Parameter Estimation for Gene Regulatory Networks from Microarray Data: Cold Shock Response in Saccharomyces cerevisiae

Modeling the Immune Response for Pathogenic and Nonpathogenic Orthohantavirus Infections in Human Lung Microvasculature Endothelial Cells

Hantaviruses, genus Orthohantavirus, family Hantaviridae, order Bunyavirales, are negative-sense, single-stranded, tri-segmented RNA viruses that persistently infect rodents, shrews, and moles. Of these, only certain virus species harbored by rodents are pathogenic to humans. Infection begins with inhalation of virus particles into the lung and trafficking to the lung microvascular endothelial cells (LMVEC). The reason why certain rodent-borne hantavirus species are pathogenic has long been hypothesized to be related to their ability to downregulate and dysregulate the immune response as well as increase vascular permeability of infected endothelial cells. We set out to study the temporal dynamics of host immune response modulation in primary human LMVECs following infection by Prospect Hill (nonpathogenic), Andes (pathogenic), and Hantaan (pathogenic) viruses. We measured the level of RNA transcripts for genes representing antiviral, proinflammatory, anti-inflammatory, and metabolic pathways from 12 to 72 h with time points every 12 h. Gene expression analysis in conjunction with mathematical modeling revealed a similar profile for all three viruses in terms of upregulated genes that partake in interferon signaling (TLR3, IRF7, IFNB1), host immune cell recruitment (CXCL10, CXCL11, and CCL5), and host immune response modulation (IDO1). We examined secreted protein levels of IFN-β, CXCL10, CXCL11, CCL5, and IDO in two male and two female primary HLMVEC donors at 48 and 60 h post infection. All three viruses induced similar levels of CCL5, CXCL10, and CXCL11 within a particular donor, and the levels were similar in three of the four donors. All three viruses induced different protein secretion levels for both IFN-β and IDO and secretion levels differed between donors. In conclusion, we show that there was no difference in the transcriptional profiles of key genes in primary HLMVECs following infection by pathogenic and nonpathogenic hantaviruses, with protein secretion levels being more donor-specific than virus-specific.

The potential of cold-shock promoters for the expression of recombinant proteins in microbes and mammalian cells

BACKGROUND

Low-temperature expression of recombinant proteins may be advantageous to support their proper folding and preserve bioactivity. The generation of expression vectors regulated under cold conditions can improve the expression of some target proteins that are difficult to express in different expression systems. The cspA encodes the major cold-shock protein from Escherichia coli (CspA). The promoter of cspA has been widely used to develop cold shock-inducible expression platforms in E. coli. Moreover, it is often necessary to employ expression systems other than bacteria, particularly when recombinant proteins require complex post-translational modifications. Currently, there are no commercial platforms available for expressing target genes by cold shock in eukaryotic cells. Consequently, genetic elements that respond to cold shock offer the possibility of developing novel cold-inducible expression platforms, particularly suitable for yeasts, and mammalian cells.

CONCLUSIONS

This review covers the importance of the cellular response to low temperatures and the prospective use of cold-sensitive promoters to direct the expression of recombinant proteins. This concept may contribute to renewing interest in applying white technologies to produce recombinant proteins that are difficult to express.

Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns

A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell’s gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.

Systems biology is a fast growing field largely powered by advances in high-performance computing and sophisticated mathematical modeling of biological systems. Based on these advances, we are now in a position to mechanistically understand and accurately predict the behavior of complex biological processes, including cell differentiation and spatial pattern formation during embryogenesis. In this article, we use an in silico approach to probe the design space of multi-input, three-node Gene Regulatory Networks (GRNs) capable of generating a striped gene expression pattern in the context of a simplified 1-D morphogenetic field.

Hierarchical parameter estimation of GRN based on topological analysis

Reverse engineering of gene regulatory network (GRN) is an important and challenging task in systems biology. Existing parameter estimation approaches that compute model parameters with the same importance are usually computationally expensive or infeasible, especially in dealing with complex biological networks.In order to improve the efficiency of computational modeling, the paper applies a hierarchical estimation methodology in computational modeling of GRN based on topological analysis. This paper divides nodes in a network into various priority levels using the graph-based measure and genetic algorithm. The nodes in the first level, that correspond to root strongly connected components(SCC) in the digraph of GRN, are given top priority in parameter estimation. The estimated parameters of vertices in the previous priority level ARE used to infer the parameters for nodes in the next priority level. The proposed hierarchical estimation methodology obtains lower error indexes while consuming less computational resources compared with single estimation methodology. Experimental outcomes with insilico networks and a realistic network show that gene networks are decomposed into no more than four levels, which is consistent with the properties of inherent modularity for GRN. In addition, the proposed hierarchical parameter estimation achieves a balance between computational efficiency and accuracy.