Search arXiv⌕ Search

SEARCH · Search arXiv

Results for “q-bio.MN”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13Linked to original sources
arXiv2011

Reduced models of networks of coupled enzymatic reactions

The Michaelis-Menten equation has played a central role in our understanding of biochemical processes. It has long been understood how this equation approximates the dynamics of irreversible enzymatic reactions. However, a similar approximation in the case of networks, where the product of one reaction can act as an enzyme in another, has not been fully developed. Here we rigorously derive such an approximation in a class of coupled enzymatic networks where the individual interactions are of Michaelis-Menten type. We show that the sufficient conditions for the validity of the total quasi steady state assumption (tQSSA), obtained in a single protein case by Borghans, de Boer and Segel can be extended to sufficient conditions for the validity of the tQSSA in a large class of enzymatic networks. Secondly, we derive reduced equations that approximate the network's dynamics and involve only protein concentrations. This significantly reduces the number of equations necessary to model such systems. We prove the validity of this approximation using geometric singular perturbation theory and results about matrix differentiation. The ideas used in deriving the approximating equations are quite general, and can be used to systematize other model reductions.

math-ph↗
arXiv2011

Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions

Genetic interactions have been widely used to define functional relationships between proteins and pathways. In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions between domains of those proteins. The domain genetic interactions rarely overlap with the domain physical interactions from iPfam database and provide a complementary view about domain relationships. Moreover, we found that domains in multidomain yeast proteins contribute to their genetic interactions differently. The domain genetic interactions help more precisely define the function related to the synthetic lethal genetic interactions, and then help understand how domains contribute to different functionalities of multidomain proteins. Using the probabilities of domain genetic interactions, we were able to predict novel yeast synthetic lethal genetic interactions. Furthermore, we had also identified novel compensatory pathways from the predicted synthetic lethal genetic interactions. Our study significantly improved the understanding of yeast mulitdomain proteins, the synthetic lethal genetic interactions and the functional relationships between proteins and pathways.

q-bio.MN↗
arXiv2011

Why aren't the small worlds of protein contact networks smaller

Computer experiments are performed to investigate why protein contact networks (networks induced by spatial contacts between amino acid residues of a protein) do not have shorter average shortest path lengths in spite of their importance to protein folding. We find that shorter average inter-nodal distances is no guarantee of finding a global optimum more easily. Results from the experiments also led to observations which parallel an existing view that neither short-range nor long-range interactions dominate the protein folding process. Nonetheless, runs where there was a slight delay in the use of long-range interactions yielded the best search performance. We incorporate this finding into the optimization function by giving more weight to short-range links. This produced results showing that randomizing long-range links does not yield better search performance than protein contact networks au natural even though randomizing long-range links significantly reduces average path lengths and retains much of the clustering and positive degree-degree correlation inherent in protein contact networks. Hence there can be explanations, other than the excluded volume argument, beneath the topological limits of protein contact networks.

q-bio.MN↗
arXiv2011

Identification of the key parameters in a mathematical model of PAR1-mediated signaling in endothelial cells

Biophysical models are often populated by a large number of input parameters that are difficult to predict or measure experimentally. The validity and robustness of a given model can be evaluated by a sensitivity test to its input parameters. In this study, we performed local (based on a Taylor-like method) and global sensitivity (based on Monte Carlo filtering techniques) analyses of a previously derived PAR1-mediated activation model of endothelial cells. This activation model previously demonstrated that peptide-activated PAR1 has a different receptor/G-protein binding affinity that favors Galpha_q activation over Galpha_12/13 by approximately 800-fold. Interestingly, the present study shows that the parameter regulating the binding rate of activated PAR1 to Galpha_12/13 is indeed important to obtain the expected RhoGTP response. Moreover, we show that the parameters representing the rate of PAR1 deactivation and the rate of PAR1 binding to G_q, are the most important parameters in the system. Finally, we illustrate that the kinetic model considered in this study is robust and we provide complementary insights into the biological meaning and importance of its kinetic parameters.

q-bio.MN↗
arXiv2011

Timing is everything: on the stochastic origins of cell-to-cell variability in cancer cell death decisions

