ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast

The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast

GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae

Motivation Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data. Availability and implementation GEMMER is freely available at http://gemmer.barberislab.com. Source code, written in Python, JavaScript library D3js, PHP and JSON, is freely available at https://github.com/barberislab/GEMMER.</p

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges

A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases

NET works after all? Engineering robustness through diversity

Classical Thermodynamics restricts engineering. These restrictions are independent of mechanism and kinetics, and thereby inescapable. Forgetting these restrictions can lead to over-optimistic designs for making bio-plastics from waste, and to erroneous ideas on early or new Life on this or other planets. This can be rectified by putting the thermodynamics in place. Is every biochemical network design feasible, provided one puts classical thermodynamics in place? Or, are there other, ill-recognized, generic restrictions to bioengineering? For a while a Non-Equilibrium Thermodynamics (NET) has been trying to discover behaviors of dynamical systems away from equilibrium that are completely independent of kinetics and mechanism and thereby not engineerable. The principles discovered were of limited use to bioengineering however. We here show that processes away from equilibrium must indeed depend on kinetics and mechanism, but, importantly, not on all kinetic and mechanistic details: There are limitations to what the engineering of mechanisms and kinetics can achieve. It is of course better to recognize what is impossible before trying to engineer the impossible. Importantly, the new NET methodology also shows that system properties that are possible, can be engineered only in certain ways. The new NET methodology also enables to understand, and perhaps engineer towards, a performance that, by adjusting the network, remains optimal when conditions are changing. Using our in silico discovery tool, we show that this may indeed occur in the Archeon S. solfataricus. What we call ‘variomatic’ gear shifting is a way that some cells may use to self-engineer their ways to maximal growth rates in environments that lack robust resources, such as in environments with fluctuating oxygen levels. Population heterogeneity is another mechanism that can increase the robustness of a cell factory. We discuss a NET principle that suggests ways in which one can engineer the cells’ diversity. Transcription burst size rather than kinetics should be modulated for making a diverse population perform much better than its average

Learning to read and write in evolution:From static pseudoenzymes and pseudosignalers to dynamic gear shifters

We present a systems biology view on pseudoenzymes that acknowledges that genes are not selfish: the genome is. With network function as the selectable unit, there has been an evolutionary bonus for recombination of functions of and within proteins. Many proteins house a functionality by which they 'read' the cell's state, and one by which they 'write' and thereby change that state. Should the writer domain lose its cognate function, a 'pseudoenzyme' or 'pseudosignaler' arises. GlnK involved in Escherichia coli ammonia assimilation may well be a pseudosignaler, associating 'reading' the nitrogen state of the cell to 'writing' the ammonium uptake activity. We identify functional pseudosignalers in the cyclin-dependent kinase complexes regulating cell-cycle progression. For the mitogen-activated protein kinase pathway, we illustrate how a 'dead' pseudosignaler could produce potentially selectable functionalities. Four billion years ago, bioenergetics may have shuffled 'electron-writers', producing various networks that all served the same function of anaerobic ATP synthesis and carbon assimilation from hydrogen and carbon dioxide, but at different ATP/acetate ratios. This would have enabled organisms to deal with variable challenges of energy need and substrate supply. The same principle might enable 'gear-shifting' in real time, by dynamically generating different pseudo-redox enzymes, reshuffling their coenzymes, and rerouting network fluxes. Non-stationary pH gradients in thermal vents together with similar such shuffling mechanisms may have produced a first selectable proton-motivated pyrophosphate synthase and subsequent ATP synthase. A combination of functionalities into enzymes, signalers, and the pseudo-versions thereof may offer fitness in terms of plasticity, both in real time and in evolution

Clb3-centered regulations are recurrent across distinct parameter regions in minimal autonomous cell cycle oscillator designs

Some biological networks exhibit oscillations in their components to convert stimuli to time-dependent responses. The eukaryoticcell cycle is such a case, being governed by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with specifictiming and guarantee its timely occurrence. Disruption of cyclin/Cdk oscillations could result in dysfunction through reduced celldivision. Therefore, it is of interest to capture properties of network designs that exhibit robust oscillations. Here we show that aminimal yeast cell cycle network is able to oscillate autonomously, and that cyclin/Cdk-mediated positive feedback loops (PFLs) andClb3-centered regulations sustain cyclin/Cdk oscillations, in known and hypothetical network designs. We propose that Clb3-mediated coordination of cyclin/Cdk waves reconciles checkpoint and oscillatory cell cycle models. Considering the evolutionaryconservation of the cyclin/Cdk network across eukaryotes, we hypothesize that functional (“healthy”) phenotypes require thecapacity to oscillate autonomously whereas dysfunctional (potentially“diseased”) phenotypes may lack this capacity

Simultaneous integration of gene expression and nutrient availability for studying the metabolism of hepatocellular carcinoma cell lines

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes

Maps for when the living gets tough: Maneuvering through a hostile energy landscape

With genome sequencing of thousands of organisms, a scaffold has become available for data integration: molecular information can now be organized by attaching it to the genes and their gene-expression products. It is however, the genome that is selfish not the gene, making it necessary to organize the information into maps that enable functional interpretation of the fitness of the genome. Using flux balance analysis one can calculate the theoretical capabilities of the living organism. Here we examine whether according to this genome organized information, organisms such as the ones present when life on Earth began, are able to assimilate the Gibbs energy and carbon that life needs for its reproduction and maintenance, from a relatively poor Gibbs-energy environment. We shall address how Clostridium ljungdahlii may use at least two special features and one special pathway to this end: gear-shifting, electron bifurcation and the Wood-Ljungdahl pathway. Additionally, we examined whether the C. ljungdahlii map can also help solve the problem of waste management. We find that there is a definite effect of the choices of redox equivalents in the Wood-Ljungdahl pathway and the hydrogenase on the yield of interesting products like hydroxybutyrate. We provide a drawing of a subset of the metabolic network that may be utilized to project flux distributions onto by the community in future works. Furthermore, we make all the code leading to the results discussed here publicly available for the benefit of future work

ROS networks:designs, aging, Parkinson’s disease and precision therapies

How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism