854 research outputs found
Does acetogenesis really require especially low reduction potential?
AbstractAcetogenesis is one of the oldest metabolic processes on Earth, and still has a major global significance. In this process, acetate is produced via the reduction and condensation of two carbon dioxide molecules. It has long been assumed that acetogenesis requires ferredoxin with an exceptionally low reduction potential of ≈−500mV in order to drive CO2 reduction to CO and the reductive carboxylation of acetyl-CoA to pyruvate. However, no other metabolic pathway requires electron donors with such low reduction potential. Is acetogenesis a special case, necessitating unique cellular conditions? In this paper, I suggest that it is not. Rather, by keeping CO as a bound metabolite, the CO-dehydrogenase-acetyl-CoA-synthase complex can couple the unfavorable CO2 reduction to CO with the favorable acetyl-CoA synthesis, thus enabling the former process to proceed using ferredoxin of moderate reduction potential of −400mV. I further show that pyruvate synthesis can also take place using the same ferredoxins. In fact, the synthesis of pyruvate from CO2, methylated-protein-carrier and −400mV ferredoxins is an energy-neutral process. These findings suggest that acetogenesis can take place at normal cellular redox state. Mechanistic coupling of reactions as suggested here can flatten energetic landscapes and diminish thermodynamic barriers and can be another role for enzymatic complexes common in nature and a useful tool for metabolic engineering
The protein cost of metabolic fluxes: prediction from enzymatic rate laws and cost minimization
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts of
different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to solve
this optimization problem directly. By treating enzyme cost as a function of
metabolite levels, we formulated ECM as a numerically tractable, convex
optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 3.8 and 2.7,
respectively, for the two kinds of data. ECM can be used to predict enzyme
levels and protein cost in natural and engineered pathways, establishes a
direct connection between protein cost and thermodynamics, and provides a
physically plausible and computationally tractable way to include enzyme
kinetics into constraint-based metabolic models, where kinetics have usually
been ignored or oversimplified
Synthetic metabolism: metabolic engineering meets enzyme design.
Metabolic engineering aims at modifying the endogenous metabolic network of an organism to harness it for a useful biotechnological task, for example, production of a value-added compound. Several levels of metabolic engineering can be defined and are the topic of this review. Basic 'copy, paste and fine-tuning' approaches are limited to the structure of naturally existing pathways. 'Mix and match' approaches freely recombine the repertoire of existing enzymes to create synthetic metabolic networks that are able to outcompete naturally evolved pathways or redirect flux toward non-natural products. The space of possible metabolic solution can be further increased through approaches including 'new enzyme reactions', which are engineered on the basis of known enzyme mechanisms. Finally, by considering completely 'novel enzyme chemistries' with de novo enzyme design, the limits of nature can be breached to derive the most advanced form of synthetic pathways. We discuss the challenges and promises associated with these different metabolic engineering approaches and illuminate how enzyme engineering is expected to take a prime role in synthetic metabolic engineering for biotechnology, chemical industry and agriculture of the future
Hitting Diamonds and Growing Cacti
We consider the following NP-hard problem: in a weighted graph, find a
minimum cost set of vertices whose removal leaves a graph in which no two
cycles share an edge. We obtain a constant-factor approximation algorithm,
based on the primal-dual method. Moreover, we show that the integrality gap of
the natural LP relaxation of the problem is \Theta(\log n), where n denotes the
number of vertices in the graph.Comment: v2: several minor changes
Conflict-Free Coloring Made Stronger
In FOCS 2002, Even et al. showed that any set of discs in the plane can
be Conflict-Free colored with a total of at most colors. That is,
it can be colored with colors such that for any (covered) point
there is some disc whose color is distinct from all other colors of discs
containing . They also showed that this bound is asymptotically tight. In
this paper we prove the following stronger results:
\begin{enumerate} \item [(i)] Any set of discs in the plane can be
colored with a total of at most colors such that (a) for any
point that is covered by at least discs, there are at least
distinct discs each of which is colored by a color distinct from all other
discs containing and (b) for any point covered by at most discs,
all discs covering are colored distinctively. We call such a coloring a
{\em -Strong Conflict-Free} coloring. We extend this result to pseudo-discs
and arbitrary regions with linear union-complexity.
\item [(ii)] More generally, for families of simple closed Jordan regions
with union-complexity bounded by , we prove that there exists
a -Strong Conflict-Free coloring with at most colors.
\item [(iii)] We prove that any set of axis-parallel rectangles can be
-Strong Conflict-Free colored with at most colors.
