Computational characterization and exploitation of biological networks.
Many aspects of biology can increasingly be represented as networks. Modeling and
manipulating these networks creates needs and opportunities for faculty who are focused on the
computational characterization and exploitation of biological networks. Genomics approaches
are generating high dimensional datasets that provide the foundation for simulations of these
networks. There are however many challenges that require non-traditional combinations of
expertise and approaches. Biological processes span very large time and geographical scales
from nanoseconds to millennia as well as from nanometers to ecosystems. Biological systems
encompass multibody problems in which components interact in specific combinations and
orders. In addition, biological processes are multivariate systems in which many variables
change over time. Successful analysis and manipulation of biological networks will require
extensive collaborations between several types of computational and experimental groups.
Improvements in analytical techniques and increases in computational power are
enhancing our ability to measure, analyze and model network-based phenomena on large
scales. For example, the ability to comprehensively and quantitatively monitor dynamic changes
in gene expression, protein levels and metabolite concentrations, together with new genomescale computational methods, is enabling detailed characterization of many types of biological
networks. As understanding of biological networks increases, methods of genetic engineering
provide possibilities for existing or novel biological networks to be synthesized and manipulated
for clinical, agricultural, or industrial benefits.
Many of key mathematical, statistical and computational issues that are applicable to
network biology also arise in fields unrelated to biology, where networks may have been
intensively studied for a long time. Therefore, pertinent concepts and analytical tools exist that
have yet to be applied to biology. Mathematical concepts underlying networks are broadly
applicable to multiple fields. The impact of interdisciplinary research and cooperation depends
on abilities and opportunities to see beyond superficial domain differences and to exploit deeper
structural similarities. The coordinated recruitment of a small number of new FTE focused on
network biology combined with the existing strengths at UC Davis provides a unique opportunity
for such interdisciplinary interactions.
For UC Davis to become a world leader in this rapidly emerging field, we request a
cluster of eight faculty hires that span eight departments in five colleges coordinated by the
Genome Center. One each would go into Biomedical Engineering (BME), Computer Science,
Evolution and Ecology (EVE), Math, Molecular & Cellular Biology (MCB), Pharmacology, Plant
Biology, and Plant Sciences. We will advertise the positions together and have a search
committee with a member from each unit who would identify appropriate candidates. Each unit
would then review the files and conduct interviews and recruitments. This cluster hire will help
recruit top faculty and provide research opportunities that would not occur if such hires were
made independently.
The new faculty will be housed in each of the eight participating departments to foster
interactions between them and the existing faculty strengths. The Genome Center will
coordinate the recruitment process and catalyze interactions between the faculty once recruited.
It will coordinate a seminar series focused on network biology and provide forums for networkoriented discussion groups. It will also assist in the recruiting of graduate students to multiple
graduate groups and facilitate the writing of collaborative research and training grants. In
addition, it will provide some of the computational resources and complementary expertise that
will be utilized by the recruited faculty.
This proposal addresses the campus-wide need to increase research in computational
biology. It builds on the existing strengths of the wet-lab faculty that are generating a wealth of
genomic data but need the tools to analyze them in a systems biology context. These positions
match and extend current departmental plans as well as complement each other. These
positions are only a focused contribution to a larger trend on campus. Several other
departments could have equally well participated. The requested FTE complement the more
physics and chemistry-oriented hires being made by the Computational Science and
Engineering Center. The impact of these eight FTE will be felt well beyond the eight
participating departments.
The position in BME will aim to elucidate the design principles for biological networks in
their complex natural setting, to employ these principles for the re-design of dysfunctional
networks, and to design, construct and evaluate synthetic biological networks with therapeutic
potential. A component of this research will be aimed at understanding the robustness and the
sensitivity of biological networks in a noisy environment that may be a key issue in controlling
responses of biological networks. This will allow the manipulation of biological networks for
translational research aimed at clinical application, based on principles to guide the design
process and understanding of the functional consequences of genetic manipulations. This
position will complement the current strengths in BME in system level study of genetic networks
and signal transduction networks using engineering principles.
The position in Computer Science will apply approaches developed for diverse types of
networks. The study of networks in biology is an active and natural topic for computer science,
since computer science has long been concerned with networks in many applications
(communication networks, networks of computers, networks as data-structures, and networkflow and graph-theoretic algorithms in addition to direct applications in biology). Computer
scientists are now active in three major topics in the study of networks in biology: the
characterization and analysis of regulatory and metabolic networks; the characterization and
exploitation of networks of biological interactions extracted by text mining the huge databases of
biological literature; the inference of phylogenetic and genealogical networks that are the key to
understanding reticulated evolution, driven by genomic data that are not well-suited to tree
models. Research in Computer Science will mesh well with the group developing in
Mathematics and will complement laboratory studies of biological networks in multiple
departments.
The position in EVE will utilize comparative genomics to analyze genomic networks that
occur at both the intracellular level, involving the interactions that specify phenotypes and
govern metabolism, and at the population genetic level, involving non-bifurcating genealogical
networks produced by recombination and selection. This will extend our studies of evolutionary
biology that span all levels of biological organization: from molecules and molecular pathways to
phenotypes, populations, and ecosystems. Genetic networks occur at all levels of organization
including the intracellular level, involving the interactions that specify phenotypes and govern
metabolism; the population level, involving non-bifurcating genealogical networks produced by
recombination and selection; and the level of inter-specific relationships. Networks at all of
these levels are receiving increasing attention from evolutionists. For instance, a central postgenomic question is how developmental pathways translate molecular variation into phenotypic
variance and evolutionary divergence. Similarly, we are just beginning to understand how to
analyze the genealogical networks that characterize the history of most eukaryotic DNA
sequences, whose transmission involves sexual reproduction and recombination. The current
EVE academic plan involves hires in both theoretical population genetics and comparative
genomics/phylogenomics. EVE would provide a productive environment for an FTE that
addresses genomic networks and comparative genomics.
