"Electricity meets Chemistry: Fast and Slow Signaling in Memory "
Upinder S. Bhalla
National Centre for Biological Sciences, TIFR, Bangalore, India
Deliberations on memory mechanisms often seem to proceed on at least three independent tracks. One of these involves biochemical mechanisms for plasticity, including feedback loops and cellular activation. Space is another dimension, and is the arena for interactions between synapses, and propagation of signals between synapses, dendrites, and the cell body. Finally, electrical activity is a function of cell as well as network dynamics, and here too feedback may play a role through reverberating activity in network loops. It is an interesting process to develop models that impinge on all of these levels, because of the wide range of timescales, numerical techniques, and sheer computational load. It is especially tricky to get parameters for such models. I will describe a study where we have used coupled electrical and biochemical compartmental modeling, and weeded out several candidate models by comparing their predictions to our experiments. The surviving models incorporate chemical, spatial and electrical ingredients. They exhibit network-activity controlled single-cell reverberating activation, with interesting spatial consequences. We suggest that this is a form of short-term and spatially deﬁned memory. It sits at the interface between individual synapses and dendrites, and also between network and cellular attributes of memory.
RIKEN Brain Science Institute
"Why bio-imaging, i.e. real time ﬂuorescence imaging?" Currently, this is a topic of great interest in the bioscience community. Many molecules involved in signal transduction have been identiﬁed, and the hierarchy among those molecules has also been elucidated. It is not uncommon to see a signal transduction diagram in which arrows are used to link molecules to show enzyme reactions and intermolecular interactions. To obtain a further understanding of a signal transduction system, however, the diagram must contain the three axes in space as well as a fourth dimension, time, because all events are controlled ingeniously in space and time. Since the isolation of green ﬂuorescent protein (GFP) from the bioluminescent jellyﬁsh in 1992 and later with its relatives, researchers have been awaiting the development of a tool, which enables the direct visualization of biological functions. This has been increasingly enhanced by the marriage of GFP with ﬂuorescence resonance energy transfer (FRET) or ﬂuorescence cross-correlation spectroscopy (FCCS), and is further expanded upon by the need for "post-genomic analyses." It is not my intent to discourage the trend seeking the visualization of biological function. I would like to propose that it is time to evaluate the true asset of "bio-imaging" for its potential and limitations in order to utilize and truly beneﬁt from this novel technique.
"Biological Large Scale Integration"
Dept of Bioengineering and (by courtesy) Applied Physics, Stanford University and Howard Hughes Medical Institute
The integrated circuit revolution changed our lives by automating computational tasks on a grand scale. My group has been asking whether a similar revolution could be enabled by automating biological tasks. To that end, we have developed a method of fabricating very small plumbing devices – chips with small channels and valves that manipulate ﬂuids containing biological molecules and cells, instead of the more familiar chips with wires and transistors that manipulate electrons. Using this technology, we have fabricated chips that have thousands of valves in an area of one square inch. We are using these chips in applications ranging from bioreactors to structural genomics to systems biology. However, there is also a substantial amount of basic physics to explore with these systems – the properties of ﬂuids change dramatically as the working volume is scaled from milliliters to nanoliters!
P4: 12:00-12:30, October 9 Main Hall
"Evolvability and hierarchy in rewired bacterial gene networks"
Mark Isalan*1, Caroline Lemerle2, Konstantinos Michalodimitrakis2, Barbara Di Ventura2, Pedro Beltrao2, Carsten Horn2 and Emanuele Raineri2
1. EMBL-CRG Systems Biology Programme, Centre for Genomic Regulation, Spain, 2. EMBL, Germany
Bacterial gene networks are highly plastic, allowing radical reconnections at the summit of the gene network hierarchy, fuelling evolvability.Sequencing of genetic material from several organisms has revealed that duplication and drift of existing genes has primarily molded the contents of a given genome. Though the effect of knocking out or over-expressing a particular gene has been studied in many organisms, no study has systematically explored the effect of adding new links in a biological network. To explore network plasticity, we constructed 598 recombinations of promoters (including regulatory regions) with different transcription or s-factors in Escherichia coli, over the genetic background of the wild-type. We found that ~95% of reconnected networks are tolerated by the bacterial cell and very few give different growth proﬁles. Expression levels correlate with the position of the factor in the wild-type network hierarchy. Most importantly, we ﬁnd that certain combinations consistently survive over the wild-type under various selection pressures. This suggests that new links in the network could readily confer a ﬁtness advantage to individuals in a population and hence may fuel evolution.
Stephen G. Oliver
Faculty of Life Sciences, The University of Manchester, U.K.
Systems biology aims at taking a more synthetic or holistic approach to deciphering the workings of living organisms. Although the ultimate aim is to construct mathematical models of complete cells or organisms that have both explanatory and predictive power, we are some way from achieving such global syntheses and we need a principled way of reducing the complexity of the problem. Accordingly, we require a top-down strategy to provide an initial coarse-grained model of the cell, and a bottom-up strategy in which individual sub-systems are modeled.
Metabolic Control Analysis (MCA) is a conceptual and mathematical formalism that models the relative contributions of individual effectors in a pathway to both the ﬂux through the pathway and the concentrations of individual intermediates within it. To exploit MCA in an initial top-down systems analysis of the eukaryotic cell, two categories of experiments are required. In category 1 experiments, ﬂux is changed and the impact on the levels of the direct and indirect products of gene action is measured. We have measured the impact of changing the ﬂux on the transcriptome, proteome, and metabolome of Saccharomyces cerevisiae. In this whole-cell analysis, ﬂux equates to growth rate. In category 2 experiments, the levels of individual gene products are altered, and the impact on the ﬂux is measured. We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high ﬂux control coefﬁcients.
For the bottom-up approach, the initial problem is one of systems identiﬁcation. While a lot of time is currently spent debating the question “What is Systems Biology?”, why (in an organism where we know so much about its biochemistry, physiology, and cell biology as S. cerevisiae) should it be a problem to identify the biological sub-systems that must be fully characterised and built into a comprehensive model of the eukaryotic cell? This problem arises because we have previously studied these biological systems in isolation and in a rigorously reductionist fashion. Now, we must study them as parts of an integrated whole. The problem is that our current view of, say, a metabolic or signal transduction pathway is often two-dimensional (rather than four-dimensional) and is frequently poorly integrated, if at all, with other cellular pathways. Thus our view of the network of metabolic pathways may not be the same as the yeast’s. In order to gain a “yeast’s eye view”, we have coupled ﬂux balance analysis with both metabolomics and genetics. Although the initial aim of these approaches is the identiﬁcation of the ‘natural’ metabolic systems of yeast, the principles involved should be more widely applicable to the problem of biological systems identiﬁcation.