Roles of positive and negative feedback in biological systems

Canalizing kernel for cell fate determination

The tendency for cell fate to be robust to most perturbations, yet sensitive to certain perturbations raises intriguing questions about the existence of a key path within the underlying molecular network that critically determines distinct cell fates. Reprogramming and trans-differentiation clearly show examples of cell fate change by regulating only a few or even a single molecular switch. However, it is still unknown how to identify such a switch, called a master regulator, and how cell fate is determined by its regulation. Here, we present CAESAR, a computational framework that can systematically identify master regulators and unravel the resulting canalizing kernel, a key substructure of interconnected feedbacks that is critical for cell fate determination. We demonstrate that CAESAR can successfully predict reprogramming factors for de-differentiation into mouse embryonic stem cells and trans-differentiation of hematopoietic stem cells, while unveiling the underlying essential mechanism through the canalizing kernel. CAESAR provides a system-level understanding of how complex molecular networks determine cell fates.

Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson's disease in an Egyptian cohort

Introduction: Parkinson's disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls. Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH). Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease.

Alternations of vitamin D and cognitive function in first-diagnosed and drug-naïve BD patients: Physical activity as a moderator

BACKGROUND

The pathophysiological mechanism of cognitive impairments of bipolar disorder (BD) has not yet been completely revealed. It is well known that Vitamin D and physical activity (PA) are associated with BD. However, specific links between Vitamin D and cognitive deficits in BD are still unclear.

METHOD

The serum levels of vitamin D were measured. The cognitive performances of 102 first-diagnosed and drug-naïve BD patients were evaluated for analysis. The repeatable battery for the assessment of neuropsychological status (RBANS) and the Stroop Color-Word test was used in this study. PA was collected by international physical activity questionnaire.

RESULT

Patients with BD had high levels of serum vitamin D. Furthermore, immediate and delayed memory was negatively associated with vitamin D levels in patients' group. The serum levels of vitamin D in patients with low PA were positively associated with memory. However, increased PA attenuated the protective effect of vitamin D on executive cognition.

CONCLUSION

It is concluded that the increased levels of vitamin D were observed in the serum of patients with BD. Thus, it is found that more PA is less beneficial to cognition of patients with BD than longer resting.

Targeting cancer-specific metabolic pathways for developing novel cancer therapeutics

Cancer is a heterogeneous disease characterized by various genetic and phenotypic aberrations. Cancer cells undergo genetic modifications that promote their proliferation, survival, and dissemination as the disease progresses. The unabated proliferation of cancer cells incurs an enormous energy demand that is supplied by metabolic reprogramming. Cancer cells undergo metabolic alterations to provide for increased energy and metabolite requirement; these alterations also help drive the tumor progression. Dysregulation in glucose uptake and increased lactate production via "aerobic glycolysis" were described more than 100 years ago, and since then, the metabolic signature of various cancers has been extensively studied. However, the extensive research in this field has failed to translate into significant therapeutic intervention, except for treating childhood-ALL with amino acid metabolism inhibitor L-asparaginase. Despite the growing understanding of novel metabolic alterations in tumors, the therapeutic targeting of these tumor-specific dysregulations has largely been ineffective in clinical trials. This chapter discusses the major pathways involved in the metabolism of glucose, amino acids, and lipids and highlights the inter-twined nature of metabolic aberrations that promote tumorigenesis in different types of cancer. Finally, we summarise the therapeutic interventions which can be used as a combinational therapy to target metabolic dysregulations that are unique or common in blood, breast, colorectal, lung, and prostate cancer.

