SEARCH · Search bioRxiv

Results for “systems biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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

At least 1,171 records · Page 65Linked to original sources

bioRxiv2018

Enzyme promiscuity shapes evolutionary innovation and optimization

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are poorly understood. Furthermore, it remains untested whether knowledge of an organisms promiscuous reaction set ( underground metabolism) can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in E. coli K-12 MG1655. After as few as 20 generations, the evolving populations repeatedly acquired the capacity to grow on five predicted novel substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate-none of which could support growth in wild-type cells. Promiscuous enzyme activities played key roles in multiple phases of adaptation. Altered promiscuous activities not only established novel high-efficiency pathways, but also suppressed undesirable metabolic routes. Further, structural mutations shifted enzyme substrate turnover rates towards the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Computational approaches will be essential to synthesize the complex role of promiscuous activities in applied biotechnology and in models of evolutionary adaptation.

systems biology↗

bioRxiv2018

Intracellular absorption underlies collective bacterial tolerance towards an antimicrobial peptide

The collective tolerance towards antimicrobial peptides (APs) is thought to occur primarily through mechanisms associated with live bacterial cells. In contrast to the focus on live cells, we discover that the LL37 antimicrobial peptide kills Escherichia coli, forming a subpopulation of dead cells that absorbs the remaining LL37 into its intracellular space. Combining mathematical modeling with population and single-cell experiments, we show that bacteria absorb LL37 at a timing that coincides with the permeabilization of their cytoplasmic membranes. Furthermore, we show that one bacterial strain can absorb LL37 and protect another strain from killing by LL37. Finally, we demonstrate that the intracellular absorption of LL37 can be reduced using a peptide adjuvant. In contrast to the existing collective tolerance mechanisms, we show that the dead-bacterial absorption of APs is a dynamic process that leads to emergent population behavior, and the work suggests new directions to enhance the efficacy of APs.

systems biology↗

bioRxiv2018

Analysis of the human kinome and phosphatome reveals diseased signaling networks induced by overexpression

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance in many cancer types. Signaling networks reprogrammed by protein overexpression remain largely uncharacterized, hindering discovery of paths to therapeutic intervention. We previously developed a single cell proteomics approach based on mass cytometry that enables quantitative assessment of overexpression effects on the signaling network. Here we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulate the cancer-related signaling network in HEK293T cells. Based on these data we expanded the functional classification of human kinases and phosphatases and detected 208 novel signaling relationships. In the signaling dynamics analysis, we showed that increased ERK-specific phosphatases sustained proliferative signaling, and using a novel combinatorial overexpression approach, we confirmed this phosphatase-driven mechanism of cancer progression. Finally, we identified 54 proteins that caused ligand-independent ERK activation with potential as biomarkers for drug resistance in cells carrying BRAF mutations.

systems biology↗

bioRxiv2018

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

The in vivo erythropoiesis, which is the generation of mature red blood cells in the bone marrow of whole organisms, has been described by a variety of mathematical models in the past decades. However, the in vitro erythropoiesis, which produces red blood cells in cultures, has received much less attention from the modelling community. In this paper, we propose the first mathematical model of in vitro erythropoiesis. We start by formulating different models and select the best one at fitting experimental data of in vitro erythropoietic differentiation. It is based on a set of linear ODE, describing 3 hypothetical populations of cells at different stages of differentiation. We then compute confidence intervals for all of its parameters estimates, and conclude that our model is fully identifiable. Finally, we use this model to compute the effect of a chemical drug called Rapamycin, which affects all states of differentiation in the culture, and relate these effects to specific parameter variations. We provide the first model for the kinetics of in vitro cellular differentiation which is proven to be identifiable. It will serve as a basis for a model which will better account for the variability which is inherent to experimental protocol used for the model calibration.

systems biology↗

bioRxiv2018

Cerebral organoid proteomics reveals signatures of dysregulated cortical development associated with human trisomy 21