The diversity of cell populations is regulated by extracellular and intracellular variability. The latter includes genetic, epigenetic and stochastic variability, all contributing to the experimentally observed heterogeneity in response to external death-inducing stimuli. Studies of sources and regulation of variability in commitment to apoptotic cancer cell death are likely to identify the fundamental features of apoptotic protein networks that are responsible for determining the ultimate cell fate. Systems biology approaches, involving computer simulations of the biochemical reactions accompanied, if possible, by experimental verification of selected components of the model, are proving useful in determining the origins of cell-to-cell variability in response to external stress stimuli. Here we summarize our current understanding of the origins of stochastic variability in cells' commitment to apoptosis, and its implications in the field on cancer therapy.

q-bio.CB↗
arXiv2011

Construction and Functional Analysis of Human Genetic Interaction Networks with Genome-wide Association Data

Genetic interaction measures how different genes collectively contribute to a phenotype, and can reveal functional compensation and buffering between pathways under genetic perturbations. Recently, genome-wide screening for genetic interactions has revealed genetic interaction networks that provide novel insights either when analyzed by themselves or when integrated with other functional genomic datasets. For higher eukaryotes such as human, the above reverse-genetics approaches are not straightforward since the phenotypes of interest for higher eukaryotes are difficult to study in a cell based assay. We propose a general framework for constructing and analyzing human genetic interaction networks from genome-wide single nucleotide polymorphism (SNP) data used for case-control studies on complex diseases. Specifically, the approach contains three major steps: (1) estimating SNP-SNP genetic interactions, (2) identifying linkage disequilibrium (LD) blocks and mapping SNP-SNP interactions to block-block interactions, and (3) functional mapping for LD blocks. We performed two sets of functional analyses for each of the six datasets used in the paper, and demonstrated that (i) the constructed genetic interaction networks are supported by functional evidence from independent biological databases, and (ii) the network can be used to discover pairs of compensatory gene modules (between-pathway models) in their joint association with a disease phenotype. The proposed framework should provide novel insights beyond existing approaches that either ignore interactions between SNPs or model different SNP-SNP pairs with genetic interactions separately. Furthermore, our study provides evidence that some of the core properties of genetic interaction networks based on reverse genetics in model organisms like yeast are also present in genetic interactions revealed by natural variation in human populations.

q-bio.MN↗
arXiv2011

Integration of Differential Gene-combination Search and Gene Set Enrichment Analysis: A General Approach

Gene Set Enrichment Analysis (GSEA) and its variations aim to discover collections of genes that show moderate but coordinated differences in expression. However, such techniques may be ineffective if many individual genes in a phenotype-related gene set have weak discriminative power. A potential solution is to search for combinations of genes that are highly differentiating even when individual genes are not. Although such techniques have been developed, these approaches have not been used with GSEA to any significant degree because of the large number of potential gene combinations and the heterogeneity of measures that assess the differentiation provided by gene groups of different sizes. To integrate the search for differentiating gene combinations and GSEA, we propose a general framework with two key components: (A) a procedure that reduces the number of scores to be handled by GSEA to the number of genes by summarizing the scores of the gene combinations involving a particular gene in a single score, and (B) a procedure to integrate the heterogeneous scores from combinations of different sizes and from different gene combination measures by mapping the scores to p-values. Experiments on four gene expression data sets demonstrate that the integration of GSEA and gene combination search can enhance the power of traditional GSEA by discovering gene sets that include genes with weak individual differentiation but strong joint discriminative power. Also, gene sets discovered by the integrative framework share several common biological processes and improve the consistency of the results among three lung cancer data sets.

q-bio.GN↗
arXiv2011

Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment.

stat.ME↗
arXiv2011

Computational modeling of differences in the quorum sensing induced luminescence phenotypes of \textit{Vibrio harveyi} and \textit{Vibrio cholerae}

