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bioRxiv2017

Butterflyfishes as a System for Investigating Pair Bonding

For many animals, affiliative relationships such as pair bonds form the foundation of society, and are highly adaptive. Animal systems amenable for comparatively studying pair bonding are important for identifying underlying biological mechanisms, but mostly exist in mammals. Better establishing fish systems will enable comparison of pair bonding mechanisms across taxonomically distant lineages that may reveal general underlying principles. We examined the utility of wild butterflyfishes (f: Chaetodontidae; g: Chaetodon) for comparatively studying pair bonding. Stochastic character mapping inferred that within the family, pairing is ancestral, with at least seven independent transitions to group formation and seven transition to solitary behavior from the late Miocene to recent. In six sympatric and wide-spread species representing a clade with one ancestrally reconstructed transition from paired to solitary grouping, we then verified social systems at Lizard Island, Australia. In situ observations confirmed that Chaetodon baronessa, C. lunulatus, and C. vagabundus are predominantly pair bonding, whereas C. rainfordi, C. plebeius, and C. trifascialis are predominantly solitary. Even in the predominantly pair bonding species, C. lunulatus, a proportion of adults (15 %) are solitary. Importantly, inter- and intra-specific differences in social systems do not co-vary with other previously established attributes (geographic occurrence, parental care, diet, or territoriality). Hence, the proposed butterflyfish populations are promising for comparative analyses of pair bonding and its mechanistic underpinnings. Avenues for further developing the system are proposed, including determining whether the utility of these species applies across their geographic disruptions.

ecology↗

bioRxiv2017

EIF4A3 associated splicing and nonsense mediated decay defined by a systems analysis with novel small molecule inhibitors

Chemical biology approaches to the global functions of splicing reactions are gaining momentum, with an increasing repertoire of small molecule probes becoming available. Here we map the association of eIF4A3 with transcript expression, NMD and alternative splicing using a set of selective novel small molecule allosteric helicase inhibitors whose synthesis and chemical properties we have recently described. We show through analysis of dose monotonic transcriptional responses to increasing inhibition that both full length and NMD prone transcripts link eIF4A3 to normal functioning of cell division including chromosome segregation and cell cycle checkpoints, pointing to a conserved role of splicing and transcript quality processing in cell cycle functions. Cell cycle analysis and microscopy of inhibitor treated cells demonstrates chromosome mis-segregation and spindle defects, associated with a G2/M arrest, validating this observation. Through analysis of conserved alternative splicing patterns exhibiting monotonic responses, we find that eIF4A3 dependent alternative splicing involves exons that are longer and introns that are shorter than transcripts not modulated by eIF4A3. Moreover we observe conservation of over/under representation of RBP binding motif density over introns and exons implicated eIF4A3 modulated skipped exon and retained introns. The distribution of motif densities over 5 and branch intron sites and 5 exons is consistent with function of the exon-junction complex. Taken together we have defined a fraction of the transcrip-tome dependent on eIF4A3 functions and revealed a link between eIF4A3 and cell cycle regulation. The systems approach described here suggests additional avenues for therapeutic exploitation of eIF4A3 functions in cancer and related diseases.

genomics↗

bioRxiv2018

Guiding the refinement of biochemical knowledgebases with ensembles of metabolic networks and semi-supervised learning

Mechanistic models are becoming common in biology and medicine. These models are often more generalizable than data-driven models because they explicitly represent biological knowledge, enabling simulation of scenarios that were not used to construct the model. While this generalizability has advantages, it also creates a dilemma: how should model curation efforts be focused to improve model performance? Here, we develop a machine learning-guided solution to this problem for genome-scale metabolic models. We generate an ensemble of candidate models consistent with experimental data, then perform in silico ensemble simulations for which improved predictiveness is desired. We apply unsupervised and supervised learning to the simulation output to identify structural variation in ensemble members that maximally influences variance in simulation outcomes across the ensemble. The resulting structural variants are high priority candidates for curation through targeted experimentation. We demonstrate this approach, called Automated Metabolic Model Ensemble-Driven Elimination of Uncertainty with Statistical learning (AMMEDEUS), by applying it to 29 bacterial species to identify curation targets that improve gene essentiality predictions. We then compile these curation targets from all 29 species to prioritize refinement of the entire biochemical database used to generate them. AMMEDEUS is a fully automated, scalable, and performance-driven recommendation system that complements human intuition during the curation of hypothesis-driven models and biochemical databases.\n\nSignificanceMechanistic computational models, such as metabolic and signaling networks, are becoming common in biology. These models contain a comprehensive representation of components and interactions for a given system, making them generalizable and often more predictive than simpler models. However, their size and connectivity make it difficult to identify which parts of a model need to be changed to improve performance further. Here, we develop a strategy to guide this process and apply it to metabolic models for a set of bacterial species. We use this strategy to identify model components that should be investigated, and demonstrate that it can improve predictive performance. This approach systematically aides the curation of metabolic models, and the databases used to construct them, without relying on the intuition of the curator.

