Michael Schatz - US grants

The research will explore the fundamental of controlling fluid flow in convective systems. Experiments will be carried out on surface tension driven (Marangoni) convection where the driving forces, thermally induced surface tension gradients confined to an interface, can be probed and altered for both laminar and chaotic convection. The driving will be determined via infrared imaging of the interfacial temperature field and altered direclty via nearly simultaneous, multipoint heating by an infrared laser scanner. Dynamics in three areas will be investigated: 1) wavenumber selection mechanism of spatially periodic patterns; 2) defect dynamics in disordered convective flow; 3) the role of unstable periodic orbits in spatiotemporal chaos.

The goals of this project are to develop techniques for the detection and prediction of low-dimensional features in high-dimensional, spatially extended systems, to investigate how coherent structures within the system affect predictability, to test control strategies for high-dimensional, spatially extended systems, and to improve understanding of uncertainty as a function of spatial scale in such systems. One motivation for the work is the desire to develop better state estimation techniques for complex environmental systems. The model system for this work is that of shallow thermal convection. The project combines laboratory experiment with theory and modeling. As well as being a good model system, convection is important in many environmental systems in the atmosphere, ocean and interior of the Earth, and in industrial settings. Thermal convection exhibits multi-scale spatial and temporal complexity and laboratory convection experiments provide plentiful, high quality, observational data under relatively well-controlled conditions. The laboratory experiments use carbon dioxide as the convecting medium in a shallow cell. Specific spiral-defect chaos flow patterns can be initiated using a computer-steered laser system that selectively heats parts of the fluid. The primary theoretical tool is a state estimation technique in which a local ensemble Kalman filter is applied to a numerical Navier-Stokes solver. This will be applied to a range of flow patterns of increasing complexity exhibited by the convection cell as the Rayleigh number is increased. The data to be assimilated will be taken from shadowgraph images of the convection. Later stages of the project will include experiments in control of the convection using selective heating guided by output from the state estimation system. It is anticipated that the results of this work will be applicable to other spatially-extended complex systems.

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Schatz

The PIs plan novel laboratory tests of a new and fundamentally different theoretical view of wall turbulence. Organized flows (coherent structures) near smooth walls play a central role in turbulence production over a wide range of Reynolds numbers. Recent theoretical work with large scale numerical computation has revealed a class of unstable, exact Navier-Stokes solutions termed "exact coherent structures" (ECS), which capture essential features of classic coherent structures. In particular, theory suggests ECS can be used construct simplified descriptions of wall turbulence. While theory/numerics have made a compelling case for an ECS-based description in highly idealized turbulent flows with periodic boundary conditions, very little is known about whether this viewpoint can describe turbulence in the lab. The PIs plan experimental studies of ECS in circular Couette flow where turbulence is initialized by precise, optically-imposed disturbances and measured by 3D velocimetry. The PI's unique method of optical distributed flow actuation has already been tested in other flows; thus, the experiments will focus directly on flow physics instead of actuator development. Laboratory testing of an ECS-based description will begin first with the adaptation of theoretical state space visualization techniques to experiments. The experiments will then identify important ECS. In particular, the PIs will focus on the Lower Branch ECS, which appears to play a key "gatekeeper" role in the transition between laminar and turbulent flow. Characterization of Lower Branch ECS will set the stage for novel approaches to turbulence flow control. Turbulence is a major consideration in the design of transport vehicles on land, at sea or in the air. In practical applications; even modest improvements in turbulence control could have enormous economic impact. The tools of dynamical systems are becoming ever more important in solving engineering problems. Thus, our planned work will implement a laboratory module on state space visualization of pendulum dynamics that will be included in a novel introductory calculus-based engineering physics curriculum at Georgia Tech and at Spelman College, the country's leading Historically Black College/University for women.