Human trisomy 21 (Down syndrome) is the most common genetic cause of intellectual disability, and is associated with complex perturbations in protein expression during development. Brain region-specific alterations in neuronal density and composition originate prenatally in trisomy 21 individuals, and are presumed to underlie the intellectual disability and early onset neurodegeneration that characterizes Down syndrome. However, the mechanisms by which chromosome 21 aneuploidy drives alterations in the central nervous system are not well understood, particularly in brain regions that are uniquely human and thus inaccessible to established animal models. Cerebral organoids are pluripotent stem cell derived models of prenatal brain development that have been used to deepen our understanding of the atypical processes associated with human neurobiological disorders, and thus provide a promising avenue to explore the molecular basis for neurodevelopmental alterations in trisomy 21. Here, we employ high-resolution label-free mass spectrometry to map proteomic changes over the course of trisomy 21 cerebral organoid development, and evaluate the proteomic alterations in response to treatment with harmine, a small molecule inhibitor of the chromosome 21 encoded protein kinase DYRK1A. Our results reveal trisomy 21 specific dysregulation of networks associated with neurogenesis, axon guidance and extracellular matrix remodeling. We find significant overlap of these networks show significant overlap with previously identified dysregulated gene expression modules identified in trisomy 21 fetal brain tissue. We show that harmine leads to partial normalization of key regulators of cortical development, including WNT7A and the transcription factors TBR1, BCL11A, and POU3F2, pointing to a causative role for DYRK1A over-expression in neurodevelopmental effects of human trisomy 21.

systems biology↗

bioRxiv2018

Prioritizing network communities

Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRO_SCPCAPANKC_SCPCAP, a mathematically principled approach for prioritizing network communities. CRO_SCPCAPANKC_SCPCAP efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRO_SCPCAPANKC_SCPCAP can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRO_SCPCAPANKC_SCPCAP can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRO_SCPCAPANKC_SCPCAP effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.

bioinformatics↗

bioRxiv2018

Adipose tissue in health and disease through the lens of its building blocks

BackgroundHighly specialized cells work in synergy forming tissues to perform functions required for the survival of organisms. Understanding this tissue-specific cellular heterogeneity and homeostasis is essential to comprehend the development of diseases within the tissue and also for developing regenerative therapies. Cellular subpopulations in the adipose tissue have been related to disease development, but efforts towards characterizing the adipose tissue cell type composition are limited due to lack of robust cell surface markers, limited access to tissue samples, and the labor-intensive process required to identify them.\n\nResultsWe propose a framework, identifying cellular heterogeneity while providing state-of-the-art cellular markers for each cell type present in tissues using transcriptomics level analysis. We validate our approach with an independent dataset and present the most comprehensive study of adipose tissue cell type composition to date, determining the relative amounts of 21 different cell types in 779 adipose tissue samples detailing differences across four adipose tissue depots, between genders, across ranges of BMI and in different stages of type-2 diabetes. We also highlight the heterogeneity in reported marker-based studies of adipose tissue cell type composition and provide novel cellular markers to distinguish different cell types within the adipose tissue.\n\nConclusionsOur study provides a systematic framework for studying cell type composition in a given tissue and valuable insights into adipose tissue cell type heterogeneity in health and disease.

systems biology↗

bioRxiv2018

An information theoretic treatment of sequence-to-expression modeling

Studying a genes regulatory mechanisms is a tedious process that involves identification of candidate regulators by transcription factor (TF) knockout or over-expression experiments, delineation of enhancers by reporter assays, and demonstration of direct TF influence by site mutagenesis, among other approaches. Such experiments are often chosen based on the biologists intuition, from several testable hypotheses. We pursue the goal of making this process systematic by using ideas from information theory to reason about experiments in gene regulation, in the hope of ultimately enabling rigorous experiment design strategies. For this, we make use of a state-of-the-art mathematical model of gene expression, which provides a way to formalize our current knowledge of cis- as well as trans-regulatory mechanisms of a gene. Ambiguities in such knowledge can be expressed as uncertainties in the model, which we capture formally by building an ensemble of plausible models that fit the existing data and defining a probability distribution over the ensemble. We then characterize the impact of a new experiment on our understanding of the genes regulation based on how the ensemble of plausible models and its probability distribution changes when challenged with results from that experiment. This allows us to assess the value of the experiment retroactively as the reduction in entropy of the distribution (information gain) resulting from the experiments results. We fully formalize this novel approach to reasoning about gene regulation experiments and use it to evaluate a variety of perturbation experiments on two developmental genes of D. melanogaster. We also provide objective and biologist-friendly descriptions of the information gained from each such experiment. The rigorously defined information theoretic approaches presented here can be used in the future to formulate systematic strategies for experiment design pertaining to studies of gene regulatory mechanisms.\n\nAuthor summaryIn-depth studies of gene regulatory mechanisms employ a variety of experimental approaches such as identifying a genes enhancer(s) and testing its variants through reporter assays, followed by transcription factor mis-expression or knockouts, site mutagenesis, etc. The biologist is often faced with the challenging problem of selecting the ideal next experiment to perform so that its results provide novel mechanistic insights, and has to rely on their intuition about what is currently known on the topic and which experiments may add to that knowledge. We seek to make this intuition-based process more systematic, by borrowing ideas from the mature statistical field of experiment design. Towards this goal, we use the language of mathematical models to formally describe what is known about a genes regulatory mechanisms, and how an experiments results enhance that knowledge. We use information theoretic ideas to assign a value to an experiment as well as explain objectively what is learned from that experiment. We demonstrate use of this novel approach on two extensively studied developmental genes in fruitfly. We expect our work to lead to systematic strategies for selecting the most informative experiments in a study of gene regulation.

