Revised Michaelis-Menten rate law with time-varying molecular concentrations

Despite over a century's use as a dominant paradigm in the description of biochemical rate processes, the Michaelis-Menten (MM) rate law stands on the restrictive assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium much faster than the molecular concentration changes. The increasingly-appreciated, remedied form of the MM rate law is also based on this quasi-steady state assumption. Although this assumption may be valid for a range of biochemical systems, the exact extent of such systems is not clear. In this study, we relax the quasi-steady state requirement and propose the revised MM rate law for the interactions of molecules with active concentration changes over time. Our revised rate law, characterized by rigorously-derived time delay effects in molecular complex formation, improves the accuracy of models especially for protein-protein and protein-DNA interactions. Our simulation and empirical data analysis show that the improvement is not limited to the quantitatively better characterization of the dynamics, but also allows the prediction for qualitatively new patterns in the systems of interest. The latter include the oscillation condition and period patterns of the mammalian circadian clock and the spontaneous rhythmicity in the degradation rates of circadian proteins, both not properly captured by the previous approaches. Moreover, our revised rate law is capable of more accurate parameter estimation. This work offers an analytical framework for understanding rich dynamics of biomolecular systems, which goes beyond the quasi-steady state assumption.

Conservation of Differential Animal MicroRNA Processing by Drosha and Dicer

In complex biochemical systems, an enzyme, protein, or RNA, symbolized as E, has hundreds or thousands of substrates or interacting partners. The relative specificity hypothesis proposes that such an E would differentially interact with and influence its many distinct, downstream substrates, thereby regulating the underlying biological process (es). The importance of relative specificity has been underappreciated, and evidence of its physiological consequences particularly lacking. Previously we showed that human Drosha and Dicer ribonucleases (RNases) both discriminate their respective microRNA (miRNA) substrates, and that differential cleavage by Drosha contributes to global differential miRNA expression. If relative specificity is an important biological mechanism, it should be evolutionarily conserved. To test this hypothesis, we hereby examined the cleavage of hundreds of zebrafish and fruitfly miRNA intermediates by Drosha and Dicer and the impact on miRNA biogenesis in these organisms. We showed that Drosha action regulates differential miRNA expression in zebrafish and fruitflies and identified the conserved secondary structure features and sequences in miRNA transcripts that control Drosha activity and miRNA expression. Our results established the conservation of miRNA processing mechanisms and regulatory functions by Drosha and Dicer, greatly strengthened the evidence for the physiological consequences of relative specificity as well as demonstrated its evolutionary significance.

Perovskite-Based Nanocomposite Electrocatalysts: An Alternative to Platinum ORR Catalyst in Microbial Fuel Cell Cathodes

Microbial fuel cells (MFCs) are biochemical systems having the benefit of producing green energy through the microbial degradation of organic contaminants in wastewater. The efficiency of MFCs largely depends on the cathode oxygen reduction reaction (ORR). A preferable ORR catalyst must have good oxygen reduction kinetics, high conductivity and durability, together with cost-effectiveness. Platinum-based electrodes are considered a state-of-the-art ORR catalyst. However, the scarcity and higher cost of Pt are the main challenges for the commercialization of MFCs; therefore, in search of alternative, cost-effective catalysts, those such as doped carbons and transition-metal-based electrocatalysts have been researched for more than a decade. Recently, perovskite-oxide-based nanocomposites have emerged as a potential ORR catalyst due to their versatile elemental composition, molecular mechanism and the scope of nanoengineering for further developments. In this article, we discuss various studies conducted and opportunities associated with perovskite-based catalysts for ORR in MFCs. Special focus is given to a basic understanding of the ORR reaction mechanism through oxygen vacancy, modification of its microstructure by introducing alkaline earth metals, electron transfer pathways and the synergistic effect of perovskite and carbon. At the end, we also propose various challenges and prospects to further improve the ORR activity of perovskite-based catalysts.

The chemical Langevin equation for biochemical systems in dynamic environments

Modelling and simulation of complex biochemical reaction networks form cornerstones of modern biophysics. Many of the approaches developed so far capture temporal fluctuations due to the inherent stochasticity of the biophysical processes, referred to as intrinsic noise. Stochastic fluctuations, however, predominantly stem from the interplay of the network with many other - and mostly unknown - fluctuating processes, as well as with various random signals arising from the extracellular world; these sources contribute extrinsic noise. Here we provide a computational simulation method to probe the stochastic dynamics of biochemical systems subject to both intrinsic and extrinsic noise. We develop an extrinsic chemical Langevin equation - a physically motivated extension of the chemical Langevin equation - to model intrinsically noisy reaction networks embedded in a stochastically fluctuating environment. The extrinsic CLE is a continuous approximation to the Chemical Master Equation (CME) with time-varying propensities. In our approach, noise is incorporated at the level of the CME, and can account for the full dynamics of the exogenous noise process, irrespective of timescales and their mismatches. We show that our method accurately captures the first two moments of the stationary probability density when compared with exact stochastic simulation methods, while reducing the computational runtime by several orders of magnitude. Our approach provides a method that is practical, computationally efficient and physically accurate to study systems that are simultaneously subject to a variety of noise sources.

Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D

Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.

Molecular convolutional neural networks with DNA regulatory circuits

Complex biomolecular circuits enable cells with intelligent behavior for survival before neural brains evolved. Synthesized DNA circuits in liquid phase developed as computational hardware can perform neural-network-like computation that harness the collective properties of complex biochemical systems, however the scaling up in complexity remains challenging to support more powerful computation. we present a systematic molecular implementation of the convolutional neural network (ConvNet) algorithm with synthetic DNA regulatory circuits based on a simple DNA switching gate architecture. We experimentally demonstrated that a DNA-based ConvNet based on shared-weight architecture of a 3×6 sized kernel can simultaneously implement parallel multiply-accumulate (MAC) operations for 144 bits inputs and recognize patterns up to 8 categories autonomously. Furthermore, it can connect with another DNA circuits to construct hierarchical networks, which can recognize patterns up to 32 categories with a two-step classification approach of performing coarse classification on language (Arabic numerals, Chinese oracles, English alphabets and Greek alphabets) and then classifying them into specific handwritten symbols. With a simple cyclic freeze/thaw approach, we can decrease computation time from hours to minutes. Our approach shows great promise in the realization of high computing power molecular computer with ability to classify complex and noisy information.

Universally valid reduction of multiscale stochastic biochemical systems using simple non-elementary propensities

Biochemical systems consist of numerous elementary reactions governed by the law of mass action. However, experimentally characterizing all the elementary reactions is nearly impossible. Thus, over a century, their deterministic models that typically contain rapid reversible bindings have been simplified with non-elementary reaction functions (e.g., Michaelis-Menten and Morrison equations). Although the non-elementary reaction functions are derived by applying the quasi-steady-state approximation (QSSA) to deterministic systems, they have also been widely used to derive propensities for stochastic simulations due to computational efficiency and simplicity. However, the validity condition for this heuristic approach has not been identified even for the reversible binding between molecules, such as protein-DNA, enzyme-substrate, and receptor-ligand, which is the basis for living cells. Here, we find that the non-elementary propensities based on the deterministic total QSSA can accurately capture the stochastic dynamics of the reversible binding in general. However, serious errors occur when reactant molecules with similar levels tightly bind, unlike deterministic systems. In that case, the non-elementary propensities distort the stochastic dynamics of a bistable switch in the cell cycle and an oscillator in the circadian clock. Accordingly, we derive alternative non-elementary propensities with the stochastic low-state QSSA, developed in this study. This provides a universally valid framework for simplifying multiscale stochastic biochemical systems with rapid reversible bindings, critical for efficient stochastic simulations of cell signaling and gene regulation. To facilitate the framework, we provide a user-friendly open-source computational package, ASSISTER, that automatically performs the present framework.

Programming ultrasensitive threshold response through chemomechanical instability

The ultrasensitive threshold response is ubiquitous in biochemical systems. In contrast, achieving ultrasensitivity in synthetic molecular structures in a controllable way is challenging. Here, we propose a chemomechanical approach inspired by Michell’s instability to realize it. A sudden reconfiguration of topologically constrained rings results when the torsional stress inside reaches a critical value. We use DNA origami to construct molecular rings and then DNA intercalators to induce torsional stress. Michell’s instability is achieved successfully when the critical concentration of intercalators is applied. Both the critical point and sensitivity of this ultrasensitive threshold reconfiguration can be controlled by rationally designing the cross-sectional shape and mechanical properties of DNA rings.

BioSANS: A Software Package for Symbolic and Numeric Biological Simulation

Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for such systems biology modeling, but most support only certain types of modeling tasks. Desirable features including ease in preparing input, symbolic or analytical computation, parameter estimation, graphical user interface, and systems biology markup language (SBML) support are not seen concurrently in one software package. In this study, we developed a python-based software that supports these features, with both deterministic and stochastic propagations. The software can be used by graphical user interface, command line, or as a python import. We also developed a semi-programmable and intuitively easy topology input method for the biochemical reactions. We tested the software with semantic and stochastic SBML test cases. Tests on symbolic solution and parameter estimation were also included. The software we developed is reliable, well performing, convenient to use, and compliant with most of the SBML tests. So far it is the only systems biology software that supports symbolic, deterministic, and stochastic modeling in one package that also features parameter estimation and SBML support. This work offers a comprehensive set of tools and allows for better availability and accessibility for studying kinetics and dynamics in biochemical systems.

Epilogue

This chapter reveals the importance of bacterial sex in evolution. After scientists realized that antibiotic resistance and the development of MDR pathogens were caused by bacterial sex, some evolutionary biologists began to wonder about the potential role of HGT in evolution. Scientists speculated that mobile genes might have played a significant role in microbial evolution. James Shapiro has argued genome change in evolution results from a natural genetic engineering process utilizing the biochemical systems for reorganizing DNA structures present in living cells. For billions of years—from the beginning of evolution—bacteria have been transferring genes horizontally, between species and therefore between lineages, interconnecting the branches on the tree of life. The promiscuous process that has scrambled the tree of life is bacterial sex, discovered by Joshua and Esther Lederberg as a laboratory curiosity that helped uncover the molecular secretes of bacterial and viral genes.