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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

A plasmid-based Escherichia coli gene expression system with cell-to-cell variation below the extrinsic noise limit

Experiments in synthetic biology and microbiology can benefit from protein expression systems with low cell-to-cell variability (noise) and expression levels precisely tunable across a useful dynamic range. Despite advances in understanding the molecular biology of microbial gene regulation, many experiments employ protein-expression systems exhibiting high noise and nearly all-or-none responses to induction. I present an expression system that incorporates elements known to reduce gene expression noise: negative autoregulation and bicistronic transcription. I show by stochastic simulation that while negative autoregulation can produce a more gradual response to induction, bicistronic expression of a repressor and gene of interest can be necessary to reduce noise below the extrinsic limit. I synthesized a plasmid-based system incorporating these principles and studied its properties in Escherichia coli cells, using flow cytometry and fluorescence microscopy to characterize induction dose-response, induction/repression kinetics and gene expression noise. By varying ribosome binding site strengths, expression levels from 55-- 10,740 molecules/cell were achieved with noise below the extrinsic limit. Individual strains are inducible across a dynamic range greater than 20-fold. Experimental comparison of different regulatory networks confirmed that bicistronic autoregulation reduces noise, and revealed unexpectedly high noise for a conventional expression system with a constitutively expressed transcriptional repressor. I suggest a hybrid, low-noise expression system to increase the dynamic range.

synthetic biology↗

bioRxiv2018

Tripal Developer Toolkit

Tripal is an open-source biological community database construction toolkit utilizing the content management system Drupal. Tripal is used to make biological, genetic and genomic data more discoverable, shareable, searchable, and standardized. As funding for community level genomics databases declines, Tripals open source codebase provides a means for sites to be built and maintained with a minimal investment in staff and new development. Tripal is ultimately as strong as the community of sites and developers that use it. We present a set of developer tools that will make building and maintaining Tripal 3 sites easier for new and returning users. These tools break down barriers to entry such as setting up developer and testing environments, acquiring and loading test datasets, working with controlled vocabulary terms, and writing new Drupal classes.

bioinformatics↗

bioRxiv2017

Genome-Enabled Insights Into The Ecophysiology Of The Comammox Bacterium Candidatus Nitrospira nitrosa

The recently discovered comammox bacteria have the potential to completely oxidize ammonia to nitrate. These microorganisms are part of the Nitrospira genus and are present in a variety of environments, including Biological Nutrient Removal (BNR) systems. However, the physiological traits within and between comammox- and nitrite oxidizing bacteria (NOB)-like Nitrospira species have not been analyzed in these ecosystems. In this study, we identified Nitrospira strains dominating the nitrifying community of a sequencing batch reactor (SBR) performing BNR under micro-aerobic conditions. We recovered metagenomes-derived draft genomes from two Nitrospira strains: (1) Nitrospira sp. UW-LDO-01, a comammox-like organism classified as Candidatus Nitrospira nitrosa, and (2) Nitrospira sp. UW-LDO-02, a nitrite oxidizing strain belonging to the Nitrospira defluvii species. A comparative genomic analysis of these strains with other Nitrospira-like genomes identified genomic differences in Ca. Nitrospira nitrosa mainly attributed to each strains niche adaptation. Traits associated with energy metabolism also differentiate comammox from NOB-like genomes. We also identified several transcriptionally regulated adaptive traits, including stress tolerance, biofilm formation and micro-aerobic metabolism, which might explain survival of Nitrospira under multiple environmental conditions. Overall, our analysis expanded our understanding of the genetic functional features of Ca. Nitrospira nitrosa, and identified genomic traits that further illuminate the phylogenetic diversity and metabolic plasticity of the Nitrospira genus.

microbiology↗

bioRxiv2018

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macromolecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plants sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to lignin-related lignin-phenotypes across the network layers. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.

