The large sensitivity, high reproducibility and essentially unlimited dynamic range of real-time PCR to measure gene expression in complex samples provides the opportunity for powerful multivariate and multiway studies of biological phenomena. In multiway studies samples are characterized by their expression profiles to monitor changes over time, effect of treatment, drug dosage etc. Here we perform a multiway study of the temporal response of four yeast Saccharomyces cerevisiae strains with different glucose uptake rates upon altered metabolic conditions.
We measured the expression of 18 genes as function of time after addition of glucose to four strains of yeast grown in ethanol. The data are analyzed by matrix-augmented PCA, which is a generalization of PCA for 3-way data, and the results are confirmed by hierarchical clustering and clustering by Kohonen self-organizing map. Our approach identifies gene groups that respond similarly to the change of nutrient, and genes that behave differently in mutant strains. Of particular interest is our finding that ADH4 and ADH6 show a behavior typical of glucose-induced genes, while ADH3 and ADH5 are repressed after glucose addition.
Multiway real-time PCR gene expression profiling is a powerful technique which can be utilized to characterize functions of new genes by, for example, comparing their temporal response after perturbation in different genetic variants of the studied subject. The technique also identifies genes that show perturbed expression in specific strains.
Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multiway interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for networks having such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here, we propose using hypergraphs to capture the uncertainty inherent in reverse engineering gene-gene networks. Some subsets of nodes may induce highly varying subgraphs across an ensemble of networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic data sets with high precision and recall, even for moderate amount of noise. We apply our techniques to a data set of pathways inferred from genetic interaction data in S. cerevisiae related to the unfolded protein response. Our approach discovers several hyperedges that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. We also show that these complexes are not discovered by an algorithm that computes frequent and dense subgraphs.
Biology and genetics; hypergraphs; graphs and networks
Computational modeling of genomic regulation has become an important focus of systems biology and genomic signal processing for the past several years. It holds the promise to uncover both the structure and dynamical properties of the complex gene, protein or metabolic networks responsible for the cell functioning in various contexts and regimes. This, in turn, will lead to the development of optimal intervention strategies for prevention and control of disease. At the same time, constructing such computational models faces several challenges. High complexity is one of the major impediments for the practical applications of the models. Thus, reducing the size/complexity of a model becomes a critical issue in problems such as model selection, construction of tractable subnetwork models, and control of its dynamical behavior. We focus on the reduction problem in the context of two specific models of genomic regulation: Boolean networks with perturbation (BNP) and probabilistic Boolean networks (PBN). We also compare and draw a parallel between the reduction problem and two other important problems of computational modeling of genomic networks: the problem of network inference and the problem of designing external control policies for intervention/altering the dynamics of the model.
A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.
Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood.
Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results.
We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.
Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed.
systems biology; Arabidopsis; transcription factors; emergent properties; modeling; genomics
Cell-to-cell variability in protein expression can be large, and its propagation through signaling networks affects biological outcomes. Here, we apply deterministic and probabilistic models and biochemical measurements to study how network topologies and cell-to-cell protein abundance variations interact to shape signaling responses.
We observe bimodal distributions of extracellular signal-regulated kinase (ERK) responses to epidermal growth factor (EGF) stimulation, which are generally thought to indicate bistable or ultrasensitive signaling behavior in single cells. Surprisingly, we find that a simple MAPK/ERK-cascade model with negative feedback that displays graded, analog ERK responses at a single cell level can explain the experimentally observed bimodality at the cell population level. Model analysis suggests that a conversion of graded input–output responses in single cells to digital responses at the population level is caused by a broad distribution of ERK pathway activation thresholds brought about by cell-to-cell variability in protein expression.
Our results show that bimodal signaling response distributions do not necessarily imply digital (ultrasensitive or bistable) single cell signaling, and the interplay between protein expression noise and network topologies can bring about digital population responses from analog single cell dose responses. Thus, cells can retain the benefits of robustness arising from negative feedback, while simultaneously generating population-level on/off responses that are thought to be critical for regulating cell fate decisions.