systems biology↗

bioRxiv2017

Single-Neuron Gene Expression Analysis Using the Maxwell 16 LEV System in the Neural Systems and Behavior Course

Gene expression analysis from single cells has become increasingly prominent across biological disciplines; thus, it is important to train students in these approaches. Here, we present an experimental and analysis pipeline that we developed for the Neural Systems & Behavior (NS&B) course at Marine Biological Laboratory. Our approach used the Maxwell(R) 16 LEV simplyRNA Tissue Kit and GoTaq(R) 2-Step RT-qPCR System for gene expression analysis from single neurons of the crustacean stomatogastric ganglion, a model system to study the generation of rhythmic motor patterns. We used double-stranded RNA to knockdown expression of a putative neuromodulator-activated sodium channel. We then examined the electrophysiological responses to known neuromodulators and confirmed that the response was reduced. Finally, we measured how mRNA levels of several ion channel genes changed in response. Our results provide new insights into the neural mechanisms underlying the generation and modulation of rhythmic motor patterns.

neuroscience↗

bioRxiv2018

Automated, predictive, and interpretable inference of C. elegans escape dynamics

The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.

systems biology↗

bioRxiv2017

Structural robustness of mammalian transcription factor networks reveals plasticity across development

Network biology aims to understand cell behavior through the analysis of underlying complex biomolecular networks. Inference of condition-specific interaction networks from epigenomic data enables the characterization of the structural plasticity that regulatory networks can acquire in different tissues of the same organism. From this perspective, uncovering specific patterns of variation by comparing network structure among tissues could provide insights into systems-level mechanisms underlying cell behavior. Following this idea, here we propose an empirical framework to analyze mammalian tissue-specific networks, focusing on characterizing and contrasting their structure and behavior in response to perturbations. We structurally represent the state of the cell/tissue by condition specific transcription factor networks generated using chromatin accessibility data, and we profile their systems behavior in terms of the structural robustness against random and directed perturbations. Using this framework, we unveil the structural heterogeneity existing among tissues at different levels of differentiation. We uncover a novel and conserved systems property of regulatory networks underlying embryonic stem cells (ESCs): in contrast to terminally differentiated tissues, the promiscuous regulatory connectivity of ESCs produces a globally homogeneous network resulting in increased structural robustness. Possible biological consequences of this property are discussed.

systems biology↗

bioRxiv2018

Coupled Reaction Networks for Noise Suppression

Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widelycited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology.

systems biology↗

bioRxiv2017

Finding analytic stationary solutions to the chemical master equation by gluing state spaces at one or two states recursively

Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Knowing analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of computing analytic stationary solutions relies on gluing simple state spaces together recursively at one or two states. We explore the capabilities of this method and introduce algorithms to derive analytic stationary solutions to the CME. We first formally characterise state spaces that can be constructed by performing single-state gluing of paths, cycles or both sequentially. We then study stochastic biochemical reaction networks that consist of reversible, elementary reactions with two-dimensional state spaces. We also discuss extending the method to infinite state spaces and designing stationary distributions that satisfy user-specified constraints. Finally, we illustrate the aforementioned ideas using examples that include two interconnected transcriptional components and chemical reactions with two-dimensional state spaces.\n\nSubject AreasSystems biology, synthetic biology, biomathematics, bioengineering

systems biology↗

bioRxiv2018

Antioxidant activity and phycoremediation ability of four cyanobacterial isolates obtained from a stressed aquatic system

Cyanobacteria are natural enormous sources of various biologically active compounds with great contributions in different industries. This study aimed to introduce molecular and biochemical characterization for four novel cyanobacterial isolates obtained from Egyptian wastewater canals. Besides, In vitro biological activity of these isolates and their potential ability to take up nutrients and heavy metals from wastewater were examined. The obtained accession numbers were KY250420.1, KY321359.1, KY296359.1 and KU373076.1 for Nostoc calcicola, Leptolyngbya sp, Nostoc sp, and Nostoc sp, respectively. The isolate Leptolyngbya sp (KY321359.1) showed the lowest identity (90%) with other deposited sequences in database. While the isolate Nostoc sp (KU373076.1) showed the highest total phenolic content as well as the highest levels of caffeic, ferulic and gallic acids. Consequently, it appeared the highest antioxidant scavenging activity. All cyanobacterial isolates revealed potent ability to take up nutrients and heavy metals from wastewater. Generally, this study provides a taxonomic and molecular evidence for four novel cyanobacterial isolates with antioxidant activity and potent phycoremediation ability.

genetics↗

bioRxiv2016

Regime shifts, alternative states and hysteresis in the Sarracenia microecosystem

Changes in environmental conditions can lead to rapid shifts in the state of an ecosystem (\"regime shifts\"), which, even after the environment has returned to previous conditions, subsequently recovers slowly to the previous state (\"hysteresis\"). Large spatial and temporal scales of dynamics, and the lack of frameworks linking observations to models, are challenges to understanding and predicting ecosystem responses to perturbations. The naturally-occurring microecosystem inside leaves of the northern pitcher plant (Sarracenia purpurea) exhibits oligotrophic and eutrophic states that can be induced by adding insect prey. Here, we further develop a model for simulating these dynamics, parameterize it using data from a prey addition experiment and conduct a sensitivity analysis to identify critical zones within the parameter space. Simulations illustrate that the microecosystem model displays regime shifts and hysteresis. Parallel results were observed in the plant itself after experimental enrichment with prey. Decomposition rate of prey was the main driver of system dynamics, including the time the system remains in an anoxic state and the rate of return to an oxygenated state. Biological oxygen demand in fluenced the shape of the systems return trajectory. The combination of simulated results, sensitivity analysis and use of empirical results to parameterize the model more precisely demonstrates that the Sarracenia microecosystem model displays behaviors qualitatively similar to models of larger ecological systems.

ecology↗

bioRxiv2018

Double-digest RAD-sequencing: do wet and dry protocol parameters impact biological results?

O_LINext-generation sequencing technologies have opened a new era of research in genomics. Among these, restriction enzyme-based techniques such as restriction-site associated DNA sequencing (RADseq) or double-digest RAD-sequencing (ddRADseq) are now widely used in many population genomics fields. From DNA sampling to SNP calling, both wet and dry protocols have been discussed in the literature to identify key parameters for an optimal loci reconstruction.\nC_LIO_LIThe impact of these parameters on downstream analyses and biological results drawn from RADseq or ddRADseq data has however not been fully explored yet. In this study, we tackled this issue by investigating the effects of ddRADseq laboratory (i.e. wet protocol) and bioinformatics (i.e. dry protocol) settings on loci reconstruction and inferred biological signal at two evolutionary scale using two systems: a complex of butterfly species (Coenonympha sp.) and populations of Common beech (Fagus sylvatica).\nC_LIO_LIResults suggest an impact of wet protocol parameters (DNA quantity, number of PCR cycles during library preparation) on the number of recovered reads and SNPs, the number of unique alleles and individual heterozygosity. We also found that bioinformatic settings (i.e. clustering and minimum coverage thresholds) impact loci reconstruction (e.g. number of loci, mean coverage) and SNP calling (e.g. number of SNPs, heterozygosity). We however do not detect an impact of parameter settings on three types of analysis performed with ddRADseq data: measure of genetic differentiation, estimation of individual admixture, and demographic inferences. In addition, our work demonstrates the high reproducibility and low rate of genotyping inconsistencies of the ddRADseq protocol.\nC_LIO_LIThus, our study highlights the impact of wet parameters on ddRADseq protocol with strong consequences on experimental success and biological conclusions. Dry parameters affects loci reconstruction and descriptive statistics but not biological conclusion for the two studied systems. Overall, this study illustrates, with others, the relevance of ddRADseq for population and evolutionary genomics at the inter- or intraspecific scales.\nC_LI

molecular biology↗

bioRxiv2016

Phosphorylation energy and nonlinear kinetics as key determinants for G2/M transition in fission yeast cell cycle

The living cell is an open nonequilibrium biochemical system, where ATP hydrolysis serves as the energy source for a wide range of intracellular processes including the assurance for decision-making. In the fission yeast cell cycle, the transition from G2 phase to M phase is triggered by the activation of Cdc13/Cdc2 and Cdc25, and the deactivation of Wee1. Each of these three events involves a phosphorylation-dephosphorylation (PdP) cycle, and together they form a regulatory circuit with feedback loops. Almost all quantitative models for cellular networks in the past have invalid thermodynamics due to the assumption of irreversible enzyme kinetics. We constructed a thermodynamically realistic kinetic model of the G2/M circuit, and show that the phosphorylation energy ({Delta}G), which is determined by the cellular ATP/ADP ratio, critically controls the dynamics and the bistable nature of Cdc2 activation. Using fission yeast nucleoplasmic extract (YNPE), we are able to experimentally verify our model prediction that increased {Delta}G, being synergistic to the accumulation of Cdc13, drives the activation of Cdc2. Furthermore, Cdc2 activation exhibits bistability and hysteresis in response to changes in phosphorylation energy. These findings suggest that adequate maintenance of phosphorylation energy ensures the bistability and robustness of the activation of Cdc2 in the G2/M transition. Free energy might play a widespread role in biological decision-making processes, connecting thermodynamics with information processing in biology.

systems biology↗

bioRxiv2016

System-wide automatic extraction of functional signatures in Pseudomonas aeruginosa with eADAGE

Cross experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with neural networks, can effectively identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a Pseudomonas aeruginosa compendium containing experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB. While we expected PhoB activity in limiting phosphate conditions, our analyses found PhoB activity in other media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for PhoB activation in this setting. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.

Systems Biology↗

bioRxiv2016

Hybrid systems approach to modeling stochastic dynamics of cell size

A ubiquitous feature of all living cells is their growth over time followed by division into two daughter cells. How a population of genetically identical cells maintains size homeostasis, i.e., a narrow distribution of cell size, is an intriguing fundamental problem. We model size using a stochastic hybrid system, where a cell grows exponentially over time and probabilistic division events are triggered at discrete time intervals. Moreover, whenever these events occur, size is randomly partitioned among daughter cells. We first consider a scenario, where a timer (i.e., cell-cycle clock) that measures the time since the last division event regulates cellular growth and the rate of cell division. Analysis reveals that such a timer-driven system cannot achieve size homeostasis, in the sense that, the cell-to-cell size variation grows unboundedly with time. To explore biologically meaningful mechanisms for controlling size we consider three different classes of models: i) a size-dependent growth rate and timer-dependent division rate; ii) a constant growth rate and size-dependent division rate and iii) a constant growth rate and division rate that depends both on the cell size and timer. We show that each of these strategies can potentially achieve bounded intercellular size variation, and derive closed-form expressions for this variation in terms of underlying model parameters. Finally, we discuss how different organisms have adopted the above strategies for maintaining cell size homeostasis.

Systems Biology↗

bioRxiv2017

Identifying (un)controllable dynamical behavior with applications to biomolecular networks

We present a technique applicable in any dynamical framework to identify control-robust subsets of an interacting system. These robust subsystems, which we call stable modules, are characterized by constraints on the variables that make up the subsystem. They are robust in the sense that if the defining constraints are satisfied at a given time, they remain satisfied for all later times, regardless of what happens in the rest of the system, and can only be broken if the constrained variables are externally manipulated. We identify stable modules as graph structures in an expanded network, which represents causal links between variable constraints. A stable module represents a system \"decision point\", or trap subspace. Using the expanded network, small stable modules can be composed sequentially to form larger stable modules that describe dynamics on the system level. Collections of large, mutually exclusive stable modules describe the systems repertoire of long-term behaviors. We implement this technique in a broad class of dynamical systems and illustrate its practical utility via examples and algorithmic analysis of two published biological network models. In the segment polarity gene network of Drosophila melanogaster, we obtain a state-space visualization that reproduces by novel means the four possible cell fates and predicts the outcome of cell transplant experiments. In the T-cell signaling network, we identify six signaling elements that determine the high-signal response and show that control of an element connected to them cannot disrupt this response.\n\nAuthor summaryWe show how to uncover the causal relationships between qualitative statements about the values of variables in ODE systems. We then show how these relationships can be used to identify subsystem behaviors that are robust to outside interventions. This informs potential system control strategies (e.g., in identifying drug targets). Typical analytical properties of biomolecular systems render them particularly amenable to our techniques. Furthermore, due to their often high dimension and large uncertainties, our results are particularly useful in biomolecular systems. We apply our methods to two quantitative biological models: the segment polarity gene network of Drosophila melanogaster and the T-cell signal transduction network.

