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| Proposal No.                         | **SI** | *      | **CCST**     |
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**[LDRD Strategic Initiative Proposal]{.underline}**

**Project Title:** Prokaryotic Simulation System: Computational Biology
and Bioinformatics -- the Science Drivers for PXRF

**Investigator(s): R. Stevens,** E. Selkov, E. Frank, M. Hereld, N.
Maltsev, M. Papka, L. Makowski (BIO), M. Thurnauer (CHM)

**Division (s): MCS, BIO, CHM**

## ALD(s): F.Y. Fradin

**(2) Opportunity:**

A major driver for Argonne's strategic initiative in petaflops computing
is the life sciences. In particular, the area we have selected for focus
in life science is computational cell biology (which complements the
focus in Biosciences division on functional genomics), the emerging
science of modeling and simulation of complete cellular systems.
Computational cell biology has as its goal integrative model based
understanding of complete biological systems. Developing the capability
to rapidly construct a computational model of a whole cell starting from
genetic sequence data and other existing sources of biological
information and to use this modeling capability to explore cellular
system dynamics is the primary goal of this project. The application of
this powerful capability will advance fundamental biological
understanding and will create many new applications, among them the
ability to engineer microorganisms for applications in energy, medicine
and the environment and to better understand the evolution of life.
DOE's Office of Biological and Environmental Research and the DOE Office
of Advanced Scientific Computing Research have identified this
discipline as one of its leading efforts, and we expect it to grow
substantially in the next several years.

**(3) Benefits/beneficiaries/customers**

The successful accomplishment of the goals of this proposal will create
a major new modeling simulation capability for the biosciences
community, which in turn will have the potential to be exploited by DOE
for research in energy production and environmental remediation,
including possible paths to cleaner energy and new sources of chemical
and industrial feedstocks. It also has an important national security
impact, since it will enable the rapid modeling and simulation of
microorganisms including possible new biological threats. Further, since
this activity is being developed with the explicit goal of exploiting
petascale systems, it will provide critical requirements and input to
the petascale technology project and will provide an example of
large-scale computational biology that may enable other groups of
biologist to accelerate their use of high-performance computers.

**(4) Abstract**

Previous work in biochemical network, genomic, and cell simulation has
led to models of well-characterized biochemical pathways. These
projects, however, have fallen short of developing a scalable
hierarchical integrative model of the cell that incorporates gene
regulation, metabolism, signaling, and transport in a spatial modeling
framework designed to scale to petaflops computer platforms.

Our long-term goal is to development a scalable hierarchical modeling
system for a complete prokaryotic cell and the related bioinformatics
infrastructure to support modeling. Our near-term goals are to (1)
develop the tools needed to accelerate the construction of models from
existing sources of biological information (e.g., G-Wiz), (2) build
software tools and a databases framework that enable the rapid
mathematical analysis of models that have been constructed in a
semi-automated fashion and to manage the model development process and
related tool chains, (3) develop a visualization environment for
constructing cell modeling and simulation inputs and for comparing and
visualizing simulation output, (4) develop and analyze mathematical
models of cellular metabolism and regulatory signaling networks, (5)
develop a set of computer simulations relating to understanding detailed
dynamics of cellular functions, cell cycle, and life cycles, (6) conduct
detailed studies of selected model organisms, and (7) develop a
verification and validation capability that will close the loop with
experimental studies.

**Technical Accomplishments in FY02**

We have prototyped an initial system (G-Wiz) for creating modeling
inputs from annotated genomic data and database of enzymes and metabolic
pathways. This system has been used to help reconstruct the core
metabolism of several model organisms relevant to this study. G-Wiz has
the following components: (1) integrated Oracle-based database
containing sequence data and genome annotations from the public data
sources (e.g., NCBI, TrEMBL) and information regarding metabolic and
regulatory networks (from EMP, KEGG, Enzyme databases), (2) the results
of our analysis of 52 bacterial and 2 eukaryotic genomes, (3) tools and
algorithms for high-throughput genetic sequence analysis and metabolic
reconstructions from the sequence data (e.g., algorithms for automated
assignment of functions to the genes, automation of the metabolic
reconstructions, tools for automation of the genome analysis), and (4) a
Web-based user interface supporting flexible querying capabilities,
visualization of the data, and interactive analysis and annotation of
the sequence data.