Lack of understanding of endocrine resistance remains one of the major challenges for breast cancer researchers, clinicians, and patients. Current reductionist approaches to understanding the molecular signaling driving resistance have offered mostly incremental progress over the past 10 years. As the field of systems biology has begun to mature, the approaches and network modeling tools being developed and applied therein offer a different way to think about how molecular signaling and the regulation of critical cellular functions are integrated. To gain novel insights, we first describe some of the key challenges facing network modeling of endocrine resistance, many of which arise from the properties of the data spaces being studied. We then use activation of the unfolded protein response (UPR) following induction of endoplasmic reticulum stress in breast cancer cells by antiestrogens, to illustrate our approaches to computational modeling. Activation of UPR is a key determinant of cell fate decision making and regulation of autophagy and apoptosis. These initial studies provide insight into a small subnetwork topology obtained using differential dependency network analysis and focused on the UPR gene XBP1. The XBP1 subnetwork topology incorporates BCAR3, BCL2, BIK, NFκB, and other genes as nodes; the connecting edges represent the dependency structures amongst these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.
Antiestrogen; autophagy; apoptosis; breast cancer; cell signaling; endoplasmic reticulum; estrogens; gene networks; unfolded protein response; computational modeling; mathematical modeling; systems biology
Strains of the Mycobacterium tuberculosis complex (MTBC) can be classified into coherent lineages of similar traits based on their genotype. We present a tensor clustering framework to group MTBC strains into sublineages of the known major lineages based on two biomarkers: spacer oligonucleotide type (spoligotype) and mycobacterial interspersed repetitive units (MIRU). We represent genotype information of MTBC strains in a high-dimensional array in order to include information about spoligotype, MIRU, and their coexistence using multiple-biomarker tensors. We use multiway models to transform this multidimensional data about the MTBC strains into two-dimensional arrays and use the resulting score vectors in a stable partitive clustering algorithm to classify MTBC strains into sublineages. We validate clusterings using cluster stability and accuracy measures, and find stabilities of each cluster. Based on validated clustering results, we present a sublineage structure of MTBC strains and compare it to the sublineage structures of SpolDB4 and MIRU-VNTRplus.
Tuberculosis; Mycobacterium tuberculosis complex; multiway models; clustering; cluster validation
The measure of the quality of a systems biology model is how well it can reproduce and predict the behaviors of a biological system such as a microbial cell. In recent years, these models have been built up in layers, and each layer has been growing in sophistication and accuracy in parallel with a global data set to challenge and validate the models in predicting the content or activities of genes (genomics), proteins (proteomics), metabolites (metabolomics), and ultimately cell phenotypes (phenomics). This review focuses on the latter, the phenotypes of microbial cells. The development of Phenotype MicroArrays, which attempt to give a global view of cellular phenotypes, is described. In addition to their use in fleshing out and validating systems biology models, there are many other uses of this global phenotyping technology in basic and applied microbiology research, which are also described.
phenotyping; phenomics; metabolomics; phenotypic analysis; cell phenotypes; phenotypic characterization
Genomic perturbations that challenge normal signaling at the pluripotent stage may trigger unforeseen ontogenic aberrancies. Anticipatory systems biology identification of transcriptome landscapes that underlie latent phenotypes would offer molecular diagnosis before the onset of symptoms. The purpose of this study was to assess the impact of calreticulin-deficient embryonic stem cell transcriptomes on molecular functions and physiological systems. Bioinformatic surveillance of calreticulin-null stem cells, a monogenic insult model, diagnosed a disruption in transcriptome dynamics, which re-prioritized essential cellular functions. Calreticulin-calibrated signaling axes were uncovered, and network-wide cartography of undifferentiated stem cell transcripts suggested cardiac manifestations. Calreticulin-deficient stem cell-derived cardiac cells verified disorganized sarcomerogenesis, mitochondrial paucity, and cytoarchitectural aberrations to validate calreticulin-dependent network forecasts. Furthermore, magnetic resonance imaging and histopathology detected a ventricular septal defect, revealing organogenic manifestation of calreticulin deletion. Thus, bioinformatic deciphering of a primordial calreticulin-deficient transcriptome decoded at the pluripotent stem cell stage a reconfigured multifunctional molecular registry to anticipate predifferentiation susceptibility toward abnormal cardiophenotype.