systems biology↗

bioRxiv2018

Tropical forest refuge in peripheral soils of slags in Caatinga

The influence of environmental factors on the structure and composition of plant communities in the Caatinga is complex and poorly explored. Spatial variation of biodiversity in Caatinga is poorly know and strictly attributed to climatic conditions. We investigated the influence of slags on floristic composition and structure of a shrubby-arboreal community in one of the driest region in Brazil. Chemical and physical analyses of soils were performed in samples of seven plots from savannic formations and from forest formations. Vegetation was characterized floristically and structurally in all plots. Habitats were structurally distinct, and diversity differed between peripheral and non-peripheral areas of slags. Nine of the ninety-seven species identified are reported to (sub)humid biomes. Soils are dominantly shallow, eutrophic and sandy loam. However, soils in the periphery of slags are more developed once paludization, melanization and bioturbation were verified. Our results suggest that soil fertility did not influence vegetable cover in Caatinga. The cover of plant species considered exclusive of (sub)humid biomes in Brazil extends beyond highlands in the semiarid, associated with high soil organic carbon content and water retention capacity of more developed soils than the typical of the Caatinga.

systems biology↗

bioRxiv2018

The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

MotivationThe application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes.\n\nResultsTo address this shortage, we created a comprehensive toolbox to model i) microbe-microbe and host-microbe metabolic interactions, and ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the COBRA Toolbox.\n\nAvailabilityThe Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

systems biology↗

bioRxiv2018

Modeling gene expression networks to predict interchromosomal organization during human embryonic stem cell differentiation

Cellular differentiation occurs through the regulation of lineage-specific gene expression networks that are facilitated by the spatial organization of the genome. Although techniques based on the chromatin conformation capture (3C) approach have yielded intrachromosomal genome-wide interaction maps, strategies to identify non-random interchromosomal associations is lacking. Therefore, we modeled the genomic organization of chromosomes based on the regulatory networks involved in the differentiation of pluripotent human embryonic stem cells (hESCs) to committed neuronal precursor cells (cNPCs). Importantly, transcriptional regulation has been demonstrated to be a driving force in non-random genome organization. Thus, we constructed coarse-grained in silico networks using gene expression data to identify potential physical associations among chromosomes occurring in situ and then analyzed the three-dimensional (3D) distribution of these chromosomes, assessing how their associations contribute to nuclear organization. Our analysis suggests that coordinate regulation of differentially expressed genes is correlated with the 3D organization of chromosomes in hESC nuclei induced to differentiate to cNPCs.\n\nAuthor SummaryThe cellular commitment and differentiation of stem cells is a hallmark of metazoan development. The ultimate fate of a stem cell is defined by the synergistic modulation of key gene regulatory networks within the nucleus. In our work, we formulate an in silico model describing how the similarity in the expression profile of differentially regulated gene networks is correlated with the higher-order organization of chromosomes during differentiation from human embryonic stem cells (hESCs) to committed neuronal precursor cells (cNPCs). Using graph statistics, we observe that the genome networks generated using the in silico model exhibit properties similar to real-world networks. In addition to modeling how gene expression relates to dynamic changes in chromosome organization, we test the model by calculating the relative proximity of multiple chromosome pairs using 3D fluorescence in situ hybridization (FISH). While various chromosomal properties, including gene density and overall length, have been attributed to chromosome organization, our previous work has identified the emergence of cell-type specific chromosomal topologies related to coordinate gene regulation during cellular differentiation. Here we extend these findings by determining whether our in silico model can predict chromosome association based upon coordinate gene expression. Our work supports the idea that gene co-regulation, in addition to inherent organizational constraints of the nucleus, influences three-dimensional chromosome organization.

systems biology↗

bioRxiv2018

Bone remodeling: A tissue-level process emerging from cell-level molecular algorithms

