Synthesis of Superbulky Amide Ligands by Addition of Polar Reagents to Sila-Imine.

The chemistry of extremely bulky amide ligands is troubled by difficulties in deprotonation of the parent amine. As an alternative route to superbulky amide reagents, the addition of polar reagents to a sila-imine has been investigated. Attempts to synthesize the superbulky amide anion (tBu3Si)2N- by addition of tBuLi to tBu2Si=N(SitBu3) failed and gave tBu3Si(tBu2HSi)NLi and isobutene. Reaction of the sila-imine with KOtBu successfully led to tBu3Si[tBu2(tBuO)Si]NK which crystallized as a separated ion-pair. Reaction with the slightly bulkier KOAd (Ad=1-adamantyl) led in presence of THF to ether ring-opening. Reaction with tBuOH gave tBu3Si[tBu2(tBuO)Si]NH but this amine cannot be easily deprotonated. Reaction with (BDI*)MgnBu in presence of THF gave (BDI*)Mg+ ⋅ (THF)2 and the non-coordinating anion tBu3Si[tBu2(nBu)Si]N-; BDI*=ß-diketiminate ligand HC[C(tBu)N-DIPP]2, DIPP=2,6-diisopropylphenyl. Reaction of Mg(nBu)2 with tBu2Si=N(SitBu3) led to a Mg complex with one amide ligand: tBu3Si[tBu2(nBu)Si]N-. The other superbulky amide anion isomerized by internal deprotonation of a tBu-substituent to give a primary carbanion that is also coordinated to Mg. Although the amide-to-carbanion isomerization is highly contrathermodynamic, it allows for coordination of both anions to a single Mg center. The new bulky amides are rare cases of halogen-free weakly coordinating anions.

Lithium Aluminium Hydride and Metallic Iron: A Powerful Team in Alkene and Arene Hydrogenation Catalysis.

Alkenes that normally do not react with LiAlH4 (3-hexene, cyclohexene, 1-Me-cyclohexene), can be reduced to the corresponding alkanes by a mixture of LiAlH4 and Fe0 (the iron was activated by Metal-Vapour-Synthesis). This alkene-to-alkane conversion with a stoichiometric quantity of LiAlH4 /Fe0 does not need quenching with water or acids, implying that both H's originate from LiAlH4 . The LiAlH4 /Fe0 combination is also a remarkably potent cooperative catalyst for hydrogenation of multi-substituted alkenes and benzene or toluene. An induction period of circa two hours and the minimally required temperature of 120 °C, suggests that the actual catalyst is a combination of Fe0 and the decomposition product of LiAlH4 (LiH and Al0 ). A thermally pre-activated LiAlH4 /Fe0 catalyst did not need an induction time and is also active at room temperature and 1 bar H2 . A combination of AliBu3 and Fe0 is an even more active hydrogenation catalyst. Without pre-activation, tetra-substituted alkenes like Me2 C=CMe2 and toluene could be fully hydrogenated.

Metallic Barium: A Versatile and Efficient Hydrogenation Catalyst.

Ba metal was activated by evaporation and cocondensation with heptane. This black powder is a highly active hydrogenation catalyst for the reduction of a variety of unactivated (non-conjugated) mono-, di- and tri-substituted alkenes, tetraphenylethylene, benzene, a number of polycyclic aromatic hydrocarbons, aldimines, ketimines and various pyridines. The performance of metallic Ba in hydrogenation catalysis tops that of the hitherto most active molecular group 2 metal catalysts. Depending on the substrate, two different catalytic cycles are proposed. A: a classical metal hydride cycle and B: the Ba metal cycle. The latter is proposed for substrates that are easily reduced by Ba0 , that is, conjugated alkenes, alkynes, annulated rings, imines and pyridines. In addition, a mechanism in which Ba0 and BaH2 are both essential is discussed. DFT calculations on benzene hydrogenation with a simple model system (Ba/BaH2 ) confirm that the presence of metallic Ba has an accelerating effect.

