Matthias Christen

Allosteric control of cyclic di-GMP signaling

Cyclic di-guanosine monophosphate is a bacterial second messenger that has been implicated in biofilm formation, antibiotic resistance, and persistence of pathogenic bacteria in their animal host. Although the enzymes responsible for the regulation of cellular levels of c-di-GMP, diguanylate cyclases (DGC) and phosphodiesterases, have been identified recently, little information is available on the molecular mechanisms involved in controlling the activity of these key enzymes or on the specific interactions of c-di-GMP with effector proteins. By using a combination of genetic, biochemical, and modeling techniques we demonstrate that an allosteric binding site for c-di-GMP (I-site) is responsible for non-competitive product inhibition of DGCs. The I-site was mapped in both multi- and single domain DGC proteins and is fully contained within the GGDEF domain itself. In vivo selection experiments and kinetic analysis of the evolved I-site mutants led to the definition of an RXXD motif as the core c-di-GMP binding site. Based on these results and based on the observation that the I-site is conserved in a majority of known and potential DGC proteins, we propose that product inhibition of DGCs is of fundamental importance for c-di-GMP signaling and cellular homeostasis. The definition of the I-site binding pocket provides an entry point into unraveling the molecular mechanisms of ligand-protein interactions involved in c-di-GMP signaling and makes DGCs a valuable target for drug design to develop new strategies against biofilm-related diseases.

PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures

Stencil calculations comprise an important class of kernels in many scientific computing applications ranging from simple PDE solvers to constituent kernels in multigrid methods as well as image processing applications. In such types of solvers, stencil kernels are often the dominant part of the computation, and an efficient parallel implementation of the kernel is therefore crucial in order to reduce the time to solution. However, in the current complex hardware micro architectures, meticulous architecture-specific tuning is required to elicit the machines full compute power. We present a code generation and auto-tuning framework \textsc{Patus} for stencil computations targeted at multi- and many core processors, such as multicore CPUs and graphics processing units, which makes it possible to generate compute kernels from a specification of the stencil operation and a parallelization and optimization strategy, and leverages the auto tuning methodology to optimize strategy-dependent parameters for the given hardware architecture.

DgrA is a member of a new family of cyclic diguanosine monophosphate receptors and controls flagellar motor function in Caulobacter crescentus.

Bacteria are able to switch between two mutually exclusive lifestyles, motile single cells and sedentary multicellular communities that colonize surfaces. These behavioral changes contribute to an increased fitness in structured environments and are controlled by the ubiquitous bacterial second messenger cyclic diguanosine monophosphate (c-di-GMP). In response to changing environments, fluctuating levels of c-di-GMP inversely regulate cell motility and cell surface adhesins. Although the synthesis and breakdown of c-di-GMP has been studied in detail, little is known about the downstream effector mechanisms. Using affinity chromatography, we have isolated several c-di-GMP-binding proteins from Caulobacter crescentus. One of these proteins, DgrA, is a PilZ homolog involved in mediating c-di-GMP-dependent control of C. crescentus cell motility. Biochemical and structural analysis of DgrA and homologs from C. crescentus, Salmonella typhimurium, and Pseudomonas aeruginosa demonstrated that this protein family represents a class of specific diguanylate receptors and suggested a general mechanism for c-di-GMP binding and signal transduction. Increased concentrations of c-di-GMP or DgrA blocked motility in C. crescentus by interfering with motor function rather than flagellar assembly. We present preliminary evidence implicating the flagellar motor protein FliL in DgrA-dependent cell motility control.

Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant

Synthetic devices for traceless remote control of gene expression may provide new treatment opportunities in future gene- and cell-based therapies. Here we report the design of a synthetic mind-controlled gene switch that enables human brain activities and mental states to wirelessly programme the transgene expression in human cells. An electroencephalography (EEG)-based brain–computer interface (BCI) processing mental state-specific brain waves programs an inductively linked wireless-powered optogenetic implant containing designer cells engineered for near-infrared (NIR) light-adjustable expression of the human glycoprotein SEAP (secreted alkaline phosphatase). The synthetic optogenetic signalling pathway interfacing the BCI with target gene expression consists of an engineered NIR light-activated bacterial diguanylate cyclase (DGCL) producing the orthogonal second messenger cyclic diguanosine monophosphate (c-di-GMP), which triggers the stimulator of interferon genes (STING)-dependent induction of synthetic interferon-β promoters. Humans generating different mental states (biofeedback control, concentration, meditation) can differentially control SEAP production of the designer cells in culture and of subcutaneous wireless-powered optogenetic implants in mice.

