The relationship between the dependent variable, y, and the independent variable, x, is described by y = x^(−k).

Bistability

The existence of two stable or equilibrium states for the same input. Hysteresis is required for a system to achieve bistability.

Boolean logic network

A network with nodes that take one of two values — for example, activated or non-activated genes. Dynamics of the network are simulated using logical operations coupled with explicit time delay.

Exact stochastic simulation algorithm

An algorithm for the numerical solution of the state of a biochemical reaction system, which therefore produces a realistic actualization of the biochemical reactions.

Stochastic simulations

Simulations that use random-number generators — that is, Monte Carlo methods that are governed by rules of chance.

Deterministic model

A model that calculates quantities using differential equations and algebraic equations.

Glutamate uncaging

The process by which chemically caged glutamate is released by focal light. It is used to study the effects of postsynaptic activation with high temporal and spatial control.

Partial differential equation

An equation that describes how the change in one variable depends on several other variables such as time and space.

Molecular crowding

A phenomenon in which the volume occupied by molecules is so large that molecules cannot diffuse freely in the cytoplasm. Reactions are either impeded or enhanced depending on the proximity of reacting molecules.

Stochastic resonance

A mechanism by which noise added to a system enhances the signal to noise ratio.

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Kotaleski, J., Blackwell, K. Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches. Nat Rev Neurosci 11, 239–251 (2010). https://doi.org/10.1038/nrn2807

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