Cardiogenesis; Cardiopoiesis; Network biology; Transcriptome; Pluripotent stem cells; Predictive medicine
Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point process regression models of individual-neuron activity with loglinear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then go on to assess the amount of data needed to reliably detect multiway spiking.
functional connectivity; loglinear models; multiple spike train analysis
One of the goals in the field of synthetic biology is the construction of cellular computation devices that could function in a manner similar to electronic circuits. To this end, attempts are made to create biological systems that function as logic gates. In this work we present a theoretical quantitative analysis of a synthetic cellular logic-gates system, which has been implemented in cells of the yeast Saccharomyces cerevisiae (Regot et al., 2011). It exploits endogenous MAP kinase signaling pathways. The novelty of the system lies in the compartmentalization of the circuit where all basic logic gates are implemented in independent single cells that can then be cultured together to perform complex logic functions. We have constructed kinetic models of the multicellular IDENTITY, NOT, OR, and IMPLIES logic gates, using both deterministic and stochastic frameworks. All necessary model parameters are taken from literature or estimated based on published kinetic data, in such a way that the resulting models correctly capture important dynamic features of the included mitogen-activated protein kinase pathways. We analyze the models in terms of parameter sensitivity and we discuss possible ways of optimizing the system, e.g., by tuning the culture density. We apply a stochastic modeling approach, which simulates the behavior of whole populations of cells and allows us to investigate the noise generated in the system; we find that the gene expression units are the major sources of noise. Finally, the model is used for the design of system modifications: we show how the current system could be transformed to operate on three discrete values.
synthetic biology; mathematical model; yeast; pheromone pathway; HOG pathway
Due to its genetic tractability and increasing wealth of accessible data, the yeast Saccharomyces cerevisiae is a model system of choice for the study of the genetics, biochemistry, and cell biology of eukaryotic lipid metabolism. Glycerolipids (e.g., phospholipids and triacylglycerol) and their precursors are synthesized and metabolized by enzymes associated with the cytosol and membranous organelles, including endoplasmic reticulum, mitochondria, and lipid droplets. Genetic and biochemical analyses have revealed that glycerolipids play important roles in cell signaling, membrane trafficking, and anchoring of membrane proteins in addition to membrane structure. The expression of glycerolipid enzymes is controlled by a variety of conditions including growth stage and nutrient availability. Much of this regulation occurs at the transcriptional level and involves the Ino2–Ino4 activation complex and the Opi1 repressor, which interacts with Ino2 to attenuate transcriptional activation of UASINO-containing glycerolipid biosynthetic genes. Cellular levels of phosphatidic acid, precursor to all membrane phospholipids and the storage lipid triacylglycerol, regulates transcription of UASINO-containing genes by tethering Opi1 to the nuclear/endoplasmic reticulum membrane and controlling its translocation into the nucleus, a mechanism largely controlled by inositol availability. The transcriptional activator Zap1 controls the expression of some phospholipid synthesis genes in response to zinc availability. Regulatory mechanisms also include control of catalytic activity of glycerolipid enzymes by water-soluble precursors, products and lipids, and covalent modification of phosphorylation, while in vivo function of some enzymes is governed by their subcellular location. Genome-wide genetic analysis indicates coordinate regulation between glycerolipid metabolism and a broad spectrum of metabolic pathways.
Genome-scale studies hold great promise for revealing novel plant biology. Because of the complexity of these techniques, numerous considerations need to be made before embarking on a study. Here we focus on the Arabidopsis model system because of the wealth of available genome-scale data. Many approaches are available that provide genome-scale information regarding the state of a given organism (e.g. genomics, epigenomics, transcriptomics, proteomics, metabolomics interactomics, ionomics, phenomics, etc.). Integration of all of these types of data will be necessary for a comprehensive description of Arabidopsis. In this review we propose that ‘triangulation’ among transcriptomics, proteomics and metabolomics is a meaningful approach for beginning this integrative analysis and uncovering a systems level perspective of Arabidopsis biology.