The last few years have seen a revolution in DNA sequencing instrumentation and technology. In that short time period sequencing instrument capabilities have increased more than 1000 fold and are likely to continue to increase about 5-fold each year for the next several years. As such it is now affordable and efficient to sequence to high coverage large plant genomes that had previously been prohibitively too expensive and complex to attempt. However, analysis methods have not improved nearly as much during the same time period and a variety of technical limitations of these new DNA sequencing instruments make it even more difficult to carry out whole genome sequencing of novel genomes (de novo sequencing). These limitations also make it more difficult to use the new instruments to carry out older clone based strategies for de novo sequencing, such as BAC-by-BAC approaches. The purpose of this meeting to be held at Cold Spring Harbor Laboratory May 18-20, 2011 are to assess the current state of next generation sequencing in terms of de novo, whole genome plant sequencing, what can be expected to develop in the near future, and then determine which advances are needed to allow these exciting technologies to be used to carry out de novo sequencing of entire complex plant genomes. The meeting will bring together stakeholders with broad range of expertise in high-throughput sequencing and genomics, plant biology, bioinformatics and databases drawn from the academic, private and international sectors. Meeting outcomes will be captured in the form of a report to be developed by participants that will be submitted for publication to Genome Research.

Numerous complex systems in nature and in technology defy concise characterization because they exhibit strongly nonlinear behaviors that lack all symmetries and are highly non-periodic on a wide range of spatial and temporal scales. Characterization by detailed measurement (in lab experiments or direct numerical simulations) is now possible in many cases using modern measurement technologies or computational techniques. However, the resulting deluge of data often leads to little insight; in particular, there is frequently no good way to connect quantitatively experimental measurements of a particular complex system with the output from simulations/models of the same system. New, computationally-based, mathematical tools from algebraic topology have the potential to bridge the gap between measurements and models; the proposed research will explore the use of algebraic topology to link numerical simulations and laboratory experiments in situations where complexity arises because the system under study is driven out of thermodynamic equilibrium. The research focuses on an outstanding paradigm for nonequilibrium complexity: fluid flow driven by temperature gradients (thermal convection). The planned work brings three unique capabilities together in a single effort:
(1) the experimental ability both to measure and to manipulate precisely complex, convective flows;
(2) efficient methods for state-of-the-art, large scale, high-resolution numerical simulations of convective flow;
(3) open source, general purpose, and efficient computational algorithms and software for computing algebraic topological invariants on large data sets.
Topological tools will be developed both to characterize and to minimize model error as well as to compare and to quantify dynamical properties including Lyapunov exponents, dimensionality and bifurcations between complex spatiotemporal flow states. This effort should ultimately identify ways in which homology-based metrics can be used for building reduced order models that permit prediction and, perhaps, control of convective flow. More generally, we expect the metrics developed for convection should find broad application to PDE-modeled problems ranging from the control of cardiac arrythmias to the prediction of weather and climate.


The behaviors of complex systems in the world around us can now both be measured with high fidelity using advanced sensing technologies and simulated with great realism using modern computer techniques. However, the enormous data sets typically produced in these cases are often difficult to interpret because there exist few good mathematical tools to connect quantitatively the experimental measurements of a given complex system with the output of computer simulations of that same system. The proposed research explores the use of the mathematics of topology to relate lab measurements to computer outputs in a particular complex system, thermal convection. The results of this work should lead to new ways to understand, to predict, and, perhaps, to control convective flow, which plays a direct role in natural processes (e.g., volcanism, earthquake dynamics, continential drift) and industrial applications (e.g., thermal regulation of many devices, the growth of semiconductor materials). Moreover, the topological tools developed for thermal convection should apply more generally to a wide variety of other problems involving complex systems including the forecasting of weather and climate; the dynamics of the biomass in the oceans; the onset of turbulence; the evolution of reagent patterns on a catalytic metal surface; and ventricular fibrillation in a human heart.

The objective of this research program is to develop and to test experimentally a revolutionary new approach to modeling and predicting two-dimensional turbulent flows. A set of weakly unstable invariant Navier-Stokes solutions will be identified and transitions between invariant solutions will be characterized to provide a coarse global description of the nonlinear dynamics of turbulent flow. Quasi-2D flow in a shallow electrolyte layer continually driven by Lorentz forces provides the setting for theoretical, analytic and experimental development of this approach. Novel and proven techniques, such as periodic orbit theory, group representation theory, Krylov-subspace numerical methods, Newton and variational solvers will be used to develop this viewpoint, which will be tested in experiments where the flow can be measured with full spatial and temporal resolution throughout the entire flow domain.

