Matthew Blow

Divisions

DOE Joint Genome Institute

• Genomic Technologies

Secondary Affiliation:

Environmental Genomics and Systems Biology

• Comparative and Functional Genomics

Research Interests

Using sequence-based approaches to understand genome function.

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Programs & Initiatives

• Phenix
  
• Computational Crystallographic Initiative
  

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Secondary Affiliation:

Environmental Genomics and Systems Biology

• Biosystems Data Science

Research Interests

A. Construction of whole genome phylogeny of living organisms, “Tree of Life”

The first task for this project is to develop one or more methods for comparing whole genome sequences of two organisms, not just a set of highly conserved gene or protein sequences, as currently practiced in Multiple Sequence Alignment (MSA) method. Our starting point was treating each whole genome sequence as a book consisting of a single string of alphabets without spaces between words for each chromosome. My group has developed the “Feature Frequency Profile (FFP)” method, which is a variation of “Word Frequency Profile” method used to compare two books describe in the field of Natural Language Analysis. Using the FFP method we were able to construct phylogenic trees of three domains of Life at an intermediate resolution, and two of the most diverse and large groups of Life, Prokaryotes (Archaea and Bacteria combined), and Fungi, the largest kingdom of Eukarya, at a high resolution. Compared to those trees based on MSA methods, our results revealed high similarities in grouping (clading) at high phylogenic levels, but substantial differences in evolutionary branching order of the clades at deeper evolutionary levels. Our next projects are to construct the phylogenic trees of other large “phylogenic” groups such as protists, Eukaryotic algae, insects, plants and others, and ultimately “Tree of Life” for all living organisms for which whole genome sequences are available.

B. Whole genome variation of human species vs. disease susceptibility

Most regions of genomes of normal human cells have been found to have the same sequences among individuals, but a small fraction, spread throughout the genome, have variations within a population. Of these, the single nucleotide polymorphisms (SNPs) account for the largest number of variations and, have been identified in over 3 million genomic “tag” positions out of 3 billion positions in a whole haploid genome. It has been widely accepted that the analysis of SNPs may be able to allow one to predict the genomic component of the susceptibility of individuals to complex diseases such as cancers, neurological diseases, autoimmune diseases, and other traits. So far, the results from the current analysis methods (e.g. Genome-wide Association Studies method) and interpretation of them have yielded information of limited predictive value of practical utility for making health-related decisions at individual or population level without information of family histories.

Recognizing the complexity and heterogeneity of cancer mechanisms, we have developed, using SNPs, an empirical approach using supervised machine-learning method, a branch of Artificial Intelligence, for predicting the relative genomic susceptibility of an individual to 9 traits consisting of 8 major cancer classes plus a healthy class. The multiclass accuracy of the combined prediction ranges from 33 to 56% depending on cancer classes of testing sets, as compared to 11% for a random prediction among 9 traits. Despite limited SNP data available and absence of rare SNPs in public databases at present, the results suggest that the framework of this approach or its improvement can predict the cancer susceptibility with probability estimates useful for making health-decisions for individuals or for a population. Our next projects are to use similar approaches to predict genomic susceptibility for various neurological diseases and autoimmune diseases.

C. Whole genome variation of non-human species vs. traits

For a longer-term projects we plan to apply similar approaches of machine learning methods as in B above to the genomic variations of various non-human species such as crop and bio-fuel plants, insects, farm animals and other to predict traits such as drought resistance, high growth, insect resistance, disease resistance etc.

Biography

Dr. Kim is a Professor of Graduate Studies in the Department of Chemistry, and a faculty member of Center for Computational Biology, University of California, Berkeley, CA, USA. He is also a Faculty Affiliate, Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA. His research area has been in Structural Biology of signal communicating proteins, such as Ras, and protein kinases that signal many cellular processes, such as cell differentiation and cancer. More recently his group have been applying machine learning methods for predicting individual’s genomic susceptibility for complex diseases such as cancer using whole genome germline sequence variations. More recently, his group has developed a new computation method to construct a “Whole-genome Tree of Life” that reveals kinship among all living organisms using whole-genome sequence information based on Information Theory. A similar approach is being used to study genomic demography of human ethnic groups. He is a member of the US National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, and a Fellow of The American Association for the Advancement of Science.