Alterations in cellular metabolisms after TKI therapy for Philadelphia chromosome-positive leukemia in children: A review

Incidence rates of chronic myeloid leukemia (CML) and Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukemia (ALL) are lower but more aggressive in children than in adults due to different biological and host factors. After the clinical application of tyrosine kinase inhibitor (TKI) blocking BCR/ABL kinase activity, the prognosis of children with CML and Ph+ ALL has improved dramatically. Yet, off-target effects and drug tolerance will occur during the TKI treatments, contributing to treatment failure. In addition, compared to adults, children may need a longer course of TKIs therapy, causing detrimental effects on growth and development. In recent years, accumulating evidence indicates that drug resistance and side effects during TKI treatment may result from the cellular metabolism alterations. In this review, we provide a detailed summary of the current knowledge on alterations in metabolic pathways including glucose metabolism, lipid metabolism, amino acid metabolism, and other metabolic processes. In order to obtain better TKI treatment outcomes and avoid side effects, it is essential to understand how the TKIs affect cellular metabolism. Hence, we also discuss the relevance of cellular metabolism in TKIs therapy to provide ideas for better use of TKIs in clinical practice.

Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change

Endocrine regulation in the hypothalamic-pituitary-thyroid (HPT) axis is orchestrated by physiological circuits which integrate multiple internal and external influences. Essentially, it provides either of the two responses to overt biological challenges: to defend the homeostatic range of a target hormone or adapt it to changing environmental conditions. Under certain conditions, such flexibility may exceed the capability of a simple feedback control loop, rather requiring more intricate networks of communication between the system's components. A new minimal mathematical model, in the form of a parametrized nonlinear dynamical system, is here formulated as a proof-of-concept to elucidate the principles of the HPT axis regulation. In particular, it allows uncovering mechanisms for the homeostasis of the key biologically active hormone free triiodothyronine (FT3). One mechanism supports the preservation of FT3 homeostasis, whilst the other is responsible for the adaptation of the homeostatic state to a new level. Together these allow optimum resilience in stressful situations. Preservation of FT3 homeostasis, despite changes in FT4 and TSH levels, is found to be an achievable system goal by joining elements of top-down and bottom-up regulation in a cascade of targeted feedforward and feedback loops. Simultaneously, the model accounts for the combination of properties regarded as essential to endocrine regulation, namely sensitivity, the anticipation of an adverse event, robustness, and adaptation. The model therefore offers fundamental theoretical insights into the effective system control of the HPT axis.

LncRNAs driving feedback loops to boost drug resistance: sinuous pathways in cancer

Feedback loops mediate signaling pathways to maintain cellular homeostasis. There are two types, positive and negative feedback loops. Both are subject to alterations, and consequently can become pathogenic in the development of diseases such as cancer. Long noncoding RNAs (lncRNAs) are regulators of signaling pathways through feedback loops hidden as the dark regulatory elements yet to be described with great impact on cancer tumorigenesis, development, and drug resistance. Several feedback loops have been studied in cancer, however, how they are regulated by lncRNAs is hardly evident, setting a trending topic in oncological research. In this review, we recapitulate and discuss the feedback loops that are regulated by lncRNAs to promote drug resistance. Furthermore, we propose additional strategies that allow us to identify, analyze and comprehend feedback loops regulated by lncRNAs to induce drug resistance or even to gain insight into novel feedback loops that are stimulated under the pressure of treatment and consequently increase its efficacy. This knowledge will be useful to optimize the therapeutic use of oncological drugs.

Controlled bio-inspired self-organised criticality

Complex biological systems are considered to be controlled using feedback mechanisms. Reduced systems modelling has been effective to describe these mechanisms, but this approach does not sufficiently encompass the required complexity that is needed to understand how localised control in a biological system can provide global stable states. Self-Organised Criticality (SOC) is a characteristic property of locally interacting physical systems, which readily emerges from changes to its dynamic state due to small nonlinear perturbations. These small changes in the local states, or in local interactions, can greatly affect the total system state of critical systems. It has long been conjectured that SOC is cardinal to biological systems, that show similar critical dynamics, and also may exhibit near power-law relations. Rate Control of Chaos (RCC) provides a suitable robust mechanism to generate SOC systems, which operates at the edge of chaos. The bio-inspired RCC method requires only local instantaneous knowledge of some of the variables of the system, and is capable of adapting to local perturbations. Importantly, connected RCC controlled oscillators can maintain global multi-stable states, and domains where power-law relations may emerge. The network of oscillators deterministically stabilises into different orbits for different perturbations, and the relation between the perturbation and amplitude can show exponential and power-law correlations. This can be considered to be representative of a basic mechanism of protein production and control, that underlies complex processes such as homeostasis. Providing feedback from the global state, the total system dynamic behaviour can be boosted or reduced. Controlled SOC can provide much greater understanding of biological control mechanisms, that are based on distributed local producers, with remote consumers of biological resources, and globally defined control.

