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Computational thinking is increasingly gaining importance in modern biology, due to the unprecedented scale at which data is nowadays produced. Bridging the cultural gap between the biological and computational sciences, this book serves as an accessible introduction to computational concepts for students in the life sciences. It focuses on teaching algorithmic and logical thinking, rather than just the use of existing bioinformatics tools or programming. Topics are presented from a biological point of view, to demonstrate how computational approaches can be used to solve problems in biology such as biological image processing, regulatory networks, and sequence analysis. The book contains a range of pedagogical features to aid understanding, including real-world examples, in-text exercises, end-of-chapter problems, colour-coded Python code, and 'code explained' boxes. User-friendly throughout, Computational Thinking for Life Scientists promotes the thinking skills and self-efficacy required for any modern biologist to adopt computational approaches in their research with confidence.

We describe a simple computational approach that can be used for the study and simulation of regulatory networks. The advantage of this approach is that it requires neither computational background nor exact quantitative data about the biological system under study. Moreover, it is suitable for examining alternative hypotheses about the structure of a biological network. We used a tool called BioNSi (Biological Network Simulator) that is based on a simple computational model, which can be easily integrated as part of the lab routine, in parallel to experimental work. One benefit of this approach is that it enables the identification of regulatory proteins, which are missing from the experimental work. We describe the general methodology for modeling a network's dynamics in the tool, and then give a point by point example for a specific known network, entry into meiosis in budding yeast.

We describe a simple computational approach that can be used for the study and simulation of regulatory networks. The advantage of this approach is that it requires neither computational background nor exact quantitative data about the biological system under study. Moreover, it is suitable for examining alternative hypotheses about the structure of a biological network. We used a tool called BioNSi (Biological Network Simulator) that is based on a simple computational model, which can be easily integrated as part of the lab routine, in parallel to experimental work. One benefit of this approach is that it enables the identification of regulatory proteins, which are missing from the experimental work. We describe the general methodology for modeling a network's dynamics in the tool, and then give a point by point example for a specific known network, entry into meiosis in budding yeast.

A new aerobic carbon-carbon bond cleavage reaction of linear di-substituted alkenes, to yield the corresponding aldehydes/ketones in high selectivity under mild reaction conditions, is described using copper(II)-substituted polyoxometalates, such as {α2-Cu(L)P2W17O61}(8-) or {[(Cu(L)]2WZn(ZnW9O34)2}(12-), as catalysts, where L = NO2. A biorenewable-based substrate, methyl oleate, gave methyl 8-formyloctanoate and nonanal in90% yield. Interestingly, cylcoalkenes yield the corresponding epoxides as products. These catalysts either can be prepared by pretreatment of the aqua-coordinated polyoxometalates (L = H2O) with NO2 or are formed in situ when the reactions are carried with nitroalkanes (for example, nitroethane) as solvents or cosolvents. Nitroethane was shown to release NO2 under reaction conditions. (31)P NMR shows that the Cu-NO2-substituted polyoxometalates act as oxygen donors to the C-C double bond, yielding a Cu-NO product that is reoxidized to Cu-NO2 under reaction conditions to complete a catalytic cycle. Stoichiometric reactions and kinetic measurements using {α2-Co(NO2)P2W17O61}(8-) as oxidant and trans-stilbene derivatives as substrates point toward a reaction mechanism for C-C bond cleavage involving two molecules of {α2-Co(NO2)P2W17O61}(8-) and one molecule of trans-stilbene that is sufficiently stable at room temperature to be observed by (31)P NMR.

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found.

The H5PV2Mo10O40 polyoxometalate catalysed the electron transfer oxidation of sulphite to yield a sulphite radical, SO3˙(-) that upon addition of O2 yielded a peroxosulphate species efficient for the H5PV2Mo10O40 catalysed epoxidation of alkenes. The acidic polyoxometalate further catalysed hydrolysis of the epoxide to give vicinal diols in high yields.

Background TGF-β signaling is a cellular pathway that functions in most cells and has been shown to play a role in multiple processes, such as the immune response, cell differentiation and proliferation. Recent evidence suggests a possible interaction between TGF-β signaling and the molecular circadian oscillator. The current study aims to characterize this interaction in the zebrafish at the molecular and behavioral levels, taking advantage of the early development of a functional circadian clock and the availability of light-entrainable clock-containing cell lines. Results Smad3a, a TGF-β signaling-related gene, exhibited a circadian expression pattern throughout the brain of zebrafish larvae. Both pharmacological inhibition and indirect activation of TGF-β signaling in zebrafish Pac-2 cells caused a concentration dependent disruption of rhythmic promoter activity of the core clock gene Per1b. Inhibition of TGF-β signaling in intact zebrafish larvae caused a phase delay in the rhythmic expression of Per1b mRNA. TGF-β inhibition also reversibly disrupted, phase delayed and increased the period of circadian rhythms of locomotor activity in zebrafish larvae. Conclusions The current research provides evidence for an interaction between the TGF-β signaling pathway and the circadian clock system at the molecular and behavioral levels, and points to the importance of TGF-β signaling for normal circadian clock function. Future examination of this interaction should contribute to a better understanding of its underlying mechanisms and its influence on a variety of cellular processes including the cell cycle, with possible implications for cancer development and progression.