Human skeleton undergoes constant remodeling during the whole life. By means of such process, which occurs at a microscopic scale, worn out bone is replaced by new, fully functional one. Multiple bone remodeling events occur simultaneously and independently throughout the body, so the whole skeleton is completely renewed about every ten years.\n\nBone remodeling is performed by groups of cells called Bone Multicellular Units (BMU). BMUs consist of different cell types; some are specialized in destroying old bone, whereas others produce new bone to replace the former. The whole process is tightly regulated so that the amount of new bone produced exactly balances that of old one removed and bone microscopic structure is maintained.\n\nTo date, many regulatory molecules involved in bone remodeling have been identified, but the precise mechanism of BMU operation remains to be fully elucidated. Given the complexity of the signaling pathways already known, the question arises of ascertaining whether such complexity is an inherent requirement of the process, or a consequence of operational redundancy.\n\nIn this work we propose a minimal model of BMU function which involves a small number of signals and accounts for fully functional BMU operation. Our main assumptions are i) at any given time, any cell within a BMU can select only one among a reduced choice of decisions: divide, die, migrate or differentiate, ii) such decision is irreversibly determined by depletion of an appropriate internal inhibitor and iii) the dynamics of any such inhibitor is coupled to that of a few external mediators. It is shown that efficient BMU operation then unfolds as an emergent property, which results from individual decisions taken by cells in the BMU unit in the absence of any external planning.\n\nAuthor summaryOur skeleton is a living organ that is being renewed throughout our life. This task is accomplished by teams of bone cells termed as Bone Multicellular Units (BMUs) that are recruited when and where needed, to operate at places where bone has lost functionality either for an excess of mechanical stress or because loss of activity. Once assembled, BMU remove old bone and replace it by new one, and disband as soon as their mission has been accomplished. No single bone evades BMU screening, so that the whole human skeleton is completely renewed approximately every ten years.\n\nIt is natural to wonder how such robust and fascinating process is regulated. Many signaling pathways involved in bone remodeling have been identified so far, but whether all of them are necessary for BMU operation remains unclear. In this work we show that just a reduced number of such signals could suffice for that purpose. This suggests that a large degree of redundancy might have been kept in place, perhaps as a consequence of different convergent strategies developed in the course of evolution.

systems biology↗

bioRxiv2018

Role of long-range aromatic clique and community in protein stability.

Aromatic interactions make an important contribution to protein structure, function, folding and have attracted intense study. Earlier studies on a recombinant xylanase from Bacillus sp. NG-27 (RBSX), which has the ubiquitous (beta/alpha)8-triosephosphate isomerase barrel fold showed that three aromatic residues to alanine substitutions, in the N-terminal and C-terminal regions, significantly decreased the stability of the enzyme. Of these mutations, F4A mutation decreased the stability of the enzyme by [~]4 degree C, whereas W6A mutation and Y343A mutation remarkably decreased the stability of the enzyme by [~]10 degree C. On the other hand, the F4W mutation did not affect the thermal stability of RBSX. We provide here a network perspective of aromatic-aromatic interactions in terms of aromatic clique community and long-range association. Our study reveals that disruption of long-range k-clique aromatic interaction cluster holding the N- and C-terminal regions are associated with the decreased stability of the enzyme. The present work reiterates as well as expands on those findings concerning the role of interactions between the N- and C-terminus in protein stability. Furthermore, comparative analyses of crystal structures of homologous pairs of proteins from thermophilic and mesophilic organisms emphasize the prevalence of long-range k-clique communities of aromatic interaction that may be playing an important role and highlights an additional source of stability in thermophilic proteins. The design principle based on clustering of long-range aromatic residues in the form of aromatic-clique and clique community may be effectively applied to enhance the stability of enzymes for biotechnological applications.\n\nDatabaseThe coordinates o fF4A, F4W, W6A, and Y343A are deposited in the PDB database under the accession numbers 5EFF, 5E58, 5EFD, and 5EBA respectively.\n\nAbbreviationsBSX, xylanase from Bacilllus sp. NG-27; RBSX, recombinant BSX xylanase; TIM, Triosephosphate isomerase; GH10, Glycosyl hydrolase family 10; 3D, three-dimensional; r.m.s.d, root mean square deviation; RSA, relative solvent accessible surface area; Tm, melting temperature; CD, Circular Dichroism; BHX, GH10 xylanase from Bacillus halodurans; BFX, GH10 xylanase from Bacillus firmus; TmxB, GH10 xylanase from Thermotoga maritima

systems biology↗

bioRxiv2018

DynamicME: Dynamic simulation and refinement of integrated models of metabolism and protein expression

Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. We develop dynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. Our dynamicME correctly predicted the substrate utilization hierarchy on mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus present two methods to calibrate ME models, specifically using time-course measurements such as from a (fed-) batch culture. Overall, dynamicME and the methods presented provide novel methods for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

systems biology↗

bioRxiv2018

3D protein structure from genetic epistasis experiments

High-throughput experimental techniques have made possible the systematic sampling of the single mutation landscape for many proteins, defined as the change in protein fitness as the result of point mutation sequence changes. In a more limited number of cases, and for small proteins only, we also have nearly full coverage of all possible double mutants. By comparing the phenotypic effect of two simultaneous mutations with that of the individual amino acid changes, we can evaluate epistatic effects that reflect non-additive cooperative processes. The observation that epistatic residue pairs often are in contact in the 3D structure led to the hypothesis that a systematic epistatic screen contains sufficient information to identify the 3D fold of a protein. To test this hypothesis, we examined experimental double mutants for evidence of epistasis and identified residue contacts at 86% accuracy, including secondary structure elements and evidence for an alternative all--helical conformation. Positively epistatic contacts - corresponding to compensatory mutations, restoring fitness - were the most informative. Folded models generated from top-ranked epistatic pairs, when compared with the known structure, were accurate within 2.4 [A] over 53 residues, indicating the possibility that 3D protein folds can be determined experimentally with good accuracy from functional assays of mutant libraries, at least for small proteins. These results suggest a new experimental approach for determining protein structure.

systems biology↗

bioRxiv2018

RAVEN 2.0: a versatile platform for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community.\n\nAuthor summaryCellular metabolism is a large and complex network. Hence, investigations of metabolic networks are aided by in silico modelling and simulations. Metabolic networks can be derived from whole-genome sequences, through identifying what enzymes are present and connecting these to formalized chemical reactions. To facilitate the reconstruction of genome-scale models of metabolism (GEMs), we have developed RAVEN 2.0. This versatile toolbox can reconstruct GEMs fast, through either metabolic pathway databases KEGG and MetaCyc, or from homology with an existing GEM. We demonstrate RAVENs functionality through generation of a metabolic model of Streptomyces coelicolor, an antibiotic-producing bacterium. Comparison of this de novo generated GEM with a previously manually curated model demonstrates that RAVEN captures most of the previous model, and we subsequently reconstructed an updated model of S. coelicolor: Sco4. Following, we used Sco4 to predict promising targets for genetic engineering, which can be used to increase antibiotic production.

systems biology↗

bioRxiv2018

The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease

A multitude of factors contribute to complex diseases and can be measured with \"omics\" methods. Databases facilitate data interpretation for underlying mechanisms. Here, we describe the Virtual Metabolic Human (VMH, http://vmh.life) database encapsulating current knowledge of human metabolism within five interlinked resources \"Human metabolism\", \"Gut microbiome\", \"Disease\", \"Nutrition\", and \"ReconMaps\". The VMH captures 5,180 unique metabolites, 17,730 unique reactions, 3,288 human genes, 255 Mendelian diseases, 818 microbes, 632,685 microbial genes, and 8,790 food items. The VMHs unique features are i) the hosting the metabolic reconstructions of human and gut microbes amenable for metabolic modeling; ii) seven human metabolic maps for data visualization; iii) a nutrition designer; iv) a user-friendly webpage and application-programming interface to access its content; and v) user feedback option for community engagement. We demonstrate with four examples the VMHs utility. The VMH represents a novel, interdisciplinary database for data interpretation and hypothesis generation to the biomedical community.

systems biology↗

bioRxiv2018

Theory of Circadian Metabolism

Many organisms repartition their proteome in a circadian fashion in response to the daily nutrient changes in their environment. A striking example is provided by cyanobacteria, which perform photosynthesis during the day to fix carbon. These organisms not only face the challenge of rewiring their proteome every 12 hours, but also the necessity of storing the fixed carbon in the form of glycogen to fuel processes during the night. In this manuscript, we extend the framework developed by Hwa and coworkers (Scott et al., Science 330, 1099 (2010)) for quantifying the relatinship between growth and proteome composition to circadian metabolism. We then apply this framework to investigate the circadian metabolism of the cyanobacterium Cyanothece, which not only fixes carbon during the day, but also nitrogen during the night, storing it in the polymer cyanophycin. Our analysis reveals that the need to store carbon and nitrogen tends to generate an extreme growth strategy, in which the cells predominantly grow during the day, as observed experimentally. This strategy maximizes the growth rate over 24 hours, and can be quantitatively understood by the bacterial growth laws. Our analysis also shows that the slow relaxation of the proteome, arising from the slow growth rate, puts a severe constraint on implementing this optimal strategy. Yet, the capacity to estimate the time of the day, enabled by the circadian clock, makes it possible to anticipate the daily changes in the environment and mount a response ahead of time. This significantly enhances the growth rate by counteracting the detrimental effects of the slow proteome relaxation.

systems biology↗