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Biomedical subjects

Upinder S Bhalla

Publications and source records attributed to Upinder S Bhalla.

At least 19 recordsLinked to original sources
PubMed2006

Rats smell in stereo.

It has been hypothesized that rats and other mammals can use stereo cues to localize odor sources, but there is limited behavioral evidence to support this hypothesis. We found that rats trained on an odor-localization task can localize odors accurately in one or two sniffs. Bilateral sampling was essential for accurate odor localization, with internasal intensity and timing differences as directional cues. If the stimulus arrived at the correct point of the respiration cycle, internasal timing differences as short as 50 milliseconds sufficed. Neuronal recordings show that bulbar neurons responded differentially to stimuli from the left and stimuli from the right.

Animals↗
PubMed2006

Synaptic plasticity in vitro and in silico: insights into an intracellular signaling maze.

Synaptic plasticity provides a record of neuronal activity and is a likely basis for memory. The early apparent simplicity of the process of synaptic plasticity has been lost in a flood of experimental data that now implicates some 200 signaling molecules in cellular memory. It is now clear that these signaling networks perform surprisingly sophisticated cellular decisions that weigh factors such as input patterns, location of stimulus, history of activity, and context. Computer models have followed experiments into this maze of molecular detail, often matching closely with their experimental counterparts, but perhaps losing simplicity in the process. Here, we suggest that the merger of models and experiment have begun to restore the earlier simplicity by outlining a few key functional roles for signaling networks in synaptic plasticity. In this review, we discuss the current state of understanding of synaptic plasticity in terms of models and experiments.

Animals↗
PubMed2005

Molecular switches at the synapse emerge from receptor and kinase traffic.

Changes in the synaptic connection strengths between neurons are believed to play a role in memory formation. An important mechanism for changing synaptic strength is through movement of neurotransmitter receptors and regulatory proteins to and from the synapse. Several activity-triggered biochemical events control these movements. Here we use computer models to explore how these putative memory-related changes can be stabilised long after the initial trigger, and beyond the lifetime of synaptic molecules. We base our models on published biochemical data and experiments on the activity-dependent movement of a glutamate receptor, AMPAR, and a calcium-dependent kinase, CaMKII. We find that both of these molecules participate in distinct bistable switches. These simulated switches are effective for long periods despite molecular turnover and biochemical fluctuations arising from the small numbers of molecules in the synapse. The AMPAR switch arises from a novel self-recruitment process where the presence of sufficient receptors biases the receptor movement cycle to insert still more receptors into the synapse. The CaMKII switch arises from autophosphorylation of the kinase. The switches may function in a tightly coupled manner, or relatively independently. The latter case leads to multiple stable states of the synapse. We propose that similar self-recruitment cycles may be important for maintaining levels of many molecules that undergo regulated movement, and that these may lead to combinatorial possible stable states of systems like the synapse.

Journal Article↗
PubMed2005

Systems modeling: a pathway to drug discovery.

Systems modeling is emerging as a valuable tool in therapeutics. This is seen by the increasing use of clinically relevant computational models and a rise in systems biology companies working with the pharmaceutical industry. Systems models have helped understand the effects of pharmacological intervention at receptor, intracellular and intercellular communication stages of cell signaling. For instance, angiogenesis models at the ligand-receptor interaction level have suggested explanations for the failure of therapies for cardiovascular disease. Intracellular models of myeloma signaling have been used to explore alternative drug targets and treatment schedules. Finally, modeling has suggested novel approaches to treating disorders of intercellular communication, such as diabetes. Systems modeling can thus fill an important niche in therapeutics by making drug discovery a faster and more systematic process.

Animals↗
PubMed2005

Minimum information requested in the annotation of biochemical models (MIRIAM).

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

Biochemistry↗
PubMed2005

Electronic data sources for kinetic models of cell signaling.