bioinformatics↗

bioRxiv2018

Surface Plasmon Resonance Imaging of Excitable Cells

Surface plasmons are highly sensitive to refractive index variations adjacent to the surface. This sensitivity has been exploited successfully for chemical and biological assays. In these systems, a surface plasmon resonance (SPR)-based sensor detects temporal variations in the refractive index at a point. SPR has also been used in imaging systems where the spatial variations of refractive index in the sample provide the contrast mechanism. A high numerical aperture objective lens has been used to design SPR microscopy systems with the ability to image adherent live cells. Addressing research questions in cell physiology and pharmacology often requires the development of a multimodal microscope where complementary information can be obtained.\n\nIn this paper, we present the development of a multimodal microscope that combines surface plasmon resonance imaging with a number of additional imaging modalities including bright-field, epi-fluorescence, total internal reflection microscopy (TIRM) and SPR fluorescence microscopy. We used a high numerical aperture objective lens to achieve SPR and TIR microscopy with the ability to image adherent live cells non-invasively. The platform has been used to image live cell cultures demonstrating both fluorescent and label-free techniques. The SPR and TIR imaging systems feature a wide field of view (300 {micro}m) that allows measurements from multiple cells while the resolution is sufficient to image fine cellular processes. The ability of the platform to perform label-free functional imaging of living cell was demonstrated by imaging the spatial variations in contraction of stem cell-derived cardiomyocytes. This technique has a promise for non-invasive imaging of the development of cultured cells over very long periods of time.

bioengineering↗

bioRxiv2017

Prototyping And Implementation Of A Novel Feedforward Loop In A Cell-Free Transcription-Translation System And Cells

Building novel synthetic biological devices is a time-consuming task because of the lengthy cell-based testing and optimization processes. Recent progress made in the cell-free field suggests that the utilization of mathematical models and cell-free transcription-translation testing platforms to systematically design and test novel synthetic biocircuits may help streamline some of the processes. Here we present a study of building a novel functional biological network motif from scratch with the aid of the mathematical modeling and the cell-free prototyping. In this work, we demonstrated that we were able to make a 3-promoter feedforward circuit from a concept to a working biocircuit in cells within a month. We started with performing simulations with a cell-free transcription-translation simulation toolbox. After verifying the feasibility of the circuit design, we used a fast assembling method to build the constructs and used the linear DNAs directly in the cell-free system for prototyping. After additional tests and assemblies, we implemented the circuit in plasmid forms in cells and showed that the in vivo results were consistent with the simulations and the outcomes in the cell-free platform. This study showed the usefulness of modeling and prototyping in building synthetic biocircuits and that we can use these tools to help streamline the process of circuit optimizations in future studies.

bioengineering↗

bioRxiv2018

Microinjection into the Caenorhabditis elegans embryo using an uncoated glass needle enables cell lineage visualization and reveals cell-non-autonomous adhesion control

Microinjection is a useful method in cell biology, with which exogenous substances are introduced into a cell in a location- and time-specific manner. The Caenorhabditis elegans embryo is an important model system for cell and developmental biology. Applying microinjection to the C. elegans embryo had been difficult due to the rigid eggshell surrounding the embryo. In 2013, microinjection method using a carbon-coated quartz needle for the C. elegans embryo was reported. To prepare the needle, unfortunately, special equipment is required and thus a limited number of researchers can use this method. In this study, we established a method for the microinjection of drugs, dyes, and microbeads into the C. elegans embryo using an uncoated glass needle that can be produced in a general laboratory. This method enabled us to easily detect cell lineage up to adult stages by injecting a fluorescent dye into a blastomere. We also found a cell-non-autonomous control mechanism of cell adhesion; specifically, the injection of an actin inhibitor into one cell at the 2-cell stage enhanced adhesion between daughter cells of the other cell. Our microinjection method is expected to be used for broad studies and could facilitate various discoveries using C. elegans.

cell biology↗

bioRxiv2016

The Fruit Fly Brain Observatory: from structure to function

The Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.

neuroscience↗

bioRxiv2018

Evolutionary forecasting of phenotypic and genetic outcomes of experimental evolution in Pseudomonas

