^May June – 2009 ^July

The leveraging of todays unprecedented capability to manipulate biological systems by state-of-the-art computational, mathematical and engineering techniques , may profoundly affect the way we approach the solution to pressing grand challenges such as the development of sustainable green energy, next generation healthcare, etc. The conceptual cornerstone of Synthetic Biology a field very much on its infancy- is that methodologies commonly used to design and construct non-biological artefacts (e.g. computer programs, airplanes, bridges, etc) might also be mastered to create designer living entities. Computational methods for modeling in Synthetic Biology consist of a list of instructions detailing an algorithm that can be executed and whose computation resembles the behavior of the biological system under study. This computational approach to modelling biological systems has been termed executable biology. In this talk I will describe current approaches for the automated generation and testing of executable biology models for synthetic biology.

The prevalence of large racks of interconnected processor nodes forces us to take another look at how to exploit parallelism when taking the join of large relations. Sometimes, there is a gain in total cost to be had by distributing pieces of each relation to several different nodes and computing the join of several large relations at once. The optimization problem is to pick the degree of replication of each relation, under the constraint that the total number of compute-nodes is fixed. We set up this problem as a nonlinear optimization and show that there is always a solution (which must be approximated by rounding to the nearest integers). For some of the most common types of join – star joins and chain joins – we give closed-form solutions to the optimization problem. Finally, we point out that the join algorithm we propose can be implemented using features already present in Hadoop, the open-source implementation of map-reduce

Power consumption is a major issue in today's datacenters. I will give an overview of the general issue of power consumption in dataceneters and will focus on storage power consumption, which typically comprises a significant percentage of datacenter power. Understanding, managing, and reducing storage power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. We developed a scalable power modeling method that estimates the power consumption of storage workloads. The modeling concept is based on identifying the major workload contributors to the power consumed by the disk arrays. To estimate the power consumed by a given host workload, our method translates the workload to the primitive activities induced on the disks. In addition, we identified that I/O queues have a fundamental influence on the power consumption. Our power estimation results are highly accurate, with only 2% deviation for typical random workloads with small transfer sizes (up to 8K), and a deviation of up to 8% for workloads with large transfer sizes. We successfully integrated our modeling into a power-aware capacity planning tool to predict system power requirements and integrated it into an online storage system to provide online estimation for the power consumed.

We study statistical properties of interacting protein interfaces and predict two strong, related effects: (i) statistically enhanced self-attraction of proteins; (ii) statistically enhanced attraction of proteins with similar structures. The effects originate in the fact that the probability to find a pattern self-match between two identical, interacting protein interfaces is always higher compared with the probability for a pattern match between two different, promiscuous protein interfaces. This theoretical finding explains statistical prevalence of homodimers in protein-protein interaction networks reported earlier. Further, our findings are confirmed by the analysis of curated database of protein complexes that showed highly statistically significant overrepresentation of dimers formed by structurally similar proteins with highly divergent sequences (“superfamily heterodimers”). We predict that significant fraction of heterodimers evolved from homodimers with the negative design evolutionary pressure applied against promiscuous homodimer formation. This is achieved through the formation of highly specific contacts formed by charged residues as demonstrated both in model and real superfamily heterodimers. In addition we introduce the notion of structural correlations of amino acid interface density. We predict that protein interfaces with enhanced structural correlations are statistically more promiscuous as compared with proteins possessing a lower degree of interface structural correlations.

References:

1. D. B. Lukatsky and E. I. Shakhnovich, Statistically Enhanced Promiscuity of Structurally Correlated Patterns, Phys. Rev. E 77, 020901(R) (2008). 2. D. B. Lukatsky, B. E. Shakhnovich, J. Mintseris, and E. I. Shakhnovich, Structural Similarity Enhances Interaction Propensity of Proteins, J. Mol. Biol. 365, 1596 (2007). 3. D. B. Lukatsky, K. B. Zeldovich, and E. I. Shakhnovich, Statistically Enhanced Self-Attraction of Random Patterns, Phys. Rev. Lett. 97, 178101 (2006). .