Quantitative Biology

Background: Herpes simplex virus type 1 (HSV-1) is a ubiquitous infectious pathogen that widely affects human health. To decipher the complicated human-HSV-1 interactions, a comprehensive protein-protein interaction (PPI) network between human and HSV-1 is highly demanded. Methods: To complement the experimental identification of human-HSV-1 PPIs, an integrative strategy to predict proteome-wide PPIs between human and HSV-1 was developed. For each human-HSV-1 protein pair, four popular PPI inference methods, including interolog mapping, the domain-domain interaction-based method, the domainmotif interaction-based method, and the machine learning-based method, were optimally implemented to generate four interaction probability scores, which were further integrated into a final probability score. Results: As a result, a comprehensive high-confidence PPI network between human and HSV-1 was established, covering 10,432 interactions between 4,546 human proteins and 72 HSV-1 proteins. Functional and network analyses of the HSV-1 targeting proteins in the context of human interactome can recapitulate the known knowledge regarding the HSV-1 replication cycle, supporting the overall reliability of the predicted PPI network. Considering that HSV-1 infections are implicated in encephalitis and neurodegenerative diseases, we focused on exploring the biological significance of the brain-specific human-HSV-1 PPIs. In particular, the predicted interactions between HSV-1 proteins and Alzheimer's-disease-related proteins were intensively investigated. Conclusion: The current work can provide testable hypotheses to assist in the mechanistic understanding of the human-HSV-1 relationship and the anti-HSV-1 pharmaceutical target discovery. To make the predicted PPI network and the datasets freely accessible to the scientific community, a user-friendly database browser was released at http:// www.zzdlab.com/HintHSV/index.php.

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT-PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images. Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria. Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19. Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

"Cells of almost all solid tissues are connected with gap junctions
which permit the direct transfer of ions and small molecules, integral to regulating coordinated function in the tissue. The pancreatic islets of Langerhans are responsible for secreting the hormone insulin in response to glucose stimulation.  Gap junctions are the only electrical contacts between the beta-cells in the tissue of these excitable islets. It is generally believed that they are responsible for synchrony of the membrane voltage oscillations among beta-cells, and thereby pulsatility of insulin secretion. Most attempts to understand connectivity in islets are often interpreted, bottom-up, in terms of measurements of gap junctional conductance. This does not, however explain systematic changes, such as a diminished junctional conductance in type 2 diabetes. We attempt to address this deficit via the model presented here, which is a learning theory of gap junctional adaptation derived with analogy to neural systems. Here, gap junctions are modelled as bonds in a beta-cell network, that are altered according to homeostatic rules of plasticity. Our analysis reveals that it is nearly impossible to view gap junctions as homogeneous across a tissue. A modified view that accommodates heterogeneity of junction strengths in the islet can explain why, for example, a loss of gap junction conductance in diabetes is necessary for an increase
in plasma insulin levels following hyperglycemia."

Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. This depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level. The nuance afforded by partitioning neural systems in this way highlights certain limitations of modular perspectives of brain function that only consider a single level of description.

Background: The past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression. Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine. Results: In this review we introduce cancer genomics from a probabilistic and statistical perspective. We start from (1) functional classification of genes into oncogenes and tumor suppressor genes, then (2) demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes, followed by (3) tumor purity analysis, which in turn leads to (4) the concept of ploidy and clonality, that is next connected to (5) tumor evolution under treatment pressure, which yields insights into cancer drug resistance. We also discuss future challenges including the non-coding genomic regions, integrative analysis of genomics and epigenomics, as well as early cancer detection. Conclusion: We believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field of cancer genomics and personalized medicine.

In many analyses of high-throughput data in systems biology, there is a need to quantify the activity of a set of genes in individual samples. A typical example is the case where it is necessary to estimate the activity of a transcription factor (which is often not directly measurable) from the expression of its target genes. We present here ROMA (Representation and quantification Of Module Activities) Java software, designed for fast and robust computation of the activity of gene sets (or modules) with coordinated expression. ROMA activity quantification is based on the simplest uni-factor linear model of gene regulation that approximates the expression data of a gene set by its first principal component. The proposed algorithm implements novel functionalities: it provides several method modifications for principal components computation, including weighted, robust and centered methods; it distinguishes overdispersed modules (based on the variance explained by the first principal component) and coordinated modules (based on the significance of the spectral gap); finally, it computes statistical significance of the estimated module overdispersion or coordination. ROMA can be applied in many contexts, from estimating differential activities of transcriptional factors to finding overdispersed pathways in single-cell transcriptomics data. We describe here the principles of ROMA providing several practical examples of its use. ROMA source code is available at https://github.com/sysbio-curie/Roma.