Experimental evolution with microbes is often highly repeatable under identical conditions, suggesting the possibility to predict short-term evolution. However, it is not clear to what degree evolutionary forecasts can be extended to related species in non-identical environments, which would allow testing of general predictive models and fundamental biological assumptions. To develop an extended model system for evolutionary forecasting, we used previous data and models of the genotype-to-phenotype map from the wrinkly spreader system in Pseudomonas fluorescens SBW25 to make predictions of evolutionary outcomes on different biological levels for Pseudomonas protegens Pf-5. In addition to sequence divergence (78% amino acid and 81% nucleotide identity) for the genes targeted by mutations, these species also differ in the inability of Pf-5 to make cellulose, which is the main structural basis for the adaptive phenotype in SBW25. The experimental conditions were also changed compared to the SBW25 system to test the robustness of forecasts to environmental variation. Forty-three mutants with increased ability to colonize the air-liquid interface were isolated, and the majority had reduced motility and was partly dependent on the pel exopolysaccharide as a structural component. Most (38/43) mutations are expected to disrupt negative regulation of the same three diguanylate cyclases as in SBW25, with a smaller number of mutations in promoter regions, including that of an uncharacterized polysaccharide operon. A mathematical model developed for SBW25 predicted the order of the three main pathways and the genes targeted by mutations, but differences in fitness between mutants and mutational biases also appear to influence outcomes. Mutated regions in proteins could be predicted in most cases (16/22), but parallelism at the nucleotide level was low and mutational hot spots were not conserved. This study demonstrates the potential of short-term evolutionary forecasting in experimental populations and provides testable predictions for evolutionary outcomes in other Pseudomonas species. Author SummaryBiological evolution is often repeatable in the short-term suggesting the possibility of forecasting and controlling evolutionary outcomes. In addition to its fundamental importance for biology, evolutionary processes are at the core of several major societal problems, including infectious diseases, cancer and adaptation to climate change. Experimental evolution allows study of evolutionary processes in real time and seems like an ideal way to test the predictability of evolution and our ability to make forecasts. However, lack of model systems where forecasts can be extended to other species evolving under different conditions has prevented studies that first predict evolutionary outcomes followed by direct testing. We showed that a well-characterized bacterial experimental evolution system, based on biofilm formation by Pseudomonas fluorescens at the surface of static growth tubes, can be extended to the related species Pseudomonas protegens. We tested evolutionary forecasts experimentally and showed that mutations mainly appear in the predicted genes resulting in similar phenotypes. We also identified factors that we cannot yet predict, such as variation in mutation rates and differences in fitness. Finally, we make forecasts for other Pseudomonas species to be tested in future experiments.

evolutionary biology↗

bioRxiv2016

Openspritzer: an open hardware pressure ejection system for reliably delivering picolitre volumes.

The ability to reliably and precisely deliver picolitre volumes is an important component of biological research. Here we describe a high-performance, low-cost, open hardware pressure ejection system (Openspritzer), which can be constructed from off the shelf components. When connected to a standard micro-pipette via suitable pneumatic tubing, the device is capable of delivering minute doses of reagents to a wide range of biological and chemical systems. In this work, we characterise the performance of the device and compare it to a popular commercial system using two-photon fluorescence microscopy. We found that Openspritzer provides the same level of control over delivered reagent dose as the commercial system. Next, we demonstrate the utility of Openspritzer in a series of standard neurobiological applications. First, we used Openspritzer to deliver precise amounts of the neurotransmitters glutamate and GABA to hippocampal neurons to elicit time- and dose-precise excitatory and inhibitory responses, respectively. Second, we used Openspritzer to deliver infectious viral and bacterial agents to living tissue. Viral transfection of hippocampal interneurons with channelrhodopsin allowed for the optogenetic manipulation of hippocampal circuitry with light. Finally, we successfully used Openspritzer to infect organotypic hippocampal slice cultures with fluorescent Mycobacterium bovis bacilli. We anticipate that due to its high performance and low cost Openspritzer will be of interest to a broad range of researchers working in the life and physical sciences.

neuroscience↗

bioRxiv2018

Study on tumor cell RNA cross-anticancer mechanism in non-tumor mice experimental model

To report the experiments of fractal structure and self-similar function in biological coupled oscillation brings life system waving growth in life organism. At the same time by self-organization the system openness come into being to maintain stability features of growth survival. From this, the orderly oscillation of tumor cell RNA was recognized that this process could lead to system sequentiality openness. According to waving growth of basic research the procedure could be sure to turn into a model as out of control of growing in tumor. Then in complete contrast that derived from the same component which have cross resistance effect. It shows that its biological behavior is converted as only a single physical power to play action by inactivated processing. Subsequent further confirms the essential roles whether essential roles link up with recovery of physiological function. The result in that it could exhibit feature of anticancer owing to be endowed tissue embryonization response. So that it also exerts preventing on senile chronic diseases. In theory, the concept of this tissue response in life organism closely link up with the single physical power of RNA component so as to bring up specific procedure of growing and subsisting that deal with keeping life vigor generalized explanatory.

cancer biology↗

bioRxiv2017

Somatic inactivating PTPRJ mutations and dysregulated pathways identified in canine melanoma by integrated comparative genomic analysis

