Two-pore domain potassium (K₂P) channels are responsible for maintaining the background conductance essential to the resting membrane potential¹. K₂P channels assemble as dimers containing two pore-forming domains and four transmembrane segments per subunits. Two fenestrations connect the lipid membrane with the central conduction cavity, which can be open or closed depending of the movements of helix TM4². TALK subfamily of K₂P channels is activated by alkaline extracellular pH and is formed by 3 members: TALK-1, TALK-2 and TASK-2. TASK-2 is also gated by intracellular pH (pH_i), being closed by intracellular acidification and activated by increasing pH_i. The neutralization of lysine positioned at the end of TM4 helix, and probably within the fenestrations, by a mutation to K245A abolishes pH_i-gating³. The molecular mechanism by which pH_i-sensing K245 exerts its gating role is unknown. A possible mechanism suggest that K245 protonated is able to open the fenestrations and therefore close the channel⁴. Through computational studies, we modeled the 3D structure of TASK-2 channel in both fenestration states, these models were used as a starting point to perform molecular dynamics simulations. The trajectories analysis reveals a good agreement between the pK_1/2 of K245 obtained experimentally and the pK_a predicted when the fenestrations are closed. Besides, we proved that Norfluoxetine compound is a potent blocker of TASK-2 channels and its putative binding site is within the fenestrations.

Synthetic Biology has the ultimate objective of design cells with predictable responses. Our ability to develop modified and synthetic organisms tailored to chemical production is fostered by our ability to recombine DNA with error-free protocols. However, our current capacities for modeling how cells work is way behind our synthesis and analysis tools that difficult the prediction of desired cell responses. Interestingly, computational modeling has impacted prominently Synthetic Biology, where the manipulation of biological systems is cost-intensive, and computational resources could leverage experimental procedures. Traditionally, Ordinary Differential Equations (ODEs) have been employed to model biological systems, but their assumptions are simply not realistic. Particularly, it has been known for a long time that biological processes are stochastic, discrete and structurally complex, hampering differential equations systems to fit these properties. Even if noise is considered, modelers would be making assumptions on how cell components traveling between compartments could affect physically separated processes, how they bind each other, and how they perform behaviors that resemble cooperativity and competition.

To further resolve a connection between modeling and designing organisms, we present a Rule-based model simulated using Gillespie’s Stochastic Simulation Algorithm. Under this approach, rules are macroscopic chemical reactions between entities that recapitulate one or several patterns necessary for a transformation. The rate associated with each rule represents how often a reaction fires in a given time. We modeled two gene regulatory networks of E. coli. These two models resemble the core network that regulates transcription and the replication of the ColEI plasmid. Average and variance of selected variables were analyzed in these examples simulated employing arbitrary rates, yet surprisingly, their properties are in close agreement with experimental data. Specifically, when the core transcription network reached pseudo-equilibrium, it predicts free RNA Polymerase Holoenzyme close to 20%, relatively near the 30% reported during exponentially growing E. coli. Similarly, the plasmid replication controlled with a negative feedback simulated a saturation dynamic, producing tens or hundreds of copies, depending strongly on the rate of interaction between its non-coding RNAs.

We are aware of limitations in our example models. We considered cells in a pseudo-stationary state, therefore disregarding the necessity to model metabolism, translation and protein degradation or dilution. Although, the processes mentioned above could be easily incorporated in successive refinements. Importantly, modeling metabolism and linking it to transcription and translation could facilitate a more reliable prediction of phenotype emergency. To this end, a Gene Regulatory Network (GRN), a Genome-Scale Metabolic Model (GSMM) and (optionally) a protein-protein and an RNA-protein interaction networks will serve as inputs to write draft models. We sought to automatically write a genome-scale model of replication, transcription, translation, RNA and protein degradation joint to metabolism. For instance, we wrote a combined metabolism and gene expression model that resemble the published central metabolism of E. coli (MODEL1505110000) employing the RegulonDB GRNs and the iJO1366 GSMM, resulting in comparable dynamics as the published ODE model.

El avance tecnológico y el quehacer científico han superado muchos de los retos que parecían una utopía hasta hace unos años. El objetivo de este trabajo es exponer la aplicación de la técnica CRIPR-Cas9 en la terapia génica aplicada en humanos y embriones. Así como la exposición de la discusión científica y ética con respecto a la edición genética de la línea germinal humana y la moratoria que se ha solicitado. Exponiendo que no existen argumentos suficientes para solicitar dicha moratoria en la investigación básica de la terapia génica germinal. Considerando también que el análisis jurídico y su intersección con la investigación científica deben primar para la implementación de enmiendas al Convenio de Oviedo en relación con las disposiciones aplicables a la edición genética.