Functional understanding of signaling pathways requires detailed information about the constituent molecules and their interactions. Simulations of signaling pathways therefore build upon a great deal of data from various sources. We first survey electronic data resources for cell signaling modeling and then based on the type of data representation the data sources are broadly classified into five groups. None of the data sources surveyed provide all required data in a ready-to-be-modeled fashion. We then put forward a "wish list" for the desired attributes for an ideal modeling centric database. Finally, we close with perspectives on how electronic data sources for cell signaling modeling have developed. We suggest that future directions in such data sources are largely model-driven and are hinged on interoperability of data sources.

Computer Simulation↗
PubMed2004

Adaptive stochastic-deterministic chemical kinetic simulations.

MOTIVATION: Biochemical signaling pathways and genetic circuits often involve very small numbers of key signaling molecules. Computationally expensive stochastic methods are necessary to simulate such chemical situations. Single-molecule chemical events often co-exist with much larger numbers of signaling molecules where mass-action kinetics is a reasonable approximation. Here, we describe an adaptive stochastic method that dynamically chooses between deterministic and stochastic calculations depending on molecular count and propensity of forward reactions. The method is fixed timestep and has first order accuracy. We compare the efficiency of this method with exact stochastic methods. RESULTS: We have implemented an adaptive stochastic-deterministic approximate simulation method for chemical kinetics. With an error margin of 5%, the method solves typical biologically constrained reaction schemes more rapidly than exact stochastic methods for reaction volumes >1-10 micro m(3). We have developed a test suite of reaction cases to test the accuracy of mixed simulation methods. AVAILABILITY: Simulation software used in the paper is freely available from http://www.ncbs.res.in/kinetikit/download.html

Algorithms↗
PubMed2004

Models of cell signaling pathways.

Cellular signaling circuits handle an enormous range of computations. Beyond the housekeeping, replicating and other functions of individual cells, signaling circuits must implement the immensely complex logic of development and function of multicellular organisms. Computer models are useful tools to understand this complexity. Recent studies have extended such models to include electrical, mechanical and spatial details of signaling, and to address the stochastic effects that arise when small numbers of molecules interact. Increasing numbers of models have been developed in close conjunction with experiments, and this interplay gives a deeper and more reliable insight into signaling function.

Cell Cycle↗
PubMed2004

A role for ERKII in synaptic pattern selectivity on the time-scale of minutes.

Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity.

Animals↗
PubMed2004

Signaling in small subcellular volumes. I. Stochastic and diffusion effects on individual pathways.

Many 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.

Calcium↗
PubMed2004

Signaling in small subcellular volumes. II. Stochastic and diffusion effects on synaptic network properties.

The 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.

Calcium↗
PubMed2003

Representation of odor habituation and timing in the hippocampus.

We performed simultaneous single-neuron recordings from the hippocampus and the olfactory bulb of anesthetized, freely breathing rats. Odor response properties of neurons in the olfactory bulb and hippocampus were characterized as firing rate changes or respiration-coupled changes. A panel of five odors was used. The rats had not been exposed to the odors on the panel before the experiment. The olfactory bulb and hippocampal neurons responded to repeated odor presentations in two ways: first, by changes in firing rate, and second, by respiratory tuning changes. Approximately 60% of bulbar neurons, 48% of hippocampal CA1 neurons, and 12% of hippocampal CA3 neurons showed statistically significant responses. None of the odor-responsive neurons in either the bulb or hippocampus responded to all of the odors on the panel. Repeated 10 sec odor stimuli presented at the intervals of 20, 30, 60, 110, and 160 sec were used to analyze the effect of the interval on odor response properties of the recorded neurons. Bulbar neurons were relatively nonselective for odor interval. Hippocampal neurons showed unexpected selectivity for the interval between repeated odor presentations. CA1 and CA3 neurons responded to only one to three of the intervals in the range. On the basis of these findings, we postulate that the hippocampus has the ability to keep track of the time elapsed between consecutive odor stimuli. This may act as a neuronal substrate for habituation and for complex tasks such as odor-guided navigation.