\item [(iv)] We provide a general framework for -Strong Conflict-Free
coloring arbitrary hypergraphs. This framework relates the notion of -Strong
Conflict-Free coloring and the recently studied notion of -colorful
coloring. \end{enumerate}
All of our proofs are constructive. That is, there exist polynomial time
algorithms for computing such colorings
Prediction from Enzymatic Rate Laws and Cost Minimization
Bacterial growth depends crucially on metabolic fluxes, which are limited by
the cell’s capacity to maintain metabolic enzymes. The necessary enzyme amount
per unit flux is a major determinant of metabolic strategies both in evolution
and bioengineering. It depends on enzyme parameters (such as kcat and KM
constants), but also on metabolite concentrations. Moreover, similar amounts
of different enzymes might incur different costs for the cell, depending on
enzyme-specific properties such as protein size and half-life. Here, we
developed enzyme cost minimization (ECM), a scalable method for computing
enzyme amounts that support a given metabolic flux at a minimal protein cost.
The complex interplay of enzyme and metabolite concentrations, e.g. through
thermodynamic driving forces and enzyme saturation, would make it hard to
solve this optimization problem directly. By treating enzyme cost as a
function of metabolite levels, we formulated ECM as a numerically tractable,
convex optimization problem. Its tiered approach allows for building models at
different levels of detail, depending on the amount of available data.
Validating our method with measured metabolite and protein levels in E. coli
central metabolism, we found typical prediction fold errors of 4.1 and 2.6,
respectively, for the two kinds of data. This result from the cost-optimized
metabolic state is significantly better than randomly sampled metabolite
profiles, supporting the hypothesis that enzyme cost is important for the
fitness of E. coli. ECM can be used to predict enzyme levels and protein cost
in natural and engineered pathways, and could be a valuable computational tool
to assist metabolic engineering projects. Furthermore, it establishes a direct
connection between protein cost and thermodynamics, and provides a physically
plausible and computationally tractable way to include enzyme kinetics into
constraint-based metabolic models, where kinetics have usually been ignored or
oversimplified
Epigenetic Landscape of Interacting Cells: A Model Simulation for Developmental Process
We propose a physical model for developmental process at cellular level to
discuss the mechanism of epigenetic landscape. In our simplified model, a
minimal model, the network of the interaction among cells generates the
landscape epigenetically and the differentiation in developmental process is
understood as a self-organization. The effect of the regulation by gene
expression which is a key ingredient in development is renormalized into the
interaction and the environment. At earlier stage of the development the energy
landscape of the model is rugged with small amplitude. The state of cells in
such a landscape is susceptible to fluctuations and not uniquely determined.
These cells are regarded as stem cells. At later stage of the development the
landscape has a funnel-like structure corresponding to the canalization in
differentiation. The rewinding or stability of the differentiation is also
demonstrated by substituting test cells into the time sequence of the model
development.Comment: The discussion, in terms of our model, on the recently reported
context-dependent behavior of STAP cells [Nature 505, 641-647 (2014)] has
been added in Appendi
An In Vivo Metabolic Approach for Deciphering the Product Specificity of Glycerate Kinase Proves that Both E. coli’s Glycerate Kinases Generate 2-Phosphoglycerate
Apart from addressing humanity’s growing demand for fuels, pharmaceuticals, plastics and other value added chemicals, metabolic engineering of microbes can serve as a powerful tool to address questions concerning the characteristics of cellular metabolism. Along these lines, we developed an in vivo metabolic strategy that conclusively identifies the product specificity of glycerate kinase. By deleting E. coli’s phosphoglycerate mutases, we divide its central metabolism into an ‘upper’ and ’lower’ metabolism, each requiring its own carbon source for the bacterium to grow. Glycerate can serve to replace the upper or lower carbon source depending on the product of glycerate kinase. Using this strategy we show that while glycerate kinase from Arabidopsis thaliana produces 3-phosphoglycerate, both E. coli’s enzymes generate 2-phosphoglycerate. This strategy represents a general approach to decipher enzyme specificity under physiological conditions
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Quantum Chemical Approach to Estimating the Thermodynamics of Metabolic Reactions
Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism.Chemistry and Chemical Biolog
Non-Hermitian approach for modeling of noise-assisted quantum electron transfer in photosynthetic complexes
We model the quantum electron transfer (ET) in the photosynthetic reaction
center (RC), using a non-Hermitian Hamiltonian approach. Our model includes (i)
two protein cofactors, donor and acceptor, with discrete energy levels and (ii)
a third protein pigment (sink) which has a continuous energy spectrum.
Interactions are introduced between the donor and acceptor, and between the
acceptor and the sink, with noise acting between the donor and acceptor. The
noise is considered classically (as an external random force), and it is
described by an ensemble of two-level systems (random fluctuators). Each
fluctuator has two independent parameters, an amplitude and a switching rate.
We represent the noise by a set of fluctuators with fitting parameters
(boundaries of switching rates), which allows us to build a desired spectral
density of noise in a wide range of frequencies. We analyze the quantum
dynamics and the efficiency of the ET as a function of (i) the energy gap
between the donor and acceptor, (ii) the strength of the interaction with the
continuum, and (iii) noise parameters. As an example, numerical results are
presented for the ET through the active pathway in a quinone-type photosystem
II RC.Comment: 15 pages, 8 figure
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