The early development stages of the frog Xenopus laevis are known as the regenerative (R) stage, because during that time, it is able to regenerate. After its metamorphosis into a frog, X. laevis goes into its non-regenerative (NR) stage. It has been reported that there are differences in the gene repertoire expressed at each stage after spinal cord injury, and also in its timing. One molecular agent that might influence gene expression and its timing are small non-coding RNAs (snc-RNAs). To gain insights into the role of snc-RNAs, in this work we aimed to investigate their differential expression between the R and the NR stages and their origins in genome regions. We have performed small RNA sequencing and differential expression analysis between the R and the NR stages. The majority of sequenced snc-RNAs have high quality, and their lengths suggest that the majority belong to the small interfering or, to the micro RNA class. In average, 98% of the ~50 million sequences completely aligned to X. laevis genome, and ~45% of them represent novel snc-RNAs. The snc-RNAs were further classified in 2.238 families, using as criteria their genomic location: ~49% of novel snc-RNA are in TEs regions. ~53% of families have their origins in genes (~33% in intronic regions) and ~47% in intergenic regions. 374 novel snc-RNAs are differentially expressed. 289 come from TEs, 250 of which are in intergenic regions and 39 are in intronic regions. These results, taken together with the differentially expressed genes, will help understanding the spinal cord regeneration process in frogs.

Multi-Objective Evolutionary Algorithms (MOEAs) are methods which are strongly recommended to approximate the Pareto frontier whenever the characteristics of objective functions and constraints make it difficult for mathematical programming (cf. Coello, 1999). These approaches construct a solution set formed by non-dominated solutions.

Alternatively, an MOEA can approximate toward the set of best compromise solutions, i.e. the Region of Interest or RoI, through the incorporation of information about the preference of a decision maker (DM). According to (Branke, Salvatore, Greco, Slowiński & Zielniewicz, 2016), the DM preference information within an MOEA search process can be motivated by the necessity of sampling of the Pareto frontier, or the reduction of the DM’s cognitive effort to handle only the RoI, or because the DM’s preference information reinforces the necessary selective pressure. Let us observe that the incorporation of preferences is a method that offers support to the limited capacity of the human mind to handle several conflicting objectives at the same time (Miller, 1956); so, this method has become a very powerful tool that aids in the solution of many-objective problems.

However, most of the time the behavior of a complex system often depends on parameters whose values are unknown in advance (Ling, 2010). Such is the case of MOEAs based on outranking approaches. The outranking approaches are methods that construct outranking relations among potential actions or decision alternatives and exploit such relations to find solutions to decision problems. These approaches have found application in approximating the RoI in Multi-objective Optimization Problems (MOPs) because they allow computational models of preferences of DM’s that can be used to guide the search in MOEAs toward solutions that are closely related to his/her interests.

Ideally, the parameter values of an outranking approach should be defined by the DM; however, given the cognitive effort required from the DM, this task can be extremely difficult and time-consuming, and hence prohibited to be handled directly. This situation is aggravated in cases when a DM is not capable of providing a clear explanation of his/her decisions. These situations prevent from the use of a direct assessment of the parameter values required by a complex system. Instead, the most convenient strategy to overcome such problems lies in the use of preference disaggregation methods.

A preference disaggregation method (PDM) is an indirect elicitation approach that can indirectly infer the values of a predefined set of parameters from a set of examples provided by the DM. In these approaches, the provision of preference information is far simpler for a DM because it can be done based on decisions taken in the past or formulated recently by means of manageable examples. So far, PDMs have been implemented using evolutionary metaheuristics, and they have shown their effectiveness on the parameter elicitation for outranking approaches used in the solution of the Portfolio Selection Problem (PSP). However, the success in the parameter elicitation does not depend only on the quality of the provided set examples but also on the performance of the used algorithms; in this aspect, it has been observed in other problems that the achievement of good solutions in a wider range of instances might require the use of several metaheuristics combined. Recently, hyperheuristics gain more attention because of their capacity to integrate characterization models of problem instances with a set of metaheuristics in order to improve the construction of solution in an optimization problem. Based on the fact that parameter elicitation has been modeled previously by optimization problems, this work proposes the study of the impact on the parameter elicitation of outranking approaches for PSP due to the implementation of a hyperheuristic that selects adequately the best metaheuristics given the instance of the problem and the state of the search process.