Emerging roles of nucleotide metabolism in cancer development: progress and prospect

Abnormal cancer metabolism occurs throughout the development of tumors. Recent studies have shown that abnormal nucleotide metabolism not only accelerates the development of tumors but also inhibits the normal immune response in the tumor microenvironment. Although few relevant experiments and reports are available, study of the interaction between nucleotide metabolism and cancer development is rapidly developing. The intervention, alteration or regulation of molecular mechanisms related to abnormal nucleotide metabolism in tumor cells has become a new idea and strategy for the treatment of tumors and prevention of recurrence and metastasis. Determining how nucleotide metabolism regulates the occurrence and progression of tumors still needs long-term and extensive research and exploration.

Feedback Control of a Two-Component Signaling System by an Fe-S-Binding Receiver Domain

Two-component signaling systems (TCSs) function to detect environmental cues and transduce this information into a change in transcription. In its simplest form, TCS-dependent regulation of transcription entails phosphoryl-transfer from a sensory histidine kinase to its cognate DNA-binding receiver protein. However, in certain cases, auxiliary proteins may modulate TCSs in response to secondary environmental cues. Caulobacter crescentus FixT is one such auxiliary regulator. FixT is composed of a single receiver domain and functions as a feedback inhibitor of the FixL-FixJ (FixLJ) TCS, which regulates the transcription of genes involved in adaptation to microaerobiosis. We sought to define the impact of fixT on Caulobacter cell physiology and to understand the molecular mechanism by which FixT represses FixLJ signaling. fixT deletion results in excess production of porphyrins and premature entry into stationary phase, demonstrating the importance of feedback inhibition of the FixLJ signaling system. Although FixT is a receiver domain, it does not affect dephosphorylation of the oxygen sensor kinase FixL or phosphoryl-transfer from FixL to its cognate receiver FixJ. Rather, FixT represses FixLJ signaling by inhibiting the FixL autophosphorylation reaction. We have further identified a 4-cysteine motif in Caulobacter FixT that binds an Fe-S cluster and protects the protein from degradation by the Lon protease. Our data support a model in which the oxidation of this Fe-S cluster promotes the degradation of FixT in vivo This proteolytic mechanism facilitates clearance of the FixT feedback inhibitor from the cell under normoxia and resets the FixLJ system for a future microaerobic signaling event.IMPORTANCE Two-component signal transduction systems (TCSs) are broadly conserved in the bacterial kingdom and generally contain two molecular components, a sensor histidine kinase and a receiver protein. Sensor histidine kinases alter their phosphorylation state in direct response to a physical or chemical cue, whereas receiver proteins "receive" the phosphoryl group from the kinase to regulate a change in cell physiology. We have discovered that a single-domain receiver protein, FixT, binds an Fe-S cluster and controls Caulobacter heme homeostasis though its function as a negative-feedback regulator of the oxygen sensor kinase FixL. We provide evidence that the Fe-S cluster protects FixT from Lon-dependent proteolysis in the cell and endows FixT with the ability to function as a second, autonomous oxygen/redox sensor in the FixL-FixJ signaling pathway. This study introduces a novel mechanism of regulated TCS feedback control by an Fe-S-binding receiver domain.

