Linking genes to microbial growth kinetics—An integrated biochemical systems engineering approach

Koutinas, Michalis; Kiparissides, Alexandros; Silva‐Rocha, Rafael; Lam, Ming-Chi; Santos, Vítor A. P. Martins dos; Pistikopoulos, Efstratios N.; Mantalaris, Athanasios

doi:10.1016/j.ymben.2011.02.001

Cited by 29 publications

(37 citation statements)

References 68 publications

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“…However, the cell mass accumulation rate of strain B0013-070 was not affected by down regulating ldhA expression (in the catabolic pathway, Table 3). This effect may be because the pronounced effect of acetic acid (Luli and Strohl, 1990;Pan et al, 1987) did not decrease (except at 40 1C) or because the microbial growth kinetics were linked to upstream molecular events that control the production of rate-limiting enzymes (Koutinas et al, 2011). After optimization of the growth temperature, the biomass yield of strain B0013-070B at 33 1C improved by 13% compared to that of strain B0013-070 at 37 1C (strain B0013-070 was previously reported to be grown at 37 1C; (Zhou et al, 2011)) with a minimal amount of lactate.…”

Section: Resultsmentioning

confidence: 99%

Genetically switched d-lactate production in Escherichia coli

Zhou

Niu

Tian

et al. 2012

Metabolic Engineering

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Section: Resultsmentioning

confidence: 99%

Genetically switched d-lactate production in Escherichia coli

Zhou

Niu

Tian

et al. 2012

Metabolic Engineering

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“…For example, Newton's Second Law of Motion states that force is proportional to the product of mass and acceleration, is a formal model between force, mass, and acceleration. Similarly, in microbiology, Monod's equation is a century old equation which formalizes the relationship biomass to the concentration of limiting substrate, and is still in use in many studies today [33,34].…”

Section: All Models Are Wrong But Some Are Usefulmentioning

confidence: 99%

Of (Biological) Models and Simulations

Ling¹

2016

MOJPB

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All models are wrong, but some are useful. Box and Draper [1].All models are right… Most are useless. Tarpey [2]The role of models has been recognized as an important aspect to the scientific method [3] for more than 70 years [4]. In recent years, there are increasing interests in metabolic modeling in biology [5,6]. The process of building a model is known as modeling. Generally speaking, models can be classified as either material models or formal models. Rosenblueth & Wiener [7] consider material models to be a physical representation of the object under study whereas formal models are symbolic or logical representations of the object under study. For example, a scale down model of an airplane for testing its aerodynamic properties in a wind tunnel is a material model. The main purpose of a scaleddown material model is the ease of study, which had been clearly demonstrated by a study on fireplace more than 200 years ago [8].A formal model, on the other hand, is an abstract but unambiguous description of a physical object or phenomenon, usually in logical or mathematical constructs. Commonly used mathematical constructs for building formal models are rate equations, such as differential equations [9], or state transitions, such as petri nets [10]. Other modeling formalisms had been reviewed by Machado [11]. Besides differentiating models by their underlying formalism, models can also be classified by their informational complexity. At the lowest level, the presence or absence of associations between 2 entities (such as cells or molecules) can be represented as a Boolean term. This results in a binary state petri net or Boolean network, which can be used to study cycles and reachability. For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17]. The main advantage of kinetic models over steady state model is the ability to simulate each state (or molecules in the context of metabolic modeling) across time, in addition to the ability to model homeostasis [12]. This will allow for identification of bottlenecks in the metabolism [18].With advancements in computing power, formal models, being constructed in mathematical or logical format, render it possible for computation. Such computation or execution of formal models is commonly known as simulation. Hence, by definition, one can "build a model", then "run or execute a model" or "run or execute a simulation" but in no means, one can "build a simulation". The ability to render execution of a model; thus, an executable or simulatable model; defines the fundamental difference between a material model an...

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“…modeling [87,88]. As shown in Figure 15.2, tandem Eσ 70 promoters Pr1 and Pr2 [89] express the master regulatory xylR gene, the product of which (XylRi) represses its own synthesis [90].…”

Section: Deconstruction Of the Ps-pr Regulatory Node Into Three Autonmentioning

confidence: 99%

“…While this distinction may not be mechanistically accurate [85], it allows us to separate the activation of Pm resulting from the master regulator XylR from that brought about by the formation of 3MB after the action of the upper pathway enzymes on m-xylene (see below). It has been possible to merge both Boolean approaches with dynamic modeling for representing the Pr/Ps node, as the lack of information about the TFs makes it necessary to use (if nothing else) a 0/1 approach to describe the interaction between, for example, Eσ 70 , Eσ 54 , and HU and the target promoters [87,88].…”

Section: Deconstruction Of the Ps-pr Regulatory Node Into Three Autonmentioning

confidence: 99%