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bioRxiv2016

Scalable parameter estimation for genome-scale biochemical reaction networks

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-and medium-scale biological processes. While the same should in principle hold for large-and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large-and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.\n\nAuthor SummaryIn this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.

systems biology↗

bioRxiv2017

Hard Limits And Performance Tradeoffs In A Class Of Sequestration Feedback Systems

Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism implemented using the sequestration binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of sequestration feedback and contribute to a more general theory of biological control systems.

systems biology↗

bioRxiv2016

Robust ergodicity and tracking in antithetic integral control of stochastic biochemical reaction networks

Controlling stochastic reactions networks is a challenging problem with important implications in various fields such as systems and synthetic biology. Various regulation motifs have been discovered or posited over the recent years, the most recent one being the so-called Antithetic Integral Control (AIC) motif [1]. Several favorable properties for the AIC motif have been demonstrated for classes of reaction networks that satisfy certain irreducibility, ergodicity and output controllability conditions. Here we address the problem of verifying these conditions for large sets of reaction networks with fixed topology using two different approaches. The first one is quantitative and relies on the notion of interval matrices while the second one is qualitative and is based on sign properties of matrices. The obtained results lie in the same spirit as those obtained in [1] where properties of reaction networks are independently characterized in terms of control theoretic concepts, linear programming conditions and graph theoretic conditions.

Systems Biology↗

bioRxiv2017

Analysis of the Quality of Macromolecular Structures

Structure determination utilizing X-ray crystallography involves collection of diffraction data, determination of initial phases followed by iterative rounds of model building and crystallographic refinement to improve the phases and minimize the differences between calculated and observed structure factors. At each of these stages, a variety of statistical filters exist to ensure appropriate validation. Biologically important observations often come from interpretations of signals that need to be carefully deciphered from noise and therefore human intervention is as important as the automated filters. Currently, all structural data are deposited in the Protein Data Bank and this repository is continuously evolving to incorporate possible new improvements in macromolecular crystallography. The journals that publish data arising from structural studies modulate their policies to take cognizance of new improved methodologies. The PDB and journals have evolved an accepted protocol to ensure the integrity of crystallographic results. As a result, the quality of available data and interpretations are becoming better over the years. However, there have been periodic efforts by some individuals who misuse validation mechanisms to selectively target published research through spurious challenges. These actions do more harm to the field of structural biology and runs counter to their claim to cleanse the system. The scientific systems in structural biology are robust and capable of self-correction and unwarranted vigilantism is counterproductive.

biophysics↗

bioRxiv2016

Multi-omic analysis of urothelial cancer patients treated with PD-L1 blockade demonstrates the contribution of both systemic and somatic factors to the biology of response and resistance

BackgroundInhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.\n\nMethods and FindingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.\n\nPatients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.\n\nConclusionsThese results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

cancer biology↗

bioRxiv2014

In Silico Design of Self-Optimizing Integrated Metabolic and Gene Regulatory Networks

Biological organisms acclimatize to varying environmental conditions via active self-regulation of internal gene regulatory networks, metabolic networks, and protein signaling networks. While much work has been done to elucidate the topologies of individual networks in isolation, understanding of inter-network regulatory mechanisms remains limited. This shortcoming is of particular relevance to synthetic biology. Synthetic biological circuits tend to lose their engineered functionality over generational time, primarily due to the deleterious stress that they exert on their host organisms. To reduce this stress (and thus minimize loss of functionality) synthetic circuits must be sensitive to the health of the host organism. Development of integrated regulatory systems is therefore essential to robust synthetic biological systems. The aim of this study was to develop integrated gene-regulatory and metabolic networks which self-optimize in response to varying environmental conditions. We performed in silico evolution to develop such networks using a two-step approach: (1) We optimized metabolic networks given a constrained amount of available enzyme. Here, we found that a proportional relationship between flux control coefficients and enzyme mass holds in all linear sub-networks of branched networks, except those sub-networks which contain allosteric regulators. Network optimization was performed by iteratively redistributing enzyme until flux through the network was maximized. Optimization was performed for a range of boundary metabolite conditions to develop a profile of optimal enzyme distributions as a function of environmental conditions. (2) We generated and evolved randomized gene regulatory networks to modulate the enzymes of a target metabolic pathway. The objective of the gene regulatory networks was to produce the optimal distribution of metabolic network enzymes given specific boundary metabolite conditions of the target network. Competitive evolutionary algorithms were applied to optimize the specific structures and kinetic parameters of the gene regulatory networks. With this method, we demonstrate the possibility of algorithmic development of integrated adaptive gene and metabolic regulatory networks which dynamically self-optimize in response to changing environmental conditions.