\textit{Vibrio harveyi} and \textit{Vibrio cholerae} have quorum sensing pathways with similar design and highly homologous components including multiple small RNAs (sRNAs). However, the associated luminescence phenotypes of strains with sRNA deletions differ dramatically: in \textit{V. harveyi}, the sRNAs act additively; however, in \textit{V. cholerae}, the sRNAs act redundantly. Furthermore, there are striking differences in the luminescence phenotypes for different pathway mutants in \textit{V. harveyi} and \textit{V. cholerae}. However these differences have not been connected with the observed differences for the sRNA deletion strains in these bacteria. In this work, we present a model for quorum sensing induced luminescence phenotypes focusing on the interactions of multiple sRNAs with target mRNA. Within our model, we find that one key parameter -- the fold-change in protein concentration necessary for luminescence activation -- can control whether the sRNAs appear to act additively or redundantly. For specific parameter choices, we find that differences in this key parameter can also explain hitherto unconnected luminescence phenotypes differences for various pathway mutants in \textit{V. harveyi} and \textit{V. cholerae}. The model can thus provide a unifying explanation for observed differences in luminescence phenotypes and can also be used to make testable predictions for future experiments.

q-bio.MN↗
arXiv2011

Enzyme sharing as a cause of multistationarity in signaling systems

Multistationarity in biological systems is a mechanism of cellular decision making. In particular, signaling pathways regulated by protein phosphorylation display features that facilitate a variety of responses to different biological inputs. The features that lead to multistationarity are of particular interest to determine as well as the stability properties of the steady states. In this paper we determine conditions for the emergence of multistationarity in small motifs without feedback that repeatedly occur in signaling pathways. We derive an explicit mathematical relationship between the concentration of a chemical species at steady state and a conserved quantity of the system such as the total amount of substrate available. We show that the relation determines the number of steady states and provides a necessary condition for a steady state to be stable, that is, to be biologically attainable. Further, we identify characteristics of the motifs that lead to multistationarity, and extend the view that multistationarity in signaling pathways arises from multisite phosphorylation. Our approach relies on mass-action kinetics and the conclusions are drawn in full generality without resorting to simulations or random generation of parameters. The approach is extensible to other systems.

q-bio.SC↗
arXiv2012

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into cumulus-type, i.e., those similar to puffy (white) clouds, and stratus-type, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an energy transfer mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by multi-trajectories; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach rarely visited but functionally-related states. We also show the role of disorder in spatial games of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.

q-bio.MN↗
arXiv2011

Chemical reaction systems with toric steady states

Mass-action chemical reaction systems are frequently used in Computational Biology. The corresponding polynomial dynamical systems are often large (consisting of tens or even hundreds of ordinary differential equations) and poorly parametrized (due to noisy measurement data and a small number of data points and repetitions). Therefore, it is often difficult to establish the existence of (positive) steady states or to determine whether more complicated phenomena such as multistationarity exist. If, however, the steady state ideal of the system is a binomial ideal, then we show that these questions can be answered easily. The focus of this work is on systems with this property, and we say that such systems have toric steady states. Our main result gives sufficient conditions for a chemical reaction system to have toric steady states. Furthermore, we analyze the capacity of such a system to exhibit positive steady states and multistationarity. Examples of systems with toric steady states include weakly-reversible zero-deficiency chemical reaction systems. An important application of our work concerns the networks that describe the multisite phosphorylation of a protein by a kinase/phosphatase pair in a sequential and distributive mechanism.

math.DS↗
arXiv2012

Weak error analysis of numerical methods for stochastic models of population processes

The simplest, and most common, stochastic model for population processes, including those from biochemistry and cell biology, are continuous time Markov chains. Simulation of such models is often relatively straightforward as there are easily implementable methods for the generation of exact sample paths. However, when using ensemble averages to approximate expected values, the computational complexity can become prohibitive as the number of computations per path scales linearly with the number of jumps of the process. When such methods become computationally intractable, approximate methods, which introduce a bias, can become advantageous. In this paper, we provide a general framework for understanding the weak error, or bias, induced by different numerical approximation techniques in the current setting. The analysis takes into account both the natural scalings within a given system and the step-size of the numerical method. Examples are provided to demonstrate the main analytical results as well as the reduction in computational complexity achieved by the approximate methods.