Algorithmic performance studies on graphics processing units

We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floating-point co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear interior-point optimization. Since a full re-implementation of these complex kernels is typically not feasible, we identify the matrix-matrix multiplication as a first natural entry-point for a minimally invasive integration of GPUs. We investigate the performance on the NVIDIA GeForce 8800 multicore chip initially architectured for intensive gaming applications. We exploit the architectural features of the GeForce 8800 GPU to design an efficient GPU-parallel sparse matrix solver. A prototype approach to leverage the bandwidth and computing power of GPUs for these matrix kernel operation is demonstrated resulting in an overall performance of over 110 GFlops/s on the desktop for large matrices and over 38 GFlops/s for sparse matrices arising in real applications. We use our GPU algorithm for PDE-constrained optimization problems and demonstrate that the commodity GPU is a useful co-processor for scientific applications.

Patus for convenient high-performance stencils: evaluation in earthquake simulations

PATUS is a code generation and auto-tuning framework for stencil computations targeting modern multi and many-core processors. The goals of the framework are productivity and portability for achieving high performance on the target platform. Its stencil specification language allows the programmer to express the computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by removing the need for manual low-level tuning. We illustrate the impact of the stencil code generation in seismic applications, for which both weak and strong scaling are important. We evaluate the performance by focusing on a scalable discretization of the wave equation and testing complex simulation types of the AWP-ODC code to aim at excellent parallel efficiency, preparing for petascale 3-D earthquake calculations.

Parallel data-locality aware stencil computations on modern micro-architectures

Novel micro-architectures including the Cell Broadband Engine Architecture and graphics processing units are attractive platforms for compute-intensive simulations. This paper focuses on stencil computations arising in the context of a biomedical simulation and presents performance benchmarks on both the Cell BE and GPUs and contrasts them with a benchmark on a traditional CPU system. Due to the low arithmetic intensity of stencil computations, typically only a fraction of the peak performance of the compute hardware is reached. An algorithm is presented, which reduces the bandwidth requirements and thereby improves performance by exploiting temporal locality of the data. We report on performance improvements over CPU implementations.

Automatic code generation and tuning for stencil kernels on modern shared memory architectures

In this paper, we present Patus, a code generation and auto-tuning framework for stencil computations targeted at multi- and manycore processors, such as multicore CPUs and graphics processing units. Patus, which stands for “Parallel Autotuned Stencils,” generates a compute kernel from a specification of the stencil operation and a strategy which describes the parallelization and optimization to be applied, and leverages the autotuning methodology to optimize strategy-specific parameters for the given hardware architecture.

Genome Calligrapher: A Web Tool for Refactoring Bacterial Genome Sequences for de Novo DNA Synthesis.

Recent advances in synthetic biology have resulted in an increasing demand for the de novo synthesis of large-scale DNA constructs. Any process improvement that enables fast and cost-effective streamlining of digitized genetic information into fabricable DNA sequences holds great promise to study, mine, and engineer genomes. Here, we present Genome Calligrapher, a computer-aided design web tool intended for whole genome refactoring of bacterial chromosomes for de novo DNA synthesis. By applying a neutral recoding algorithm, Genome Calligrapher optimizes GC content and removes obstructive DNA features known to interfere with the synthesis of double-stranded DNA and the higher order assembly into large DNA constructs. Subsequent bioinformatics analysis revealed that synthesis constraints are prevalent among bacterial genomes. However, a low level of codon replacement is sufficient for refactoring bacterial genomes into easy-to-synthesize DNA sequences. To test the algorithm, 168 kb of synthetic DNA comprising approximately 20 percent of the synthetic essential genome of the cell-cycle bacterium Caulobacter crescentus was streamlined and then ordered from a commercial supplier of low-cost de novo DNA synthesis. The successful assembly into eight 20 kb segments indicates that Genome Calligrapher algorithm can be efficiently used to refactor difficult-to-synthesize DNA. Genome Calligrapher is broadly applicable to recode biosynthetic pathways, DNA sequences, and whole bacterial genomes, thus offering new opportunities to use synthetic biology tools to explore the functionality of microbial diversity. The Genome Calligrapher web tool can be accessed at https://christenlab.ethz.ch/GenomeCalligrapher  .

A Performance Study of an Anelastic Wave Propagation Code Using Auto-tuned Stencil Computations

Abstract In this paper, we use our stencil code generation and auto-tuning framework Patus to optimize and parallelize the most compute intensive stencil calculations of an anelastic wave propagation code, which was used to conduct numerous significant simulations at the Southern California Earthquake Center. From a straight-forward specification of the stencil calculation, Patus automatically creates an implementation targeted at the chosen hardware platform and applies hardware-specific optimizations including cache blocking, loop unrolling, and explicit vectorization. We show that, using this approach, we are able to speed up individual compute kernels by a factor of 2.4× on average, and reduce the time required to compute one time step of the entire simulation by 47% in a weak and up to 129% in a strong thread scaling setting.