Notions of similarity for systems biology models.

Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly vary. A general notion of model similarity, applicable to various types of models, is still missing. Here we survey existing methods for the comparison of models, introduce quantitative measures for model similarity, and discuss potential applications of combined similarity measures. To frame model comparison as a general problem, we describe a theoretical approach to defining and computing similarities based on a combination of different model aspects. The six aspects that we define as potentially relevant for similarity are underlying encoding, references to biological entities, quantitative behaviour, qualitative behaviour, mathematical equations and parameters and network structure. We argue that future similarity measures will benefit from combining these model aspects in flexible, problem-specific ways to mimic users' intuition about model similarity, and to support complex model searches in databases.

Highly Active Superbulky Alkaline Earth Metal Amide Catalysts for Hydrogenation of Challenging Alkenes and Aromatic Rings.

Two series of bulky alkaline earth (Ae) metal amide complexes have been prepared: Ae[N(TRIP)2 ]2 (1-Ae) and Ae[N(TRIP)(DIPP)]2 (2-Ae) (Ae=Mg, Ca, Sr, Ba; TRIP=SiiPr3 , DIPP=2,6-diisopropylphenyl). While monomeric 1-Ca was already known, the new complexes have been structurally characterized. Monomers 1-Ae are highly linear while the monomers 2-Ae are slightly bent. The bulkier amide complexes 1-Ae are by far the most active catalysts in alkene hydrogenation with activities increasing from Mg to Ba. Catalyst 1-Ba can reduce internal alkenes like cyclohexene or 3-hexene and highly challenging substrates like 1-Me-cyclohexene or tetraphenylethylene. It is also active in arene hydrogenation reducing anthracene and naphthalene (even when substituted with an alkyl) as well as biphenyl. Benzene could be reduced to cyclohexane but full conversion was not reached. The first step in catalytic hydrogenation is formation of an (amide)AeH species, which can form larger aggregates. Increasing the bulk of the amide ligand decreases aggregate size but it is unclear what the true catalyst(s) is (are). DFT calculations suggest that amide bulk also has a noticeable influence on the thermodynamics for formation of the (amide)AeH species. Complex 1-Ba is currently the most powerful Ae metal hydrogenation catalyst. Due to tremendously increased activities in comparison to those of previously reported catalysts, the substrate scope in hydrogenation catalysis could be extended to challenging multi-substituted unactivated alkenes and even to arenes among which benzene.

Early Main Group Metal Catalysts for Imine Hydrosilylation.

The efficient catalytic reduction of imines with phenylsilane is achieved by using the potassium, calcium and strontium based catalysts [(DMAT)K (THF)]∞ , (DMAT)2 Ca⋅(THF)2 and (DMAT)2 Sr⋅(THF)2 (DMAT=2-dimethylamino-α-trimethylsilylbenzyl). Eight different aldimines and the ketimine Ph2 C=NPh could be successfully reduced by PhSiH3 at temperatures between 25-60 °C with catalyst loadings down to 2.5 mol %. Also, simple amides like KN(SiMe3 )2 or Ae[N(SiMe3 )2 ]2 (Ae=Ca, Sr, Ba) catalyze this reaction. Activities increase with metal size. For most substrates the activity increases along the row K

s-Block metal complexes of superbulky (tBu3Si)2N-: a new weakly coordinating anion?

