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U S Bhalla

Publications and source records attributed to U S Bhalla.

7 recordsLinked to original sources

Functional modules in biological signalling networks.

Signalling pathways carry information from the outside of the cell to cellular machinery capable of producing biochemical or physiological responses. Although linear signalling plays an important role in biological regulation, signalling pathways are often interconnected to form networks. We have used computational analysis to study emergent properties of simple networks that consist of up to four pathways, We find that when one pathway gates signal flow through other pathways which produce physiological responses, gating results in signal prolongation such that the signal may be consolidated into a physiological response. When two pathways combine to form a feedback loop such feedback loops can exhibit bistability. Negative regulators of the loop can serve as the locus for flexibility whereby the system has the capability of switching states or functioning as a proportional read-out system. Networks where bistable feedback loops are connected to gates can lead to persistent signal activation at distal locations. These emergent properties indicate system analysis of signalling networks may be useful in understanding higher-order biological functions.

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Complexity in biological signaling systems.

Biological signaling pathways interact with one another to form complex networks. Complexity arises from the large number of components, many with isoforms that have partially overlapping functions; from the connections among components; and from the spatial relationship between components. The origins of the complex behavior of signaling networks and analytical approaches to deal with the emergent complexity are discussed here.

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Emergent properties of networks of biological signaling pathways.

Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.

Animals↗

Multiday recordings from olfactory bulb neurons in awake freely moving rats: spatially and temporally organized variability in odorant response properties.

Chronic single-unit recordings were obtained from the mitral cell layer of the olfactory bulbs of awake freely moving rats placed in an odorant stream. Over periods up to five days, 618 recordings from 186 single neurons were obtained. Responses of individual neurons were found to be quite variable over time, although this variability was below chance and was not incremental. The responses of nearby neurons were more similar than expected by chance but less similar than individual neurons recorded at different times. However, responses of spatially well-separated neurons were more different than chance over short time periods. During rapid sniffing, single-unit responses became more variable, and the spatial organization of responses became less apparent. These results suggest that neuronal responses in the olfactory bulb are generally quite variable over time, with this variability increasing during periods of rapid sniffing. These results are interpreted in the context of a distributed, centrally modulated model of olfactory processing.

Animals↗

Lateralization of membrane-associated protein kinase C in rat piriform cortex: specific to operant training cues in the olfactory modality.

Rats were trained on an olfactory and a control modality (auditory or visual) discrimination task and brain membrane-associated protein kinase C (mPKC) was subsequently assessed using quantitative autoradiography of radiolabelled phorbol ester binding. In rats which received olfactory-cued training, mPKC showed a highly significant lateralization in the piriform cortex but not in the hippocampus. Both olfactory-trained rats and control modality rats showed a significant increase in mPKC in the hippocampus when compared to naive rats. Thus, while behavioral training procedures appeared to result in a hippocampal increase in the activated state of this enzyme as has been reported elsewhere, only olfactory learning produced an piriform cortex lateralization in the activated state of the enzyme. While the functional significance of such a change in the distribution of protein kinase C is still unclear, it does suggest that the monitoring of this enzyme's activational state may prove to be a useful tool in the study of memory formation in a wide variety of behavioral contexts.

Animals↗

Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb.

1. Detailed compartmental computer simulations of single mitral and granule cells of the vertebrate olfactory bulb were constructed using previously published geometric data. Electrophysiological properties were determined by comparing model output to previously published experimental data, mainly current-clamp recordings. 2. The passive electrical properties of each model were explored by comparing model output with intracellular potential data from hyperpolarizing current injection experiments. The results suggest that membrane resistivity in both cells is nonuniform, with somatas having a substantially lower resistivity than the dendrites. 3. The active properties of these cells were explored by incorporating active ion channels into modeled compartments. On the basis of evidence from the literature, the mitral cell model included six channel types: fast sodium, fast delayed rectifier (Kfast), slow delayed rectifier (K), transient outward potassium current (KA), voltage- and calcium-dependent potassium current (KCa), and L-type calcium current. The granule cell model included four channel types: rat brain sodium, K, KA, and the non-inactivating muscarinic potassium current (KM). Modeled channels were based on the Hodgkin-Huxley formalism. 4. Representative kinetics for each of the channel classes above were obtained from the literature. The experimentally unknown spatial distributions of each included channel were obtained by systematic parameter searches. These were conducted in two ways: large-scale simulation series, in which each parameter was varied in turn, and an adaptation of a multidimensional conjugate gradient method. In each case, the simulated results were compared wtih experimental data using a curve-matching function evaluating mean squared differences of several aspects of the simulated and experimental voltage waveforms. 5. Systematic parameter variations revealed a single distinct region of parameter space in which the mitral cell model best fit the data. This region of parameter space was also very robust to parameter variations. Specifically, optimum performance was obtained when calcium and slow K channels were concentrated in the glomeruli, with a lower density in the soma and proximal secondary dendrites. The distribution of sodium and fast potassium channels, on the other hand, was highest at the soma and axon, with a much lighter distribution throughout the secondary dendrites. The KA and KCa channels were also concentrated near the soma. 6. The parameter search of the granule cell model was much less restrained by experimental data. Several parameter regimes were found that gave a good match to the data.(ABSTRACT TRUNCATED AT 400 WORDS)

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Rallpacks: a set of benchmarks for neuronal simulators.

The field of computational neurobiology has advanced to the point where there are several general-purpose simulators to choose from. These cater to various niches in the world of realistic neuronal models, which range from the molecular level to descriptions of entire sensory modalities. In addition, there are numerous custom-designed simulations, adaptations of electrical circuit simulators, and other specific implementations of neurobiological models. As a first step towards evaluating this disparate set of simulators and simulations, and towards establishing standards for comparisons of speed and accuracy, we describe a set of benchmarks. These have been given the name 'Rallpacks' in honor of Wilfrid Rall, who pioneered the study of neuronal systems through analytical and numerical techniques.

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