Markus W. Covert

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

Integrating high-throughput and computational data elucidates bacterial networks

The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.

Single-cell NF-κB dynamics reveal digital activation and analogue information processing

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-α, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-κB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α-induced NF-κB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

Genome-Scale Metabolic Model of Helicobacter pylori 26695

A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.

Metabolic modeling of microbial strains in silico

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

Transcriptional Regulation in Constraints-based Metabolic Models of Escherichia coli

Full genome sequences enable the construction of genome-scale in silico models of complex cellular functions. Genome-scale constraints-based models of Escherichia coli metabolism have been constructed and used to successfully interpret and predict cellular behavior under a range of conditions. These previous models do not account for regulation of gene transcription and thus cannot accurately predict some organism functions. Here we present an in silico model of the central E. coli metabolism that accounts for regulation of gene expression. This model accounts for 149 genes, the products of which include 16 regulatory proteins and 73 enzymes. These enzymes catalyze 113 reactions, 45 of which are controlled by transcriptional regulation. The combined metabolic/regulatory model can predict the ability of mutant E. coli strains to grow on defined media as well as time courses of cell growth, substrate uptake, metabolic by-product secretion, and qualitative gene expression under various conditions, as indicated by comparison with experimental data under a variety of environmental conditions. The in silico model may also be used to interpret dynamic behaviors observed in cell cultures. This combined metabolic/regulatory model is thus an important step toward the goal of synthesizing genome-scale models that accurately represent E. coli behavior.

Single-cell NF-[kgr]B dynamics reveal digital activation and analogue information processing

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-α, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-κB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α-induced NF-κB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli

MOTIVATION The effort to build a whole-cell model requires the development of new modeling approaches, and in particular, the integration of models for different types of processes, each of which may be best described using different representation. Flux-balance analysis (FBA) has been useful for large-scale analysis of metabolic networks, and methods have been developed to incorporate transcriptional regulation (regulatory FBA, or rFBA). Of current interest is the integration of these approaches with detailed models based on ordinary differential equations (ODEs). RESULTS We developed an approach to modeling the dynamic behavior of metabolic, regulatory and signaling networks by combining FBA with regulatory Boolean logic, and ordinary differential equations. We use this approach (called integrated FBA, or iFBA) to create an integrated model of Escherichia coli which combines a flux-balance-based, central carbon metabolic and transcriptional regulatory model with an ODE-based, detailed model of carbohydrate uptake control. We compare the predicted Escherichia coli wild-type and single gene perturbation phenotypes for diauxic growth on glucose/lactose and glucose/glucose-6-phosphate with that of the individual models. We find that iFBA encapsulates the dynamics of three internal metabolites and three transporters inadequately predicted by rFBA. Furthermore, we find that iFBA predicts different and more accurate phenotypes than the ODE model for 85 of 334 single gene perturbation simulations, as well for the wild-type simulations. We conclude that iFBA is a significant improvement over the individual rFBA and ODE modeling paradigms. AVAILABILITY All MATLAB files used in this study are available at http://www.simtk.org/home/ifba/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Neuron-to-neuron transmission of α-synuclein fibrils through axonal transport

The lesions of Parkinson disease spread through the brain in a characteristic pattern that corresponds to axonal projections. Previous observations suggest that misfolded α‐synuclein could behave as a prion, moving from neuron to neuron and causing endogenous α‐synuclein to misfold. Here, we characterized and quantified the axonal transport of α‐synuclein fibrils and showed that fibrils could be transferred from axons to second‐order neurons following anterograde transport.

High-Sensitivity Measurements of Multiple Kinase Activities in Live Single Cells

Increasing evidence has shown that population dynamics are qualitatively different from single-cell behaviors. Reporters to probe dynamic, single-cell behaviors are desirable yet relatively scarce. Here, we describe an easy-to-implement and generalizable technology to generate reporters of kinase activity for individual cells. Our technology converts phosphorylation into a nucleocytoplasmic shuttling event that can be measured by epifluorescence microscopy. Our reporters reproduce kinase activity for multiple types of kinases and allow for calculation of active kinase concentrations via a mathematical model. Using this technology, we made several experimental observations that had previously been technicallyunfeasible, including stimulus-dependent patterns of c-Jun N-terminal kinase (JNK) and nuclear factor kappa B (NF-κB) activation. We also measured JNK, p38, and ERK activities simultaneously, finding that p38 regulates the peak number, but not the intensity, of ERK fluctuations. Our approach opens the possibility of analyzing a wide range of kinase-mediated processes in individual cells.