MICHAEL W. DEEM John W. Cox Professor Rice University 6100 Main Street - MS 142 Houston, TX 77005-1892 (713) 348-5852 (713) 348-5811 (confidential fax) mwdeem@rice.edu My PGP public key

Education
B.S., California Institute of Technology (1991)
Ph.D., University of California at Berkeley (1994)
Postdoctoral Fellow, Harvard University (1995-1996)

Awards, Honors, and Positions
Fannie and John Hertz Fellow (1991-1994); Senior Research Scientist, CuraGen Corporation (1994-1995); NSF Postdoctoral Fellowship in Chemistry (1995-1996); Assistant and tenured Associate Professor, UCLA (1996-2002); NSF CAREER Award (1997-2001); Northrop Grumman Outstanding Junior Faculty Research Award (1997); Visiting Professor, University of Amsterdam (1999); A Top 100 Young Innovator, MIT's Technology Review (November 1999) (Profile); Alfred P. Sloan Research Fellow (2000); Camille Dreyfus Teacher-Scholar Award (2002); John W. Cox Professor, Rice University (2002-); Allan P. Colburn Award (2004); Editorial Board Member, Protein Engineering, Design and Selection (2005-present); Fellow, American Institute for Medical and Biological Engineering (2005); Member, Board of Directors, Biomedical Engineering Society (2005-2008); Fellow, American Physical Society (2006); Associate Editor, PLoS Computational Biology (2006-present); Vaughan Lectureship, California Institute of Technology (2007); Member, Nominating Committee, Division of Biological Physics, American Physical Society (2007); Member, Board of Governors, Institute for Complex Adaptive Matter (2007-present)

Publications
Recent Invited Talks
Recent Presentations
Popular Press
U.S. Patents

Theoretical methods of statistical mechanics are developed and applied at Rice to study the collective properties of biological systems. Natural systems from our world and engineered systems from biotechnology offer a wide variety of phenomena for study. The group has 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, and the first exact solution of a mathematical model of evolution that accounts for cross-species genetic exchange. The adaptive immune response and immunological responses to cancer vaccines are studied with a variety of random energy models. Field theories are used to analyze physical theories of evolution. In the materials field, the group has developed a number of widely-used Monte Carlo methods in structure, nucleation, and function of zeolites and remains interested in these areas.

The Adaptive Immune Response

     
Evolved antibody affinity constant to a second antigen after exposure to an original antigen that differs by probability p (solid line) where the dotted line represents the affinity constant without previous exposure, dominant and subdominant epitopes of the hemagglutinin and neuraminidase proteins of the 2003/2004 flu virus, and specific lysis values for dengue fever from theory and human T cell vaccine trials.

Our immune system protects us against death by infection. A major component of the immune system is generation of antibodies, protein molecules that bind specific antigens. To recognize invading pathogens, the immune system performs a search of the amino acid sequence space of possible antibodies. To find useful antibodies in the effectively infinite protein sequence space, the immune system has evolved a hierarchical strategy. Once the immune system has been faced with an antigen, a state of memory is established, which allows the immune system to respond more rapidly and effectively upon subsequent encounters with the same antigen. Although our immune system is highly effective, some limitations have been reported. The phenomenon known as ``original antigenic sin'' is the tendency for antibodies produced in response to exposure to influenza virus antigens to suppress the creation of new, different antibodies in response to exposure to different versions of the flu. The phenomenon of original antigenic sin has been observed in the flu, dengue fever, human immunodeficiency virus (HIV), and other viruses.

Using random energy models, we investigate the dynamics of original antigenic sin in the immune system. The phenomenon of original antigenic sin is explained as stemming from localization of the immune system response in antibody sequence space. This localization is a result of the roughness in sequence space of the evolved antibody affinity constant for antigen and is observed for diseases with high year-to-year mutation rates, such as influenza. These results suggest several implications for vaccination strategies against viruses and cancers.

Cancer Vaccines

   
Immunodominance hierarchy and energy profile of different vaccination protocols for related (left) and unrelated (right) cancer-specific epitopes.

Our immune system protects us on a daily basis against a broad spectrum of possible cancers. The refractory nature of cancer to many standard therapies has led to substantial efforts to achieve immune control. Cancerous cells of many types are, however, exceptionally adept at evading the immune response. By sculpting the diversity of the effector TCR repertoire, immune evasion by tumor cells can be reduced.

We introduce a theory that captures realistic recognition characteristics between the T cell receptors (TCRs) and tumor, the primary dynamics due to TCR resource competition, and the secondary dynamics due to competition between elimination of tumor cells by effector TCRs and escape of tumor cells by epitope mutation and allele loss. We use this theory to show how the immune response to each cancer-associated epitope may be sculpted. The theory is used to generate design rules for the development of vaccines for cancers with high mutation rates or multiple specific antigens, with a focus on reducing the deleterious effects of immunodominance.