Osmoregulation by the myo-inositol biosynthesis pathway in turbot Scophthalmus maximus and its regulation by anabolite and c-Myc

The induction of the myo-inositol biosynthesis (MIB) pathway in euryhaline fishes is an important component of the cellular response to osmotic challenge. The MIPS and IMPA1 genes were sequenced in turbot and found to be highly conserved in phylogenetic evolution, especially within the fish species tested. Under salinity stress in turbot, both MIPS and IMPA1 showed adaptive expression, a turning point in the level of expression occurred at 12 h in all tissues tested. We performed an RNAi assay mediated by long fragment dsRNA prepared by transcription in vitro. The findings demonstrated that knockdown of the MIB pathway weakened the function of gill osmotic regulation, and may induce a genetic compensation response in the kidney and gill to maintain physiological function. Even though the gill and kidney conducted stress reactions or compensatory responses to salinity stress, this inadequately addressed the consequences of MIB knockdown. Therefore, the survival time of turbot under salinity stress after knockdown was obviously less than that under seawater, especially under low salt stress. Pearson's correlation analysis between gene expression and dietary myo-inositol concentration indicated that the MIB pathway had a remarkable negative feedback control, and the dynamic equilibrium mediated by negative feedback on the MIB pathway played a crucial role in osmoregulation in turbot. An RNAi assay with c-Myc in vivo and the use of a c-Myc inhibitor (10058-F4) in vitro demonstrated that c-Myc was likely to positively regulate the MIB pathway in turbot.

Digital signaling network drives the assembly of the AIM2-ASC inflammasome

The AIM2-ASC inflammasome is a filamentous signaling platform essential for mounting host defense against cytoplasmic dsDNA arising not only from invading pathogens but also from damaged organelles. Currently, the design principles of its underlying signaling network remain poorly understood at the molecular level. We show here that longer dsDNA is more effective in inducing AIM2 assembly, its self-propagation, and downstream ASC polymerization. This observation is related to the increased probability of forming the base of AIM2 filaments, and indicates that the assembly discerns small dsDNA as noise at each signaling step. Filaments assembled by receptor AIM2, downstream ASC, and their joint complex all persist regardless of dsDNA, consequently generating sustained signal amplification and hysteresis. Furthermore, multiple positive feedback loops reinforce the assembly, as AIM2 and ASC filaments accelerate the assembly of nascent AIM2 with or without dsDNA. Together with a quantitative model of the assembly, our results indicate that an ultrasensitive digital circuit drives the assembly of the AIM2-ASC inflammasome.

Parametric Sensitivity Analysis of Oscillatory Delay Systems with an Application to Gene Regulation

A parametric sensitivity analysis for periodic solutions of delay-differential equations is developed. Because phase shifts cause the sensitivity coefficients of a periodic orbit to diverge, we focus on sensitivities of the extrema, from which amplitude sensitivities are computed, and of the period. Delay-differential equations are often used to model gene expression networks. In these models, the parametric sensitivities of a particular genotype define the local geometry of the evolutionary landscape. Thus, sensitivities can be used to investigate directions of gradual evolutionary change. An oscillatory protein synthesis model whose properties are modulated by RNA interference is used as an example. This model consists of a set of coupled delay-differential equations involving three delays. Sensitivity analyses are carried out at several operating points. Comments on the evolutionary implications of the results are offered.

Coherent organization in gene regulation: a study on six networks

Structural and dynamical fingerprints of evolutionary optimization in biological networks are still unclear. Here we analyze the dynamics of genetic regulatory networks responsible for the regulation of cell cycle and cell differentiation in three organisms or cell types each, and show that they follow a version of Hebb's rule which we have termed coherence. More precisely, we find that simultaneously expressed genes with a common target are less likely to act antagonistically at the attractors of the regulatory dynamics. We then investigate the dependence of coherence on structural parameters, such as the mean number of inputs per node and the activatory/repressory interaction ratio, as well as on dynamically determined quantities, such as the basin size and the number of expressed genes.