We conducted a detailed review of the literature and of existing
modeling efforts. At least two-dozen programs are reported in the
literature and conference proceedings that simulate biological processes
relevant to this proposal. They vary as to which processes can be
simulated and which mathematical modeling techniques are supported
(e.g., stoichiometric analysis vs. integration of differential equations
vs. stochastic simulation). This work has laid the groundwork a detailed
requirements analysis for the tools to support the modeling and
simulation tool chain. Our studies to date have confirmed that
whole-cell and cellular-community simulations will require data and
model elements from multiple disciplines and multiple labs within each
discipline. These data and models will have significant volume and
significant complexity. Therefore, combining these observations, we
identified several design requirements: (1) the database / simulation /
analysis interconnect is essential in order to close the modeling loop;
(2) the software must be usable by a community, not just by an
institution; and (3) we will make exploit our experience in distributed,
collaborative science and software project management to contribute to
the simulation community at large and will work to foster collaboration.

We have generated draft reconstructions of the metabolism networks of
*Synechococcus, Synechocystis,* and *Prochloroccocus marinus,* the three
model organisms selected for this study. These three are important
cyanobacteria whose genetic sequences are available and for which
important applications are possible or there is important biological
interest in their life cycle and development. Work is currently
proceeding to develop initial flux-based models.

We have developed a preliminary metabolic network visualization tool and
have begun the development of a tool framework that can be used to
support visualization of metabolic network simulations and the
comparisons of cellular networks. We have also established a basic
modeling and simulation infrastructure in the mathematics and computer
science division by installing appropriate software systems (e.g., ERGO,
Content with Maple conduit for computing derivatives, Auto2000, R, Swarm
and related tools).

We have established a baseline wet laboratory capability to cultivate
cyanobacteria and in particular to prepare to grow synchronous cultures
needed for assays and validation of initial simulations.

**Plans for FY03**

Tools for Automating Construction of Models from Existing and New
Sources of Biological Information

In addition to the need for high-throughput gene sequence analysis,
development of a functional context for the phylogenetically diverse
data is vital for the understanding of biological systems and for the
development of practical applications. To this end, we propose to
introduce the following new features to the G-Wiz system:

-   *Representation of metabolism in a form of a hierarchy of
    interconnected metabolic networks.*  We have developed a method of
    representing metabolic networks in a form of a hierarchy of
    interconnected metabolic process modules (subnetworks). This
    approach enables us to view a microbial cell as a system and to
    study its metabolism as a whole through the coordinated activities
    of its constituent metabolic functional modules. We will continue to
    develop a method for identification and representation of metabolic
    process modules. We will develop a graphical interface that will
    allow navigation of hierarchy of metabolic and regulatory networks.
    We will also develop an XML representation of the modules.

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-   *Tools for comparative analysis of metabolic and regulatory
    networks.* Detailed comparisons of metabolic routes used by
    organisms adopted to different environments and separated by
    phylogenetic distances will lead to a deeper understanding of the
    evolution of metabolic and other biological systems in general and
    will help characterize many mechanisms that still remain poorly
    understood. We will develop a set of XML tools that will allow
    comparative analysis of metabolic networks in different organisms.
    These tools will capitalize on information in the EMP database
    describing metabolic diversity in different organisms. Connected to
    the sequence and expression data, this information will aid in
    understanding the evolution of metabolic functions and genomes. It
    will also facilitate detailed studies of diversity and conservation
    of function in biological systems.

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-   *Identification and analysis of gene regulatory cascades.* The
    development of computer models of biological systems requires
    understanding of complex signaling and regulatory networks for
    predicting transcriptional responses to different environmental and
    developmental conditions. Detailed characterization of signaling and
    regulatory proteins that constitute such networks is an essential
    preparatory step for such models. We plan to use the
    Argonne-developed Sentra database of signal transduction proteins.
    Currently, Sentra contains signal transduction proteins from 48
    (including *Synechococcus WH8201* and *P. aeroginosa*) completely
    sequenced prokaryotic genomes as well as sequences from SWISS-PROT
    and TrEMBL. Signal transduction proteins are annotated with
    information describing conserved domains, paralogous and orthologous
    sequences, and conserved chromosomal gene clusters. Such in-depth
    analysis of signal transduction proteins will allow us to generate
    hypotheses regarding architecture of some signaling cascades and the
    nature of a transmitted signal.

Software Tools and Databases to Enable Rapid Analysis of Models, Manage
Model Development, and Facilitate Construction of Scalable Simulations

-   We will develop an interconnect between our bioinformatics
    databases, especially G-Wiz, and the installed tools. The objective
    is to accelerate the biological studies, thereby producing examples
    and use cases for the workflow analysis below and for studies in
    representation and transport. As these items advance, we will revise
    the interconnects between the deployed tools to test and improve the
    interconnect designs.