Sterically hindered amide anions have found widespread application as deprotonation agents or as ligands to stabilize metals in unusual coordination geometries or oxidation states. The use of bulky amides has also been advantageous in catalyst design. Herein we present s-block metal chemistry with one of the bulkiest known amide ligands: (tBu3Si)2N- (abbreviated: tBuN-). The parent amine (tBuNH), introduced earlier by Wiberg, is extremely resistant to deprotonation (even with nBuLi/KOtBu superbases) but can be deprotonated slowly with a blue Cs+/e- electride formed by addition of Cs0 to THF. (tBuN)Cs crystallized as a separated ion-pair, even without cocrystallized solvent. As salt-metathesis reactions with (tBuN)Cs are sluggish and incomplete, it has only limited use as an amide transfer reagent. However, ball-milling with LiI led to quantitative formation of (tBuN)Li and CsI. Structural characterization shows that (tBuN)Li is a monomeric contact ion-pair with a relatively short N-Li bond, an unusual T-shaped coordination geometry around N and extremely short Li⋯Me anagostic interactions. Crystal structures are compared with Li and Cs complexes of less bulky amide ligands (iPr3Si)2N- (iPrN-) and (Me3Si)2N- (MeN-). DFT calculations show trends in the geometries and electron distributions of amide ligands of increasing steric bulk (MeN- < iPrN- < tBuN-) and confirm that tBuN- is a rare example of a halogen-free weakly coordinating anion.

Controlled vocabularies and semantics in systems biology.

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.

Teaming up main group metals with metallic iron to boost hydrogenation catalysis.

Hydrogenation of unsaturated bonds is a key step in both the fine and petrochemical industries. Homogeneous and heterogeneous catalysts are historically based on noble group 9 and 10 metals. Increasing awareness of sustainability drives the replacement of costly, and often harmful, precious metals by abundant 3d-metals or even main group metals. Although not as efficient as noble transition metals, metallic barium was recently found to be a versatile hydrogenation catalyst. Here we show that addition of finely divided Fe0, which itself is a poor hydrogenation catalyst, boosts activities of Ba0 by several orders of magnitude, enabling rapid hydrogenation of alkynes, imines, challenging multi-substituted alkenes and non-activated arenes. Metallic Fe0 also boosts the activity of soluble early main group metal hydride catalysts, or precursors thereto. This synergy originates from cooperativity between a homogeneous, highly reactive, polar main group metal hydride complex and a heterogeneous Fe0 surface that is responsible for substrate activation.

Toward Community Standards and Software for Whole-Cell Modeling.

OBJECTIVE: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS: Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION: We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE: We anticipate that these new standards and software will enable more comprehensive models.

Function of dynamic models in systems biology: linking structure to behaviour.

BACKGROUND: Dynamic models in Systems Biology are used in computational simulation experiments for addressing biological questions. The complexity of the modelled biological systems and the growing number and size of the models calls for computer support for modelling and simulation in Systems Biology. This computer support has to be based on formal representations of relevant knowledge fragments. RESULTS: In this paper we describe different functional aspects of dynamic models. This description is conceptually embedded in our "meaning facets" framework which systematises the interpretation of dynamic models in structural, functional and behavioural facets. Here we focus on how function links the structure and the behaviour of a model. Models play a specific role (teleological function) in the scientific process of finding explanations for dynamic phenomena. In order to fulfil this role a model has to be used in simulation experiments (pragmatical function). A simulation experiment always refers to a specific situation and a state of the model and the modelled system (conditional function). We claim that the function of dynamic models refers to both the simulation experiment executed by software (intrinsic function) and the biological experiment which produces the phenomena under investigation (extrinsic function). We use the presented conceptual framework for the function of dynamic models to review formal accounts for functional aspects of models in Systems Biology, such as checklists, ontologies, and formal languages. Furthermore, we identify missing formal accounts for some of the functional aspects. In order to fill one of these gaps we propose an ontology for the teleological function of models. CONCLUSION: We have thoroughly analysed the role and use of models in Systems Biology. The resulting conceptual framework for the function of models is an important first step towards a comprehensive formal representation of the functional knowledge involved in the modelling and simulation process. Any progress in this area will in turn improve computer-supported modelling and simulation in Systems Biology.

Structure, function, and behaviour of computational models in systems biology.

BACKGROUND: Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such "bio-models" necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. RESULTS: We present a conceptual framework - the meaning facets - which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model's components (structure), the meaning of the model's intended use (function), and the meaning of the model's dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. CONCLUSIONS: The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research.