Bioengineering↗

bioRxiv2018

Associations between Afrotropical bats, parasites, and microbial symbionts

Bats are among the most diverse animals on the planet and harbor numerous bacterial, viral, and eukaryotic symbionts. The interplay between bacterial community composition and parasitism in bats is not well understood and may have important implications for studies of similar systems. Here we present a comprehensive survey of dipteran and haemosporidian parasites, and characterize the gut, oral, and skin microbiota of Afrotropical bats. We identify significant correlations between bacterial community composition of the skin and dipteran ectoparasite prevalence across four major bat lineages, as well as links between the oral microbiome and malarial parasitism, suggesting a potential mechanism for host selection and vector-borne disease transmission in bats. In contrast to recent studies of host-microbe phylosymbiosis in mammals, we find no correlation between chiropteran phylogenetic distances and bacterial community dissimilarity across the three anatomical sites, suggesting that host environment is more important than shared ancestry in shaping the composition of bat-associated bacterial communities.\n\nSIGNIFICANCEAnimals rely on bacterial symbionts for numerous biological functions, such as digestion and immune system development. Increasing evidence suggests that host-associated microbes may play a role in mediating parasite burden. This study is the first to provide a comprehensive survey of bacterial symbionts from multiple anatomical sites across a broad taxonomic range of Afrotropical bats, demonstrating significant associations between the bat microbiome and parasite prevalence. This study provides a framework for future approaches to systems biology of host-symbiont interactions across broad taxonomic scales, emphasizing the interdependence between microbial symbionts and vertebrate health in the study of wild organisms and their natural history.

ecology↗

bioRxiv2018

Some lessons and perspectives for applications of stochastic models in biological and cancer research

Randomness is an unavoidable feature of inner cellular environment and its effects propagate to higher levels of living matter organization such as cells, tissues, and organisms. Approaching those systems experimentally to understand their dynamics is a complex task because of the plethora of compounds interacting in a web that combines intra and inter level elements such that a coordinate behavior come up. Such a characteristic points to the necessity of establishing principles that help on the description, categorization, classification, and the prediction of the behavior of biological systems. The theoretical machinery already available, or the ones to be discovered motivated by biological problems, can play an important role on that quest. Here we exemplify the applicability of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that we have employed aiming to provide the reader with a phenomenological, biologically motivated, perspective of the use of theoretical methods. Furthermore, we discuss some of our conclusions after employing our approach and some research perspectives that they motivate.

systems biology↗

bioRxiv2018

Turing patterns are common but not robust

Turing patterns (TPs) underlie many fundamental developmental processes, but they operate over narrow parameter ranges, raising the conundrum of how evolution can ever discover them. Here we explore TP design space to address this question and to distill design rules. We exhaustively analyze 2- and 3-node biological candidate Turing systems: crucially, network structure alone neither determines nor guarantees emergent TPs. A surprisingly large fraction (>60%) of network design space can produce TPs, but these are sensitive to even subtle changes in parameters, network structure and regulatory mechanisms. This implies that TP networks are more common than previously thought, and evolution might regularly encounter prototypic solutions. Importantly, we deduce compositional rules for TP systems that are almost necessary and sufficient (96% of TP networks contain them, and 95% of networks implementing them produce TPs). This comprehensive network atlas provides the blueprints for identifying natural TPs, and for engineering synthetic systems.

developmental biology↗

bioRxiv2017

Insuperable Problems Of The Genetic Code Initially Emerging In An RNA World

Differential equations for error-prone information transfer (template replication, transcription or translation) are developed in order to consider, within the theory of autocatalysis, the advent of coded protein synthesis. Variations of these equations furnish a basis for comparing the plausibility of contrasting scenarios for the emergence of tRNA aminoacylation, ultimately by enzymes, and the relationship of this process with the origin of the universal system of molecular biological information processing embodied in the Central Dogma. The hypothetical RNA World does not furnish an adequate basis for explaining how this system came into being, but principles of self-organisation that transcend Darwinian natural selection furnish an unexpectedly robust basis for a rapid, concerted transition to genetic coding from a peptide*RNA world.

evolutionary biology↗

bioRxiv2017

DeepMetabolism: A Deep Learning System To Predict Phenotype From Genome Sequencing