Protein Molecular Evolution

     
New hierarchical protocol for protein molecular evolution, evolution to a new three-dimensional protein fold, and conservative (green) and non-conservative (yellow) mutations in the evolution of green fluorescent protein.

Biological diversity has evolved despite the essentially infinite complexity of protein sequence space. We are developing hierarchical approaches to the efficient searching of this space. We quantify the evolutionary potential of the approach with Monte Carlo simulations. Non-homologous juxtaposition of encoded structure has been shown to be the rate-limiting step in the production of new tertiary protein folds. Indeed, non-homologous ``swapping'' of low energy secondary structures increased the binding constant of a simulated protein by 10^8 relative to base substitution alone. Applications of the approach include the generation of new protein folds and modeling the molecular evolution of disease.

Qualitative changes in protein space such as these allow viruses, parasites, bacteria, and cancers to evade the immune system, vaccines, antibiotics, and therapeutics. The successful design of vaccines and drugs must anticipate the evolutionary potential of both local and large space searching by pathogens in response to therapeutic and immune selection. The addition of disease specific constraints to the Monte Carlo simulations should be a promising approach for predicting pathogen plasticity. Experimental implementation of the hierarchical protocol should be a powerful approach to the discovery of new therapeutics. Infectious agents will continue to evolve unless we can force them down the road to extinction.

Structure and Function of Cystine-Knot Peptide Antimicrobials

       
Experimental NMR structures of two Kalata B1 isomers, a simulated structure, and the lantibiotic nisin Z used a food preservative in salad dressing served on United Airlines.

Over the past fifteen years, hundreds of membranolytic peptides have been isolated from a wide variety of plants, invertebrates, mammals (including humans), bacteria, and fungi. Most of these peptides play a key role in the host cells' defense system. A wide range of membranolytic activities have been observed. Peptide activity has been observed versus bacteria and fungi cells, tumor cells, viruses (including HIV-1), and erythrocyte cells. Optimization of the therapeutic index of these molecules requires understanding the mechanisms of their membranolytic activity and also determination of peptide structural features necessary for the activity. For example, peptide antimicrobials are useful in treating sepsis, and the lantibiotic nisin Z is used as a food preservative. However, the mechanisms that determine the peptide specificity and membrane activity are poorly understood.

We are developing biased Monte Carlo methods to study peptide drugs and toxins. The approach leads to an efficient, Boltzmann-weighted sampling of the torsional degrees of freedom in these biological molecules, a feat not possible with standard Monte Carlo and molecular dynamics methods. These techniques allow, for the first time, an examination of the conformations accessible to these peptide drugs and toxins at body temperature. The approach allows prediction of peptide transport properties and improved parameterizations of force fields. Finally, the approach can be used to design inhibitors to protein active sites, in a rigorous formulation of the GROW and SPROUT ideas used in the pharmaceutical industry.

Combinatorial Chemistry

   
Random Phase Volume Model used in the validation of combinatorial chemistry protocols.

The goal of combinatorial materials discovery is to find compositions of matter that maximize a specific material property, such as superconductivity, magnetoresistance, luminescence, ligand specificity, sensor response, or catalytic activity. We have developed new experimental protocols for searching the space of variables in combinatorial chemistry, exploiting an analogy between combinatorial materials discovery and Monte Carlo computer modeling methods. The Random Phase Volume Model (above) is used to demonstrate the effectiveness of the Monte Carlo strategies.

Efficient implementations of the search strategy are feasible with existing library creation technology. Moreover ``closing the loop'' between library design and redesign is achievable with the same database technology currently used to track and record the data from combinatorial chemistry experiments. These multiple-round protocols, when combined with appropriate robotic controls, should allow the practical application of combinatorial chemistry to more complex and interesting systems.

Transport-Limited Reactions

     
Renormalization group flow near a phase transition, the ``superfast'' reaction regime in reactive turbulent flow, and vertices of a reaction/diffusion field theory. 

Surface reactions show a variety of complex spatial and temporal patterns. Simple systems, such as oxidation of CO on single crystal Pt(110), show surprisingly rich behavior, ranging from spirals and standing waves to chemical turbulence. Such behavior arises because two dimensions is the upper critical dimension for many surface reactions, and so collective fluctuations control the dynamics. Because collective fluctuations dominate the kinetics, the traditional reaction diffusion partial differential equations are invalid in two dimensions. We have derived the exact equations for surface reactions. We study the kinetics by applying renormalization group theory to a field theoretic description of the reaction. We have found that surface defects can alter the behavior of surface desorption reactions, leading to anomalous kinetics in some cases. We have found a variety of interesting effects, such as ``superfast reaction'' in reactive turbulent flow under some conditions. Results such as these are relevant to heterogeneous metal catalysis, annealing of defects in solids, and design of microreactors (MEMS).