States Of Biological Systems Research Articles

Stabilization of Charge-Transfer Excited States in Biological Systems: A Computational Focus on the Special Pair in Photosystem II Reaction Centers.

Charge-transfer (CT) excited states play an important role in many biological processes. However, many computational approaches often inadequately address the equilibration effects of nuclear and environmental degrees of freedom on these states. One prominent example of systems in which CT states are of utmost importance is reaction centers (RC) in photosystems. Here we use a multiscale approach combined with time-dependent density functional theory to explore the lowest CT excited state of the special pair PD1-PD2 in the Photosystem II-RC of a cyanobacterium. We find that the nonequilibrium CT excited state resides near the Soret band, making an exciton the lowest-energy excited state. However, accounting for nuclear and state-specific dielectric equilibration along the CT potential energy surface (PES), the CT state PD1--PD2+ stabilizes energetically below the excitonic state. This underscores the crucial role of state-specific solvation in mapping the PES of CT states, as demonstrated in a simplified dimer model.

Transcriptomic forecasting with neural ordinary differential equations

Single-cell transcriptomics technologies can uncover changes in the molecular states that underlie cellular phenotypes. However, understanding the dynamic cellular processes requires extending from inferring trajectories from snapshots of cellular states to estimating temporal changes in cellular gene expression. To address this challenge, we have developed a neural ordinary differential-equation-based method, RNAForecaster, for predicting gene expression states in single cells for multiple future time steps in an embedding-independent manner. We demonstrate that RNAForecaster can accurately predict future expression states in simulated single-cell transcriptomic data with cellular tracking over time. We then show that by using metabolic labeling single-cell RNA sequencing (scRNA-seq) data from constitutively dividing cells, RNAForecaster accurately recapitulates many of the expected changes in gene expression during progression through the cell cycle over a 3-day period. Thus, RNAForecaster enables short-term estimation of future expression states in biological systems from high-throughput datasets with temporal information.

Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network

BackgroundThe modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications.MethodsWe have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN.ResultsOur HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network sizes and levels of stochastic noise. We found our HRNN method to be superior in terms of accuracy for nonlinear data sets with higher amounts of noise.ConclusionsThe proposed method identifies time-delayed gene-gene interactions of GRNs. The topology-based advancement of our HRNN worked as expected by more effectively modeling nonlinear data sets. As a non-fully connected network, an added benefit to HRNN was how it helped to find the few genes which regulated the target gene over different time delays.

Aquaphotomics: Near Infrared Spectroscopy and Water States in Biological Systems.

Aquaphotomics is a new discipline that provides a framework for understanding changes in water molecular system presented as a water spectral pattern, to mirror the rest of the solution and to give a holistic description related to system functionality. One of its main purposes is to identify water bands as main coordinates of future absorbance patterns to be used as a system biomarker. This chapter presents the Aquaphotomics methodology and illustrates a way to identify specific water bands using temperature change and addition of solutions of different ionic strength as perturbations. Rapid and precise measurement of low concentration solutes has been given as a strong evidence of the vast information that "the water spectral pattern as molecular mirror" approach provides. Few applications using near infrared spectroscopy and multivariate analysis as main tools of Aquaphotomics have been presented.

Robust estimation of stochastic gene-network systems

Gene networks in biological systems are highly complicated because of their nonlinear and stochastic features. Network dynamics typically involve crosstalk mechanism and they may suffer from corruption due to intrinsic and extrinsic stochastic molecular noises. Filtering noises in gene networks using biological techniques accompanied with a systematic strategy is thus an attractive topic. However, most states of biological systems are not directly accessible. In practice, these immeasurable states can only be predicted based on the measurement output. In the lab experiment, green fluorescent protein (GFP) is commonly adopted as the reporter protein since it is able to reflect intensity of the gene expression. On this basis, this study considers a nonlinear stochastic model to describe the stochastic gene networks and shows that robust state estimation using Kalman filtering techniques is possible. Stability of the robust estimation scheme is analyzed based on the Ito’s theorem and Lyapunov stability theory. Numerical examples in silico are illustrated to confirm performance of the proposed design.

Identification of Cancer Cell-Line Origins Using Fluorescence Image-Based Phenomic Screening

Universal phenotyping techniques that can discriminate among various states of biological systems have great potential. We applied 557 fluorescent library compounds to NCI's 60 human cancer cell-lines (NCI-60) to generate a systematic fluorescence phenotypic profiling data. By the kinetic fluorescence intensity analysis, we successfully discriminated the organ origin of all the 60 cell-lines.

Plausibility of quantum coherent states in biological systems

In this paper we briefly discuss the necessity of using quantum mechanics as a fundamental theory applicable to some key functional aspects of biological systems. This is especially relevant to three important parts of a neuron in the human brain, namely the cell membrane, microtubules (MT) and ion channels. We argue that the recently published papers criticizing the use of quantum theory in these systems are not convincing.