Background: Hepatitis B virus (HBV) has affected over 300 million people worldwide which causes to induce mostly liver disease and liver cancer. It is a member of the family Hepadnaviridae which is a small DNA virus with unusual characters like retroviruses. Generally, hepatoprotective drugs provoke some side effects in human beings. For the reason, this study aims to identify alternative drug molecules from the natural source of medicinal plants with smaller quantity of side effects than those conventional drugs in treating HBV. Methods: We developed computational methods for calculating drug and target binding resemblance using the Maestro v10.2 of Schrodinger suite. The target and ligand molecules were obtained from recognized databases. Ligand molecules of 40 phytoconstituents were retrieved from variety of plants after we executed crucial analyses such as molecular docking and absorption, distribution, metabolism, and excretion (ADME) analysis. Results: In the docking analysis, the natural analogues repandusinic acid showed better docking scores of-14.768 with good binding contacts. The remaining bioactive molecules corilagin, furosin, nirurin, iso-quercetin and gallocatechin also showed better docking scores. Conclusion: This computational analysis reveals that repandusinic acid is a suitable drug candidate for HBV. Therefore, we recommend that this analogue is suitable in further exploration using in vitro studies.

Background: Molecular competition brings about trade-offs of shared limited resources among the cellular components, and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions. Several molecular competition scenarios have been observed recently, but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition. Methods: Here, by abstracting the analogous competition mechanism behind diverse molecular systems, we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses. Results: We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence. We quantified how competition can shape regulator-target dose-response curve, modulate dynamic response speed, control target expression noise, and introduce correlated fluctuations between targets. Conclusions: This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network, and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.

Chromatin immunoprecipitation coupled with massive parallel sequencing (ChIP-seq) is a powerful technology to identify the genome-wide locations of DNA binding proteins such as transcription factors or modified histones. As more and more experimental laboratories are adopting ChIP-seq to unravel the transcriptional and epigenetic regulatory mechanisms, computational analyses of ChIP-seq also become increasingly comprehensive and sophisticated. In this article, we review current computational methodology for ChIP-seq analysis, recommend useful algorithms and workflows, and introduce quality control measures at different analytical steps. We also discuss how ChIP-seq could be integrated with other types of genomic assays, such as gene expression profiling and genome-wide association studies, to provide a more comprehensive view of gene regulatory mechanisms in important physiological and pathological processes. QB 54

A polymer chain pinned in space exerts a fluctuating force on the pin point in thermal equilibrium. The average of such fluctuating force is well understood from statistical mechanics as an entropic force, but little is known about the underlying force distribution. Here, we introduce two phase space sampling methods that can produce the equilibrium distribution of instantaneous forces exerted by a terminally pinned polymer. In these methods, both the positions and momenta of mass points representing a freely jointed chain are perturbed in accordance with the spatial constraints and the Boltzmann distribution of total energy. The constraint force for each conformation and momentum is calculated using Lagrangian dynamics. Using terminally pinned chains in space and on a surface, we show that the force distribution is highly asymmetric with both tensile and compressive forces. Most importantly, the mean of the distribution, which is equal to the entropic force, is not the most probable force even for long chains. Our work provides insights into the mechanistic origin of entropic forces, and an efficient computational tool for unbiased sampling of the phase space of a constrained system.

Recent advances in imaging technology now provide us with 3D images of developing organs. These can be used to extract 3D geometries for simulations of organ development. To solve models on growing domains, the displacement fields between consecutive image frames need to be determined. Here we develop and evaluate different landmark-free algorithms for the determination of such displacement fields from image data. In particular, we examine minimal distance, normal distance, diffusion-based and uniform mapping algorithms and test these algorithms with both synthetic and real data in 2D and 3D. We conclude that in most cases the normal distance algorithm is the method of choice and wherever it fails, diffusion-based mapping provides a good alternative.

We review a statistical mechanics treatment of the stability of globular proteins based on a simple model Hamiltonian taking into account protein self interactions and protein-water interactions. The model contains both hot and cold folding transitions. In addition it predicts a critical point at a given temperature and chemical potential of the surrounding water. The universality class of this critical point is new.

While there have been great advances in quantification of the genotype of organisms, including full genomes for many species, the quantification of phenotype is at a comparatively primitive stage. Part of the reason is technical difficulty: the phenotype covers a wide range of characteristics, ranging from static morphological features, to dynamic behavior. The latter poses challenges that are in the area of multimedia signal processing. Automated analysis of video and audio recordings of animal and human behavior is a growing area of research, ranging from the behavioral phenotyping of genetically modified mice or drosophila to the study of song learning in birds and speech acquisition in human infants. This paper reviews recent advances and identifies key problems for a range of behavior experiments that use audio and video recording. This research area offers both research challenges and an application domain for advanced multimedia signal processing. There are a number of MMSP tools that now exist which are directly relevant for behavioral quantification, such as speech recognition, video analysis and more recently, wired and wireless sensor networks for surveillance. The research challenge is to adapt these tools and to develop new ones required for studying human and animal behavior in a high throughput manner while minimizing human intervention. In contrast with consumer applications, in the research arena there is less of a penalty for computational complexity, so that algorithmic quality can be maximized through the utilization of larger computational resources that are available to the biomedical researcher.

