RESEARCH NEEDS FOR BIOAUGMENTATION - Bioaugmentation for Groundwater Remediation - page 332

describe a biological system by a set of constraints that can be described mathematically, and the
set of possible solutions define the range of valid states and behaviors of the biological system (Lee
et al., 2006 ). These constraints can be physicochemical (e.g., physical laws of conservation of mass
and energy), topological (e.g., spatial restrictions on metabolites within cellular compartments),
and environmental (e.g., nutrient availability and pH).
Some models attempt to predict fluxes between different metabolic pathways, both to
understand the microbial physiology and to predict growth yields and metabolite usage within
the cell. Flux balance analysis is specifically concerned with determining a set of steady-state
fluxes that optimize a specific parameter (e.g., maximizing biomass production). Once the
system is defined, optimization techniques may be applied to evaluate the performance of the
biological system to perturbation(s) using computer simulations. The resulting sets of
simulated flux data can be compared with experimental data and ultimately the collection of
possible fluxes can be used to predict the response of large-scale biochemical networks exposed
to different conditions (Lee et al., 2006 ).
This constraint-based modeling approach can accurately predict microbial growth under a
variety of environmental conditions (Edwards et al., 2001 ; Feist et al., 2009 ; Ibarra et al., 2002 ;
Mahadevan et al., 2006 ; Oh et al., 2007 ). This approach may be particularly well-suited to
modeling microorganisms in heterogeneous environments because it does not assume constant
yield coefficients and has been shown to account for the changes in the metabolic network in
response to nutrient limitations (Schuetz et al., 2007 ; Varma et al., 1993 ).
The potential of metabolic modeling for understanding and predicting microbial physiology
is evident by the rapidly expanding database of models (~40) for a diversity of microorganisms.
However, apart from a few notable exceptions, most of the research efforts have focused on
well-studied model organisms, motivating the need for expanding these modeling approaches to
non-model organisms and microbial communities. For example, the Department of Energy
(DOE) has recently initiated such a modeling effort, focused on microbial processes relevant to
the missions in bioenergy generation (Rittmann et al., 2008 ), carbon cycling and bioremediation
(Ahsanul Islam et al., 2010 ; Scheibe et al., 2009 ; Yu et al., 2010 ). Finally, most of the models are
based on metabolic networks reconstructed from pure cultures, further highlighting the need for
network reconstruction and modeling methods from metagenome sequences.

Research Needs: Basic Science - Organismal Scale
Enrich for and cultivate new organisms and newly-defined communities of organisms, on different
contaminants and under different conditions.

Refer to Section 12.3.4 on The Enrichment Paradox.

Develop new strategies for cultivating microbes from the subsurface.

Focus on both isolated organisms and defined communities.

Better understand the evolution of these organisms and the origins of the activity (e.g., dechlorination).

How were these traits acquired and why?

What are the natural substrates and niches for these organisms?

Apply genome scale models of metabolism and regulation to enhance microbial growth.

Take advantage of the large available datasets of genomic information.

Models should be applied to real systems, not model organisms.