R-3 Repository :: Browsing by Author "Deem, Michael W."

Browsing by Author "Deem, Michael W."

Now showing 1 - 20 of 46

Analysis of Hierarchical Organization in Gene Expression Networks Reveals Underlying Principles of Collective Tumor Cell Dissemination and Metastatic Aggressiveness of Inflammatory Breast Cancer
(Frontiers, 2018) Tripathi, Shubham; Jolly, Mohit Kumar; Woodward, Wendy A.; Levine, Herbert; Deem, Michael W.
Clusters of circulating tumor cells (CTCs), despite being rare, may account for more than 90% of metastases. Cells in these clusters do not undergo a complete epithelial-to-mesenchymal transition (EMT), but retain some epithelial traits as compared to individually disseminating tumor cells. Determinants of single cell dissemination versus collective dissemination remain elusive. Inflammatory breast cancer (IBC), a highly aggressive breast cancer subtype that chiefly metastasizes via CTC clusters, is a promising model for studying mechanisms of collective tumor cell dissemination. Previous studies, motivated by a theory that suggests physical systems with hierarchical organization tend to be more adaptable, have found that the expression of metastasis-associated genes is more hierarchically organized in cases of successful metastases. Here, we used the cophenetic correlation coefficient (CCC) to quantify the hierarchical organization in the expression of two distinct gene sets, collective dissemination-associated genes and IBC-associated genes, in cancer cell lines and in tumor samples from breast cancer patients. Hypothesizing that a higher CCC for collective dissemination-associated genes and for IBC-associated genes would be associated with retention of epithelial traits enabling collective dissemination and with worse disease progression in breast cancer patients, we evaluated the correlation of CCC with different phenotypic groups. The CCC of both the abovementioned gene sets, the collective dissemination-associated genes and the IBC-associated genes, was higher in (a) epithelial cell lines as compared to mesenchymal cell lines and (b) tumor samples from IBC patients as compared to samples from non-IBC breast cancer patients. A higher CCC of both gene sets was also correlated with a higher rate of metastatic relapse in breast cancer patients. In contrast, neither the levels of CDH1 gene expression nor gene set enrichment analysis (GSEA) of the abovementioned gene sets could provide similar insights. These results suggest that retention of some epithelial traits in disseminating tumor cells as IBC progresses promotes successful breast cancer metastasis. The CCC provides additional information regarding the organizational complexity of gene expression in comparison to GSEA. We have shown that the CCC may be a useful metric for investigating the collective dissemination phenotype and a prognostic factor for IBC.
Brain Modularity Mediates the Relation between Task Complexity and Performance
(The MIT Press, 2017) Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuñez, Aurora I.; Ye, Fengdan; Deem, Michael W.; Center for Theoretical Biological Physics
Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.
Computational and Theoretical Analysis of Influenza Virus Evolution and Immune System Dynamics
(2011) Pan, Keyao; Deem, Michael W.
Influenza causes annual global epidemics and severe morbidity and mortality. The influenza virus evolves to escape from immune system antibodies that bind to it. The immune system produces influenza virus specific antibodies by VDJ recombination and somatic hypermutation. In this dissertation, we analyze the mechanism of influenza virus evolution and immune system dynamics using theoretical modeling and computational simulation. The first half of this thesis discusses influenza virus evolution. The epidemiological data inspires a novel sequence-based antigenic distance measure for subtypes H1N1 and H3N2 virus, which are superior to the conventional measure using hemagglutination inhibition assay. Historical influenza sequences show that the selective pressure increases charge in immunodominant epitopes of the H3 hemagglutinin influenza protein. Statistical mechanics and high-performance computing technology predict fixation tendencies of the H3N2 influenza virus by free energy calculation. We introduce the notion of entropy from physics and informatics to identify the epitope regions of H1-subtype influenza A with application to vaccine efficacy. We also use entropy to quantify selection and diversity in viruses with application to the hemagglutinin of H3N2 influenza. Using the bacterial E. coli as a model, we show the evidence for recombination contributing to the evolution of extended spectrum β-lactamases (ES-BLs) in clinical isolates. A guinea pig experiment supports the discussion on influenza virus evolution. The second half of the thesis discusses immune system dynamics. We design a two-scale model to describe correlation in B cell VDJ usage of zebrafish. We also introduce a dynamical system to model original antigenic sin in influenza. This dissertation aims to help researchers understand the interaction between influenza virus and the immune system with a quantitative approach.