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Research Interests

Chemical accuracy in macromolecular Xray crystallography

Programs & Initiatives

• Computational Crystallography Initiative
  
• Python-based Hierarchical ENvironment for Integrated Xtallography (PHENIX) Software Suite
  

Divisions

Molecular Biophysics and Integrated Bioimaging

• Cellular and Tissue Imaging

Biography

Hoi-Ying Holman is a senior scientist in the Molecular Biophysics and Integrated Bioimaging (MBIB) Division of Berkeley Lab’s Biosciences Area and Director of the Berkeley Synchrotron Infrared Structural Biology (BSISB) imaging program at the Advanced Light Source (ALS). She received her PhD from the University of California at Berkeley. With team members Drs. Sun Choi, Liang Chen, and Giovanni Birarda, she was awarded a 2014 R&D 100 award for their development of the Berkeley Lab Multiplex Chemotyping Microarray (MCM).

Research Interests

Hoi-Ying’s research interests are in microbial ecology, geomicrobiology, bioenergy, bioremediation, and bioavailability of chemicals to ecological receptors. In the BSISB imaging program at the ALS, she focuses on developing and providing research communities new synchrotron infrared (SIR) technologies for deciphering the relationship between genome and functional processes, and identifying the connection between the genome and natural environments. Current technologies in active development are: SIR nano-spectroscopy, SIR plasmon resonance microscopy, and the integration of SIR spectromicroscopy with ambient IR laser ablation (AIRLAB) mass spectrometry.

Programs & Initiatives

• Berkeley Synchrotron Infrared Structural Biology
  

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Divisions

Environmental Genomics and Systems Biology

• Molecular EcoSystems Biology

Secondary Affiliation:

Biological Systems and Engineering

• BioEngineering & BioMedical Sciences

Research Interests

Jenny is a plant glycobiologist who is interested in understanding the myriad ways that plants synthesize and use complex sugars: to build their cell wall, and to glycosylate other molecules, including proteins and lipids. Her team applys synthetic and systems biology tools with the overal goal to develop more sustainable bioenergy crops.

As part of the Joint BioEnergy Institute (JBEI) we are deciphering how a cell wall is made and assembled, and applying this knowledge to the predictable engineering of dedicated biomass crops (primarily sorghum and switchgrass). As part of the m-CAFEs SFA (Microbial Community Analysis & Functional Evaluation in Soils Scientific Focus Area), we are exploring how roots interact with a synthetic microbial community. We aim to enhance those interactions via engineering, to support the development of more sustainable crops that require fewer inputs (e.g. irrigation, pesticides). For m-CAFEs, and other projects, we are making use of fabricated ecosystems (EcoFABs, EcoPODS), as we attempt to bridge the gap between data gathered in the lab and that collected in the field. Other projects include exploring how cell wall esters contribute to forest volatile emissions when trees undergo drought, and developing resources to support sorghum’s use in biotechnology research.

Programs & Initiatives

• m-CAFEs
  
• Joint BioEnergy Institute (JBEI)
  

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Research Interests

Molecular and Cell Biology and Chemistry

Divisions

Molecular Biophysics and Integrated Bioimaging

• Cellular and Tissue Imaging

Divisions

Environmental Genomics and Systems Biology

• Biosystems Data Science

Research Interests

Dr. Chris Mungall is a Staff Scientist in the Environmental Genomics and Systems Biology Division at LBNL, where he heads the Biosystems Data Science department. Chris’s research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. In the Gene Ontology project and others, Chris and his collaborators develop systems that help curators translate biological knowledge into a computable form, and apply that biological knowledge to answer complex biological questions.

Chris’s areas of focus include ontologies, systems biology, data science, biocuration, knowledge representation, data harmonization, reusable and interoperable software, machine learning and reasoning. A growing area of interest is translating curated basic research data into clinically actionable frameworks.

Chris is a PI on the Gene Ontology (GO), the Monarch Initiative, the Alliance of Genome Resources, Phenomics First, and the NCATS Biomedical Data Translator, as well as metadata lead for the National Microbiome Data Collaborative (NMDC). In 2017, Chris was the first person to be awarded the Exceptional Contributions to Biocuration Award by the International Society for Biocuration. In 2020, he received a Berkeley Lab Early Scientific Career Director’s Award.