-   We will investigate the use of SBW and SBML as a possible framework
    for managing and representing our models and simulations. We will
    study the relationship between representations stored in the
    database and the corresponding classes inside the simulation
    executable (the two are likely not the same). Over time, the initial
    set of tools will be extended to use these representations and/or
    they will be used to interconnect new tools. Depending upon timing,
    how well the tools perform, and how quickly our own simulation
    framework grows, we will begin to replace the tools described above
    with ones of our own, interconnected with our multiscale model
    representations and database. We will build a database in which
    models may be stored. The objective will be to facilitate the
    biology studies by reducing the time spent re-entering models and by
    exploring possibilities to collate model definitions and simulation
    invocation configurations. This database is not (logically) the same
    as the database holding the bioinformatics information. As a result
    of doing the biology modeling, we will explore the relationship
    between these databases and use this to define the workflow model
    and assign roles and responsibilities to the database compartments.

Visualization for Constructing Modeling and Simulation Inputs and for
Comparing Simulation Outputs

Our work in visualization will focus on development of tools to address
two key areas: visual presentation of networks and the output of
simulations. Enabling researchers to explore biological data in a "what
if" manner requires that results and information be presented clearly
and quickly. Therefore, much of the visualization work will focus on
understanding researchers' needs. As more information and resources
become available, the complexity of the information benefiting from
visualization will increase; we will therefore focus on scalable
solutions to visualization problems.

Guiding our approach to this development work are the following basic
tenets: Build on existing standards and formats, create import/export
ability, ensure modularity to support both addition of algorithms and
feature extensions, and seek open source solutions where possible. We
will develop a network-visualization architecture with the following
features and basic components: *Modular Layout Engine* -- will handle
the layout computation that will feed the visualization. The
architecture will allow for incorporation of different layout algorithms
-- borrowed or developed in house. We will seed the environment with at
least one useful instantiation of a layout method. *Annotation and Event
Interface* -- will interface the user and simulation agents to
components of the network representation (e.g., nodes and edges). It
will enable annotations and provide mechanism for queries to retrieve
supplementary and supporting data (images, references, experimental
notes, hypotheses). *Network Visualization Parameter Interface* -- will
enable object visualization properties to be changed (wire
representations, highlighting, textures, etc.). This will facilitate
adjustment to suit different needs and will also enable later links to
simulation output. *Network Navigation and Manipulation Tools* -- will
enable researchers to navigate the structure and to study it at
different scales and from different viewpoints. The tools will enable
collapse of subnetworks and visual comparisons of object fragments.

To support integration of the simulation functions described, we will
add the following components:

-   *Simulation Parameter Sets* -- A data type that expresses
    configuration for our simulation as well as the hierarchical lists
    of probes and clamps used in the visualization interface to the
    simulations. Here, we may be able to leverage tools under
    development elsewhere, such as the Systems Biology Markup Language,
    SBML, which is part of the Systems Biology Workbench

-   *Time-Series Input Interface* -- An interface widget that will
    enable setup of time-varying parameters that drive the simulations
    (clamped fluxes, environmental ionic concentrations, ambient
    molecular species concentrations, etc.). This interface will include
    methods for selecting as well as setting these.

-   *Metabolic Probe Visualization Tool* -- The interface to
    controlling, adding, muting sets of metabolic probes to monitor the
    output of simulations. This will include the methods for selecting
    as well as setting these.

Test Models of Cellular Metabolism and of Regulatory and Signalling
Networks

During FY03 we will begin developing series of detailed models of
cyanobacteria. We will start with a model study with analysis and
multiple computational experiments of different versions of ODE models
describing the circadian cell clock. We believe that the clock has
metabolic nature and that its major positive feedback generating the
circadian oscillations does not engage gene expression mechanisms.
However, we will consider several variants of models (described in
literature but never analyzed in depth mathematically) in which the cell
clock is based on gene expression feedbacks. We will make a comparative
analysis of metabolic and genetic mechanisms to offer scientific
community enough theoretical data in favor of one of these variants.

We will model a cell population as a population of cell clocks
interacting via common cultivation medium and specific quorum sensing
signals. Here we will demonstrate a specific role of intercellular
communication via quorum sensing mechanisms since such mechanism
maintain population synchrony under batch growth. In particular, we will
demonstrate the existence of a stable autosynchronous state in the
population of large number of cells even under conditions of large
parameter scattering among the cell. To refute a widely distributed idea
that the parametric scatter is the main cause of the "inevitable"
desynchronization of the cells, we will analyze an ideal population with
identical cells and identical initial phases of their clocks and show
that such ideal populations can, under certain conditions, lose its
synchrony. To help design and plan synchronous cultivation of
cyanobacteria for this project, we will analyze variants of the
cultivation, which will enable us to maintain a high degree of synchrony
in cultivated bacteria. Some variants of instrumental feedbacks will be
considered to help maintenance of synchrony in a real bioreactor.