Alanine racemase (AlaR) catalyzes the interconversion between l-Ala and d-Ala with the aid of the cofactor pyridoxal 5'-phosphate (PLP). The pyridine nitrogen in PLP in the wild-type enzyme is unprotonated due to interaction with Arg219, a rare feature among PLP-dependent enzymes. Herein, we performed combined quantum mechanics and molecular mechanics molecular dynamics simulations to study the Arg219Glu mutant AlaR. In this form of the enzyme, the PLP-pyridine nitrogen is protonated. This study suggests that the catalytic effect in the Arg219Glu mutant enzyme is due to a combined solvent and inherent stabilizing effect of the protonated cofactor, in contrast to the wild-type enzyme where the catalytic effect may be ascribed to solvent effects alone. Furthermore, we find that the quinonoid intermediate is greatly stabilized in the mutant enzyme, opening the possibility for side reactions such as transamination. We show that a computed 1,3-proton transfer in PLP due to the catalytic Lys39 is a feasible side reaction en route to transamination.

The H5PV2Mo10O40 polyoxometalate catalyzes the electron transfer oxidation of sulphite to yield a sulphite radical.

Molecular dynamics simulations using a combined quantum mechanical and molecular mechanical (QM/MM) potential have been carried out to investigate the internal proton transfer equilibrium of the external aldimine species in l-dopa decarboxylase, and carbanion stabilization by the enzyme cofactor in the active site of alanine racemase. Solvent effects lower the free energy of the O-protonated PLP tautomer both in aqueous solution and in the active site, resulting a free energy difference of about -1 kcal/mol relative to the N-protonated Schiff base in the enzyme. The external aldimine provides the dominant contribution to lowering the free energy barrier for the spontaneous decarboxylation of l-dopa in water, by a remarkable 16 kcal/mol, while the enzyme l-dopa decarboxylase further lowers the barrier by 8 kcal/mol. Kinetic isotope effects were also determined using a path integral free energy perturbation theory on the primary (13)C and the secondary (2)H substitutions. In the case of alanine racemase, if the pyridine ring is unprotonated as that in the active site, there is destabilizing contribution to the formation of the α-carbanion in the gas phase, although when the pyridine ring is protonated the contribution is stabilizing. In aqueous solution and in alanine racemase, the α-carbanion is stabilized both when the pyridine ring is protonated and unprotonated. The computational studies illustrated in this article show that combined QM/MM simulations can help provide a deeper understanding of the mechanisms of PLP-dependent enzymes. This article is part of a Special Issue entitled: Pyridoxal Phosphate Enzymology.

The origin of the catalytic proficiency of the cofactor-independent enzyme proline racemase (ProR) has been investigated by a combined classical and quantum simulation approach with a hybrid quantum mechanics/molecular mechanics potential energy surface. The present study shows that the ProR reaction mechanism is asynchronous concerted with no distinct intermediate. Various mechanisms are investigated, and it is concluded that active site residues other than the Cys dyad are not involved in chemical catalysis. When compared to an analogous aqueous solution-phase reaction, we find that the free-energy barrier is reduced by 14 kcal/mol in ProR, although the reaction mechanisms in the enzyme and in water are similar. The computed catalytic effect is comparable to that in the isofunctional enzyme alanine racemase (AlaR). However, in AlaR the catalytic burden is divided between the cofactor pyridoxal 5'-phosphate and the enzyme environment, whereas in ProR it is borne entirely by the enzyme environment. This is ascribed to a highly preorganized active site facilitating transition state stabilization via a tight network of hydrogen bonds donated by nearby active site residues.

Modeling and analysis of genetic regulatory networks is essential both for better understanding their dynamic behavior and for elucidating and refining open issues. We hereby present a discrete computational model that effectively describes the transient and sequential expression of a network of genes in a representative developmental pathway. Our model system is a transcriptional cascade that includes positive and negative feedback loops directing the initiation and progression through meiosis in budding yeast. The computational model allows qualitative analysis of the transcription of early meiosis-specific genes, specifically, Ime2 and their master activator, Ime1 . The simulations demonstrate a robust transcriptional behavior with respect to the initial levels of Ime1 and Ime2. The computational results were verified experimentally by deleting various genes and by changing initial conditions. The model has a strong predictive aspect, and it provides insights into how to distinguish among and reason about alternative hypotheses concerning the mode by which negative regulation through Ime1 and Ime2 is accomplished. Some predictions were validated experimentally, for instance, showing that the decline in the transcription of IME1 depends on Rpd3, which is recruited by Ime1 to its promoter. Finally, this general model promotes the analysis of systems that are devoid of consistent quantitative data, as is often the case, and it can be easily adapted to other developmental pathways.

Background Bench biologists often do not take part in the development of computational models for their systems, and therefore, they frequently employ them as “black-boxes”. Our aim was to construct and test a model that does not depend on the availability of quantitative data, and can be directly used without a need for intensive computational background. Results We present a discrete transition model. We used cell-cycle in budding yeast as a paradigm for a complex network, demonstrating phenomena such as sequential protein expression and activity, and cell-cycle oscillation. The structure of the network was validated by its response to computational perturbations such as mutations, and its response to mating-pheromone or nitrogen depletion. The model has a strong predicative capability, demonstrating how the activity of a specific transcription factor, Hcm1, is regulated, and what determines commitment of cells to enter and complete the cell-cycle. Conclusion The model presented herein is intuitive, yet is expressive enough to elucidate the intrinsic structure and qualitative behavior of large and complex regulatory networks. Moreover our model allowed us to examine multiple hypotheses in a simple and intuitive manner, giving rise to testable predictions. This methodology can be easily integrated as a useful approach for the study of networks, enriching experimental biology with computational insights.