Osmolyte-Induced Folding of an Intrinsically Disordered Protein: Folding Mechanism in the Absence of Ligand

Understanding the interconversion between thermodynamically distinguishable states present in a protein folding pathway provides not only the kinetics and energetics of protein folding but also insights into the functional roles of these states in biological systems. The protein component of the bacterial RNase P holoenzyme from Bacillus subtilis (P protein) was previously shown to be unfolded in the absence of its cognate RNA or other anionic ligands. P protein was used in this study as a model system to explore general features of intrinsically disordered protein (IDP) folding mechanisms. The use of trimethylamine N-oxide (TMAO), an osmolyte that stabilizes the unliganded folded form of the protein, enabled us to study the folding process of P protein in the absence of ligand. Transient stopped-flow kinetic traces at various final TMAO concentrations exhibited multiphasic kinetics. Equilibrium "cotitration" experiments were performed using both TMAO and urea during the titration to produce a urea-TMAO titration surface of P protein. Both kinetic and equilibrium studies show evidence of a previously undetected intermediate state in the P protein folding process. The intermediate state is significantly populated, and the folding rate constants are relatively slow compared to those of intrinsically folded proteins similar in size and topology. The experiments and analysis described serve as a useful example for mechanistic folding studies of other IDPs.

On Robust State Estimation of Gene Networks

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

Biophysical Cancer Transformation Pathway

Coherent vibration states in biological systems excited in nonlinear electrically polar structures by metabolic energy supply were postulated by H. Fröhlich. Fröhlich's requirements for coherent vibrations and generation of electromagnetic field are satisfied by microtubules whose subunits are electric dipoles. Static electric field around mitochondria and “wasted energy” efflux from them provide nonlinear conditions and coherent excitation. Numerical models are used for analysis of coherent vibration states.A hypothesis is presented that dysfunction of mitochondria (i.e., extinction of the zones of the static electric field and of the efflux of “wasted energy”) and disintegration of the cytoskeleton on the pathway of cancer transformation result in disturbances of coherence of the cellular electrically polar oscillations and of the generated electromagnetic field with consequences in cellular organization and interactions between cells. Local invasion, detachment, and metastasis of cancer cells are subsequent events of disturbed electromagnetic interactions.

Statistical physics of pairwise probability models.

Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the mean values and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.

Concepts, challenges, and prospects on Multiagent Data Warehousing (MADWH) and Multiagent Data Mining (MADM)

Multiagent Data Warehousing (MADWH) and Multiagent Data Mining (MADM) presents a multidimensional agent-oriented approach for brain modelling and decision making based on the hypothesis that a brain system consists of a society of semiautonomous neural agents and full autonomy is the result of coordination of semiautonomous functionalities. The agent-oriented approach leads to the following concepts, challenges, and prospects: agent laws, agentisation, agent discovery, law discovery, self-organisation, and reorganisation; mining agent association rules in 1st-order logic; modelling full autonomy as the result of coordination of semiautonomous agents; modelling evolving processes like growing and aging; modelling healthy states as well as unhealthy states of biological systems.

A new principle for information storage in an enzymatic pathway model.

Strong experimental evidence indicates that protein kinase and phosphatase (KP) cycles are critical to both the induction and maintenance of activity-dependent modifications in neurons. However, their contribution to information storage remains controversial, despite impressive modeling efforts. For instance, plasticity models based on KP cycles do not account for the maintenance of plastic modifications. Moreover, bistable KP cycle models that display memory fail to capture essential features of information storage: rapid onset, bidirectional control, graded amplitude, and finite lifetimes. Here, we show in a biophysical model that upstream activation of KP cycles, a ubiquitous mechanism, is sufficient to provide information storage with realistic induction and maintenance properties: plastic modifications are rapid, bidirectional, and graded, with finite lifetimes that are compatible with animal and human memory. The maintenance of plastic modifications relies on negligible reaction rates in basal conditions and thus depends on enzyme nonlinearity and activation properties of the activity-dependent KP cycle. Moreover, we show that information coding and memory maintenance are robust to stochastic fluctuations inherent to the molecular nature of activity-dependent KP cycle operation. This model provides a new principle for information storage where plasticity and memory emerge from a single dynamic process whose rate is controlled by neuronal activity. This principle strongly departs from the long-standing view that memory reflects stable steady states in biological systems, and offers a new perspective on memory in animals and humans.

Singularly perturbed periodic parabolic equations with alternating boundary layer type solutions

We consider a class of singularly perturbed parabolic equations for which the degenerate equations obtained by setting the small parameter equal to zero are algebraic equations that have several roots. We study boundary layer type solutions that, as time increases, periodically go through two fairly long lasting stages with extremely fast transitions in between. During one of these stages the solution outside the boundary layer is close to one of the roots of the degenerate (reduced) equation, while during the other stage the solution is close to the other root. Such equations may be used as models for bio‐switches where the transitions between various stationary states of biological systems are initiated by comparatively slow changes within the systems.