Computational discovery of metal-organic frameworks with high gas deliverable capacity
(2017-04-20) Bao, Yi; Deem, Michael W.
Metal-organic frameworks (MOFs) are a rapidly emerging class of nanoporous materials with largely tunable chemistry and diverse applications in gas storage, gas purification, catalysis, sensing and drug delivery. Efforts have been made to develop new MOFs with desirable properties both experimentally and computationally for decades. To guide experimental synthesis, we here develop a computational methodology to explore MOFs with high gas deliverable capacity. This de novo design procedure applies known chemical reactions, considers synthesizability and geometric requirements of organic linkers, and efficiently evolves a population of MOFs to optimize a desirable property. We identify 48 MOFs with higher methane deliverable capacity at 65–5.8 bar condition than the MOF-5 reference in nine networks. In a more comprehensive work, we predict two sets of MOFs with high methane deliverable capacity at a 65–5.8 bar loading–delivery condition or a 35–5.8 bar loading–delivery condition. We also optimize a set of MOFs with high methane accessible internal surface area to investigate the relationship between deliverable capacities and internal surface area. This methodology can be extended to MOFs with multiple types of linkers and multiple SBUs. Flexibile MOFs may allow for sophisticated heat management strategies and also provide higher gas deliverable capacity than rigid frameworks. We investigate flexible MOFs, such as MIL-53 families, and Fe(bdp) and Co(bdp) analogs, to understand the structural phase transition of frameworks and the resulting influence on heat of adsorption. Challenges of simulating a system with a flexible host structure and incoming guest molecules are discussed. Preliminary results from isotherm simulation using the hybrid MC/MD simulation scheme on MIL-53(Cr) are presented. Suggestions for proceeding to understand the free energy profile of flexible MOFs are provided.
Design of organic structure directing agents for polymorph A zeolite beta
(Royal Society of Chemistry, 2019) Daeyaert, Frits; Deem, Michael W.
Zeolite beta is a crystalline material with layer-type faulting. The two end members with perfect crystalline order are beta A and beta B. While zeolite beta in faulted form is a widely used industrial catalyst, additional applications may be possible with the perfect crystalline forms. We here computationally design chemically synthesizable organic structure directing agents that may aid the nucleation and growth of pure zeolite beta A, excluding the competing product zeolite beta B.
Development of modularity in the neural activity of childrenʼs brains
(IOP Publishing, 2015) Chen, Man; Deem, Michael W.; Center for Theoretical Biological Physics
We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from functional magnetic resonance imaging (fMRI) data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease.
Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling
(Nature Publishing Group, 2015) Bai, Peng; Jeon, Mi Yeong; Ren, Limin; Knight, Chris; Deem, Michael W.; Tsapatsis, Michael; Siepmann, J. Ilja
Zeolites play numerous important roles in modern petroleum refineries and have the potential to advance the production of fuels and chemical feedstocks from renewable resources. The performance of a zeolite as separation medium and catalyst depends on its framework structure. To date, 213 framework types have been synthesized and >330,000 thermodynamically accessible zeolite structures have been predicted. Hence, identification of optimal zeolites for a given application from the large pool of candidate structures is attractive for accelerating the pace of materials discovery. Here we identify, through a large-scale, multi-step computational screening process, promising zeolite structures for two energy-related applications: the purification of ?ethanol from fermentation broths and the hydroisomerization of alkanes with 18-30 carbon atoms encountered in petroleum refining. These results demonstrate that predictive modelling and data-driven science can now be applied to solve some of the most challenging separation problems involving highly non-ideal mixtures and highly articulated compounds.
DNA Free Energy Landscapes and RNA Nano-Self-Assembly Using Atomic Force Microscopy
(2014-03-26) Frey, Eric William; Kiang, Ching-Hwa; Deem, Michael W.; Ajayan, Pulickel M.