Cells of almost all solid tissues are connected with gap junctions which permit the direct transfer of ions and small molecules, integral to regulating coordinated function in the tissue. The pancreatic islets of Langerhans are responsible for secreting the hormone insulin in response to glucose stimulation. Gap junctions are the only electrical contacts between the beta-cells in the tissue of these excitable islets. It is generally believed that they are responsible for synchrony of the membrane voltage oscillations among beta-cells, and thereby pulsatility of insulin secretion. Most attempts to understand connectivity in islets are often interpreted, bottom-up, in terms of measurements of gap junctional conductance. This does not, however, explain systematic changes, such as a diminished junctional conductance in type 2 diabetes. We attempt to address this deficit via the model presented here, which is a learning theory of gap junctional adaptation derived with analogy to neural systems. Here, gap junctions are modelled as bonds in a beta-cell network, that are altered according to homeostatic rules of plasticity. Our analysis reveals that it is nearly impossible to view gap junctions as homogeneous across a tissue. A modified view that accommodates heterogeneity of junction strengths in the islet can explain why, for example, a loss of gap junction conductance in diabetes is necessary for an increase in plasma insulin levels following hyperglycemia.

In many analyses of high-throughput data in systems biology, there is a need to quantify the activity of a set of genes in individual samples. A typical example is the case where it is necessary to estimate the activity of a transcription factor (which is often not directly measurable) from the expression of its target genes. We present here ROMA (Representation and quantification Of Module Activities) Java software, designed for fast and robust computation of the activity of gene sets (or modules) with coordinated expression. ROMA activity quantification is based on the simplest uni-factor linear model of gene regulation that approximates the expression data of a gene set by its first principal component. The proposed algorithm implements novel functionalities: it provides several method modifications for principal components computation, including weighted, robust and centered methods; it distinguishes overdispersed modules (based on the variance explained by the first principal c...

Synaptic vesicle recycling has long served as a model for the general mechanisms of cellular trafficking. We used an integrative approach, combining quantitative immunoblotting and mass spectrometry to determine protein numbers; electron microscopy to measure organelle numbers, sizes, and positions; and super-resolution fluorescence microscopy to localize the proteins. Using these data, we generated a three-dimensional model of an "average" synapse, displaying 300,000 proteins in atomic detail. The copy numbers of proteins involved in the same step of synaptic vesicle recycling correlated closely. In contrast, copy numbers varied over more than three orders of magnitude between steps, from about 150 copies for the endosomal fusion proteins to more than 20,000 for the exocytotic ones.

A feed-forward neural net with adaptable synaptic weights and fixed, zero or non-zero threshold potentials is studied, in the presence of a global feedback signal that can only have two values, depending on whether the output of the network in reaction to its input is right or wrong. It is found, on the basis of four biologically motivated assumptions, that only two forms of learning are possible, Hebbian and Anti-Hebbian learning. Hebbian learning should take place when the output is right, while there should be Anti-Hebbian learning when the output is wrong. For the Anti-Hebbian part of the learning rule a particular choice is made, which guarantees an adequate average neuronal activity without the need of introducing, by hand, control mechanisms like extremal dynamics. A network with realistic, i.e., non-zero threshold potentials is shown to perform its task of realizing the desired input-output relations best if it is sufficiently diluted, i.e. if only a relatively low fraction of all possible synaptic connections is realized.

Background: Time-lapse live cell imaging of a growing cell population is routine in many biological investigations. A major challenge in imaging analysis is accurate segmentation, a process to define the boundaries of cells based on raw image data. Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging. Methods: Combined with a multi-layer training set strategy, we developed a neural-network-based algorithm-Cellbow. Results: Cellbow can achieve accurate and robust segmentation of cells in broad and general settings. It can also facilitate long-term tracking of cell growth and division. To facilitate the application of Cellbow, we provide a website on which one can online test the software, as well as an ImageJ plugin for the user to visualize the performance before software installation. Conclusion: Cellbow is customizable and generalizable. It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed. For bright-field images, only a small set of sample images of the specific cell type from the user may be needed for training.