Programs & Initiatives

• The Monarch Initiative
  
• Gene Ontology Consortium
  
• The Environmental Ontology (EnvO)
  

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Research Interests

My early scientific career began with the characterization of drug delivery in Low-Density Lipoprotein and mastering solution X-ray scattering (SAXS) techniques at the Austrian Academy of Sciences in Graz, Austria.  I have been bridging crystallography with solution scattering since 2000. My early developments in integrative structural biology were later awarded Erwin Schrödinger fellowship to pursue my postdoctoral research in Marseille at Centre National de la Recherche Scientifique. Here I developed an ensemble modeling approach to investigate flexible and dynamic macromolecules by combining solution scattering, crystallography, and molecular dynamics. This early innovation becomes the groundwork for our current worldwide used FoXS software package.

Besides developing novel SAXS methods, my research focuses on visualizing the large megadalton biological assemblies that included the cellulosome, human complement, and non-homologous end-joining. Characterizing these dynamic molecules impacts medical research, like defining the allosteric mechanism for complement inhibition by the Staphylococcus aureus and discovering the extended grooved scaffold for DNA ligation in DNA break repair.

My effort as the beamline scientist at the SIBYLS beamline at the Advanced Light Source (ALS) makes the SAXS technique accessible to more investigators and pushes SAXS onto a new paradigm. We have been the first to create a high throughput SAXS pipeline and established a mail-in program at SIBYLS to collect SAXS data for scientists from all around the world.

As a research scientist at the LBNL, my research focuses on the modulation of transcription by DNA control elements.  Using an altered bacterial histone-like protein, we show that reorganization of the bacterial chromatin can dynamically modulate the cellular transcription pattern.  We anticipate making histone-like protein interactions an attractive target for controlling pathogenesis and microbial systems in general with this ongoing research.

I’m co-directing Structural Cell Biology Core of the NCI-funded Structural Cell Biology of DNA Repair (SBDR) group.

Divisions

Molecular Biophysics and Integrated Bioimaging

• Bioenergetics

Research Interests

The goal of our work is to develop efficient, robust photocatalytic subsystems and complete systems for the synthesis of renewable fuels and chemicals using carbon dioxide and water as starting materials, and sunlight as energy source. All-inorganic oxo-bridged heterobinuclear light absorbers coupled to metal oxide nanoclusters are being developed for accomplishing visible light induced multi-electron catalysis for carbon dioxide reduction and water oxidation, using photodeposition methods for the proper coupling of chromophore and nanocluster catalyst. Structures, charge transfer processes and catalytic mechanisms are elucidated by FT-infrared, Raman and optical spectroscopy, X-ray spectroscopy, and atomic resolution imaging. The mechanistic understanding gained from the time-resolved FT-infrared studies combined with electron transfer investigations of the heterobinuclear charge transfer units by transient optical spectroscopy guide the design of units for improved photocatalytic efficiency under visible and near infrared light. Using low temperature atomic layer deposition and nanofabrication methods, metal oxide core-shell constructs are being developed for separating the water oxidation catalysis from light absorption and reduction chemistry by a nanoscale silica-based membrane. Synthetic methods have been established for embedding electron or hole conducting molecular wires into the insulating silica membrane, and proton transmission properties of the silica have been quantified. Emphasis is on tightly controlled electron transport from light absorber to catalyst through the molecular wires, on atomically defined contacts between the components, and on the elucidation of charge transport kinetics and efficiency across the assembly. The long term objective is to close the photocatalytic cycle of carbon dioxide reduction and water oxidation on the nanoscale while achieving product separation on the macroscale.

The ultrathin silica membranes with embedded molecular wires open up opportunities for exploring single integrated biohybrid assemblies to create function that combines the best of biology with the best of inorganic catalysis (BSP milestone). The membranes allow electronic coupling of life organisms with inorganic catalysis, with the incompatible reaction environments held in nanometer proximity. The approach drastically reduces efficiency losses due to transport of charges and chemical species over macroscale distances. Major effort will focus on spectroscopic and electrochemical studies to understand and control electron and proton transport pathways across both the silica and biological membrane, and to elucidate the catalytic mechanisms.