systems biology↗

bioRxiv2017

High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow

BackgroundCancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous host systems. Therapies act on this combined cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic computational models can augment traditional laboratory and clinical studies, helping identify the factors driving a treatments success or failure. However, given the uncertainties regarding the underlying biology, these multiscale computational models can take many potential forms, in addition to encompassing high-dimensional parameter spaces. Therefore, the exploration of these models is computationally challenging. We propose that integrating two existing technologies--one to aid the construction of multiscale agent-based models, the other developed to enhance model exploration and optimization--can provide a computational means for high-throughput hypothesis testing, and eventually, optimization.\n\nResultsIn this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in applying PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a generalized PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where hundreds or thousands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization.\n\nConclusionsWhile key notational and computational challenges remain, mechanistic agent-based models and high-throughput model exploration environments can be combined to systematically and rapidly explore key problems in cancer. These high-throughput computational experiments can improve our understanding of the underlying biology, drive future experiments, and ultimately inform clinical practice.

systems biology↗

bioRxiv2018

Life Inside A Dinosaur Bone: A Thriving Microbiome

Fossils were long thought to lack original organic material, but the discovery of organic molecules in fossils and sub-fossils, thousands to millions of years old, has demonstrated the potential of fossil organics to provide radical new insights into the fossil record. How long different organics can persist remains unclear, however. Non-avian dinosaur bone has been hypothesised to preserve endogenous organics including collagen, osteocytes, and blood vessels, but proteins and labile lipids are unstable during diagenesis or over long periods of time. Furthermore, bone is porous and an open system, allowing microbial and organic flux. Some of these organics within fossil bone have therefore been identified as either contamination or microbial biofilm, rather than original organics. Here, we use biological and chemical analyses of Late Cretaceous dinosaur bones and sediment matrix to show that dinosaur bone hosts a diverse microbiome. Fossils and matrix were freshly-excavated, aseptically-acquired, and then analysed using microscopy, spectroscopy, chromatography, spectrometry, DNA extraction, and 16S rRNA amplicon sequencing. The fossil organics differ from modern bone collagen chemically and structurally. A key finding is that 16S rRNA amplicon sequencing reveals that the subterranean fossil bones host a unique, living microbiome distinct from that of the surrounding sediment. Even in the subsurface, dinosaur bone is biologically active and behaves as an open system, attracting microbes that might alter original organics or complicate the identification of original organics. These results suggest caution regarding claims of dinosaur bone soft tissue preservation and illustrate a potential role for microbial communities in post-burial taphonomy.

paleontology↗

bioRxiv2015

Principles of studying a cell - a non-boastful paper for all molecular biologists

Studies of a cell rely on either observational approaches or perturbational/genetic approaches to define the contribution of a gene to specific cellular traits. It is unclear, however, under what circumstances each of the two approaches can be most successful and when they are doomed to fail. By analyzing over 500 complex traits of the yeast Saccharomyces cerevisiae we show that the trait relatedness to fitness determines the performance of observational approaches. Specifically, in traits subject to strong natural selection, genes identified using observational approaches are often highly coordinated in expression, such that the gene-trait associations are readily recognizable; in sharp contrast, the lack of such coordination in traits subject to weak selection leads to no detectable activity-trait associations for any individual genes and thus the failure of observational approaches. We further show that genetic approaches can be successful when the genes responsible for coordinating the target genes of observational approaches are perturbed. However, because the system-level cellular responses to a random mutation affect more or less every gene and consequently every trait, most genetic effects convey no trait-specific functional information for understanding the traits, which is particularly true for traits subject to weak selection.\n\nSignificance statementCell research is nearly exclusively based on empirical data obtained through either observational approaches or perturbational/genetic approaches. It is, however, increasingly clear that an analytical framework able to guide the empirical strategies is necessary to drive the field further ahead. This study analyzes ~500 complex traits of the yeast Saccharomyces cerevisiae and reveals the organizing principles of a cell. Specifically, a cell can be viewed as a factory, with each trait being the product of a production line operated directly by workers who are supervised by managers. For a cellular trait produced by many workers, the coordination level of the workers determines the performance of observational approaches. Meanwhile, the coordination of workers is realized by managers that are recruited and/or maintained by natural selection. Thus, observational approaches are expected to fail for traits subject to little selection, and genetic approaches can be successful only when the managers of fitness-tightly-coupled traits are perturbed. The manager-worker architecture built by natural selection explains well the origins of global epistasis and ubiquitous genetic effects, two major issues confusing current genetics and molecular and cellular biology, providing a clear guideline on how to study a cell.

Systems Biology↗