Complex systems dynamics in aging: new evidence, continuing questions

There have long been suggestions that aging is tightly linked to the complex dynamics of the physiological systems that maintain homeostasis, and in particular to dysregulation of regulatory networks of molecules. This review synthesizes recent work that is starting to provide evidence for the importance of such complex systems dynamics in aging. There is now clear evidence that physiological dysregulation—the gradual breakdown in the capacity of complex regulatory networks to maintain homeostasis—is an emergent property of these regulatory networks, and that it plays an important role in aging. It can be measured simply using small numbers of biomarkers. Additionally, there are indications of the importance during aging of emergent physiological processes, functional processes that cannot be easily understood through clear metabolic pathways, but can nonetheless be precisely quantified and studied. The overall role of such complex systems dynamics in aging remains an important open question, and to understand it future studies will need to distinguish and integrate related aspects of aging research, including multi-factorial theories of aging, systems biology, bioinformatics, network approaches, robustness, and loss of complexity.

Coherent regulation in yeast's cell-cycle network

We define a measure of coherent activity for gene regulatory networks, a property that reflects the unity of purpose between the regulatory agents with a common target. We propose that such harmonious regulatory action is desirable under a demand for energy efficiency and may be selected for under evolutionary pressures. We consider two recent models of the cell-cycle regulatory network of the yeast, Saccharomyces cerevisiae as a case study and calculate their degree of coherence. A comparison with random networks of similar size and composition reveals that the yeast's cell-cycle regulation is wired to yield an exceptionally high level of coherent regulatory activity. We also investigate the mean degree of coherence as a function of the network size, connectivity and the fraction of repressory/activatory interactions.

Stability, complexity and robustness in population dynamics

The problem of stability in population dynamics concerns many domains of application in demography, biology, mechanics and mathematics. The problem is highly generic and independent of the population considered (human, animals, molecules,…). We give in this paper some examples of population dynamics concerning nucleic acids interacting through direct nucleic binding with small or cyclic RNAs acting on mRNAs or tRNAs as translation factors or through protein complexes expressed by genes and linked to DNA as transcription factors. The networks made of these interactions between nucleic acids (considered respectively as edges and nodes of their interaction graph) are complex, but exhibit simple emergent asymptotic behaviours, when time tends to infinity, called attractors. We show that the quantity called attractor entropy plays a crucial role in the study of the stability and robustness of such genetic networks.

Dynamic processes in regulation and some implications for biofeedback and biobehavioral interventions

Systems theory has long been used in psychology, biology, and sociology. This paper applies newer methods of control systems modeling for assessing system stability in health and disease. Control systems can be characterized as open or closed systems with feedback loops. Feedback produces oscillatory activity, and the complexity of naturally occurring oscillatory patterns reflects the multiplicity of feedback mechanisms, such that many mechanisms operate simultaneously to control the system. Unstable systems, often associated with poor health, are characterized by absence of oscillation, random noise, or a very simple pattern of oscillation. This modeling approach can be applied to a diverse range of phenomena, including cardiovascular and brain activity, mood and thermal regulation, and social system stability. External system stressors such as disease, psychological stress, injury, or interpersonal conflict may perturb a system, yet simultaneously stimulate oscillatory processes and exercise control mechanisms. Resonance can occur in systems with negative feedback loops, causing high-amplitude oscillations at a single frequency. Resonance effects can be used to strengthen modulatory oscillations, but may obscure other information and control mechanisms, and weaken system stability. Positive as well as negative feedback loops are important for system function and stability. Examples are presented of oscillatory processes in heart rate variability, and regulation of autonomic, thermal, pancreatic and central nervous system processes, as well as in social/organizational systems such as marriages and business organizations. Resonance in negative feedback loops can help stimulate oscillations and exercise control reflexes, but also can deprive the system of important information. Empirical hypotheses derived from this approach are presented, including that moderate stress may enhance health and functioning.