math.PR↗
arXiv2011

Inferring Disease and Gene Set Associations with Rank Coherence in Networks

A computational challenge to validate the candidate disease genes identified in a high-throughput genomic study is to elucidate the associations between the set of candidate genes and disease phenotypes. The conventional gene set enrichment analysis often fails to reveal associations between disease phenotypes and the gene sets with a short list of poorly annotated genes, because the existing annotations of disease causative genes are incomplete. We propose a network-based computational approach called rcNet to discover the associations between gene sets and disease phenotypes. Assuming coherent associations between the genes ranked by their relevance to the query gene set, and the disease phenotypes ranked by their relevance to the hidden target disease phenotypes of the query gene set, we formulate a learning framework maximizing the rank coherence with respect to the known disease phenotype-gene associations. An efficient algorithm coupling ridge regression with label propagation, and two variants are introduced to find the optimal solution of the framework. We evaluated the rcNet algorithms and existing baseline methods with both leave-one-out cross-validation and a task of predicting recently discovered disease-gene associations in OMIM. The experiments demonstrated that the rcNet algorithms achieved the best overall rankings compared to the baselines. To further validate the reproducibility of the performance, we applied the algorithms to identify the target diseases of novel candidate disease genes obtained from recent studies of GWAS, DNA copy number variation analysis, and gene expression profiling. The algorithms ranked the target disease of the candidate genes at the top of the rank list in many cases across all the three case studies. The rcNet algorithms are available as a webtool for disease and gene set association analysis at http://compbio.cs.umn.edu/dgsa_rcNet.

q-bio.GN↗
arXiv2011

Reverse Engineering of Molecular Networks from a Common Combinatorial Approach

The understanding of molecular cell biology requires insight into the structure and dynamics of networks that are made up of thousands of interacting molecules of DNA, RNA, proteins, metabolites, and other components. One of the central goals of systems biology is the unraveling of the as yet poorly characterized complex web of interactions among these components. This work is made harder by the fact that new species and interactions are continuously discovered in experimental work, necessitating the development of adaptive and fast algorithms for network construction and updating. Thus, the "reverse-engineering" of networks from data has emerged as one of the central concern of systems biology research. A variety of reverse-engineering methods have been developed, based on tools from statistics, machine learning, and other mathematical domains. In order to effectively use these methods, it is essential to develop an understanding of the fundamental characteristics of these algorithms. With that in mind, this chapter is dedicated to the reverse-engineering of biological systems. Specifically, we focus our attention on a particular class of methods for reverse-engineering, namely those that rely algorithmically upon the so-called "hitting-set" problem, which is a classical combinatorial and computer science problem, Each of these methods utilizes a different algorithm in order to obtain an exact or an approximate solution of the hitting set problem. We will explore the ultimate impact that the alternative algorithms have on the inference of published in silico biological networks.

q-bio.MN↗
arXiv2011

Probabilistic analysis of the human transcriptome with side information

Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.

stat.ML↗
arXiv2011

Estimating Reaction Rate Parameters in Cell Signaling Pathways Using Extreme Decomposition and Belief Propagation Tailored for Data-Rich Cases

Motivation: Modeling biological signaling networks using ordinary differential equations (ODEs) has proven to be a powerful technique for generating insight into cellular dynamics, but it typically requires estimating rate parameters based on experimentally observed concentrations. New measurement methods can measure concentrations for all molecular species in a pathway, which creates a new opportunity to decompose the optimization of rate parameters. Results: In contrast with conventional methods that minimize the disagreement between simulated and observed concentrations, the BPPE method fits a spline curve through the observed concentration points, and then matches the derivatives of the spline-curve to the production and consumption of each species. Whereas traditional methods follow the ODEs exactly and then attempt to match the data, BPPE follows the data exactly and then attempts to match the ODEs. The new objective function is an extreme decomposition of the problem because each factor in the function is enforcing the equality of one ODE at one timeslice. A "loopy belief propagation" algorithm solves this factorized approximation of the parameter estimation problem providing systematic coverage of the search space and unique asymptotic behavior; the run time is polynomial in the number of molecules and timepoints, but exponential in the degree of the biochemical network. The implementation is a global-local hybrid optimization, and we compare with the performance of local, global, and hybrid methods. BPPE is demonstrated for a novel model of Akt activation dynamics including redox-mediated inactivation of PTEN. Availability: Software and supplementary information are available at http://webbppe.nus.edu.sg:8080/opal2/WebBPPE . Contact: LisaTK@nus.edu.sg . Keywords: probabilistic graphical models, physico-chemical modeling, systems biology, signal transduction.

q-bio.MN↗