Application of theoretical methods to increase succinate production in engineered strains.

Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.

The evolution of synapse models--from numbers to networks to spaces.

We review the evolution of synapse models over the last sixty-five years in terms of the changing paradigms: initially, the synapse was modelled only as part of a neuronal system, both as a number (weight of an edge in a connectionist network) and as a channel in a conductance-based model. With the availability of more structural and kinetic data, it came to be seen as a full-fledged biochemical network, accompanied by a shift from the previous "top-down" approach to a more "bottom-up" network reconstruction. Most recently, the synapse is seen as a geometric 3-dimensional space with various processes driving the dynamics. A particular focus is placed on models of the dopamine synapse and their connections to schizophrenia. The advances of detailed modelling on the synaptic level have highlighted the challenges of integrating the various functional levels, which are tightly coupled with processes on different scales in time and space. On the other hand, this effort will contribute to bridging the currently perceived gap between computational neuroscience and (computational) systems biology.

Concept maps and canonical models in neuropsychiatry.

Most bioscientists engage in informal modelling in their research and explicitly document this activity's results in diagrams or "concept maps". While canonical modelling approaches such as Biochemical Systems Theory (BST) immediately allow the construction of a corresponding system of equations, the problem of determining appropriate parameter values remains. Goel et al. introduced Concept Map Modelling (CMM) as a framework to address this problem through an interactive dialogue between experimenters and modellers. The CMM dialogue extracts the experimenters' implicit knowledge about dynamical behaviour of the parts of the system being modelled in form of rough sketches and verbal statements, e.g. value ranges. These are then used as inputs for parameter and initial value estimates for the symbolic canonical model based on the diagram. Canonical models have the big advantage that a great variety of parameter estimation methods have been developed for them in recent years. The paper discusses the suitability of this approach for neuropsychiatry using recent work of Qi et al. on a canonical model of presynaptic dopamine metabolism. Due to the complexity of systems encountered in neuropsychiatry, hybrid models are often used to complement the canonical models discussed here.

Optimization of tryptophan production in bacteria. Design of a strategy for genetic manipulation of the tryptophan operon for tryptophan flux maximization.

In the present work we have applied the indirect optimization method (Torres, N. V. et al. Biotechnol. Bioeng. 1996, 49, 247-258) to the maximization of tryptophan biosynthesis in Escherichia coli. The optimization procedure is applied to an updated model of this biochemical system (Xiu, Z-L et al., J. Biotechnol. 1997, 58, 125-140) and thus extended to a problem that includes the processes of transcription and translation. The model representation used by these authors is first translated into the corresponding S-system version. Then, to guarantee cell viability, we impose a set of constraints on some variable and parameter values, all of which are able to be modulated by available techniques. Our results show that it is possible to attain a stable and robust steady state with a rate of tryptophan production increased more than 4 times. This is achieved by changing four key parameters related to the efflux of tryptophan, the growth rate, the inhibition constant, and the tryptophan repressor level. Moreover, it is demonstrated that we can reach this optimum state in a sequential manner, each step leading us to a better situation in relation to the previous one. Thus, only by doubling the tryptophan excretion we can triplicate the rate of tryptophan production. A further, although lesser, improvement can be attained by increasing 4-fold the rate of growth and subsequently by weakening the inhibitory feedback interaction of tryptophan on the enzymes leading to its synthesis. Finally, a significant jump in the rate of production can be obtained if the level of the trp operon could be decreased. When a second approach was considered, in which the growth rate is kept constant in the optimized profile, we found out that by modulation of the parameters it is possible to increase more than 2-fold the rate of tryptophan production.

Hybrid modeling in computational neuropsychiatry.

The aim of building mathematical models is to provide a formal structure to explain the behaviour of a whole in terms of its parts. In the particular case of neuropsychiatry, the available information upon which models are to be built is distributed over several fields of expertise. Molecular and cellular biologists, physiologists and clinicians all hold valuable information about the system which has to be distilled into a unified view. Furthermore, modelling is not a sequential process in which the roles of field and modelling experts are separated. Model building is done through iterations in which all the parts have to keep an active role. This work presents some modelling techniques and guidelines on how they can be combined in order to simplify modelling efforts in neuropsychiatry. The proposed approach involves two well known modelling techniques, Petri nets and Biochemical System Theory that provide a general well proven structured definition for biological models.