Erwin Schrödinger pointed out in his 1944 book “What is Life” that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.
reductionism; emergentism; systems biology; self-organization; agent-based modeling; tissue morphogenesis; early carcinogenesis
Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by “phase space diagrams”, where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system s dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent “omics descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.
system biology; cancer; fractal analysis; complexity; carcinogenesis; non linear dynamics
Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.
In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.
Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.
Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.
The rapid advancement in basic biology knowledge, especially in the stem cell field, has created new opportunities to develop biomaterials capable of orchestrating the behavior of transplanted and host cells. Based on our current understanding of cellular differentiation, a conceptual framework for the use of materials to program cells in situ is presented, namely a domino versus a switchboard model, to highlight the use of single versus multiple cues in a controlled manner to modulate biological processes. Further, specific design principles of material systems to present soluble and insoluble cues that are capable of recruiting, programming and deploying host cells for various applications are presented. The evolution of biomaterials from simple inert substances used to fill defects, to the recent development of sophisticated material systems capable of programming cells in situ is providing a platform to translate our understanding of basic biological mechanisms to clinical care.
Biomaterials; scaffolds; stem cells; translational medicine; tissue engineering
Current therapy for sickle cell disease (SCD) is limited to supportive treatment of complications, red blood cell transfusions, hydroxyurea, and stem cell transplantation. Difficulty in the translation of mechanistically based therapies may be the result of a reductionist approach focused on individual pathways, without having demonstrated their relative contribution to SCD complications. Many pathophysiologic processes in SCD are likely to interact simultaneously to contribute to acute vaso-occlusion or chronic vasculopathy. Applying concepts of systems biology and network medicine, models were developed to show relationships between the primary defect of sickle hemoglobin (Hb S) polymerization and the outcomes of acute pain and chronic vasculopathy. Pathophysiologic processes such as inflammation and oxidative stress are downstream by-products of Hb S polymerization, transduced through secondary pathways of hemolysis and vaso-occlusion. Pain, a common clinical trials endpoint, is also complex and may be influenced by factors outside of sickle cell polymerization and vascular occlusion. Future sickle cell research needs to better address the biologic complexity of both sickle cell disease and pain. The relevance of individual pathways to important sickle cell outcomes needs to be demonstrated in vivo before investing in expensive and labor-intensive clinical trials.
The mythological story of the Golden Fleece symbolizes the magical regenerative power of skin appendages. Similar to the adventurous pursuit of the Golden Fleece by the multi-talented Argonauts, today we also need an integrated multi-disciplined approach to understand the cellular and molecular processes during development, regeneration and evolution of skin appendages. To this end, we have explored several aspects of skin appendage biology that contribute to the Turing activator / inhibitor model in feather pattern formation, the topo-biological arrangement of stem cells in organ shape determination, the macro-environmental regulation of stem cells in regenerative hair waves, and potential novel molecular pathways in the morphological evolution of feathers. Here we show our current integrative biology efforts to unravel the complex cellular behavior in patterning stem cells and the control of regional specificity in skin appendages. We use feather / scale tissue recombination to demonstrate the timing control of competence and inducibility. Feathers from different body regions are used to study skin regional specificity. Bioinformatic analyses of transcriptome microarrays show the potential involvement of candidate molecular pathways. We further show Hox genes exhibit some region specific expression patterns. To visualize real time events, we applied time-lapse movies, confocal microscopy and multiphoton microscopy to analyze the morphogenesis of cultured embryonic chicken skin explants. These modern imaging technologies reveal unexpectedly complex cellular flow and organization of extracellular matrix molecules in three dimensions. While these approaches are in preliminary stages, this perspective highlights the challenges we face and new integrative tools we will use. Future work will follow these leads to develop a systems biology view and understanding in the morphogenetic principles that govern the development and regeneration of ectodermal organs.
feathers; hairs; systems biology; stem cells; regeneration; microarray analyses; imaging; multiphoton microscopy; regional specificity
The use of functional genomics has largely increased our understanding of cell biology and promises to help the development of systems biology needed to understand the complex order of events that regulates cellular differentiation in vivo. One model system clearly dependent on the integration of extra and intra cellular signals is the development of B-lymphocytes from hematopoietic stem cells in the bone marrow. This developmental pathway involves several defined differentiation stages associated with specific expression of genes including surface markers that can be used for the prospective isolation of the progenitor cells directly from the bone marrow to allow for ex vivo gene expression analysis. The developmental process can be simulated in vitro making it possible to dissect information about cell/cell communication as well as to address the relevance of communication pathways in a rather direct manner. Thus we believe that B-lymphocyte development represents a useful model system to take the first steps towards systems biology investigations in the bone marrow.
