Faculty Bibliography

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

MOTIVATION/BACKGROUND:Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions. RESULTS:We present here, a review of the current state of systems biology verification and a detailed methodology to address its shortcomings. This methodology named 'Industrial Methodology for Process Verification in Research' or IMPROVER, consists on evaluating a research program by dividing a workflow into smaller building blocks that are individually verified. The verification of each building block can be done internally by members of the research program or externally by 'crowd-sourcing' to an interested community. www.sbvimprover.com IMPLEMENTATION/METHODS:This methodology could become the preferred choice to verify systems biology research workflows that are becoming increasingly complex and sophisticated in industrial and academic settings.

Nanopore-based technologies have attracted much attention recently for their promising use in low-cost and high-throughput genome sequencing. To achieve single-base resolution of DNA sequencing, it is critical to slow and control the translocation of DNA, which has been achieved in a protein nanopore but not yet in a solid-state nanopore. Using all-atom molecular dynamics simulations, we investigated the dynamics of a single-stranded DNA (ssDNA) molecule in an aqueous glycerol solution confined in a SiO(2) nanopore. The friction coefficient ξ of the ssDNA molecule is found to be approximately 18 times larger in glycerol than in water, which can dramatically slow the motion of ssDNA. The electrophoretic mobility μ of ssDNA in glycerol, however, decreases by almost the same factor, yielding the effective charge (ξμ) of ssDNA being roughly the same as in water. This is counterintuitive since the ssDNA effective charge predicted from the counterion condensation theory varies with the dielectric constant of a solvent. Due to the larger friction coefficient of ssDNA in glycerol, we further show that glycerol can improve trapping of ssDNA in the DNA transistor, a nanodevice that can be used to control the motion of ssDNA in a solid-state nanopore. Simulation results of slowing ssDNA translocation were confirmed in our nanopore experiment.

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Major advances in high-throughput technology platforms, coupled with increasingly sophisticated computational methods for systematic data analysis, have provided scientists with tools to better understand the complexity of signaling networks. In this era of massive and diverse data collection, standardization efforts that streamline data gathering, analysis, storage, and sharing are becoming a necessity. Here, we give an overview of current technologies to study signal transduction. We argue that along with the opportunities the new technologies open, their heterogeneous nature poses critical challenges for data handling that are further increased when data are to be integrated in mathematical models. Efficient standardization through markup languages and data annotation is a sine qua non condition for a systems-level analysis of signaling processes. It remains to be seen the extent to which and the speed at which the emerging standardization efforts will be embraced by the signaling community.

Solid state nanopores are a core element of next-generation single molecule tools in the field of nano-biotechnology. Thin film electrodes integrated into a pore can interact with charges and fields within the pore. In order to keep the nanopore open and thus functional electrochemically induced surface alteration of electrode surfaces and bubble formation inside the pore have to be eliminated. This paper provides electrochemical analyses of nanopores drilled into TiN membranes which in turn were employed as thin film electrodes. We studied physical pore integrity and the occurrence of water decomposition yielding bubble formation inside pores by applying voltages between -4.5 and +4.5 V to membranes in various protection stages continuously for up to 24 h. During potential application pores were exposed to selected electrolyte-solvent systems. We have investigated and successfully eliminated electrochemical pore oxidation and reduction as well as water decomposition inside nanopores of various diameters ranging from 3.5 to 25 nm in 50 nm thick TiN membranes by passivating the nanopores with a plasma-oxidized layer and using a 90% solution of glycerol in water as KCl solvent. Nanopore ionic conductances were measured before and after voltage application in order to test for changes in pore diameter due to electrochemical oxidation or reduction. TEM imaging was used to confirm these observations. While non-passivated pores were electrochemically oxidized, neither electrochemical oxidation nor reduction was observed for passivated pores. Bubble formation through water decomposition could be detected in non-passivated pores in KCl/water solutions but was not observed in 90% glycerol solutions. The use of a protective self-assembled monolayer of hexadecylphosphonic acid (HDPA) was also investigated.

Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs-that is, outsourcing the task to the interested community-may be an effective strategy for network inference.