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09:25 to 09:30 |
Spenta Wadia |
Opening remarks |
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09:30 to 10:15 |
Mukund Thattai |
Rapporteur talk on Natural numbers Abstract:
I will present a few snapshots of open problems in biology. The idea is not to give a comprehensive overview of biology, which is impossible, but rather a flavour of some areas where theorists should not fear to tread. A common theme runs through these areas: the idea that small integer numbers of basic microscopic units (DNA bases; genes; proteins; neurons) can be mapped onto macroscopic features of living things and of the world we perceive.
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10:15 to 10:30 |
Madan Rao |
Active Mechanics as a way of managing information in the cell Abstract:
I will describe how the active mechanics of energy consuming entities can regulate the organization and management of information in the cell.
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10:30 to 10:45 |
Kavita Jain |
Population genetics of molecular evolution Abstract:
I will briefly discuss the questions that we are trying to address in biological evolution and indicate the kind of information that we may obtain from systems biology.
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10:45 to 11:00 |
Shyamala Mani |
Does structure inform function: the cerebellum as a case study |
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11:00 to 11:30 |
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Coffee break |
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11:30 to 11:45 |
Vidyanand Nanjundiah |
Theories in evolutionary biology Abstract:
Natural selection, the theory proposed by Darwin and Wallace, continues to be treated as the 'default' explanation for evolution. Even when first proposed it had an implicity algorithmic form. The form was made explicit after it was combined with Mendel's laws of genetics in the form of population genetics. Advances in our understanding of the functioning of living matter have reinforced early qualms regarding the sufficiency of classical population genetics as an explanation for evolution. In addition to that, the evolution of cooperative groups - whether in multicellular development or social behaviour - continues to raise questions regarding the appropriate units and levels of natural selection. Also, it has once again brought into debate the possibility of alternatives to natural selection.
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11:45 to 12:00 |
Sandeep Krishna |
Combining theory and experiments to understand sugar regulation in bacteria Abstract:
I will describe our developing understanding of sugar uptake and metabolism in bacteria using the example of galactose regulation in E. coli. I'll especially emphasise the constant interplay between experiments and theory that was involved in this process.
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12:00 to 12:15 |
Sanjay Jain |
Structure, Dynamics and Evolution of complex systems |
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12:15 to 13:15 |
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Discussion Session |
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13:15 to 14:30 |
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Lunch |
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14:30 to 15:15 |
Mayank Mehta |
Rapporteur talk on “Neurophysics of learning and memory Abstract:
A defining feature of the brain is that it learns and remembers. Learning is thought to occur via changes in synaptic strengths. The human brain has about 100 trillion (~10^14) synapses. The synaptic strength changes over time scales ranging from milliseconds to years (~10^11 milliseconds). The synapses and neural responses are quite noisy. Yet, we often learn facts and events after a single, brief experience. Hence, a key challenge for neurophysics is to understand how rapid and reliable learning can occur in large and noisy networks with multiple time scales. I will describe some of our attempts to address this challenge.
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15:15 to 15:30 |
Collins Assisi |
Role of network topology in the generation of coordinated neuronal activity Abstract:
A number of studies have characterized the structure of real–world networks. However, much less is known about how the structure constrains the behaviour of dynamical systems embedded at the nodes of the network. Neuronal networks consisting of interacting excitatory and inhibitory units exhibit a rich dynamical repertoire, a consequence of both the intrinsic properties of neurons and their interactions over the network. I will show that a structural feature of the inhibitory sub–network, its colourings, may be used to predict the dynamics of the complete network. This description allows the identification of groups of inhibitory neurons that switch between active and quiescent states in a coordinated manner. Using the colouring of the inhibitory sub–network we can construct a space in which the collective activity of the excitatory neurons reliably forms a series of orthogonally propagating waves. In effect, we illustrate how low dimensional collective dynamics may be identified in a complex network by merely reordering its nodes according to a prescription dictated by its structure.
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15:30 to 15:45 |
Suhita Nadkarni |
Sophisticated Synapses - A quantitative insight into complex components of neuronal networks Abstract:
A synapse is a fundamental component of the framework required for information processing in the nervous system. Synapses are immensely complex with a large number of ion channels and a plethora of neurotransmitters operating over multiple of timescales. This complexity contributes to the rich spatiotemporal repertoire of the nervous system required to respond appropriately to complex patterns of stimulus and carry out extensive computations. However several downstream signaling pathways implicated in information processing overlap. Therefore pharmacological and genetic tools to tease out the individual functional roles at the cellular and subcellular level are not always viable.
I will discuss the potential of detailed models in the context of some of our recent work on the CA3-CA1 synapse. It is a small synapse in the hippocampus that has been extensively studied for its role in synaptic plasticity- the cellular underpinning of memory formation and learning. We show that a well calibrated spatially explicit model of the synapse can be used to carry out ‘in-silico’ experiments and can be a bridge between experiments and theory. This paradigm allows for the investigation of, otherwise experimentally inaccessible, structure - function relationships at the level of a synapse.
Our approach is to devise biophysical models which incorporate a more sophisticated view of synapses. calibrate and test these models with experimental data that can then be used to make testable predictions and finally work towards understanding the implications of component sophistication for network dynamics.
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15:45 to 16:00 |
Rajesh Kasturirangan |
Cognitive Theory Abstract:
While physicists and mathematicians interested in the mind sciences have mostly concerned themselves with neuroscientific questions, I claim that cognitive theory offers the best prospects for the theoretically minded scientist. Thermodynamics has to precede statistical mechanics. I will mention some interesting questions and lines of inquiry from the cognitive world and show how they can and should be tackled by theorists from various backgrounds.
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16:00 to 16:30 |
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Coffee Break |
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16:30 to 16:45 |
S P Arun |
Why our computers can play chess but cannot see like us? Abstract:
It is a common misconception that there was nothing more to seeing than the sensing of light in the eye. But we now know that visual information from the eyes is processed along a series of visual areas that occupy nearly 40% of our brains. This transformation is remarkable because we have programmed computers to play chess and Jeopardy but we still cannot make them see the way we do. Why are computers so bad at vision? What makes our brain so good at vision? Can we understand this at the level of single neurons? I will describe the problem and some of our work in trying to address these questions.
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16:45 to 17:45 |
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Discussion Session |
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17:45 to 18:45 |
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High Tea |
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