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The turn of the millennium has seen a growing interest in the study of live cells by infrared (IR) spectroscopy, driven by the versatility, wealth of molecular information, and potential for high-throughput screening of the technique. Measurements on individual cells, either isolated or within a multi-cellular structure, provide information that is not available from ensemble samples. The present review discusses the use of infrared (IR) microscopy to analyse live single cells from a biochemical perspective, seeking information on real-time processes. The emphasis is on the use of the technique to quantify metabolic turnover, with the aim of providing a complementary method for metabolomics, and for toxicological and pharmacological studies. The work highlights the methodological advances and proof-of-concept experiments that took place over the past few years in this direction. It discusses current advantages and limitations of the technique, including the possibility of detecting specific biomolecules and their reactivity, and it concludes with a brief outline of future perspectives.

Evolution is often understood through genetic mutations driving changes in an organism's fitness, but there is potential to extend this understanding beyond the genetic code. We propose that natural products - complex molecules central to Earth's biochemistry can be used to uncover evolutionary mechanisms beyond genes. By applying Assembly Theory (AT), which views selection as a process not limited to biological systems, we can map and measure evolutionary forces in these molecules. AT enables the exploration of the assembly space of natural products, demonstrating how the principles of the selfish gene apply to these complex chemical structures, selecting vastly improbable and complex molecules from a vast space of possibilities. By comparing natural products with a broader molecular database, we can assess the degree of evolutionary contingency, providing insight into how molecular novelty emerges and persists. This approach not only quantifies evolutionary selection at the molecular level but also offers a new avenue for drug discovery by exploring the molecular assembly spaces of natural products. Our method provides a fresh perspective on measuring the evolutionary processes b

Biology is perhaps the most complex of the sciences, given the incredible variety of chemical species that are interconnected in spatial and temporal pathways that are daunting to understand. Their interconnections lead to emergent properties such as memory, consciousness, and recognition of self and non-self. To understand how these interconnected reactions lead to cellular life characterized by activation, inhibition, regulation, homeostasis, and adaptation, computational analyses and simulations are essential, a fact recognized by the biological communities. At the same time, students struggle to understand and apply binding and kinetic analyses for the simplest reactions such as the irreversible first-order conversion of a single reactant to a product. This likely results from cognitive difficulties in combining structural, chemical, mathematical, and textual descriptions of binding and catalytic reactions. To help students better understand dynamic reactions and their analyses, we have introduced two kinds of interactive graphs and simulations into the online educational resource, Fundamentals of Biochemistry, a multivolume biochemistry textbook that is part of the LibreText c

The online estimation of the derivative of an input signal is widespread in control theory and engineering. In the realm of chemical reaction networks (CRN), this raises however a number of specific issues on the different ways to achieve it. A CRN pattern for implementing a derivative block has already been proposed for the PID control of biochemical processes, and proved correct using Tikhonov's limit theorem. In this paper, we give a detailed mathematical analysis of that CRN, thus clarifying the computed quantity and quantifying the error done as a function of the reaction kinetic parameters. In a synthetic biology perspective, we show how this can be used to design error correcting terms to compute online functions involving derivatives with CRNs. In the systems biology perspective, we give the list of models in BioModels containing (in the sense of subgraph epimorphisms) the core derivative CRN, most of which being models of oscillators and control systems in the cell, and discuss in detail two such examples: one model of the circadian clock and one model of a bistable switch.

Undergraduate biochemistry laboratory courses often do not provide students with an authentic research experience, particularly when the express purpose of the laboratory is purely instructional. However, an instructional laboratory course that is inquiry- and research-based could simultaneously impart scientific knowledge and foster a student's research expertise and confidence. We have developed a year-long undergraduate biochemistry laboratory curriculum wherein students determine, via experiment and computation, the function of a protein of known three-dimensional structure. The first half of the course is inquiry-based and modular in design; students learn general biochemical techniques while gaining preparation for research experiments in the second semester. Having learned standard biochemical methods in the first semester, students independently pursue their own (original) research projects in the second semester. This new curriculum has yielded an improvement in student performance and confidence as assessed by various metrics. To disseminate teaching resources to students and instructors alike, a freely accessible Biochemistry Laboratory Education resource is available at

Would another origin of life resemble Earth's biochemical use of amino acids? Here we review current knowledge at three levels: 1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we don't know it. Amino acids thus remain important and tractable targets for astrobiological research. 2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. 3) Would we expect a similar set of side chains to those within the genetic code? Not only do many plausible alternatives exist and evidence exists for both evolutionary advantage and physicochemical constraint for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side ch

In the context of Mediterranean aquaculture little attention has been paid to the manipulation of feeding frequency at the on-growing phase. The effects of different feeding frequencies: one meal per day, two meals per day, three meals per day on growth, digestive enzyme activity, feed digestibility and plasma biochemistry were studied in gilthead sea bream (Sparus aurata, L. 1758) fed with high and low fishmeal and fish oil levels. Isonitrogenous and isolipidic extruded diets were fed to triplicate fish groups by a fixed ration over 109 days. No significant effects of feeding frequency on overall performance, feed efficiency and feed digestibility during the on-growing of gilthead sea bream fed high or low fishmeal and fish oil dietary level were observed. Pepsin activity showed an apparent decrease in fish receiving more than one meal a day which was not compensated by an increased production of alkaline proteases particularly in fish fed on low FM. Although there were no effects on growth and feed utilisation at increasing feeding frequency, trypsin decreased significantly with an increasing number of meals only under low FMFO diet. Thus, it seemed that consecutive meals could h