We believe that the modern flux analysis, very popular among biologists
and theoreticians, may successfully describe behavior of the large
asynchronous cell populations. We will show that a version of flux
analysis, a "phased flux analysis," can successfully represent
periodically changing flux distribution in cells undergoing profound
cyclic changes along metabolic cycle. The standard enzyme kinetics
assumes that the total concentration of all enzyme forms is constant. It
is an obvious oversimplification of the real situation, in which each
enzyme is subject to a continuous, very slow degradation (or dilution
caused by growing cell volume.) This degradation (dilution) flow is
counterbalanced by a biosynthesis of enzyme molecules de novo. Although
the fluxes of enzyme synthesis and degradation are very slow (thousand
times slower than the metabolite fluxes), their influence on enzyme
kinetics and the overall cell metabolism is substantial at the times
comparable to the enzyme turnover. Damped or parametric resonance
phenomena, limit cycles of the nature, quite different from what is
known so far, and stochastic limit cycles in such systems may arise in
metabolic system quite easily. By studying these processes we will gain
insight into what kinds of regulatory mechanism prevent potential chaos
in cellular organization.

Models and Simulations of Dynamics of Cellular Functions, Cell Cycle,
and Cell Clock

We will build a dynamic model of *Synechococcus sp*. intermediary
metabolism and qualitatively fit it to experimental data obtained with a
metabolically synchronous population of these cyanobacteria. The model
will describe the temporal separation of carbon and nitrogen metabolisms
orchestrated by a redox cell clock. Such separation is well established
for a number of cyanobacteria and phototrophic Eukaryotes. However, its
mechanism and the role the clock are still a matter of hot theoretical
discussions. The suggested project may deliver direct answers to many
theoretical questions, thus making an essential contribution to a deeper
understanding of the most intimate process of cellular life.

The mathematical model will be built from a detailed metabolic
reconstruction made from sequenced genome of *Synechococcus sp*. The
accuracy of the reconstruction will be essentially enhanced by automated
annotation of a large number of publicly available sequenced genomes,
including all available cyanobacterial genomes. The metabolic
reconstructions of cyanobacteria will be made accessible for public. The
most critical theoretical predictions about expected gene functions;
allosteric regulation and gene expression control mechanisms will be
experimentally verified. We plan to use the model to demonstrate
fundamental features of circadian cell clock, common for all
cyanobacteria and the vast majority of eukaryotes studied so far. These
will include a along oscillation period (about 24 hr) and its stability
in a wide range of environmental conditions. We will evaluate the
relative contribution of metabolic and genetic regulation in the
functioning and homeostatic properties of the clock

Computational Studies of Selected Model Organisms

In FY03 we will begin a number of experiments aimed at addressing a
number of significant computational and theoretical questions:

1.  To what extent gene expression is important for the cell clock
    mechanism?

2.  Can the cell clock tick under the total block of protein synthesis
    at least for one metabolic cycle? In particular, can the clock be
    ticking under a complete block of cell division?

3.  Will phase shifting of this concentration result the corresponding
    phase shifts of the cell clock?

4.  Is glycogen is the only slow changing storage compound responsible
    for the circadian period of the clock? Some cyanobacteria have
    additional carbohydrate buffers like trehalose and sucrose. Can
    these disaccharides substitute glycogen when its exchange disturbed
    by a mutation?

5.  Can *Synechococcus* and Synechocystis grow well on maltose, as
    metabolic reconstructions indicate? If so, can one use a bleached
    mutant of these cyanobacteria or grow them in the dark to see a
    typical organotrophic growth of these bacteria with nearly the same
    clock period?

6.  In Eukaryotic cells, cell clock gates cell division: the worse
    environmental conditions to support the growth the lower the
    probability to divide under continuous running clock. Does the
    phenomenon exist in cyanobacteria?

7.  We will use metabolic models to optimize some cyanobacteria to
    maximize production of H2 under in the dark. Can we then demonstrate
    it experimentally?

8.  Theory predicts existence of the auto synchrony in continuous
    cultures will arise spontaneously from the asynchronous state. Can
    we compute the conditions for such regime and confirm it
    experimentally?

9.  Theory predicts existence of a partial synchrony in the population
    at which the whole initially synchronous population can
    spontaneously split onto two subpopulations shifted by a half of
    period. One could erroneously claim such partially synchronous
    population as dividing two times faster with respect to the clock
    period. Can we compute the conditions for such situation and to
    confirm it experimentally?