Life science is entering a new era of petabyte-level sequencing data. Converting such \"big data\" to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

systems biology↗

bioRxiv2017

Spatially organizing biochemistry: choosing a strategy to translate synthetic biology to the factory

Natural biochemical systems are ubiquitously organized both in space and time. Engineering the spatial organization of biochemistry has emerged as a key theme of synthetic biology, with numerous technologies promising improved biosynthetic pathway performance. One strategy, however, may produce disparate results for different biosynthetic pathways. We propose a spatially resolved kinetic model to explore this fundamental design choice in systems and synthetic biology. We predict that two example biosynthetic pathways have distinct optimal organization strategies that vary based on pathway-dependent and cell-extrinsic factors. Moreover, we outline this design space in general as a function of kinetic and biophysical properties, as well as culture conditions. Our results suggest that organizing biosynthesis has the potential to substantially improve performance, but that choosing the appropriate strategy is key. The flexible mathematical framework we propose can be adapted to diverse biosynthetic pathways, and lays a foundation to rationally choose organization strategies for biosynthesis.

synthetic biology↗

bioRxiv2016

Retargeting: an unrecognized consideration in endonuclease-based gene drive biology

There is intense interest surrounding the use of gene editing nucleases in gene drive systems to control agricultural insect pests and insect vectors of infectious diseases such as malaria, dengue and Zika virus. While gene drive systems offer immense promise for the beneficial modification of deleterious insect populations, their unique mechanism of action also raises novel safety concerns and regulatory issues. A recent US National Academies of Science report provides a list of potential regulatory issues associated with implementation of homologous recombination (HR)-mediated gene drive systems, based on the premise that all such systems would exhibit similar biological behaviors. Here we examine how HR-mediated gene drive systems based on different gene editing nuclease platforms could be affected by mutations that occur during host cell transcription, genome replication, and, in conjunction with gene editing nuclease activity, during HR-mediated gene drive. Our analysis suggests that the same feature that makes RNA-guided nucleases such attractive research tools--their ease of reprogramming by alterations to their guide RNA components--might also contribute to increased rates of retargeting that could influence the long term behavior of RNA-guided gene drive systems. Predictability of behavior over time is an issue that should be addressed by in-depth risk assessment before field testing of organisms incorporating nuclease-mediated gene drives.

synthetic biology↗

bioRxiv2017

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature, thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have training data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of protein length and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multi-protein synthetic circuits, RNA circuits or the cell free production of small molecule products.

synthetic biology↗

bioRxiv2017

Thermodynamic Projection Of The Antibody Interaction Network: The Binding Fountain Energy Landscape

The complexity of the adaptive immune system in humans is comparable to that of the central nervous system in terms of cell numbers, cellular diversity and the network of interactions between components. While the application of molecular biological methods and bioinformatics has brought about an ever deepening and sharpening static description of the molecular and cellular components of the system, a unifying theoretical understanding of the laws governing the dynamics of the system is still lacking.\n\nWe have recently developed a quantitative model for the description of antibody homeostasis as defined by the dimensions of antigen concentration, antigen-antibody interaction affinity and antibody concentration. In this paper we develop the concept of a novel thermodynamic representation of multiple molecular interactions in a system, the fountain energy landscape of binding. We show that the hypersurface of the binding fountain corresponds to the antibody-antigen interaction network projected onto an energy landscape defined by conformational entropy and free energy of binding. We demonstrate that thymus independent and thymus dependent antibody responses show distinct patterns of changes in the energy landscape. Overall, the binding fountain energy landscape concept allows a systems biological, thermodynamic perception of the functioning of the clonal humoral immune system.

biophysics↗

bioRxiv2016

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology↗

bioRxiv2017

Genome-wide analysis of cell-gene interactions

The study presents an analysis of how different cellular functions link cell size to the expression of synthetic genes in E. coli. The Size-Expression interaction was mapped with a two-gene genetic probe across 3800 single-gene deletion strains. Through regression analysis, expression-specific effects and gene-specific effects were derived from size effects and generic expression effects, respectively. The entire compendium of cell functions broadly mapped to four systems of distinct primary influence on the Size-Expression map. Specifically, membrane structural components primarily affected size, whereas protein and RNA stability primarily affected gene expression. In addition, major Size-Expression shifts showed no substantial gene-specific effects unless they were mediated by key components of the protein synthesis apparatus.\n\nSubject Category: Synthetic Biology

systems biology↗

bioRxiv2017

Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

Here, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

neuroscience↗