In recent years considerable portion of the computer science community has focused its attention on understanding living cell biochemistry and efforts to understand such complication reaction environment have spread over wide front, ranging from systems biology approaches, through network analysis (motif identification) towards developing language and simulators for low level biochemical processes. Apart from simulation work, much of the efforts are directed to using mean field equations (equivalent to the equations of classical chemical kinetics) to address various problems (stability, robustness, sensitivity analysis, etc.). Rarely is the use of mean field equations questioned. This review will provide a brief overview of the situations when mean field equations fail and should not be used. These equations can be derived from the theory of diffusion controlled reactions, and emerge when assumption of perfect mixing is used.

Biochemists know that the law of mass action is not exact and not very useful because we cannot transfer it (with unchanged parameters) from one condition to another. I argue that exact equations require calibrated multiscale analysis to deal with ions. Exact theories in biochemistry must use mathematics of interactions because biological ionic solutions, derived from seawater, are complex (not simple) fluids. The activity of one ion depends on every other ion. Mathematics of conservative interactions is well understood but friction is another matter. Mathematicians now have now an energetic variational calculus dealing with friction. Complex fluids need variational methods because everything interacts with everything else. Mathematics designed to handle interactions is needed to produce exact equations. If interactions are not addressed with variational mathematics, they are bewildering. The mathematics must include the global properties of the electric field. Flow of charge in one place changes the flow everywhere by Kirchoff and Maxwell laws. Charge changes physical nature as it flows through a circuit. It is ions in salt water; it is electrons in a vacuum tube; it is quasi-part

We investigate the prospects for the past or current existence of habitable conditions deep underneath the surfaces of the Moon and Mars as well as generic bound and free-floating extrasolar rocky objects. We construct a simple model that takes into account the thermal limits of life as well as the size, surface temperature, and relative radionuclide abundance of a given object and yields the spatial extent of the subsurface habitable region. We also investigate the constraint imposed by pressure on habitability, and show that it is unlikely to rule out the prospects for life altogether. We estimate the maximum biomass that might be sustainable in deep subsurface environments as a function of the aforementioned parameters from an energetic perspective. We find that it might be a few percent that of Earth's subsurface biosphere, and three orders of magnitude smaller than Earth's global biomass, under ideal circumstances. We conclude with a brief exposition of the prevalence of rocky objects with deep biospheres and methods for detecting signatures of biological activity through forthcoming missions to visit the Moon and Mars.

The motion of a eukaryotic cell presents a variety of interesting and challenging problems from both a modeling and a computational perspective. The processes span many spatial scales (from molecular to tissue) as well as disparate time scales, with reaction kinetics on the order of seconds, and the deformation and motion of the cell occurring on the order of minutes. The computational difficulty, even in 2D, resides in the fact that the problem is inherently one of deforming, non-stationary domains, bounded by an elastic perimeter, inside of which there is redistribution of biochemical signaling substances. Here we report the results of a computational scheme using the immersed boundary method to address this problem. We adopt a simple reaction-diffusion system that represents an internal regulatory mechanism controlling the polarization of a cell, and determining the strength of protrusion forces at the front of its elastic perimeter. Using this computational scheme we are able to study the effect of protrusive and elastic forces on cell shapes on their own, the distribution of the reaction-diffusion system in irregular domains on its own, and the coupled mechanical-chemical syst

Biochemistry and mechanics are closely coupled in cell adhesion. At sites of cell-matrix adhesion, mechanical force triggers signaling through the Rho-pathway, which leads to structural reinforcement and increased contractility in the actin cytoskeleton. The resulting force acts back to the sites of adhesion, resulting in a positive feedback loop for mature adhesion. Here we model this biochemical-mechanical feedback loop for the special case when the actin cytoskeleton is organized in stress fibers, which are contractile bundles of actin filaments. Activation of myosin II molecular motors through the Rho-pathway is described by a system of reaction-diffusion equations, which are coupled into a viscoelastic model for a contractile actin bundle. We find strong spatial gradients in the activation of contractility and in the corresponding deformation pattern of the stress fiber, in good agreement with experimental findings.

Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex non-linear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets.

Recent biological research has sought to understand how biochemical signaling pathways, such as the mitogen-activated protein kinase (MAPK) family, influence the migration of a population of cells during wound healing. Fisher's Equation has been used extensively to model experimental wound healing assays due to its simple nature and known traveling wave solutions. This partial differential equation with independent variables of time and space cannot account for the effects of biochemical activity on wound healing, however. To this end, we derive a structured Fisher's Equation with independent variables of time, space, and biochemical pathway activity level and prove the existence of a self-similar traveling wave solution to this equation. We also consider a more complicated model with different phenotypes based on MAPK activation and numerically investigate how various temporal patterns of biochemical activity can lead to increased and decreased rates of population migration.