In the last decade, different omic technologies have experiences an exponential technological advancement. However, metabolomics has not followed a similarly vertiginous improving improvement and are far from genomics or transcriptomics in terms of throughput, cost and even accuracy. Therefore, genome-scale in-silico methodologies to estimate metabolic activities from genomic data constitute an active field. The solutions available fall into two extremes: those with few assumptions about the relationships among proteins and metabolites, which are easy-to-use but less accurate and those that account for the complex relationships among molecules and proteins defined in the metabolic pathways, which are more accurate but require mathematical skills. Here, we introduce Metabolica, an algorithm that considers the complex functional relationships among all the molecules and proteins involved in the metabolic pathway analyzed but keeping an easy use that do not require of advanced mathematical skills. Metabolica has been implemented in a freely available software R package. The software inputs transcriptomic data and infers the activities of the reactions that produce the different metabolites in the pathway analyzed. An example shows how detected dysregulated metabolites in several cancers are related to patient survival.

This two-part series describes how to test hypotheses on molecular mechanisms that underlie biological phenomena, using preclinical drug testing as a simplified example. While pursuing drug testing in preclinical research, it is important for students to understand the limitations of descriptive as well as mechanistic studies. The former does not identify any causal links between two or more variables; it identifies the presence or absence of correlations. The latter has caveats presented in Parts I and II of this series. Part II also describes how to test for a causal link between drug-induced activation of biological targets and therapeutic outcomes. Here, the mechanism of action of the drug is identified with pharmacological or genetic approaches that modify the expression/activity of the drug targets. Without interference with the proposed mechanism of action, a causal link between activation (or inhibition) of the target P and the therapeutic outcomes of drug D cannot be established. Using pharmacological agonists and antagonists, gene knockout and overexpression, or protein knockdown tools, designing a full-factorial three-way ANOVA forces the investigator to include the appropriate control groups, mitigating the risk of false positive or false negative conclusions. Upon completion of this series, the educator and student will have some of the tools in hand to design mechanistic studies and interpret various experimental outcomes, with knowledge of strengths and limitations of preclinical research.

Many discoveries in the biological sciences have emerged from observational studies, but student researchers also need to learn how to design experiments that distinguish correlation from causation. For example, identifying the physiological mechanism of action of drugs with therapeutic potential requires the establishment of causal links. Only by specifically interfering with the purported mechanisms of action of a drug can the researcher determine how the drug “causes” its physiological effects. Typically, pharmacological or genetic approaches are employed to modify the expression and/or activity of the biological drug target or downstream pathways, to test if the salutary properties of the drug are thereby abolished. However, experimental techniques have caveats that tend to be underappreciated, particularly for the newer methods. In this two-part series, the caveats and strengths of mechanistic preclinical research are described, using the intuitive example of pharmaceutical drug testing in experimental models of human diseases. This series is not intended to tackle the perpetual clash between the frequentist approach to statistics and other schools of thought. Rather, Part I focuses on technical practicalities and common pitfalls of cellular and animal models designed for drug testing, and Part II describes in simple terms how to leverage a full-factorial three-way ANOVA, to test for causality in the link between drug-induced activation (or inhibition) of a biological target and therapeutic outcomes. Upon completion of this series, the student is expected to appreciate the strengths as well as limitations of mechanistic research and to avoid some of its pitfalls.

As the era of omics continues to expand, omics-based experiments have become popular in industrial biotechnology. They promise deeper biological understanding, which may be leveraged to develop novel solutions to bioprocess optimization and cell line engineering strategies. Despite this expansion, naivety about what omic data offers and how to best handle such data can challenge the extraction of actionable value. However, the value of omic experiments in biotechnology research and development can be maximized with deliberate application of omic approaches and forethought about analysis techniques. Here we describe important considerations when designing and implementing omic-based experiments, and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in biomanufacturing.

Mathematical modelling of bioprocesses has a long and notable history, with eminent contributions from fields including microbiology, ecology, biophysics, chemistry, statistics, control theory and mathematical theory. This richness of ideas and breadth of concepts provide great motivation for inquisitive engineers and intrepid scientists to try their hand at modelling, and this collaboration of disciplines has also delivered significant milestones in the quality and application of models for both theoretical and practical interrogation of engineered biological systems. The focus of this review is the anaerobic digestion process, which, as a technology that has come in and out of fashion, still remains a fundamental process for addressing the global climate emergency. Whether with conventional anaerobic digestion systems, biorefineries, or other anaerobic technologies, mathematical models are important tools that are used to design, monitor, control and optimise the process. Both highly structured, mechanistic models and data-driven approaches have been used extensively over half a decade, but recent advances in computational capacity, scientific understanding and diversity and quality of process data, presents an opportunity for the development of new modelling paradigms, augmentation of existing methods, or even incorporation of tools from other disciplines, to ensure that anaerobic digestion research can remain resilient and relevant in the face of emerging and future challenges.

For more than one hundred years, scientists dispute the correct interpretation of Lorentz transformations within the framework of the special theory of relativity of Albert Einstein. On the one hand, the changes in length, time and mass with increasing velocity are interpreted as apparent due to the observer dependence within special relativity. On the other hand, real changes are described corresponding to the experimental evidence of mass increase in particle accelerators or of clock delay. This ambiguity is accompanied by an ongoing controversy about valid Lorentz-transformed thermodynamic quantities such as entropy, pressure and temperature. In this paper is shown that the interpretation problem of the Lorentz transformations is genuinely anchored within the postulates of special relativity and can be solved on the basis of the thermodynamic approach of matter-energy equivalence, i.e. an energetic distinction between matter and mass. It is suggested that the velocity-dependent changes in state quantities are real in each case, in full agreement with the experimental evidence.

1) Background: Climate change may subject forests to climate conditions to which they are not adapted. Elevated temperatures reduce net photosynthesis by increasing respiration rates and increasingly long droughts dramatically increase morbidity. CO₂ enrichment enhances productivity, but it is not clear to what extent CO₂ enrichment can offset the negative effects of elevated temperatures and longer droughts. 2) Methods: We used a mechanistic landscape model to conduct controlled simulation experiments manipulating CO₂ concentration, temperature, drought length and soil water capacity. 3) Results: We found that elevated CO₂ stimulates productivity such that it dwarfs the negative effect caused by elevated temperature. Energy reserves were not as strongly mitigated by elevated CO₂, and mortality of less competitive cohorts increased. Drought length had a surprisingly small effect on productivity measures, but had a marked negative effect on mortality risk. 4) Conclusions: Elevated CO₂ compensated for the negative effect of longer droughts in terms of productivity measures, but not survival measures.

The particulars of stimulus-response experiments done on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures – so that modal analysis can be used constructively in systems biology model building – for nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input-output data associated with a structural model – one that includes inputs and outputs explicitly – and for NL as well as linear biosystems. The methodology establishes model dimensionality (size, complexity) from particular data sets; helps select among multiple candidate models (model distinguishability); helps in designing new experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. For NL biosystems, results are not as comprehensive, similarly informative about their dominant dynamical properties, and unified with linear models on invariant 2-dimensional manifolds in phase space. Some automation of these highly technical aspects of biomodeling is also introduced.