If successful, the results of this research will impact several areas of science, engineering, and medicine. Although the focus of this investigation is on fluid turbulence in two dimensions, the proposed approach has the potential to provide new ways to model, and ultimately control, a wide range of spatiotemporally chaotic systems, such as magnetic confinement fusion reactors and abnormal cardiac dynamics (from mild arrhythmias to potentially lethal fibrillation). The most immediate practical application, however, is the reduction of turbulent drag responsible for a significant part of the fuel consumed in the automotive, aviation, and shipping industries. Even an incremental reduction of drag by the proposed flow control methodology would have a tremendous economic impact. All software and data produced by the research program will be made publicly available with a central aim of lowering the barrier of entry to dynamical systems research by providing well-documented, easy-to-use interfaces to state-of-the-art numerical algorithms.

The Cold Spring Harbor Laboratory is awarded a CAREER grant for the PI Michael Schatz to develop new computational methods for processing DNA sequencing data from the latest high-throughput sequencing technologies. DNA sequencing costs and throughput have improved by orders of magnitudes over the last three decades, although many questions remain unsolved, especially because of the short sequence lengths currently available. Emerging "third generation" sequencing technology from Pacific Biosciences, Moleculo, Oxford Nanopore, and other companies are poised to revolutionize genomics by enabling the sequencing of long, individual molecules of DNA and RNA. The sequence lengths with these technologies can reach up to tens of thousands of nucleotides, however few or no analysis packages are capable of dealing with these types of genetic sequence data. This project will overcome these limitations by developing several novel analysis algorithms specifically for long read single molecule sequencing and their associated complex error models. The outcomes will help answer biological questions of profound significance to all of society, such as: What were the genetic implications of the domestication of rice? What genes and regulatory elements give rise to the incredible regenerative properties of the flatworm? or, What can be understood from assembling reference genomes of sugarcane and pineapple towards breeding more robust plant crops and biofuels?

Specific objectives of the research include working towards assembling entire plant and animal chromosomes into complete, haplotype-phased sequences; identifying fusion genes and complex alternative splicing patterns responsible for diseases or adaptability; and searching for structural variations associated with improved crop yield or human diseases such as cancer or autism. Even if some future technology is capable of directly reading entire transcripts or entire genomes, this research will remain necessary to examine the higher level relationships across populations of genomes or in measuring the dynamics of gene expression and splicing.

This project will tightly integrate research and education, promoting opportunities at high school through postdoctoral levels with the development of new course materials, hands-on research opportunities, and one-on-one mentoring experiences. This effort will specifically target the intersection of computer science and biology, promoting interdisciplinary education, and ensuring the next generation of scientists are ready for the complexities of quantitative and digital biology. To engage the widest possible audience, Dr Schatz will also develop novel online teaching materials made available through a yearly bioinformatics contest. The first round of the contest reached nearly 1000 students around the world and at all levels of education, engaging students far beyond our physical limits. The products of the research will be made available as open-source software, and installed into the graphical iPlant Discovery Environment making them easily accessible to the large community of plant researcher around the world.

This REU Site award to Cold Spring Harbor Laboratory (CSHL), located in Cold Spring Harbor, NY, will support the training of ten students for ten weeks during the summers of 2016-2018. This award is supported by the Division of Biological Infrastructure in the Directorate for Biological Sciences (BIO) and the Division for Mathematical Sciences in the Directorate for Mathematics and Physical Sciences (MPS).CSHL's REU in Bioinformatics and Computational Neuroscience (BCN) provides participants with an exceptional research experience, integrating genomics and neuroscience through shared analysis tools. Spanning genomes, cells, organisms and the brain, the program trains students to approach complex biological systems quantitatively. Students conduct full-time, independent research under the mentorship of one of CSHL's approximately 50 faculty members working in genomics, quantitative biology, and neuroscience. Participants have access to state-of-the-art technologies, such as high-throughput sequencing and two-photon imaging, and attend lab meetings and research seminars. The REU curriculum includes workshops on quantitative techniques, responsible conduct of research, scientific communication, and scientific careers. The REU culminates with a symposium in which participants present their work to CSHL's scientific community. Students are housed on CSHL's 110-acre campus, within walking distance of laboratories and dining halls. Participants receive room and board and a summer stipend and have access to campus amenities. Students apply online, supplying a personal statement, two letters of recommendation, and academic records. REU participants are selected based on academics, motivation, and demonstrated potential.

It is anticipated that 30 students, primarily from schools with limited research opportunities or those from underrepresented groups, will be trained in CSHL's REU in BCN. Participants will learn to interrogate biological questions with computational tools and techniques. Through the 10-week REU experience, participants will learn how research is conducted. Many will present the results of their work at national scientific conferences, furthering their identity as independent scientists.

