Title: Genome regulation in fission yeast
Abstract: Data on absolute molecule numbers will empower the modeling, understanding, and comparison of cellular functions and biological systems. We quantified transcriptomes and proteomes in fission yeast during cellular proliferation and quiescence. This rich resource provides the first comprehensive reference for all RNA and most protein concentrations in a eukaryote under two key physiological conditions. The integrated data set supports quantitative biology and affords unique insights into cell regulation. Although mRNAs are typically expressed in a narrow range above 1 copy/cell, most long, noncoding RNAs, except for a distinct subset, are tightly repressed below 1 copy/cell. Cell-cycle-regulated transcription tunes mRNA numbers to phase-specific requirements but can also bring about more switch-like expression. Proteins greatly exceed mRNAs in abundance and dynamic range, and concentrations are regulated to functional demands. Upon transition to quiescence, the proteome changes substantially, but, in stark contrast to mRNAs, proteins do not uniformly decrease but scale with cell volume.
Title: Complexity, Pattern Formation and Chaos in the heart; a combined high performance computation and experimental approach for the study and treatment of arrhythmias.
Abstract: The heart is an electro-mechanical system in which, under normal conditions, electrical waves propagate in a coordinated manner to initiate an efficient contraction. In pathologic states, single and multiple rapidly rotating spiral and scroll waves of electrical activity can appear and generate complex spatiotemporal patterns of activation that inhibit contraction and can be lethal if untreated. Despite much study, many questions remain regarding the mechanisms that initiate, perpetuate, and terminate reentrant waves in cardiac tissue.
In this talk, we will show how a combined experimental and computational approach is used to better understand the dynamics of cardiac arrhythmias. From a computational point of view we will discuss from the numerical models derived to represent the dynamics of single cells to the coupling of millions of cells to represent the three-dimensional structure of a working heart. Some of the major difficulties of computer simulations for these kinds of systems include: i) Different orders of magnitude in time scales, from milliseconds to seconds; ii) millions of degrees of freedom over millions of integration steps within irregular domains; and iii) the need for near-real-time simulations. Advances in these areas will be discussed as well as the use of GPUs for large scale simulations. Finally we will show how computer simulations guide the development of new low energy defibrillation methods that are being tested experimentally that require only 10 percent the energy of current standard methods.
Title: Moment based estimation and control of cell populations
Abstract: We touch upon modeling, analysis and system identification issues arising in population systems. We will investigate how modeling and analysis methods can be extended to account for stochasticity both at the individual and at the population level. We will then discuss how equations characterizing the dynamics of the moments of the resulting stochastic descriptions can be used for state estimation and system identification tasks based on population level measurements. We also discuss optimal experiment design in cases were external signals are available to affect the evolution of the system. The developments will be motivated by and applied to specific problems in systems biology. The potential of the methods, however, is not restricted to biology and extends to numerous problems in engineering and beyond.
Title: From High-throughput Approaches to Molecular Mechanism
Abstract: A combination of new technologies, resources and methodologies has enabled researchers to move traditional reverse genetics approaches to a genome-wide level. High-throughput gene-gene, gene-drug and drug-drug interaction maps, pioneered mostly in yeast, have provided a plethora of mechanistic insights in gene function, pathway architecture and drug mode of action. Starting with E. coli, we have implemented analogous high-throughput approaches in a number of bacteria and used them to study different aspects of their biology. Here I will illustrate how these system-approaches can be used to assign function to uncharacterized genes, discover new layers of regulation for known biological processes-pathways, map higher-order interconnections in the genetic network, and identify the molecular mechanism behind drug mode-of-action and drug-drug synergy.
Title: Partial moment closures and rare event methods for stochastic reaction networks
Abstract: Stochastic modeling of biochemically reacting systems is an important branch of quantitative systems biology. Stochastic simulation is in widespread use for analyzing such models, but it is computationally expensive and only provides estimates that are subject to statistical uncertainty. Therefore, if accurate and more efficient numerical solutions are possible they should be preferred over statistical methods. In this talk we will discuss numerical approaches for two challenging problems that are currently gaining considerable interest in the community: (1) estimation of kinetic parameters, (2) approximation of rare event probabilities.
For problem (1) we will assume that noisy observations of certain parts of the system are available and that our model contains several unknown kinetic parameters. We will resort to models that are hybrid in the sense that a moment closure is employed for chemical species with large molecular populations. For the remaining parts of the model the classical discrete stochastic description based on the chemical master equation is used. In particular, the efficiency and accuracy of the recently proposed method of conditional moments will be discussed as well as its applicability to the estimation of parameters.
The second part of the talk will focus on numerically solving problem (2) using a method that is inspired by ideas from importance sampling and recent weighted stochastic simulation algorithms for estimating rare event probabilities. A guided state space exploration will be employed to bias the system parameters such that the rare event of interest becomes less rare. In this way, we can numerically integrate the equations that describe the time evolution of the system and dynamically truncate those portions of the system that do not significantly contribute to the rare event probability.