The position in Math will apply nonlinear dynamical systems to the mathematical
modeling of molecular pathways. This position is consistent with the academic plan of the
Department of Mathematics to expand their existing strengths in partial differential equations,
mathematical physics, and geometric topology where dynamical systems play a central role.
One of the most exciting areas of nonlinear dynamical systems is the mathematical modeling of
biochemical pathways. The dynamics of such a system will include rate equations for each
reaction step in the pathway, plus the equations governing the expression of the genes and the
synthesis of their proteins. The systems involved will include nonlinear equations with many
unknowns. Because of the small numbers of molecules involved, stochastic effects are
expected to be important. The mathematical models needed for full simulations are thus
expected to be complex. The identification of good paradigm problems that are simple enough
to be tractable and characterized sufficiently to make modeling possible as well as biologically
important enough so that the utility of the modeling is evident to wetlab scientists, will require
close collaborations with biologists. The proposed initiative places the Mathematics Department
in an ideal position in its pursuit of its long term goal of increasing research collaborations with
biological sciences.
The position in MCB will focus on the computational analysis of metabolic and
developmental networks. Both the maintenance of normal cellular homeostasis and the
development of complex multicellular organisms are dependent on the precise regulation of
molecular networks. In metabolism, the flux of molecules through specific biochemical
pathways and the regulation of these pathways play a key role whereas the precise temporal
regulation of gene expression is essential for normal development. Integration of computational
biology with wet-lab experimentation will provide new insights into how these networks function.
This FTE would make a key contribution to MCB’s academic plan that emphasizes biochemistry
and developmental biology.
The position in Pharmacology will exploit the rapidly increasing amounts of
proteomic data that are being generated as the result of increased resolution and
throughput of mass spectrometers as well as other approaches such as protein
microarrays. Such proteomic analyses are critical to the analysis of the components and
dynamics of protein interaction networks. Our ability to generate proteomics data has
exceeded the tools to analyze it. This position will focus on devising and implementing
new statistical and computational approaches to analyze proteomic data such as
statistical models that predict and test the stoichiometry, stability, and dynamics of
macromolecular protein complexes as well as computational methods to compare
quantitative datasets generated by microarray and proteomics data. This FTE will
closely complement the recent Genome Center hires into Pharmacology and MCB who
are involved in wet lab proteomics research.
The FTE in Plant Biology will be an experimentalist who uses computational approaches
to analyze for example hormone signaling, long-distance cellular communication networks, or
developmental responses to light or other environmental cues. The positions in Plant Biology
and Plant Science will exploit several unique features of plants that make them excellent models
for biological networks. Plants can be grown in large numbers under controlled environments
and exhibit a much wider range of developmental responses to environmental stimuli than
animals, making them ideal for quantitative modeling of interactions of multi-cellular organisms
with their environment. Plants also exhibit a high degree of intra-specific genetic polymorphism
relative to animals; such diversity provides both natural variants and molecular markers to allow
rapid identification of the genetic components underlying the observed responses. The data
generated in the research by this FTE will allow the construction of models for gene networks,
metabolic pathways, long-range communications, and responses to external signals. A longterm goal is to model a “virtual plant”. This FTE will complement existing strengths in plant
research at UC Davis and will allow UC Davis to maintain its leading position in plant biology.
The FTE in Plant Sciences will be a quantitative geneticist who will develop and apply
methods for high-throughput and high-resolution identification of quantitative trait loci and
analysis of the phenotypic consequences of natural allelic variation. Domesticated and natural
plant systems provide excellent models to understand the underlying relationship between
phenotype and genotype. Genomic technologies now in place at the Genome Center, the Plant
Genomics Program and the Department of Plant Sciences provide the platform for large-scale
genotyping and phenotyping in plant populations. New theoretical and computational methods
are required to mine between the large multi-dimensional datasets that will be generated. Such
methods are rooted in quantitative genetic and statistical theory. Quantitative genomics is an
emerging discipline formed at the interface of classical quantitative genetic theory and the
molecular dissection of complex traits, including gene regulation.
In summary: these eight positions will provide a unique combination of perspectives on
an overlapping set of biological problems. They will expose non-traditional, non-biological
expertise to biologists and vice versa. This will result in novel research projects and establish
UC Davis as a leader in network analysis and manipulation.
Respectfully submitted
Richard Michelmore
Director, The Genome Center
Michael Savageau
Chair, Department of Biomedical Engineering
College of Engineering
Zhaojun Bai
Chair, Department of Computer Science
College of Engineering
Michael Turelli
Chair, Department of Ecology and Evolution
Division of Biological Sciences
Motohico Mulase
Chair, Department of Mathematics
College of Letters and Science
Michael Dahmus
Chair, Department of Molecular and Cellular Biology
Division of Biological Sciences
Peter Cala
Chair, Department of Pharmacology and Toxicology
School of Medicine
Venkatesan Sundaresan
Chair, Section of Plant Biology
Division of Biological Sciences
Chris van Kessel
Chair, Department of Plant Sciences
College of Agricultural and Environmental Sciences