10. Theory predicts, that mutation in quorum sensing prevents synchrony
    in batch cultivation. However, communication among the cells via
    common substrate/products makes the auto-synchrony possible still.
    Can we confirm that?

We believe that answering such questions will enormously advance our
understanding in the cellular life and prepare theory to attack much
more complicated problems associated with spatial organization of
bacterial and eukaryotic cells, with complicated behavior of mixed cell
population.

Verification and Validation Capability

Our goal is building quantitative dynamic models of selected
cyanobacteria. The only way of getting reliable experimental data about
intracellular dynamics is synchronous cultivation of these bacteria in a
specially designed cultivation system allowing us automate all basic
procedures in the course of experiment and to minimize human support
around the clock. For this purpose, we will use a specially designed a
version of IS 2 cultivation plant built for us by WB Moore, Inc. The
device will have a two-liter working volume and will be equipped with
all standard features required for continuous cultivation of
microorganisms in the industry (sterilization in place, pH, light, and
temperature control, continuous registration of gas levels in gas and
liquid phase). Two sanitary ports will be available for programmed or
manual sampling of culture for fixation and further analysis. The
cultivator has a standard computer interface allowing us to computerize
long-term experiments with cyanobacteria. We will monitor experiment via
the network during the whole cultivation experiment. A typical
experiment will take about two weeks. The first week will be used to
develop an initial amount of culture and establish a metabolic synchrony
among the cells. The second one will be used to further grow a
synchronous biomass to sample it in the end of the second week with 1 h
interval within one full metabolic cycle. Although the main purpose of
the experimental is to supply numeric experimental data for fitting to
theoretical models, its significance goes far beyond this simple
function. We will use this cultivation facility to generate biomass that
will be analyzed for metabolites in BIO and CHM divisions

FY 03 Milestones:

-   Improve functions and features of G-Wiz in support of rapid model
    construction and develop representations of model inputs including
    stoichiometric matrices, increase the number of genomes available to
    G-Wiz

-   Install Systems Biology Workbench and connect bioinformatics
    databases in G-Wiz to SBW

-   Build tool chain infrastructure for managing model development
    workflow and automated conversion of model representations

-   Design initial multiscale model representation in SBML, and
    construct database for model storage

-   Design modular graph layout engine, including evaluation of existing
    layout tools to understand features needed. Construct the overall
    framework to allow for a modular construction of a generic framework
    that easily supports extensions (e.g., new tools to be added and
    existing tools to be plugged in through wrappers).

-   Prototype annotation and event interface, allowing for queries from
    supplementary and supporting databases.

-   Design network visualization parameter interface. Examples will be
    produced for researchers to test and give feedback.

-   Prototype network navigation and manipulation tools

-   Work with simulation scientists to design the set of requirements
    for the time-series input interface and metabolic probe tools.

-   Build detailed metabolic reconstruction from sequenced genome of
    *Synechococcus sp*.

-   Conduct series of mathematical analysis and experiments with initial
    flux based mathematical model

-   Develop prototype dynamic model of *Synechococcus sp*, with the
    focus on the separation of the carbon and nitrogen metabolisms

-   Demonstrate features of circadian clock for cyanobacteria

-   Conduct a bifurcation analysis on metabolic models, with the
    objective of formulating a theoretical basis for auto synchronous
    growth of cyanobacterial cells

-   Validate one or more simulations in the wet lab

**(5) Resources required (dependencies, key skills, and new hires)**

This project has required the hiring new staff and postdocs. In FY02 we
have added additional staff that will perform the work summarized in
this proposal. This has included computational molecular biologists and
microbiologists with an interest in modeling prokaryotic systems,
expertise in applied mathematics of biochemical network modeling and
analysis, and modern software engineering. A major requirement for the
successful completion of this project is access to experimental
verification and validation capabilities. These are actively being
developed through joint interactions between MCS and BIO and CHM
divisions.

**(6) Future funding opportunities (direct follow-on and related
programs) for next year and subsequent years**

This project has potential for direct and follow-on funding from the DOE
Office of Advanced Scientific Computing Research and the Office of
Biological and Environmental Research, particularly from the Genomes to
Life program. It also positions ANL to compete for funding for related
programs at NIH and DARPA.

**(7) Prior year(s) accomplishments to milestones**

We have made considerable progress toward several of our near-term
goals, including (1) literature review of modeling efforts leading to
identification of design requirements, (2) initial metabolic
reconstructions of three model organisms, (3) prototype visualization
tool, and (4) baseline wet laboratory capability.