Acetylcholine promotes Ca2+ and NO-oscillations in adipocytes implicating Ca2+→NO→cGMP→cADP-ribose→Ca2+ positive feedback loop--modulatory effects of norepinephrine and atrial natriuretic peptide

PURPOSE

This study investigated possible mechanisms of autoregulation of Ca(2+) signalling pathways in adipocytes responsible for Ca(2+) and NO oscillations and switching phenomena promoted by acetylcholine (ACh), norepinephrine (NE) and atrial natriuretic peptide (ANP).

METHODS

Fluorescent microscopy was used to detect changes in Ca(2+) and NO in cultures of rodent white adipocytes. Agonists and inhibitors were applied to characterize the involvement of various enzymes and Ca(2+)-channels in Ca(2+) signalling pathways.

RESULTS

ACh activating M3-muscarinic receptors and Gβγ protein dependent phosphatidylinositol 3 kinase induces Ca(2+) and NO oscillations in adipocytes. At low concentrations of ACh which are insufficient to induce oscillations, NE or α1, α2-adrenergic agonists act by amplifying the effect of ACh to promote Ca(2+) oscillations or switching phenomena. SNAP, 8-Br-cAMP, NAD and ANP may also produce similar set of dynamic regimes. These regimes arise from activation of the ryanodine receptor (RyR) with the implication of a long positive feedback loop (PFL): Ca(2+)→NO→cGMP→cADPR→Ca(2+), which determines periodic or steady operation of a short PFL based on Ca(2+)-induced Ca(2+) release via RyR by generating cADPR, a coagonist of Ca(2+) at the RyR. Interplay between these two loops may be responsible for the observed effects. Several other PFLs, based on activation of endothelial nitric oxide synthase or of protein kinase B by Ca(2+)-dependent kinases, may reinforce functioning of main PFL and enhance reliability. All observed regimes are independent of operation of the phospholipase C/Ca(2+)-signalling axis, which may be switched off due to negative feedback arising from phosphorylation of the inositol-3-phosphate receptor by protein kinase G.

CONCLUSIONS

This study presents a kinetic model of Ca(2+)-signalling system operating in adipocytes and integrating signals from various agonists, which describes it as multivariable multi feedback network with a family of nested positive feedback.

"Immunetworks", intersecting circuits and dynamics

This paper proposes a study of biological regulation networks based on a multi-level strategy. Given a network, the first structural level of this strategy consists in analysing the architecture of the network interactions in order to describe it. The second dynamical level consists in relating the patterns found in the architecture to the possible dynamical behaviours of the network. It is known that circuits are the patterns that play the most important part in the dynamics of a network in the sense that they are responsible for the diversity of its asymptotic behaviours. Here, we pursue further this idea and argue that beyond the influence of underlying circuits, intersections of circuits also impact significantly on the dynamics of a network and thus need to be payed special attention to. For some genetic regulation networks involved in the control of the immune system ("immunetworks"), we show that the small number of attractors can be explained by the presence, in the underlying structures of these networks, of intersecting circuits that "inter-lock".

Modeling metabolic adjustment in Mycobacterium tuberculosis upon treatment with isoniazid

