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MICHAEL W. DEEM Research Interests Page
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MICHAEL W. DEEM Research Interests Page
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Publications
Recent Invited Talks
U.S. Patents
Michael Deem is an American researcher. He works in the life science and materials science areas. Deem has published over 200 papers, given over 300 invited talks, invented 16 U.S. patents, and taught over 10000 students.
Michael W. Deem develops theoretical methods of statistical mechanics to study the collective properties of biological systems. He studies both nature and synthetic, engineered biology. He developed methods to quantify vaccine effectiveness and antigenic distance for influenza, methods to sculpt the immune system to mitigate immunodominance in dengue fever, a physical theory of the competition that allows HIV to escape from the immune system, the first exact solution of a mathematical model of evolution that accounts for cross-species genetic exchange, a hierarchical approach to protein molecular evolution, a `thermodynamic' formulation of evolution, and a theory for how biological modularity spontaneously arises in an evolving system. He developed structured random energy models to study the adaptive immune response to viruses and vaccines. He used field theories to analyze evolution. He showed the connection between horizontal gene transfer and modularity. In the materials field, he developed a number of widely-used Monte Carlo methods in structure, nucleation, and function of zeolites. He remains interested in these areas.
For nearly a decade, Deem participated in a project to find fundamental mathematical laws of biology, FunBio. Building on his work showing that evolvability is a selectable trait, he contributed the idea that changes in environmental pressure stimulate the spontaneous formation of modular structure, with a scaling factor that depends on the ruggedness of the fitness landscape. Modular partitioning of the geometry of biological space shows up in protein structure, genetics, and biological networks. In this body of work, he explained how biology nucleated from among the many possibilities in chemistry. He described the emergence of modularity in biology as a symmetry-breaking phase transition, with modularity as the order parameter. He used the theory to explain observations in pathogen structure, metabolic networks, gene networks, and protein-protein interaction networks. He further used the theory to explain observations in ecological food webs, developmental pathways, physiology, cancer, social networks, and world trade networks. He used this theory to discover and explain new methods for protein evolution. He used this theory to understand how modularity of the human brain changes as babies develop into adults. He used this theory to postulate and then demonstrate the relation between task complexity and modularity in adult human brains.
Biological health is not a single, stable, fixed point. Rather, health reflects a rich interplay of complex dynamics. Recent observations suggest that one of the most significant damaging effects of trauma or illness may be erosion of natural physiological complexity. For example, loss of heart rate variability leads to a deterioration of health. Similarly, loss of natural correlations in gait leads to postural imbalance. To preempt that erosion, there is a critical need for predictive physiology. Physiologists currently use pattern classification to predict clinical outcomes. By echoing predictive meteorology, Deem and colleagues made an important step in developing a framework for predictive physiology. The rich Deem approach used dense data, high-speed computing, and repeated application of simple physical laws.
Thus, he seeks a novel marriage between the mathematics of fluctuations and clinical medicine. Clinically, this project's importance lies in two realms. First, there is high clinical value in predicting what will happen "five minutes from right now." This information is very helpful for safely managing acute care patients in the emergency department, operating room, and intensive care unit. Second, there is a critical need to predict when patients might veer away from their predicted physiology. When patients become "off trajectory," their medical team can provide heightened surveillance and early intervention. This mathematical framework, when fully formed, may serve as part of the basis for personalized critical care.
Following an exact solution of a simplified Eigen model by Luca Peliti, Deem mapped the classical Crow-Kimura and Eigen physical theories of evolution onto quantum field theories. In this setting, he mapped exactly solved a wide class of evolution models. These exact solutions give us a 'thermodynamic' formulation of evolution. This discovery makes precise the analogies between energy and replication rate, mutation rate and temperature, and population size and temperature. He developed these theories for fitness landscapes with multiple peaks and extended them to include recombination and horizontal gene transfer. He showed how alphabet size affects the error threshold phase transition and extended the classical theories to finite populations.
The Michael Deem Rice group spent two decades on the immune response to, evolution of, and vaccines for influenza A virus. Deem developed a theory of the immune pressure on the virus. This theory considers both the sequence level and the molecular level. He developed a method based upon sequence clustering that identifies an emerging influenza virus before it becomes dominant. This strain detection tool is useful for selection of the annual influenza vaccine.