References

Bechikh, S. (2013). Incorporating Decision Maker’s Preference Information in Evolutionary Multi-objective Optimization, Diss. PhD thesis, High Institute of Management of Tunis, University of Tunis, Tunisia.

Branke, J., Salvatore, C., Greco, S., Slowiński, Zielniewicz, P. (2016). Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research, 250:884–901.

Coello, C.A. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, an International Journal, 1(3):269–308.

Burke, E. K., Hyde, M. R., Kendall, G., Ochoa, G., Ozcan, E., & Woodward, J. R. (2009). Exploring hyper-heuristic methodologies with genetic programming. In Computational intelligence (pp. 177-201). Springer Berlin Heidelberg.

Burke, E. K., Hyde, M., Kendall, G., Ochoa, G., Özcan, E., & Woodward, J. R. (2010). A classification of hyper-heuristic approaches. In Handbook of metaheuristics (pp. 449-468). Springer US.

Coello, C.A. (1999). A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, an International Journal, 1(3):269–308. DOI:10.1007/ BF03325101.

Coello, C. C. (2000). Handling preferences in evolutionary multiobjective optimization: A survey. In Evolutionary Computation, 2000. Proceedings of the 2000 Congress on (Vol. 1, pp. 30-37). IEEE.

Coello, C. (2017). Introducción a la computación evolutiva. Notas de Curso CINVESTAV-IPN.

Cruz-Reyes, L, Fernandez, E., Rangel-Valdez, N. (2017). A metaheuristic optimization-based indirect elicitation of preference parameters for solving many-objective problems. International Journal of Computational Intelligence Systems, 10(2017): 56 – 77.

Dias, L., Mousseau, V. (2006). Inferring electre’s veto-related parameters from outranking examples. European Journal of Operational Research, 170:172–191.

Fernandez, E., Lopez, E., Lopez, F., and Coello Coello, C.A. (2011). Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method. Information Science (IS). 181: 44 – 56.

Ling-Ko, L., Hsu, D., Lee, W.S., Ong, S.C.W. (2010). Structured Parameter Elicitation. APPEARED IN: Proc. AAAI Conference on Artificial Intelligence, 2010. Available at https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1798. Date accessed: 1/Nov/2018.

Miller, G.A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2):81–97, 1956.

Rangel-Valdez, N. Fernandez, E., Cruz Reyes, L., Gomez-Santillan, C., Hernández-López, R.I. (2015). Multiobjective Optimization Approach for Preference-Disaggregation Analysis Under Effects of Intensity. MICAI (2) 2015: 451-462.

Roy, B. (1991). The outranking approach and the foundations of electre methods. Theory and Decisions, 31(1):49–73. DOI:

Soubeiga, E. (2003). Development and application of hyperheuristics to personnel scheduling (Doctoral dissertation, University of Nottingham).

Zhu, Y. & Luo, Y. (2016) Multi-objective optimisation and decision-making of space station logistics strategies. International Journal of Systems Science, 47(13):3132 – 3148.

Project portfolio optimization is one of the most important strategic-decision problems faced by any organization. The construction of the best portfolio that accomplishes a certain balance among the selected projects can be defined as follows:

(1)

where R_F is the space of feasible portfolios, and represents the functions z_i that characterize the impact of a portfolio x over the considered criteria.

Although typical, Problem (1) is not the unique way in which decision makers (DMs) are concerned with project portfolio selection. Several papers (Fernandez 2009, Fernandez 2013, Bastiani 2015 and Fernandez 2017) have approached a problem with a distinctive feature, which is that the only information available about the projects is their rank, (they are ordered according to the DM’s preferences) and their budgetary requirements. This situation is related to the fact that sometimes a DM may i) prefer simpler decision methods; ii) agree easily on a priority ranking, or when the DM is a complex collective entity for which is very hard to evaluate the project objectives and to solve a multi-criteria optimization problem like (1).

Bastiani et al. (2015) y Fernández et al. (2017) proposed an optimization approach based on maximizing the cardinality and minimizing the discrepancies in a portfolio; cardinality refers to the total number of supported projects; the term discrepancy is a concept that reflects the negative effect that is applied over the DM's thinking because one of the projects, when it is compared against others, seems to have merits that belong to the portfolio but it is not in it.