Complex biological systems exhibit a property of robustness at all levels of organization. Through different mechanisms, the system tries to sustain stress such as due to starvation or drug exposure. To explore whether reconfiguration of the metabolic networks is used as a means to achieve robustness, we have studied possible metabolic adjustments in Mtb upon exposure to isoniazid (INH), a front-line clinical drug. The redundancy in the genome of M. tuberculosis (Mtb) makes it an attractive system to explore if alternate routes of metabolism exist in the bacterium. While the mechanism of action of INH is well studied, its effect on the overall metabolism is not well characterized. Using flux balance analysis, inhibiting the fluxes flowing through the reactions catalyzed by Rv1484, the target of INH, significantly changes the overall flux profiles. At the pathway level, activation or inactivation of certain pathways distant from the target pathway, are seen. Metabolites such as NADPH are shown to reduce drastically, while fatty acids tend to accumulate. The overall biomass also decreases with increasing inhibition levels. Inhibition studies, pathway level clustering and comparison of the flux profiles with the gene expression data indicate the activation of folate metabolism, ubiquinone metabolism, and metabolism of certain amino acids. This analysis provides insights useful for target identification and designing strategies for combination therapy. Insights gained about the role of individual components of a system and their interactions will also provide a basis for reconstruction of whole systems through synthetic biology approaches.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11693-011-9075-6) contains supplementary material, which is available to authorized users.

[Medical applications of systems biology]

We illustrate in this review some applications of systems biology in the medical and biological areas. After a brief summary of time scales experienced by medical -observations and of the general scheme of dynamic systems, we describe how some techniques underlying the complex systems theory can be applied to model medical issues in immunology, medical genetics, developmental morphogenesis, biochemistry, epidemiology, telemedecine and multiple platforms of expertise. In concluding, we will discuss the issue of "clinomics" coupling clinical and -omics data in a unique patient-specific file.

Synchrony in reaction-diffusion models of morphogenesis: applications to curvature-dependent proliferation and zero-diffusion front waves

The paper presents the classical age-dependent approach of the morphogenesis in the framework of the von Foerster equation, in which we introduce a new constraint and study a new feature: (i) the new constraint concerns cell proliferation along the contour lines of the cell density, depending on the local curvature such as it favours the amplification of the concavities (like in the gastrulation process) and (ii) the new feature consists of considering, on the cell density surface, a remarkable line (the null mean Gaussian curvature line), on which the normal diffusion vanishes, favouring local coexistence of diffusing morphogens, metabolites or cells, and hence the auto-assemblages of these entities. Two applications to biological multi-agents systems are studied, gastrulation and feather morphogenesis.

Robustness in regulatory interaction networks. A generic approach with applications at different levels: physiologic, metabolic and genetic

Regulatory interaction networks are often studied on their dynamical side (existence of attractors, study of their stability). We focus here also on their robustness, that is their ability to offer the same spatiotemporal patterns and to resist to external perturbations such as losses of nodes or edges in the networks interactions architecture, changes in their environmental boundary conditions as well as changes in the update schedule (or updating mode) of the states of their elements (e.g., if these elements are genes, their synchronous coexpression mode versus their sequential expression). We define the generic notions of boundary, core, and critical vertex or edge of the underlying interaction graph of the regulatory network, whose disappearance causes dramatic changes in the number and nature of attractors (e.g., passage from a bistable behaviour to a unique periodic regime) or in the range of their basins of stability. The dynamic transition of states will be presented in the framework of threshold Boolean automata rules. A panorama of applications at different levels will be given: brain and plant morphogenesis, bulbar cardio-respiratory regulation, glycolytic/oxidative metabolic coupling, and eventually cell cycle and feather morphogenesis genetic control.

Bistability in a model of mesoderm and anterior mesendoderm specification in Xenopus laevis

In this paper we develop a model of mesendoderm specification in Xenopus laevis based on an existing gene regulation network. The mesendoderm is a population of cells that may contribute to either the mesoderm or endoderm. The model that we develop encompasses the time evolution of transcription factor concentrations in a single cell and is shown to have stable steady states that correspond to mesoderm and anterior mesendodermal cell types, but not endoderm (except in cells where Goosecoid expression is inhibited). Both in vitro and in vivo versions of the model are developed and analysed, the former indicating how cell fate is determined in large part by the concentration of Activin administered to a cell, with the model results comparing favourably with current quantitative experimental data. A numerical investigation of the in vivo model suggests that cell fate is determined largely by a VegT and beta-Catenin pre-pattern, subsequently being reinforced by Nodal. We argue that this sensitivity of the model to a VegT and beta-Catenin pre-pattern indicates that a key VegT self-limiting mechanism (for which there is experimental evidence) is absent from the model. Furthermore, we find that the lack of a steady state corresponding to endoderm is entirely consistent with current in vivo data, and that the in vivo model corresponds to mesendoderm specification on the dorsal, but not the ventral, side of the embryo.