Unique oligomeric intermediates of bovine liver catalase.

Catalases, although synthesized from single genes and built up from only one type of subunit, exist in heterogeneous form with respect to their conformations and association states in biological systems. This heterogeneity is not of genetic origin, but rather reflects the instability of this oligomeric heme enzyme. To understand better the factors that stabilize the various association states of catalase, we performed studies on the multimeric intermediates that are stabilized during guanidine-hydrochloride- and urea-induced unfolding of bovine liver catalase (BLC). For the first time, we have observed an enzymatically active, folded dimer of native BLC. This dimer has slightly higher enzymatic activity and altered structural properties compared to the native tetramer. Comparative studies of the effect of NaCl, GdmCl, and urea on BLC show that cation binding to negatively charged groups present in amino acid side chains of the enzyme leads to stabilization of an enzymatically active, folded dimer of BLC. Besides the folded dimer, an enzymatically active expanded tetramer and a partially unfolded, enzymatically inactive dimer of BLC were also observed. A complete recovery of native enzyme was observed on refolding of expanded tetramers and folded dimers; however, a very low recovery (maximum of approximately 5%) of native enzyme was observed on refolding of partially unfolded dimers and fully unfolded monomers.

Reverse intersystem crossing in rose bengal. II. Fluence dependence of fluorescence following 532 nm laser excitation.

A fluence-dependent fluorescence technique was used to observe reverse intersystem crossing from a certain higher-lying triplet state of rose bengal populated by a single pulse of 532 nm light. The quantum yield of reverse intersystem crossing from this state was determined to be 0.12+/-0.02 for rose bengal in phosphate-buffered saline. The importance of including molecular rotation effects in the analysis of fluorescence resulting from reverse intersystem crossing is discussed. Differences in the photochemical reactivity of upper triplet states in biological systems have been previously hypothesized to result from photophysical differences, particularly substantial differences in their reverse intersystem crossing yields. In this work this hypothesis is analyzed quantitatively, using numerical models of the population dynamics. These models suggest that reverse intersystem crossing alone cannot adequately explain the differences in biological response.

Quantum mechanical concepts of coherent states in biological systems

Abstract In order to demonstrate the possibility of coherent behaviour in biological systems, Fröhlich wrote down the rate equations and showed that if energy is supplied above a critical rate to the branch or branches of electromagnetic modes, Bose-Einstein condensation into the lowest energy state occurs. The general forms of the rate equations were dictated by requiring a Bose-Einstein distribution for thermal equilibrium when there is no energy supplied. Using microscopic theory and perturbation calculations, we were able to obtain the rate equations of Fröhlich from the Hamiltonian of the biological system under study. Two simple cases were presented to demonstrate the enhancement of excitations in biological systems by external stimulation. Fröhlich's model has been particularly useful in understanding recent experiments revealing the effects of low level microwaves on various biological systems. One important general characteristic reported in these experiments is the existence of a time threshold for the initiation of the differing biological effects. We obtained an approximation of this time threshold.

Different lethal effects by enzyme-generated triplet indole-3-aldehyde in different Escherichia coli strains

Strains of Escherichia coli which lack 4-thiouridine (S 4U) exhibit a higher survival rate than their wild-type parents which contain S 4U after treatment with enzyme-generated triplet indole-3-aldehyde. In a similar manner to results obtained with monochromatic 334 nm UV light, the survival is related to single-strand breakage of DNA in E. coli containing the pBR 322 plasmid. The effects of the excited states generated by an enzymatic system suggest that S 4U is an important chromophore in the lethal effects observed. The results also suggest that the energy transferred from triplet indole-3-aldehyde to S 4U may also be passed from S 4U of T-RNA to DNA, possibly through a singlet oxygen intermediate generated by excited S 4U, resulting in a decrease in the survival rate of E. coli containing S 4U. These results emphasize the importance of excited states in biological systems.

Production of coherent states in biological systems

It is shown that Glauber-type coherent states exist in biological systems as a result of the broken symmetry of the free phonons. The symmetry of the system is reduced by the presence of the interaction terms in the hamiltonian. The coherent states so produced allow for the existence of giant dipoles capable of long-range interactions.

Multiple Fröhlich coherent states in biological systems: Computer simulation

Computer simulation of the energy storage in the band of normal modes with energy supply based on the kinetic equation with nonlinear (parametric) energy exchange terms indicates the possibility of the multiple Fröhlich coherent states excitation. It is mainly connected with the non-uniform transmission probabilities ξ of the combined emission-absorption processes between vibration modes. The effect of the coherent states changes on the long-range interaction forces and cancer cell migration is discussed.