Synergic effects in subsets of projects are not considered by the works from Bastiani et al. (2015) and Fernández et al. (2017), what is perhaps their most important limitation. The purpose of this contribution is in incorporating synergy in the proposal of Fernández et al. (2017). Synergy is related to the existence of complex interdependencies among projects. They compete for resources, but some can share them, becoming more advantageous when they are supported together. Likewise, it is very common for synergy to be manifested in subsets of projects and that, therefore, the combined contribution of them to the impact of the portfolio is greater than the sum of their separate contributions. We propose here to use a strategy based on creating artificial projects that represent synergic coalitions, with their own budgetary requirements (usually less than the sum of their components), and their specific rank (better than the rank of their component projects). Such strategy would need a re-definition of the objectives in the optimization problem. The objectives related to discrepancies may keep their original meaning, but those related to cardinality should be modified through some way to take into account the impact increased by synergy.

[Bastiani et al., 2015] Bastiani, S. Samantha, et al. "Portfolio Optimization From a Set of Preference Ordered Projects Using an Ant Colony Based Multi-objective Approach." International Journal of Computational Intelligence Systems 8.sup2 (2015): 41-53.

[Fernandez, 2009] E. Fernandez, L. F. Felix and G. Mazcorro, “Multiobjective Optimisation of an Outranking Model for Public Resources Allocation on Competing Projects”, International Journal of Operational Research, Vol. 5, No. 2, pp. 190-210, 2009.

[Fernandez, 2013] E. Fernandez and R. Olmedo, “Public Project Portfolio Optimization under a Participatory Paradigm”, Applied Computational Intelligence and Soft Computing, 2013, doi: 10.1155/2013/891781.

[Fernandez, 2017]Eduardo Fernandez, Claudia Gómez-Santillán, Laura Cruz-Reyes, Nelson Rangel-Valdez, and Shulamith Bastiani, “Design and Solution of a Surrogate Model for Portfolio Optimization Based on Project Ranking,” Scientific Programming, vol. 2017, Article ID 1083062, 10 pages, 2017.

The molecular recognition of carbohydrates by proteins is characterized by the presence of classical hydrogen bonds stabilizing binding together with an important contribution from other intermolecular interactions conferring high specificity. [1] The design of glycomimetic ligands as modulators of protein-carbohydrate binding events is a common approach in the context of chemical glycobiology [2] and carbohydrate-based drug discovery. [3]

While a diversity of functional groups has been successfully introduced in carbohydrate structures,[2] the use of halogens has been largely neglected, except for fluorine. However, heavier halogens (X = Cl, Br, or I) can establish highly directional, R–X...B interactions with Lewis bases (B), known as halogen bonds (HaB).These interactions have been mostly explained by the presence of an electropositive site at the outermost region of X species, named sigma-hole.[4] HaB-mediated molecular recognition phenomena are widespread across biological systems and have been used as tools in medicinal chemistry, [5] amongst other fields.

In the search for novel glycomimetics with the potential to modulate carbohydrate-protein recognition via HaB interactions, we performed a quantum mechanical study on the HaB donor propensity of model halogenated carbohydrate derivatives by computing the respective molecular electrostatic potential surface maxima. This procedure allowed us to map the chemical space of halogenated sugars in terms of their potential to act as HaB interaction partners with HaB acceptor species commonly found in biomolecules and the results encourage further in silico optimization towards new halogen-bonding glycomimetic ligands.

Acknowledgements:

Investigador FCT (IF/00069/2014, IF/00069/2014/CP1216/CT0006), PhD grant SFRH/BD/116614/2016, IUD projects UID/QUI/UI0612/2013, UID/MULTI/04046/2013. Lisboa 2020, Portugal 2020, FEDER/FN, and European Union (LISBOA-01-0145-FEDER-028455, PTDC/QUI-QFI/28455/2017).

References:

[1] Lacetera, A. et al. in Chemical Biology, No. 3, Computational Tools for Chemical Biology (Ed.: S. Martín-Santamaría), Royal Society of Chemistry, Croydon, UK, 2018, pp.145-164.

[2] Sliba, R. C., Pohl, N. L. B. Curr. Opin. Chem. Biol. 2016, 34, 127.

[3] Ernst, B., Magnani, J. L. Nat. Rev. Drug. Discov. 2009, 8, 661.

[4] Clark, T. et al. J. Mol. Model. 2007, 13, 291.

[5] Wilcken, R. et al. J. Med. Chem. 2013, 56, 1363.