A common web-based assessment tool used by all REU programs funded by the Division of Biological Infrastructure (Directorate for Biological Sciences) is used to determine the effectiveness of the training program. Students are tracked after the program to determine career paths. Students will be asked to respond to an automatic email sent via the NSF reporting system. More information about the program is available by visiting http://www.cshl.edu/Education/NSF-REU-in-Bioinformatics-and-Computational-Neuroscience.html, or by contacting the PI (Dr. Anne Churchland, churchland@cshl.edu), the co-PI (Dr. Michael Schatz, mschatz@cshl.edu).

The weather we experience is driven by convection, sunlight warms the earth which heats the atmosphere which is cooled by the cold temperatures of outer space. Most people are not interested in microscopic behavior, for example the behavior of the individual molecules in the air, nor macroscopic behavior, such as worldwide average temperature. What is of interest are mesoscopic patterns, for example weather fronts which result in local changes in temperature. This interest in mesoscopic, as opposed to micro- or macroscopic features, of large scale systems occurs in a wide variety of complex large scale physical phenomena such as combustion in engines, dynamics of biomass in the oceans, ventricle fibrillation in a human heart, etc. These mesoscopic patterns take on many different shapes and sizes and change with time, sometimes slowly and sometimes rapidly. The form of these patterns and how they evolve in time is often very dependent on parameters. New technologies are greatly increasing our abilities to measure and simulate these physical phenomena, resulting in enormous data sets, but our ability to extract and quantify this information in a way that leads to understanding, predictability, and control of these systems is not keeping pace. We will explore the use of new mathematical tools to address this problem.

The spatial and temporal complexity of Rayleigh-Bénard convection produces high dimensional time series data. A relatively new algebraic topological tool called Persistent Homology will be used to provide new tools for nonlinear dimension reduction. To ensure the applicability of these methods and that physically important mesoscopic features of the dynamics are preserved they will be developed in conjunction with the further development of carefully controlled high precision convection experiments and state-of-the-art, large scale, high-resolution numerical simulations of the Boussinesq equations. This includes the analysis of the geometry of covariant Lyapunov exponents. The new computational tools developed in this work should find broad application in a wide variety of problems involving complex nonequilibrium systems in nature (oceanic and atmospheric flows, climate and weather forecasting) and in technology (nonlinear optical systems, combustion and chemical reactions) where understanding and prediction of complex behavior is desired.

This research project explores and experimentally tests a radically new mathematical framework for understanding and predicting complicated behaviors in numerous fundamental and practical problems in science, engineering, and medicine (e.g., weather forecasting, characterization of cardiac arrhythmias, etc.). Complex behaviors in many such problems are often governed by patterns that appear fleetingly but repeatedly. The research develops general, powerful techniques for identifying and quantifying key patterns, including the temporal sequences in which the patterns may appear; knowledge of the patterns and sequences can then be harnessed to construct "road maps" for predicting future behaviors. This study will focus on demonstrating "proof of principle" by constructing road maps of complex behavior observed in turbulent fluid flow in laboratory experiments. If successful, the results of this study will lead directly to the development of faster and more accurate ways to make predictions of complicated behavior in large real world problems. For example, the ability to identify and quantify important patterns and sequences in atmospheric turbulence should enable weather forecasts that are better and more rapid than those currently possible today. All software and useful solution data produced by the research activities will be made publicly available. The research program tightly integrates with teaching and learning at the undergraduate and graduate levels and includes activities to increase participation of underrepresented groups.

The primary goal of this research program is to develop a novel geometrical/topological approach to modeling and prediction of turbulent flows and to validate it experimentally. Investigation will focus on a weakly turbulent flow in a shallow electrolyte fluid layer. A combination of existing numerical methods and new methods developed as a part of this program will be used to compute a large set of unstable states (known as exact coherent states in fluid dynamics) and the network of connections between these states, given by the numerically exact solutions of the mathematical model of the flow. Temporal averages will be compared with state averages in experiment and simulations to verify the statistical predictions of periodic orbit theory. A low-dimensional predictive model for the dynamics based on the topology of the network of connections will similarly be validated against experiment and simulations. Understanding, prediction, and control of spatiotemporally chaotic dynamics, in general, and of turbulent fluid flows, in particular, is largely an open problem of both practical and fundamental significance. The geometrical/topological framework that will be developed and tested under this project will provide a novel reduced-order, predictive, dynamical description of turbulent fluid flows. This framework will also provide a connection between the dynamical description and the conventional statistical description of fluid turbulence. In addition, this framework should serve as a foundation for a radically new way to control complex dynamical regimes in a wide range of applications.