Canine malignant melanoma, a significant cause of mortality in domestic dogs, is a powerful comparative model for human melanoma, but little is known about its genetic etiology. We mapped the genomic landscape of canine melanoma through multi-platform analysis of 37 tumors (31 mucosal, 3 acral, 2 cutaneous, and 1 uveal) and 17 matching constitutional samples including long- and short-insert whole genome sequencing, RNA sequencing, array comparative genomic hybridization, single nucleotide polymorphism array, and targeted Sanger sequencing analyses. We identified novel predominantly truncating mutations in the putative tumor suppressor gene PTPRJ in 19% of cases. No BRAF mutations were detected, but activating RAS mutations (24% of cases) occurred in conserved hotspots in all cutaneous and acral and 13% of mucosal subtypes. MDM2 amplifications (24%) and TP53 mutations (19%) were mutually exclusive. Additional low-frequency recurrent alterations were observed amidst low point mutation rates, an absence of ultraviolet light mutational signatures, and an abundance of copy number and structural alterations. Mutations that modulate cell proliferation and cell cycle control were common and highlight therapeutic axes such as MEK and MDM2 inhibition. This mutational landscape resembles that seen in BRAF wild-type and sun-shielded human melanoma subtypes. Overall, these data inform biological comparisons between canine and human melanoma while suggesting actionable targets in both species.\n\nAUTHOR SUMMARYMelanoma, an aggressive cancer arising from transformed melanocytes, commonly occurs in pet dogs. Unlike human melanoma, which most often occurs in sun-exposed cutaneous skin, canine melanoma typically arises in sun-shielded oral mucosa. Clinical features of canine melanoma resemble those of human melanoma, particularly the less common sun-shielded human subtypes. However, whereas the genomic basis of diverse human melanoma subtypes is well understood, canine melanoma genomics remain poorly defined. Similarly, although diverse new treatments for human melanoma based on a biologic disease understanding have recently shown dramatic improvements in outcomes for these patients, treatments for canine melanoma are limited and outcomes remain universally poor. Detailing the genomic basis of canine melanoma thus provides untapped potential for improving the lives of pet dogs while also helping to establish canine melanoma as a comparative model system for informing human melanoma biology and treatment. In order to better define the genomic landscape of canine melanoma, we performed multi-platform characterization of 37 tumors. Our integrated analysis confirms that these tumors commonly contain mutations in canine orthologs of human cancer genes such as RAS, MDM2, and TP53 as well mutational patterns that share important similarities with human melanoma subtypes. We have also found a new putative cancer gene, PTPRJ, frequently mutated in canine melanoma. These data will guide additional biologic and therapeutic studies in canine melanoma while framing the utility of comparative studies of canine and human cancers more broadly.

cancer biology↗

bioRxiv2017

Genome Architecture Leads a Bifurcation in Cell Identity

Genome architecture is important in transcriptional regulation and study of its features is a critical part of fully understanding cell identity. Altering cell identity is possible through overexpression of transcription factors (TFs); for example, fibroblasts can be reprogrammed into muscle cells by introducing MYOD1. How TFs dynamically orchestrate genome architecture and transcription as a cell adopts a new identity during reprogramming is not well understood. Here we show that MYOD1-mediated reprogramming of human fibroblasts into the myogenic lineage undergoes a critical transition, which we refer to as a bifurcation point, where cell identity definitively changes. By integrating knowledge of genome-wide dynamical architecture and transcription, we found significant chromatin reorganization prior to transcriptional changes that marked activation of the myogenic program. We also found that the local architectural and transcriptional dynamics of endogenous MYOD1 and MYOG reflected the global genomic bifurcation event. These TFs additionally participate in entrainment of biological rhythms. Understanding the system-level genome dynamics underlying a cell fate decision is a step toward devising more sophisticated reprogramming strategies that could be used in cell therapies.

cell biology↗

bioRxiv2017

Uncovering cell-free protein expression dynamics by a promoter library with diverse strengths

Cell-free protein expression systems have been widely used for synthetic biology and metabolic engineering applications in recent years. Yet little is known about protein expression in the cell-free systems. Here we take a systems approach to uncover underlying dynamics of cell-free protein expression. We construct a set of T7 promoter variants to express proteins at different transcription rates in a reconstituted and E. coli extract-based cell-free systems. We find that the maximum expression level and the rate of protein synthesis as responses to the transcription rate change are different in the two cell-free systems, suggesting they are driven by different expression dynamics. We confirm this by constructing a simple mathematical model for each cell-free system, which well reproduce the experimental results and also identify different limiting factors for better protein expression in the two cell-free systems. In particular, they revealed there is a negative feedback effect in the mRNA-protein translation by the PURE system and also identified different limiting factors for better protein expression in the two cell-free systems.

synthetic biology↗

bioRxiv2017

From word models to executable models of signaling networks using automated assembly