Novel agricultural technologies are required to improve food production in saline and dry soils. Based on a finding made by farmers who noticed a good growth and a reduced incidence of phytopathogenic infections of wheat grown between the rows of olive trees, we have screened diazotrophic endophytic PGPB associated with olive tree for plant stress tolerance improving capability. Strains were selected following a biochemical characterization of plant growth promotion activities such as ability of antimicrobial production, azote fixation, ACC deaminase production, growth hormone production, Phosphate solubilization…

Among the selected strains, BCLRB2 was the strain that shown the most efficient capacity to fix atmospheric nitrogen, which is the most prominent factor of all plant growth parameters under stressful environments. The strain BCLRB2, identified as Bacillus sp, had ACC deaminase, and highly stimulatory effect in vitro associated with high production of hydrolytic enzymes, AIA, and solubilization of tricalcium phosphate. The efficiency of BCLRB2 strain was explored for in vivo pot plant growth. As a result, inoculated plants with Bacillus sp. BCLRB2, showed the best growth of durum wheat seedlings compared to a control under salt stress and natural conditions. Total length, fresh weight, and total dry weight were significantly higher in inoculated plants compared to uninoculated ones.

The Bacillus sp.BCLRB2 is approved to be an efficient diazotrophic Halotolerant PGPB strain

Salinity is one of the most severe abiotic stresses limiting crop yield. Salt-affected area in Tunisia is fast escalating due to intrusion of saline water on arable land and use of chemical fertilizers and pesticides. Thus, a great effort is required to preserve crop production under limiting factors.

The present study was conducted to isolate and identify PGPB associated with two halophyte plants from coastal saline site. These strains were tested for improved crop productivity under salinity conditions. Four strains namely MA9, MA14, MA17 and MA19 were selected

The PGPB-inoculated plants were relatively healthy and hydrated, whereas the uninoculated plant leaves were desiccated in the presence of 125 mM NaCl. The percentage of water content (PWC) in the plant was also significantly higher in inoculated plants compared to uninoculated ones. Under greenhouse experiments, our data revealed that experiments using seed biopriming on non-sterile soil supplemented with NaCl permitted to identify the most efficient isolates which offered the best vegetable criteria by significantly increasing root and shoot length, root and shoot dry weights, area of the root system and thousand seed mass in plant growth trials. The benefic effect of seed biopriming was more pronounced in soil samples added with NaCl than that of untreated soil.

Seed biopriming by efficient PGPB strains induced salinity tolerance of wheat and therefore enhanced their productivity under salinity.

Dihydroflavonol 4-reductase (DFR) is a pivotal enzyme in the flavonoid biosynthesis pathway catalyzing the last common step that leads to anthocyanins and proanthocyanidins. DFR promotes the reduction of three dihydroflavonols: dihydrokaempferol (DHK), dihydroquercetin (DHQ) and dihydromyricetin (DHM) to leucoanthocyanidins. These substrates differ only in the number of hydroxyl groups on the B phenyl ring: only one, two or three to DHK, DHQ and DHM respectively. Recently, a new variant of DFR (DFR1), which showed an unusual preference for only DHK was identified in strawberry, meanwhile DFR2 can convert any of the three dihydroflavonols. A region of 26 amino acid residues could be relevant to identify the substrates, proposed as the binding pocket of B phenyl ring of dihydroflavonols, where an asparagine residue could be critical. To determine the importance of these differences in both proteins, a characterization at structural level by homology model methodology was carried out to FcDFR1 and FcDFR2 from the Chilean strawberry (Fragaria chiloensis). Additionally, by molecular dynamics simulation we identify differences in substrate binding mode of the proteins with DHK and DHQ. Phylogenetic analyses grouped FcDFR1 and FcDFR2 into separate clades. FcDFR1 and FcDFR2 sequences consist of 341 and 350 amino acid residues respectively, and share 78.6% sequence identity. The most important differences were found in the region that is important for substrate identification. FcDFR1 and FcDFR2 structures were obtained through comparative modeling, showing a RMSD of 2.39 Å. Regarding protein-ligand interactions, in FcDFR2 a strong and stable interaction between Asn133 and the 3'-OH group on ring B of DHQ was determined by molecular dynamics simulations, but not in FcDFR1, where the equivalent residue is Ala135. In contrast, DHK without 3'-OH group could be transformed by both enzymes as stable interactions were determined. The data provides an explanation of why DFR1 could interact with DHK and not with DHQ.