There is an important conceptual lesson which has long been appreciated by those who work in biophysics and related interdisciplinary fields. While the extraordinary behavior of biological matter is governed by its detailed atomic structure and random fluctuations, and is therefore difficult to predict, it can nevertheless be understood within simplified frameworks. Such frameworks model the system as consisting of only one or a few components, and model the behavior of the system as the occupation of a single state out of a small number of states available. The emerging widespread application of nanotechnology, such as atomic force microscopy (AFM), has expanded this understanding in eye-opening new levels of detail by enabling nano-scale control, measurement, and visualization of biological molecules. This thesis describes two independent projects, both of which illuminate this understanding using AFM, but which do so from very different perspectives. The organization of this thesis is as follows. Chapter 1 begins with an experimental background and introduction to AFM, and then describes our setup in both single-molecule manipulation and imaging modes. In Chapter 2, we describe the first project, the motivation for which is to extend methods for the experimental determination of the free energy landscape of a molecule. This chapter relies on the analysis of single-molecule manipulation data. Chapter 3 describes the second project, the motivation for which is to create RNA-based nano-structures suitable for future applications in living mammalian cells. This chapter relies mainly on imaging. Chapters 2 and 3 can thus be read and understood separately.
Enantiomerically enriched, polycrystalline molecular sieves
(2023-05-09) Davis, Mark E.; Brand, Stephen Kramer; Schmidt, Joel E.; Deem, Michael W.; Rice University; California Institute of Technology; William Marsh Rice University; United States Patent and Trademark Office
This disclosure describes enantiomerically enriched chiral molecular sieves and methods of making and using the same. In some embodiments, the molecular sieves are silicates or germanosilicates of STW topology.
Enantiomerically enriched, polycrystalline molecular sieves
(2021-12-21) Davis, Mark E.; Brand, Stephen Kramer; Schmidt, Joel E.; Deem, Michael W.; Rice University; California Institute of Technology; United States Patent and Trademark Office
This disclosure describes enantiomerically enriched chiral molecular sieves and methods of making and using the same. In some embodiments, the molecular sieves are silicates or germanosilicates of STW topology.
Evolutionary processes in finite populations
(American Physical Society, 2013) Lorenz, Dirk M.; Park, Jeong-Man; Deem, Michael W.
We consider the evolution of large but finite populations on arbitrary fitness landscapes. We describe the evolutionary process by a Markov-Moran process.We show that toO(1/N), the time-averaged fitness is lower for the finite population than it is for the infinite population.We also showthat fluctuations in the number of individuals for a given genotype can be proportional to a power of the inverse of the mutation rate. Finally, we show that the probability for the system to take a given path through the fitness landscape can be nonmonotonic in system size.
Experimental Free Energy Landscape Reconstruction of DNA Unstacking Using Crooks Fluctuation Theorem
(2013-06-05) Frey, Eric; Kiang, Ching-Hwa; Deem, Michael W.; Nordlander, Peter J.
Nonequilibrium work theorems, such as the Jarzynski equality and the Crooks fluctuation theorem, allow one to use nonequilibrium measurements to determine equilibrium free energies. For example, it has been demonstrated that the Crooks fluctuation theorem can be used to determine RNA folding energies. We used single-molecule manipulation with an atomic force microscope to measure the work done on poly(dA) as it was stretched and relaxed. This single-stranded nucleic acid exhibits unique base-stacking transitions in its force-extension curve due to the strong interactions among A bases, as well as multiple pathways. Here we showed that free energy curves can be determined by using the Crooks fluctuation theorem. The nonequilibrium work theorem can be used to determine free energy curves even when there are multiple pathways.
Facile Synthesis and Catalysis of Pure-Silica and Heteroatom LTA
(American Chemical Society, 2015) Boal, Ben W.; Schmidt, Joel E.; Deimund, Mark A.; Deem, Michael W.; Henling, Lawrence M.; Brand, Stephen K.; Zones, Stacey I.; Davis, Mark E.
Zeolite A (LTA) has many large-scale uses in separations and ion exchange applications. Because of the high aluminum content and lack of high-temperature stability, applications in catalysis, while highly desired, have been extremely limited. Herein, we report a robust method to prepare pure-silica, aluminosilicate (product Si/Al = 12–42), and titanosilicate LTA in fluoride media using a simple, imidazolium-based organic structure-directing agent. The aluminosilicate material is an active catalyst for the methanol-to-olefins reaction with higher product selectivities to butenes as well as C5 and C6 products than the commercialized silicoalumniophosphate or zeolite analogue that both have the chabazite framework (SAPO-34 and SSZ-13, respectively). The crystal structures of the as-made and calcined pure-silica materials were solved using single-crystal X-ray diffraction, providing information about the occluded organics and fluoride as well as structural information.
