Hans Liljenström

Translation rate modification by preferential codon usage: Intragenic position effects

We present a model for calculating the protein production rate as a function of the translation rate. The model takes into account that the elongation rate along an mRNA molecule is non-uniform as a result of different tRNA availabilities for different codons. Initiation of ribosomes on an mRNA is normally the rate-limiting step in the translation process, and blocking of the initiation site can be avoided if the codons closest to this site allow fast translation by the ribosome. Hence, different selective forces may act on the choice of synonymous codons in the initiation region than elsewhere on a given mRNA. We show that the elongation rate along the whole mRNA influences the production rate of abundant proteins, whereas only the elongation rate in the initiation region is of importance for the production rate of rare proteins. We also present an analysis of the codon distribution along known mRNAs coding for abundant and rare proteins.

Regulating the nonlinear dynamics of olfactory cortex

The dynamic behaviour of cortical structures can be changed significantly in character by different types of neuromodulators. We simulate such effects in a neural network model of the olfactory cortex and analyse the resulting nonlinear dynamics of this system, including during both learning and recall. The model has simple network units and realistic network connectivity. The input-output relation of populations of neurons is represented as a sigmoid function, with a single parameter determining threshold, slope and amplitude of the curve. This parameter can be thought of as corresponding to the concentration of a particular neuromodulator in the system. It can also be related to the level of arousal of an animal. By varying this ‘gain parameter’ we show that the model can give point attractor, limit cycle attractor and strange chaotic or non-chaotic attractor behaviour. We also display ‘transient chaos’ phenomena, which begin with chaos-like behaviour but eventually converge to a limit cycle. We demonst...

Autonomous learning with complex dynamics

Traditionally, associative memory models are based on point attractor dynamics, where a memory state corresponds to a stationary point in state space. However, biological neural systems seem to display a rich and complex dynamics whose function is still largely unknown. We use a neural network model of the olfactory cortex to investigate the functional significance of such dynamics, in particular with regard to learning and associative memory. the model uses simple network units, corresponding to populations of neurons connected according to the structure of the olfactory cortex. All essential dynamical properties of this system are reproduced by the model, especially oscillations at two separate frequency bands and aperiodic behavior similar to chaos. By introducing neuromodulatory control of gain and connection weight strengths, the dynamics can change dramatically, in accordance with the effects of acetylcholine, a neuromodulator known to be involved in attention and learning in animals. With computer simulations we show that these effects can be used for improving associative memory performance by reducing recall time and increasing fidelity. the system is able to learn and recall continuously as the input changes, mimicking a real world situation of an artificial or biological system in a changing environment.

Spontaneously active cells induce state transitions in a model of olfactory cortex

The existence of neurons with intrinsic oscillations does not in itself explain the synchronization of local populations of neurons, but it is likely to pace population rhythms when the neurons are suitably coupled by chemical and/or electrical synapses. In the present study, we have investigated the role of spontaneously active cells as noisy or pacemaker units in setting global oscillations in a three-layered cortical model. The presence of a small number of noisy (spontaneously active) units induce oscillations at the network level in the range of the gamma rhythm. The number of noisy units in the network and their type (excitatory or inhibitory or excitatory and inhibitory together) determines the emergence of regular oscillations or aperiodic (chaotic) behaviour. It also determines the onset of the global behaviour. On replacing a noisy unit by a pacemaker unit, similar gamma oscillations were generated. With both noisy and pacemaker units, we found that certain characteristics of the spontaneous activity determine the delay period for the onset of global activity. Preliminary studies have been carried out with spontaneously active units having a chaotic dynamics but the results are much similar to that with a noisy burst. Different functional roles have been suggested for cortical oscillations, such as determining global functional states and specifying connectivity during development. Oscillations at different frequency bands, in particular in the gamma band (around 40 Hz), have also been associated with memory and attention. The presence of spontaneously active neurons, either with noisy or oscillatory activity, could be responsible for global oscillations in the absence of external stimuli in certain cortical areas in the mature brain.

The tRNA cycle and its relation to the rate of protein synthesis

With the aid of a kinetic model, we have investigated how the adaptation between the various components of the tRNA cycle and the codon frequencies affects the rate of protein synthesis. Depending on the relative amounts of total tRNA, synthetase and ribosomes, the optimal correlations vary between a situation where all tRNA species are either present in equal amounts or are present in amounts proportional to the square-root of the corresponding codon frequencies, and a situation where the amounts of the different tRNA species present are linearly proportional to the codon frequencies.

Theoretical modelling of protein synthesis.

This article provides an overview of the use of mathematical and computer modelling in furthering the understanding of protein synthesis. In particular, we discuss issues such as the nature of the rate limiting step(s), error rates, tRNA-codon adaptation, codon bias, attenuation control, and problems of selection and error corrections, focussing on their theoretical treatment.

Investigating amplifying and controlling mechanisms for random events in neural systems

Microscopic and individual events in the nervous system, such as single channel openings and individual action potentials, often drown in the summed activity of the surround. Yet, under certain circumstances such events can be amplified and have meso— and macroscopic effects. We use experimental as well as computational methods to investigate mechanisms by which neural systems can amplify weak signals and individual events, and control the system at a larger scale. By this multilevel approach we try specify the rules and constraints for the resulting state transitions.

A cortical network model for clinical EEG data analysis

Abstract We use computational models of neo-cortex to investigate how cortical neurodynamics may depend on network properties and on intrinsic and external signals and fluctuations. We have previously demonstrated plausible relations between structure, dynamics and function of a neural network model of paleo-cortex, and now use a similar paradigm for the neo-cortical case. We investigate the role of various network properties and external input on electroencephalography (EEG) dynamics, in particular relating to electroconvulsive therapy (ECT) of patients with major depression. Our results are suggestive for the neural mechanisms underlying EEG, as well as for the dynamical effects of ECT on human EEG.

Modelling ECT effects by connectivity changes in cortical neural networks

Biomathematical methods were applied to investigate how cortical neurodynamics depends on network connectivity. In particular, we study changes in the EEG pattern of depressed patients, following electroconvulsive therapy (ECT). The aim is to gain a better understanding of the neural mechanisms responsible for these changes, which include clear phase shifts in the EEG dynamics. This understanding is intended to provide clinical guidance in predicting ECT dose and response in depressed patients.

Analysis of phase shifts in clinical EEG evoked by ECT

We propose a new strategy for studying the phase shifts of electroencephalography (EEG) after electroconvulsive therapy (ECT) of patients with major depression. We divide each ECT EEG time series into four phases and calculate the power spectrum and coherence of left and right prefrontal EEGs for each phase. Previously, we have qualitatively demonstrated certain ECT EEG dynamical patterns by using a neo-cortical neural network model. Now we quantitatively analyze the dynamical phase shifts of the ECT EEG data. Our results are suggestive for a deeper understanding of the ECT EEG patterns and for building more realistic cortical models.