Project Summary VISION: ValIdated Systematic IntegratiON of hematopoietic epigenomes Technological advances enabling the production of large numbers of rich, genome-wide, sequence-based datasets have transformed biology. However, the volume of data is overwhelming for most investigators. Also, we do not know the mechanisms by which the vast majority of epigenetic features regulate normal differentiation or lead to aberrant function in disease. We have formed an interdisciplinary, collaborative team of investigators to address the problem of how to effectively utilize the enormous amount of epigenetic data both for basic research and precision medicine. At this point, acquisition of data is no longer the major barrier to understanding mechanisms of gene regulation during normal and pathological tissue development. The chief challenges are how to: (i) integrate epigenetic data in terms that are accessible and understandable to a broad community of researchers, (ii) build validated quantitative models explaining how the dynamics of gene expression relates to epigenetic features, and (iii) translate information effectively from mouse models to potential applications in human health. These needs are addressed by the proposed ValIdated Systematic IntegratiON (VISION) of epigenetic data to analyze mouse and human hematopoiesis, a tractable system with clear clinical significance and importance to NIDDK. By pursuing the following Specific Aims, the interdisciplinary collaboration will deliver comprehensive catalogs of cis regulatory modules (CRMs), extensive chromatin interaction maps and deduced regulatory domains, validated quantitative models for gene regulation, and a guide for investigators to translate insights from mouse models to human clinical studies. These deliverables will be provided to the community in readily accessible, web-based platforms including customized genome browsers, databases with facile query interfaces, and data-driven on-line tools. Specifically, the proposed work in Aim 1 will build comprehensive, integrative catalogs of hematopoietic CRMs and transcriptomes by compiling and determining informative epigenetic features and transcript levels in hematopoietic stem and progenitor cells and in mature cells. CRMs will be predicted using the novel IDEAS (Integrative and Discriminative Epigenome Annotation System) method. Work proposed in Aim 2 will build and validate quantitative models for gene regulation informed by chromatin interaction maps and epigenetic data. Compiling and determining chromosome interaction frequencies will predict likely target genes for CRMs. Gene regulatory models will be built that predict the contributions of CRMs and specific proteins to regulated expression; these models will be validated by extensive testing using genome-editing in ten reference loci. Finally, work in Aim 3 will produce a guide for investigators to translate insights from mouse models to human clinical studies. This effort will include categorizing orthologous mouse and human genes by conservation versus divergence of expression patterns, assigning CRMs to informative categories of epigenomic evolution, and testing the interspecies functional maps experimentally by genome-editing.

Project Summary Cancer research is now a data-driven discipline, but only a minority of cancer researchers are data scientists. This severely restricts our ability to effectively study and cure the disease. The far reaching significance of our project is in federating disparate data and computational resources in order to provide a unifying analysis platform for computational cancer research. We will extend the popular scientific workbench Galaxy (https://galaxyproject.org) so that it can integrate with distributed data and compute resources used and needed by cancer researchers, including those resources in the NCI Cancer Research Data Commons (NCR DC). Our Federated Galaxy system will allow users to seamlessly access NCR DC data across multiple resources. It will support multiple analysis scenarios tuned to skills and computational requirements of individual researchers. The aims of this project are: Aim 1. Extend Galaxy for working with distributed cancer genomics and phenotypic data. This will enable Galaxy users to access both public and private cancer data regardless of their actual physical location. Best-practice approaches will be used for accessing restricted datasets. Aim 2. Enhance Galaxy for context-aware, distributed cancer genomics analyses using shared workflow representations. This will enable Galaxy users to run genomics analyses on different clouds, ultimately reducing the time, cost, and data transfer associated with analyses. Aim 3. Apply Federated Galaxy to precision oncology research. Workflows developed in this aim will leverage the technologies in Aims 1 and 2 to benchmark machine learning algorithms for predicting tumor phenotype and drug response. Interactive reports will summarize benchmarking results and utilize ITCR visualizations for deep dives into results. Our system will provide a singular access point to distributed cancer datasets and will enable these data to be analyzed within a single portal in a way that satisfies multiple analysis scenarios and utilizes diverse computational resources. Finally, a cloud-centric Galaxy built for the NCR DC will substantially grow the community of users working with the GDC and the NCR DC. This is because Galaxy brings with itself a vibrant world-wide community of users and developers, which numbers tens of thousands of scientists. These individuals will help to tune the GDC and other resources within the NCR DC to the needs of real-life analysis scenarios and will enrich the set of tools accessible to cancer researchers.

