James Garbe

Divisions

Biological Systems and Engineering

• BioEngineering & BioMedical Sciences

Divisions

Biological Systems and Engineering

• BioEngineering & BioMedical Sciences

Divisions

Biological Systems and Engineering

• Process Engineering & Analytics

Research Interests

My research interests lie in the use of metabolomics analyses to characterize engineered biosynthetic and metabolic pathways to understand primary and secondary cellular metabolism and their regulation, in vivo. This is directed towards addressing key environmental challenges and engineering robust microbial platforms for producing valuable renewable bioproducts.

Divisions

Energy Analysis and Environmental Impacts

Secondary Affiliation:

Biological Systems and Engineering

• Process Engineering & Analytics

Research Interests

Technoeconomic analysis, life-cycle assessment, bioenergy scenario modeling, energy-water nexus, uncertainty analysis, infrastructure systems modeling, economic input-output modeling, transportation fleet modeling, comparative analysis of energy carriers/energy storage technologies

Divisions

Biological Systems and Engineering

• Biodesign

Research Interests

My research has focused on understanding mechanistic and structural features of modular polyketide synthases and exploiting that knowledge for producing a variety of high-value chemicals in microbes from renewable carbon sources. 

Divisions

DOE Joint Genome Institute

Secondary Affiliation:

Environmental Genomics and Systems Biology

• Molecular EcoSystems Biology

Biography

Nigel Mouncey joined us on March 15, 2017 as the Director of the DOE Joint Genome Institute.  Hailing from England, Mouncey earned his Bachelor of Science degree in microbiology with honors from the University of Glasgow and received his Doctor of Philosophy in biochemistry at the University of Sussex. He completed post-doctoral research at Harvard Medical School and the University of Texas Medical School in Houston. Mouncey spent 10 years at Roche Vitamins in New Jersey and DSM Nutritional Products in Switzerland before joining Dow AgroSciences in Indianapolis, Indiana. He has a demonstrated strong track record in microbiology and management, in both the academic and industry sectors.

Divisions

Climate & Ecosystems

Secondary Affiliation:

Environmental Genomics and Systems Biology

• Molecular EcoSystems Biology

Biography

As an environmental microbiologist, Chakraborty has pioneered identification of unique microbes mediating critical carbon and nitrogen metabolisms in diverse ecosystems. She is involved in extensive outreach and mentorship and her efforts have led to very positive impacts in the community, at the Laboratory, and on the career trajectories of a wide range of early career scientists.

Divisions

Environmental Genomics and Systems Biology

• Molecular EcoSystems Biology

Secondary Affiliation:

DOE Joint Genome Institute

Biography

I received my undergraduate training at Northwestern University where I majored in Biology. While at Northwestern, I worked for Dr. James A. Lippincott on Agrobacterium tumefaciens Ti plasmid transfer and integration. It was here that I first developed an interest in molecular biology and plant pathology. I then went to the University of California at Berkeley were I eventually ended up working the laboratory of Dr. Steven Lindow as a staff research associate for almost eight years. I was rapidly educated in the workings of the news media and public perceptions of scientific research with the lab’s release of Ice Minus Pseudomonas syringae into the environment. I went to graduate school in the Department of Plant Pathology at UC Berkeley with Dr. Lindow as my advisor and Dr. Andy O. Jackson as my mentor in molecular biology. My interest was in the bacterium’s response to stress in the environment and received my Ph.D. under Steve Lindow in the characterization of epiphytic fitness mutants of P. syringae subjected to UV and desiccation stresses. The mid-90s was not the best time for agricultural research funding, so for my post-doc I changed fields, still keeping my interest in bacterial interactions in the environment. I joined Dr. Ken Wilson’s lab in the Infectious Diseases Division of Duke University Medical Center where I studied culture-resistant human pathogens and the epidemiology of the anthrax pathogen, Bacillus anthracis. While at Duke University I was fortunate enough to be the first to discover sequence-based differences for the differentiation of strains of B. anthracis. The variable number tandem repeats (VNTR) that I discovered became the basis for a strain identification system for B. anthracis that is still in use today. I joined Lawrence Livermore National Laboratory as a Principle Investigator in the Biology and Biotechnology Research Program. While at LLNL, I set up a research program in bacterial diagnostics and environmental monitoring of pathogens. I joined Lawrence Berkeley National Laboratory in 2003 as a Scientist in the Earth Sciences Division. I have set up a laboratory to study bacterial responses to selected environments. I have been very excited to be at a place where I am exposed to the latest technological breakthroughs and to be right next door to the UC Berkeley campus where I am able to interact with the leaders in the field of microbiology. I am the former Head of the Ecology Department at LBNL.

Research interests from EESA’s webpage:

My research focus is in the area of microbial ecology and includes the examination of phylogenetic diversity in natural environments. My laboratory uses molecular approaches to study the dynamics of microbial community structure under changing environmental conditions. This includes the development of new techniques to dissect the microbial diversity of complex ecosystems. The long-term goal of this research is to integrate different fields of biology (i.e., genomics, ecology, molecular biology, proteomics and bioinformatics) to provide insight into the interactions of environmental microorganisms under stressful conditions. We hope to harness the existing capabilities of beneficial microbes to improve water quality, reduce contamination and limit the amount of carbon released into the atmosphere. My current research focuses on the remediation of oil spills, limiting sulfate reduction in oil reservoirs, tracking sources of fecal pollution in marine and freshwater systems and using thermophilic composting to reduce the impact of wastes on watersheds and to improve land use productivity. My laboratory has developed Greengenes, a 16S rRNA gene sequence repository and online toolset, for aligning/annotating novel sequences, interpreting microarray results and developing phylogenetically specific probes. We have been part of the Data Analysis and Coordination Center for the Human Microbiome Project and have examined the tremendous diversity of human-associated microbes. We have developed numerous microarray systems, including the third-generation (G3) PhyloChip, for the measurement of microbial diversity and the identification of bacterial communities by 16S rRNA gene sequences. We have also developed additional microarray systems, including microarrays for whole-genome expression profiling in response to various environmental stimuli.

A complete listing of current research projects can be found on The Andersen Lab website or on UC Berkeley’s Gary Andersen Page

Research Interests

I am interested in using molecular approaches to study the dynamics of microbial community structure in the environment. This includes the development of new techniques to dissect the microbial diversity of complex ecosystems. The long-term goal of this research is to integrate different fields of biology (i.e., genomics, ecology, molecular biology, proteomics and bioinformatics) to provide insight into the interactions of environmental microorganisms under stressful conditions. My current research focuses on the microbial ecology of airborne and aquatic bacteria with an emphasis on the natural distribution of pathogens in the environment. I have developed numerous microarray systems for the measurement of microbial diversity and the identification of bacterial communities by 16S and other gene sequences. I have also used microarrays for whole genome expression of genes in response to environmental stimuli.

Divisions

Molecular Biophysics and Integrated Bioimaging

• Structural Biology

Research Interests

1) New algorithms and methods development for crystallographic structure determination of small- and macro(bio)-molecules.

2) Software (Phenix) development for automated crystallographic structure solution, analysis and validation using X-ray, neutron and cryo-EM data.

3) Three-dimensional structure determination of macro-molecules.

Programs & Initiatives

• Computational Crystallography Initiative (CCI)
  
• PHENIX Software Suite
  
• Quantum Refinement Project
  

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.