Finite population size effects in quasispecies models with single-peak fitness landscape
(IOP Publishing, 2012-04) Saakian, David B.; Deem, Michael W.; Hu, Chin-Kun
We consider finite population size effects for Crow-Kimura and Eigen quasispecies models with single-peak fitness landscape. We formulate accurately the iteration procedure for the finite population models, then derive the Hamilton-Jacobi equation (HJE) to describe the dynamic of the probability distribution. The steady-state solution of HJE gives the variance of the mean fitness. Our results are useful for understanding the population sizes of viruses in which the infinite population models can give reliable results for biological evolution problems.
Hierarchy in gene expression is predictive of risk, progression, and outcome in adult acute myeloid leukemia
(IOP Publishing, 2015) Tripathi, Shubham; Deem, Michael W.
Cancer progresses with a change in the structure of the gene network in normal cells. We define a measure of organizational hierarchy in gene networks of affected cells in adult acute myeloid leukemia (AML) patients. With a retrospective cohort analysis based on the gene expression profiles of 116 AML patients, we find that the likelihood of future cancer relapse and the level of clinical risk are directly correlated with the level of organization in the cancer related gene network. We also explore the variation of the level of organization in the gene network with cancer progression. We find that this variation is non-monotonic, which implies the fitness landscape in the evolution of AML cancer cells is non-trivial. We further find that the hierarchy in gene expression at the time of diagnosis may be a useful biomarker in AML prognosis.
Hierarchy of gene expression data is predictive of future breast cancer outcome
(IOP Publishing, 2013) Chen, Man; Deem, Michael W.
We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to the correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with the values of network hierarchy in the expression network of all genes, due to the dedifferentiation of gene pathways and circuits. These results suggest that the hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis.
Improving the Effectiveness of the Annual Flu Vaccine
(James A. Baker III Institute for Public Policy, 2018) Deem, Michael W.; Bonomo, Melia E.; Matthews, Kirstin R.W.; James A. Baker III Institute for Public Policy
In Silico Discovery of High Deliverable Capacity Metal-Organic Frameworks
(American Chemical Society, 2015) Bao, Yi; Martin, Richard L.; Simon, Cory; Haranczyk, Maciej; Smit, Berend; Deem, Michael W.
Metal-organic frameworks (MOFs) are actively being explored as potential adsorbed natural gas storage materials for small vehicles. Experimental exploration of potential materials is limited by the throughput of synthetic chemistry. We here describe a computational methodology to complement and guide these experimental efforts. The method uses known chemical transformations in silico to identify MOFs with high methane deliverable capacity. The procedure explicitly considers synthesizability with geometric requirements on organic linkers. We efficiently search the composition and conformation space of organic linkers for 9 MOF networks, finding 48 materials with higher predicted deliverable capacity (at 65 bar storage, 5.8 bar depletion, and 298 K) than MOF-5 in 4 of the 9 networks. The best material has a predicted deliverable capacity 8% higher than that of MOF-5.
In silico prediction of MOFs with high deliverable capacity or internal surface area
(Royal Society of Chemistry, 2015) Bao, Yi; Martin, Richard L.; Haranczyk, Maciej; Deem, Michael W.
Metal–organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal–organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. The correlation between deliverable capacities and surface area is network dependent.
Influenza evolution and H3N2 vaccine effectiveness, with application to the 2014/2015 season
(Oxford University Press, 2016) Li, Xi; Deem, Michael W.
Influenza A is a serious disease that causes significant morbidity and mortality, and vaccines against the seasonal influenza disease are of variable effectiveness. In this article, we discuss the use of the pepitope method to predict the dominant influenza strain and the expected vaccine effectiveness in the coming flu season. We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 [clade 3C.1] vaccine against the A/California/02/2014 [clade 3C.3a] strain that emerged in the population can be estimated via pepitope. In addition, we show by a multidimensional scaling analysis of data collected through 2014, the emergence of a new A/New Mexico/11/2014-like cluster [clade 3C.2a] that is immunologically distinct from the A/California/02/2014-like strains.

Browsing by Author "Deem, Michael W."

Results Per Page

Sort Options