PROJECT SUMMARY The last twenty years have experienced extensive growth in the sequencing of cancer genomes, leading to a dramatically increased understanding of the role of genetic and epigenetic mutations in cancer. This has largely been enabled by developments in high-throughput ?second-generation? sequencing technology and analysis that characterize cancer genomes using short-reads. Recently, a new generation of high-throughput long-read sequencing instruments, primarily from Pacific Biosciences and Oxford Nanopore, have become available that are poised to displace short-read sequencing for many applications. We and others have used these technologies to discover tens of thousands of variants per cancer genome that are not detectable using short-reads, including structural variants and differentially methylated regions in known oncogenes and cancer risk genes. These technologies carry the potential to address many open questions in cancer biology, however, the analysis of long-read sequencing data is computationally demanding and needs specialized algorithms that are either too inefficient to use at scale or do not yet exist. In this proposal, we will address several gaps in the application of long-read technology for basic research and clinical use in cancer genomics. First, we will develop improved methods for finding structural variants and complex repeat expansions from long-reads, both of which are major diagnostic and prognostic indicators of disease, yet are not accurately identified using existing methods. Leveraging the improved phasing capabilities of long reads, this work will include the detection of mosaic variants, revealing tumor heterogeneity and variants in precancerous tissues. Next, we will apply machine learning and systems level advances to accelerate and improve the comparison of variants across large patient cohorts. Critically, this will compensate for the error prone nature of single molecule long-read sequencing to make these comparisons more accurate when comparing tumor-normal samples or pedigrees of related patients so that recurrent driving mutations can be accurately identified. Finally, we will develop integrative methods for the joint analysis of genome, transcriptome, and epigenetic profiling of cancer genomes. These advances will improve the identification of fusion genes, and allow for entirely new forms of epigenetic analysis, such as the allele-specific analysis of methylation across transposable elements and other repetitive elements. Synthesizing the many thousands of novel variants we will detect using our methods, we will then develop algorithms that will identify and evaluate recurrent genetic or epigenetic variations as putative driving mutations. All methods will be released open-source and will empower us, our ITCR collaborators, and the cancer genomics community at large to study genetic and epigenetic variants with near perfect accuracy and thereby unlock many new associations to treatment and disease.

Flows containing particles (suspension flows) are found in countless settings in nature and in technology; examples range from silt-laden water streaming in a river to blood coursing through a cell-counting analyzer. Pure Newtonian fluids are well-known to undergo instabilities that lead to significant changes in the flow behavior. Suspension flows also experience instabilities; however, the mechanisms that drive suspension flow instabilities are not yet understood. In this project, proven techniques for characterizing instabilities in pure Newtonian fluids will be applied to suspension flows instabilities. This approach should reveal how such instabilities can be probed and manipulated in service of developing better ways to predict how the particles move and are distributed in practical applications. The proposed project is also expected to have significant educational impacts, including providing training on complex flow problem-solving for the next generation of scientists and engineers, attracting and training new graduate and undergraduate students from underrepresented groups and communicating the main ideas in a non-technical form to students at all levels of the educational system and the general public.<br/> <br/>The primary goal of this project is to demonstrate that the vast fundamental and applied knowledge of instabilities in pure (Newtonian) flows can be harnessed to achieve breakthrough understanding of instabilities in suspension flows. Specifically, this project will test the main Newtonian insight that structuring the flow geometry can unfold the transition process to reveal well-separated, non-turbulent transitions arising from instabilities that can be manipulated by imposing suitably designed perturbations. The project employs new laboratory experiments and existing theory to explore suspension flows in structured channels. First, the laminar steady state will be characterized as a function of Reynolds number for a specified particle size and selected average particle volume fractions. The research then examines both pure Newtonian fluid and suspension flows instabilities. The outcomes of this project should lay the foundations for future studies to investigate new and heretofore uncharted fundamental fluid physics that arises when inertial particles are added to the flow. The results of our work should set the stage for the discovery of new methods to manipulate flow and particles.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.