Action Potentials↗
PubMed2003

The Database of Quantitative Cellular Signaling: management and analysis of chemical kinetic models of signaling networks.

MOTIVATION: Analysis of cellular signaling interactions is expected to pose an enormous informatics challenge, perhaps even larger than analyzing the genome. The complex networks arising from signaling processes are traditionally represented as block diagrams. A key step in the evolution toward a more quantitative understanding of signaling is to explicitly specify the kinetics of all chemical reaction steps in a pathway. Technical advances in proteomics and high-throughput protein interaction assays promise a flood of such quantitative data. While annotations, molecular information and pathway connectivity have been compiled in several databases, and there are several proposals for general cell model description languages, there is currently little experience with databases of chemical kinetics and reaction level models of signaling networks. RESULTS: The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It is intended both to serve the growing field of chemical-reaction level simulation of signaling networks, and to anticipate issues in large-scale data management for signaling chemistry. AVAILABILITY: The Database of Quantitative Cellular Signaling is available at http://doqcs.ncbs.res.in. Links to the signaling model simulator, GENESIS/Kinetikit are at http://www.ncbs.res.in/~bhalla/kkit/index.html and are also provided from within the database. The database source code is available under the GNU Public License.

Abstracting and Indexing↗
PubMed2003

Understanding complex signaling networks through models and metaphors.

Signaling 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.

Algorithms↗
PubMed2003

Temporal computation by synaptic signaling pathways.

Synaptic 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. 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.

Algorithms↗
PubMed2002

MAP kinase phosphatase as a locus of flexibility in a mitogen-activated protein kinase signaling network.

Intracellular signaling networks receive and process information to control cellular machines. The mitogen-activated protein kinase (MAPK) 1,2/protein kinase C (PKC) system is one such network that regulates many cellular machines, including the cell cycle machinery and autocrine/paracrine factor synthesizing machinery. We used a combination of computational analysis and experiments in mouse NIH-3T3 fibroblasts to understand the design principles of this controller network. We find that the growth factor-stimulated signaling network containing MAPK 1, 2/PKC can operate with one (monostable) or two (bistable) stable states. At low concentrations of MAPK phosphatase, the system exhibits bistable behavior, such that brief stimulus results in sustained MAPK activation. The MAPK-induced increase in the amounts of MAPK phosphatase eliminates the prolonged response capability and moves the network to a monostable state, in which it behaves as a proportional response system responding acutely to stimulus. Thus, the MAPK 1, 2/PKC controller network is flexibly designed, and MAPK phosphatase may be critical for this flexible response.

3T3 Cells↗
PubMed2002

Simulations of inositol phosphate metabolism and its interaction with InsP(3)-mediated calcium release.

Inositol phosphates function as second messengers for a variety of extracellular signals. Ins(1,4,5)P(3) generated by phospholipase C-mediated hydrolysis of phosphatidylinositol bisphosphate, triggers numerous cellular processes by regulating calcium release from internal stores. The Ins(1,4,5)P(3) signal is coupled to a complex metabolic cascade involving a series of phosphatases and kinases. These enzymes generate a range of inositol phosphate derivatives, many of which have signaling roles of their own. We have integrated published biochemical data to build a mass action model for InsP(3) metabolism. The model includes most inositol phosphates that are currently known to interact with each other. We have used this model to study the effects of a G-protein coupled receptor stimulus that activates phospholipase C on the inositol phosphates. We have also monitored how the metabolic cascade interacts with Ins(1,4,5)P(3)-mediated calcium release. We find temporal dynamics of most inositol phosphates to be strongly influenced by the elaborate networking. We also show that Ins(1,3,4,5)P(4) plays a key role in InsP(3) dynamics and allows for paired pulse facilitation of calcium release. Calcium oscillations produce oscillatory responses in parts of the metabolic network and are in turn temporally modulated by the metabolism of InsP(3).

Animals↗