Faculty Bibliography

BACKGROUND:Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. RESULTS:We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. CONCLUSIONS:A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely defined, as in challenge 2. Importantly, we found that aggregating independent parameter predictions and network topology across submissions creates a solution that can be better than the one from the best-performing submission.

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

A nanofluidic channel, with a pair of perpendicularly aligned nanoelectrodes, is proposed to electrically control the motion of DNA molecules. Using all-atom molecular dynamics simulations, we studied electrostatic responses of a charged DNA molecule in the nanochannel and investigated optimized operating conditions for controlling the DNA molecule. When the transversal electric field was periodically turned on and off, the DNA molecule was correspondingly immobilized on and released from the channel surface. Under simultaneously applied longitudinal biasing and transversal trapping electric fields, the DNA molecule moved forward in a 'ratchet'-like fashion. It is expected that achieving the controlled motion of DNA in the channel can advance studies and applications of a nanochannel-based sensor for analyzing DNA (e.g., DNA sequencing).

We introduce a method to fabricate solid-state nanopores with sub-20 nm diameter in membranes with embedded metal electrodes across a 200 mm wafer using CMOS compatible semiconductor processes. Multi-layer (metal-dielectric) structures embedded in membranes were demonstrated to have high uniformity (± 0.5 nm) across the wafer. Arrays of nanopores were fabricated with an average size of 18 ± 2 nm in diameter using a Reactive Ion Etching (RIE) method in lieu of TEM drilling. Shorts between the membrane-embedded metals were occasionally created after pore formation, but the RIE based pores had a much better yield (99%) of unshorted electrodes compared to TEM drilled pores (<10%). A double-stranded DNA of length 1 kbp was translocated through the multi-layer structure RIE-based nanopore demonstrating that the pores were open. The ionic current through the pore can be modulated with a gain of 3 using embedded electrodes functioning as a gate in 0.1 mM KCl aqueous solution. This fabrication approach can potentially pave the way to manufacturable nanopore arrays with the ability to electrically control the movement of single or double-stranded DNA inside the pore with embedded electrodes.

Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.

Previous measurements of the electronic conductance of DNA nucleotides or amino acids have used tunnel junctions in which the gap is mechanically adjusted, such as scanning tunneling microscopes or mechanically controllable break junctions. Fixed-junction devices have, at best, detected the passage of whole DNA molecules without yielding chemical information. Here, we report on a layered tunnel junction in which the tunnel gap is defined by a dielectric layer, deposited by atomic layer deposition. Reactive ion etching is used to drill a hole through the layers so that the tunnel junction can be exposed to molecules in solution. When the metal electrodes are functionalized with recognition molecules that capture DNA nucleotides via hydrogen bonds, the identities of the individual nucleotides are revealed by characteristic features of the fluctuating tunnel current associated with single-molecule binding events.

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

BACKGROUND:Although much is understood about the enzymatic cascades that underlie cellular biosynthesis, comparatively little is known about the rules that determine their cellular organization. We performed a detailed analysis of the localization of E.coli GFP-tagged enzymes for cells growing exponentially. RESULTS:We found that out of 857 globular enzymes, at least 219 have a discrete punctuate localization in the cytoplasm and catalyze the first or the last reaction in 60% of biosynthetic pathways. A graph-theoretic analysis of E.coli's metabolic network shows that localized enzymes, in contrast to non-localized ones, form a tree-like hierarchical structure, have a higher within-group connectivity, and are traversed by a higher number of feed-forward and feedback loops than their non-localized counterparts. A Gene Ontology analysis of these enzymes reveals an enrichment of terms related to essential metabolic functions in growing cells. Given that these findings suggest a distinct metabolic role for localization, we studied the dynamics of cellular localization of the cell wall synthesizing enzymes in B. subtilis and found that enzymes localize during exponential growth but not during stationary growth. CONCLUSIONS:We conclude that active biochemical pathways inside the cytoplasm are organized spatially following a rule where their first or their last enzymes localize to effectively connect the different active pathways and thus could reflect the activity state of the cell's metabolic network.