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage; (ii) adaptive drug resistance in BRAF-V600E mutant melanomas; and (iii) the RAS signaling pathway. The use of natural language for modeling makes routine tasks more efficient for modeling practitioners and increases the accessibility and transparency of models for the broader biology community.\n\nStandfirst textINDRA uses natural language processing systems to read descriptions of molecular mechanisms and assembles them into executable models.\n\nHighlightsO_LIINDRA decouples the curation of knowledge as word models from model implementation\nC_LIO_LIINDRA is connected to multiple natural language processing systems and can draw on information from curated databases\nC_LIO_LIINDRA can assemble dynamical models in rule-based and reaction network formalisms, as well as Boolean networks and visualization formats\nC_LIO_LIWe used INDRA to build models of p53 dynamics, resistance to targeted inhibitors of BRAF in melanoma, and the Ras signaling pathway from natural language\nC_LI

systems biology↗

bioRxiv2015

Dynamic effects of extrinsic noise in a simple oscillatory gene network with delayed negative-feedback regulation: an electronic modeling approach

Gene expression is intrinsically stochastic due to the small number of molecules involved in some of the underlying biochemical reactions. The resulting molecule-count random fluctuations are known as biochemical noise. The dynamic effects of intrinsic noise (that originated within the system) have been widely studied. However, the effects of the noise coming from other sources the system is in contact with, or extrinsic noise, is not so well understood. In this work we introduce an electronic model for a simple gene oscillatory network, with delayed negative-feedback regulation. Notably, this model accounts for the intrinsic biochemical noise due to the slow promoter switching between the active and inactive states; but dismisses biochemical noise due to mRNA and protein production and degradation. We characterize the oscillatory behavior of this gene network by varying all the relevant parameter values within biologically meaningful ranges. Finally, we investigate how different sources of extrinsic noise affect the system dynamic behavior. To simulate extrinsic noise we consider stochastic time series coming from another circuit simulating a gene network. Our results indicate that, depending on the parameter affected by extrinsic noise and the power spectra of the stochastic time series, the system quasi-periodic behavior is affected in different ways.

Systems Biology↗

bioRxiv2017

Tools for engineering coordinated system behaviour in synthetic microbial consortia

Advancing synthetic biology to the multicellular level requires the development of multiple orthogonal cell-to-cell communication channels to propagate information with minimal signal interference. The development of quorum sensing devices, the cornerstone technology for building microbial communities with coordinated system behaviour, has largely focused on reducing signal leakage between systems of cognate AHL/transcription factor pairs. However, the use of non-cognate signals as a design feature has received limited attention so far. Here, we demonstrate the largest library of AHL-receiver devices constructed to date with all cognate and non-cognate chemical signal interactions quantified and we develop a software tool that allows automated selection of orthogonal chemical channels. We use this approach to identify up to four orthogonal channels in silico and experimentally demonstrate the simultaneous use of three channels in co-culture. The development of multiple non-interfering cell-to-cell communication channels will facilitate the design of synthetic microbial consortia for novel applications including distributed bio-computation, increased bioprocess efficiency, cell specialisation, and spatial organisation.

synthetic biology↗

bioRxiv2017

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

We introduce a new conceptual framework and a model-based interventional calculus to steer, manipulate, and reconstruct the dynamics and generating mechanisms of non-linear dynamical systems from partial and disordered observations based on the contributions of each of the systems, by exploiting first principles from the theory of computability and algorithmic information. This calculus entails finding and applying controlled interventions to an evolving object to estimate how its algorithmic information content is affected in terms of positive or negative shifts towards and away from randomness in connection to causation. The approach is an alternative to statistical approaches for inferring causal relationships and formulating theoretical expectations from perturbation analysis. We find that the algorithmic information landscape of a system runs parallel to its dynamic attractor landscape, affording an avenue for moving systems on one plane so they can be controlled on the other plane. Based on these methods, we advance tools for reprogramming a system that do not require full knowledge or access to the systems actual kinetic equations or to probability distributions. This new approach yields a suite of universal parameter-free algorithms of wide applicability, ranging from the discovery of causality, dimension reduction, feature selection, model generation, a maximal algorithmic-randomness principle and a systems (re)programmability index. We apply these methods to static (e.coli Transcription Factor network) and to evolving genetic regulatory networks (differentiating naive from Th17 cells, and the CellNet database). We highlight their ability to pinpoint key elements (genes) related to cell function and cell development, conforming to biological knowledge from experimentally validated data and the literature, and demonstrate how the method can reshape a systems dynamics in a controlled manner through algorithmic causal mechanisms.

systems biology↗