Cancer stem cell (CSC) theory suggests a cell-lineage structure in cancer that CSCs are capable of giving rise to the other non-stem cancer cells (NSCCs) but not vice versa. However, an alternative scenario of bidirectional interconversions between CSCs and NSCCs was proposed in [Gupta PB, et al. (2011) Cell 146: 633644]. Here we present computational analyses for further investigating the relation between CSCs and NSCCs through population modeling of cancer cells, where not only can CSCs differentiate into NSCCs by asymmetric cell division, NSCCs can also dedifferentiate into CSCs by cell state conversion. By validating our model with recent experimental data, it is shown that the conversion from CSCs to NCSSs explains the transient increase in the proportion of CSCs initiated from the purified NSCCs subpopulation. Our results indicate that cell-state conversions in cancer play important role s in effectively keeping the heterogeneity in the population of cancer cells.

Over the past 15 years, the field of synthetic biology has seen a rapid rise in research activities and innovation, as evident by the increase of publications that quote the term "synthetic biology" from one in 2002 to~1400 in 2016 (from PubMed). Furthermore, synthetic biology projects now involve biological parts that work at wide length-scales and synthetic cells that are created from the bottom-up. With the expansion of synthetic biology in terms of project scale and research activity, have we fulfilled the initial goals of the field? In my personal perspective, there are at least two primary goals of synthetic biology that differentiate it from other fields. First, synthetic biology will enable rational and quantitative control of biological systems that are robust to their environmental context. Second, synthetic biology will expand our ability to create and program biological systems from the bottom-up. Achieving the goals has broad impact on applications of synthetic biology in the manufacturing of therapeutic cells, production of biological commodities, disease diagnostics, and therapeutic treatment. However, assessing our overall advancement toward the goals is non-trivial. We do not use a standard metric to report functions of engineered cells, and we do not study the same model system within the same context. My own research has met similar obstacles in pursuing the goals. A meta-analysis of platereader protocols in synthetic biology reveals a lack of consistent protocols and consensus reporting of the protocols, which may affect their reproducibility [1]. In addition, my lab synthesizes artificial cells that mimic living cells from the bottom-up . Despite the minimality of the artificial cells, it remains difficult to rationally control the cells in varying environmental conditions due to the lack of knowledge on how their parts interact with one another and with environmental factors.

Life is composed of complex networks that manifests itself into the adaptability. Using quasistatic dynamical systems, biology can be generalized into a dynamical system. Quasistatic dynamical systems are processes where the observed system transforms innitesimally due to external inuence to trace a continous path over an innite path. Assuming that biological time states are deterministic and the initial state is random. This assumptions renders the entire network stochastic. In a standard setting with chaos, a biological system could be modeled based on its statistical behavior, which would be determined by a stochastic diusion process. Furthermore, this method could be applied to form networks with unique connection patterns. Modularity of the networks creates a formal semantic that allows dynamics of biology to be predicted.

In this paper we present NPEST, a novel tool for the analysis of expressed sequence tags (EST) distributions and transcription start site (TSS) prediction. This method estimates an unknown probability distribution of ESTs using a maximum likelihood (ML) approach, which is then used to predict positions of TSS. Accurate identification of TSS is an important genomics task, since the position of regulatory elements with respect to the TSS can have large effects on gene regulation, and performance of promoter motif-finding methods depends on correct identification of TSSs. Our probabilistic approach expands recognition capabilities to multiple TSS per locus that may be a useful tool to enhance the understanding of alternative splicing mechanisms. This paper presents analysis of simulated data as well as statistical analysis of promoter regions of a model dicot plant Arabidopsis thaliana. Using our statistical tool we analyzed 16520 loci and developed a database of TSS, which is now publicly available at www.glacombio.net/NPEST.

Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

In the human genome, distal enhancers are involved in regulating target genes through proximal promoters by forming enhancer-promoter interactions. However, although recently developed high-throughput experimental approaches have allowed us to recognize potential enhancer-promoter interactions genome-wide, it is still largely unknown whether there are sequence-level instructions encoded in our genome that help govern such interactions. Here we report a new computational method (named "SPEID") using deep learning models to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Our results across six different cell types demonstrate that SPEID is effective in predicting enhancer-promoter interactions as compared to state-of-the-art methods that use non-sequence features from functional genomic signals. This work shows for the first time that sequence-based featu...

In many analyses of high-throughput data in systems biology, there is a need to quantify the activity of a set of genes in individual samples. A typical example is the case where it is necessary to estimate the activity of a transcription factor (which is often not directly measurable) from the expression of its target genes. We present here ROMA (Representation and quantification Of Module Activities) Java software, designed for fast and robust computation of the activity of gene sets (or modules) with coordinated expression. ROMA activity quantification is based on the simplest uni-factor linear model of gene regulation that approximates the expression data of a gene set by its first principal component. The proposed algorithm implements novel functionalities: it provides several method modifications for principal components computation, including weighted, robust and centered methods; it distinguishes overdispersed modules (based on the variance explained by the first principal c...

Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.