In order to identify extra cellular signals that promote B lymphocyte development we created a database with approximately 400 receptor ligand pairs and software matching gene expression data from two cell populations to obtain information about possible communication pathways. Using this database and gene expression data from NIH3T3 cells (unable to support B cell development), OP-9 cells (strongly supportive of B cell development), pro-B and pre-B cells as well as mature peripheral B-lineage cells, we were able to identify a set of potential stage and stromal cell restricted communication pathways. Functional analysis of some of these potential ways of communication allowed us to identify BMP-4 as a potent stimulator of B-cell development in vitro. Further, the analysis suggested that there existed possibilities for progenitor B cells to send signals to the stroma. The functional consequences of this were investigated by co-culture experiments revealing that the co-incubation of stromal cells with B cell progenitors altered both the morphology and the gene expression pattern in the stromal cells.
We believe that this gene expression data analysis method allows for the identification of functionally relevant interactions and therefore could be applied to other data sets to unravel novel communication pathways.
Calcium is a universal signaling molecule with a central role in a number of vascular functions including in the regulation of tone and blood flow. Experimentation has provided insights into signaling pathways that lead to or affected by Ca2+ mobilization in the vasculature. Mathematical modeling offers a systematic approach to the analysis of these mechanisms and can serve as a tool for data interpretation and for guiding new experimental studies. Comprehensive models of calcium dynamics are well advanced for some systems such as the heart. This review summarizes the progress that has been made in modeling Ca2+ dynamics and signaling in vascular cells. Model simulations show how Ca2+ signaling emerges as a result of complex, nonlinear interactions that cannot be properly analyzed using only a reductionist's approach. A strategy of integrative modeling in the vasculature is outlined that will allow linking macroscale pathophysiological responses to the underlying cellular mechanisms.
In this note we illustrate on a few examples of cells and proteins behavior that microscopic biological systems can exhibit a complex probabilistic behavior which cannot be described by classical probabilistic dynamics. These examples support authors conjecture that behavior of microscopic biological systems can be described by quantum-like models, i.e., models inspired by quantum-mechanics. At the same time we do not couple quantum-like behavior with quantum physical processes in bio-systems. We present arguments that such a behavior can be induced by information complexity of even smallest bio-systems, their adaptivity to context changes. Although our examples of the quantum-like behavior are rather simple (lactose-glucose interference in E. coli growth, interference effect for differentiation of tooth stem cell induced by the presence of mesenchymal cell, interference in behavior of PrPC and PrPSc prions), these examples may stimulate the interest in systems biology to quantum-like models of adaptive dynamics and lead to more complex examples of nonclassical probabilistic behavior in molecular biology.
Quantum-like interference; Nonclassical probabilistic behavior; Lactose-glucose interference; E. coli growth; Differentiation of tooth stem cell; Mesenchymal cell; Interference of PrPC and PrPSc prions
Mathematical modeling of signaling and gene regulatory networks has provided unique insights about systems behaviors for many cell biological problems of medical importance. Quantitative single cell monitoring has a crucial role in advancing systems modeling of molecular networks. However, due to the multidisciplinary techniques that are necessary for adaptation of such systems biology approaches, dissemination to a wide research community has been relatively slow. In this essay, I focus on some technical aspects that are often under-appreciated, yet critical in harnessing live cell imaging methods to achieve single-cell-level understanding and quantitative modeling of molecular networks. The importance of these technical considerations will be elaborated with examples of successes and shortcomings. Future efforts will benefit by avoiding some pitfalls and by utilizing the lessons collectively learned from recent applications of imaging in systems biology.
quantitative microscopy; live cell imaging; fluorescent proteins; mathematical modeling; network models