Over the last decade, he has become interested in the bacterial immune system, CRISPR. He has explained, for example, the increased diversity of the leader-proximal spacer sequences in CRISPR as resulting from antigen selection pressure. He developed a physical model to study the dynamics of bacteria and phage coevolution. He quantified the impact of mutation and recombination on bacteria CRISPR recognition and phage evasion. He discovered an intriguing phase diagram of the phage extinction probability, mediated by the CRISPR system, that is more complex than that of the classical predator–prey model. He built on this work to review phage mechanisms of evasion from CRISPR-protected prokaryotes. He described how the CRISPR-Cas system is useful in biotechnology. He discussed examples in from cell lines and animal models, cell labeling and information storage, antibiotic resistance, and human therapeutics. He described how CRISPR makes efficient use of its limited repertoire to recognize as many different phages as possible.
Deem developed the generalized NK (GNK) model of protein evolution. Phil Anderson suggested in the 1980s that a spin glass model may describe the fitness landscape of life. Kauffman and Levin introduced the NK model as an instance of the spin glass model. Deem pointed out that biology is modular, and this modular structure must, therefore, be present in a theoretical description. The GNK model that Deem developed accounts for the formation of and interaction between secondary structures of a protein and for the presence of a protein active site or binding site. He used the GNK model to validate the performance of a new, hierarchical approach to protein molecular evolution.
The Michael W. Deem Rice group spent two decades developing and applying the GNK model. Deem used the GNK model to predict the usefulness of reduced alphabets in protein molecular evolution experiments. He computed the expected efficacy of vaccines with the GNK model, as a function of the difference between the vaccine and infecting virus. He showed that the pEpitope metric for antigenic distance correlates to a higher degree with H3N2 vaccine efficacy in humans than do the data from ferret animal models. He explained why original antigenic sin could occur, in the first theory to explain both positive and negative vaccine effectiveness. He used the GNK model to predict influenza H3N2 vaccine effectiveness; the predictions were within 10% of the data from human studies. He used the GNK model to predict immunity in an agent-based influenza epidemiology model, predicting epidemic progression in accordance with WHO data. He used the theory to predict the impact of vaccination strategy, time of vaccination, and extent of vaccination. He used the GNK model to look at autoimmune disease, describing how the immune system benefits from seemingly puzzling glassy dynamics-through alleviation of autoimmune disease.
Deem used the GNK model to identify new designs for a vaccine against the four viruses that cause dengue fever. He showed that immunodominance in the immune system can limit the T cell immunity generated by traditional vaccines. He showed that a novel multi-site vaccination provides excellent protection. Recent experiments have validated this strategy. Deem generalized the GNK model to T cell immunity and parametrized it on altered peptide ligand experiments. He predicted immunological averages and correlations. He applied the GNK model to T cell immunodominance data; the predictions were within the error bars of human vaccine data.
He extended this work to look at the pressure on HIV to escape from the immune system. He suggested an approach to corral the virus with several vaccines to prevent escape from the immune system.
He used the GNK model to examine the T cell response to cancer. He put forward a mechanism that explains the immunodominance which occurs in cancer vaccine studies is as a result of competition for access to antigen in the body's lymph nodes. He made a quantitative comparison of GNK model predictions to specific lysis data. From this theory, he evaluated the multi-site vaccination strategy. He further used the GNK model to predict tumor escape and immune elimination probabilities as a function of expressed epitopes and vaccination strategy. He showed that allele loss is more significant than point mutation for tumor escape.
Michael W Deem developed the widely-used DIFFAX and ZEFSA methods for solving zeolite crystal structures from powder diffraction data. These tools are applicable to natural zeolites and synthetic zeolites. He provided the first atomistic simulations, as a step for fundamental understanding, of silica nucleation under zeolite synthesis conditions. More recently he developed a database of predicted zeolite-like frameworks that contains over 4 million structures. The database is offered as a tool for the zeolite material community, and it is part of the predicted crystallography open database (PCOD). Researchers are currently exploring the possibilities that this database contains. Researchers are identifying structures with promising properties in carbon capture, adsorption, and catalysis for further modeling and targeted synthesis. This database of molecular sieves greatly expands the possibilites for a new synthetic zeolite. Michael Deem developed a method to discover chemically-synthesizable organic structure directing agents (OSDAs) for zeolites. He applied this approach to find new molecules for the synthesis of several zeolites, including chiral zeolites. He further extended this approach to optimize linkers in metal organic frameworks (MOFs) for maximal gas storage. He remains interested in these areas of materials chemistry.
Michael Deem has contributed a number of fundamental advances to statistical mechanics. He developed an efficient, recursive formalism for calculating diffraction patterns from faulted crystals. He showed that Monte Carlo simulation requires balance, not detailed balance as commonly assumed. He developed the parallel tempering method for materials simulations. He developed the coherent states quantum field theory for chemical reactions in disordered media. He developed the coherent states formalism for evolution. He invented the hierarchical spin glass models for evolution.