Cell-free transcription–translation is an expanding field in synthetic biology as a rapid prototyping platform for blueprinting the design of synthetic biological devices. Exemplar efforts include translation of prototype designs into medical test kits for on-site identification of viruses (Zika and Ebola), while gene circuit cascades can be tested, debugged and re-designed within rapid turnover times. Coupled with mathematical modelling, this discipline lends itself towards the precision engineering of new synthetic life. The next stages of cell-free look set to unlock new microbial hosts that remain slow to engineer and unsuited to rapid iterative design cycles. It is hoped that the development of such systems will provide new tools to aid the transition from cell-free prototype designs to functioning synthetic genetic circuits and engineered natural product pathways in living cells.

Heteromeric amino acid transporters (HATs) are relevant targets for structural studies. On the one hand, HATs are involved in inherited and acquired human pathologies. On the other hand, these molecules are the only known examples of solute transporters composed of two subunits (heavy and light) linked by a disulfide bridge. Unfortunately, structural knowledge of HATs is scarce and limited to the atomic structure of the ectodomain of a heavy subunit (human 4F2hc-ED) and distant prokaryotic homologues of the light subunits that share a LeuT-fold. Recent data on human 4F2hc/LAT2 at nanometer resolution revealed 4F2hc-ED positioned on top of the external loops of the light subunit LAT2. Improved resolution of the structure of HATs, combined with conformational studies, is essential to establish the structural bases for light subunit recognition and to evaluate the functional relevance of heavy and light subunit interactions for the amino acid transport cycle.

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR–ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in ‘real-time’ and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX 2 R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allows ab initio approaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure–function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.

In recent years, bio-based chemicals have gained interest as a renewable alternative to petrochemicals. However, there is a significant need to assess the technological, biological, economic and environmental feasibility of bio-based chemicals, particularly during the early research phase. Recently, the Multi-scale framework for Sustainable Industrial Chemicals (MuSIC) was introduced to address this issue by integrating modelling approaches at different scales ranging from cellular to ecological scales. This framework can be further extended by incorporating modelling of the petrochemical value chain and the de novo prediction of metabolic pathways connecting existing host metabolism to desirable chemical products. This multi-scale, multi-disciplinary framework for quantitative assessment of bio-based chemicals will play a vital role in supporting engineering, strategy and policy decisions as we progress towards a sustainable chemical industry.

ABC transporters are primary active transporters found in all kingdoms of life. Human multidrug resistance transporter ABCB1, or P-glycoprotein, has an extremely broad substrate spectrum and confers resistance against chemotherapy drug treatment in cancer cells. The bacterial ABC transporter MsbA is a lipid A flippase and a homolog to the human ABCB1 transporter, with which it partially shares its substrate spectrum. Crystal structures of MsbA and ABCB1 have been solved in multiple conformations, providing a glimpse into the possible conformational changes the transporter could be going through during the transport cycle. Crystal structures are inherently static, while a dynamic picture of the transporter in motion is needed for a complete understanding of transporter function. Molecular dynamics (MD) simulations and electron paramagnetic resonance (EPR) spectroscopy can provide structural information on ABC transporters, but the strength of these two methods lies in the potential to characterise the dynamic regime of these transporters. Information from the two methods is quite complementary. MD simulations provide an all atom dynamic picture of the time evolution of the molecular system, though with a narrow time window. EPR spectroscopy can probe structural, environmental and dynamic properties of the transporter in several time regimes, but only through the attachment sites of an exogenous spin label. In this review the synergistic effects that can be achieved by combining the two methods are highlighted, and a brief methodological background is also presented.

The papers resulting from the recent Biochemical Society Focused Meeting ‘G-Protein-Coupled Receptors: from Structural Insights to Functional Mechanisms’ held in Prato in September 2012 are introduced in the present overview. A number of future goals for GPCR (G-protein-coupled receptor) research are considered, including the need to develop biophysical and computational methods to explore the full range of GPCR conformations and their dynamics, the need to develop methods to take this into account for drug discovery and the importance of relating observations on isolated receptors or receptors expressed in model systems to receptor function in vivo .

Modelling and optimization principles become a key concept in many biological areas, especially in biochemistry. Definitions of objective function, fitness and co-evolution, although they differ between biology and mathematics, are similar in a general sense. Although successful in fitting models to experimental data, and some biochemical predictions, optimization and evolutionary computations should be developed further to make more accurate real-life predictions, and deal not only with one organism in isolation, but also with communities of symbiotic and competing organisms. One of the future goals will be to explain and predict evolution not only for organisms in shake flasks or fermenters, but for real competitive multispecies environments.

Mizoribine induces the differentiation of promyelocytes by an unknown mechanism that relies on compromised guanine nucleotide synthesis. I have found that mizoribine also perturbs adenosine nucleotide levels in HL-60 promyelocytes, particularly ATP. To reconcile these observations with the known actions of mizoribine I have adapted an existing model of human purine metabolism composed as an S-system familiar from Biochemical Systems Theory. Mizoribine's actions were then simulated and compared with experimental data.

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

Systems biology describes the collection of a set of measurements on a system, integrated with a mathematical model of that system. The model and the measurements must be made together and refined iteratively, requiring close collaboration between biologists and modellers. A complete cell is probably too large and complicated to model yet, but simplified subsystems will probably produce valuable results. I consider various ways of simplifying the system and conclude that the biggest challenge is to get everyone working together productively.

Ligand-gated ion channels function as rapid signal transducers, converting chemical signals (in the form of neurotransmitters) into electrical signals in the postsynaptic neuron. This is achieved by the recognition of neurotransmitter at its specific-binding sites, which then triggers the opening of an ion channel (‘gating’). For this to occur rapidly (<1 ms), there must be an efficient coupling between the agonist-binding site and the gate, located more than 30 Å (1 Å=0.1 nm) away. Whereas a great deal of progress has been made in elucidating the structure and function of both the agonist-binding site and the ion permeation pathway in ligand-gated ion channels, our knowledge of the coupling mechanism between these domains has been limited. In this review, we summarize recent studies of the agonist-binding site and the ion channel in the γ-aminobutyric acid type A receptor, and discuss those structural elements that may mediate coupling between them. We will also consider some possible molecular mechanisms of receptor activation.

The mathematical structures known as Petri Nets have recently become the focus of much research effort in both the structural and quantitative analysis of all kinds of biological networks. This review provides a very brief summary of these interesting new research directions.