The impact of time delays on the robustness of biological oscillators and the effect of bifurcations on the inverse problem

Differential equation models for biological oscillators are often not robust with respect to parameter variations. They are based on chemical reaction kinetics, and solutions typically converge to a fixed point. This behavior is in contrast to real biological oscillators, which work reliably under varying conditions. Moreover, it complicates network inference from time series data. This paper investigates differential equation models for biological oscillators from two perspectives. First, we investigate the effect of time delays on the robustness of these oscillator models. In particular, we provide sufficient conditions for a time delay to cause oscillations by destabilizing a fixed point in two-dimensional systems. Moreover, we show that the inclusion of a time delay also stabilizes oscillating behavior in this way in larger networks. The second part focuses on the inverse problem of estimating model parameters from time series data. Bifurcations are related to nonsmoothness and multiple local minima of the objective function.

Deactivation of Src family kinases: hypothesis testing using a Monte Carlo sensitivity analysis of systems-level properties

Src family tyrosine kinases play a key role in many cellular signalling networks, but due to the high complexity of these networks their precise function remains elusive. Many factors involved in Src regulation, such as specific kinases and phosphatases, are still unknown. Mathematical models have been constructed to improve the understanding of the system and its dynamic behavior. Using a computational random parameter search, we characterized and compared the dynamics of three alternative models in order to assess their likelihoods. For this, we investigated how systems-level properties such as bistability and excitable behavior relate to kinetic and physiological parameters and how robust these properties were. Our results suggest the existence of a putative negative feedback loop in the Src system. A previously suggested role for PTPalpha in the deactivation of Src was not supported by the model.

De-stabilization of the positive vago-vagal reflex in bulimia nervosa

Bulimia nervosa is characterized by consuming large amounts of food over a defined period with a loss of control over the eating. This is followed by a compensatory behavior directed at eliminating the consumed calories, usually vomiting. Current treatments include antidepressants and/or behavioral therapies. Consensus exists that these treatments are not very effective and are associated with high relapse rates. We review evidence from literature and present original data to evaluate the hypothesis that bulimia involves alterations in vago-vagal function. Evidence in support of this include (1) laboratory studies consistently illustrate deficits in meal size, meal termination, and satiety in bulimia; (2) basic science studies indicate that meal size and satiation are under vagal influences; (3) anatomical, behavioral and physiological data suggest that achieving satiety and the initiation of emesis involve common neural substrates; (4) abnormal vagal and vago-vagal reflexive functions extend to non-eating activational stimuli; and (5) studies from our laboratory modulating vagal activation have shown significant effects on binge/vomit frequencies and suggest a return of normal satiation. We propose a model for the pathophysiology of bulimia based upon de-stabilization of a bi-stable positive vago-vagal feedback loop. This model is not meant to be complete, but rather to stimulate anatomical, psychobiological, and translational neuroscience experiments aimed at elucidating the pathophysiology of bulimia and developing novel treatment strategies.

Graph-theoretic methods for the analysis of chemical and biochemical networks. I. Multistability and oscillations in ordinary differential equation models

A chemical mechanism is a model of a chemical reaction network consisting of a set of elementary reactions that express how molecules react with each other. In classical mass-action kinetics, a mechanism implies a set of ordinary differential equations (ODEs) which govern the time evolution of the concentrations. In this article, ODE models of chemical kinetics that have the potential for multiple positive equilibria or oscillations are studied. We begin by considering some methods of stability analysis based on the digraph of the Jacobian matrix. We then prove two theorems originally given by A. N. Ivanova which correlate the bifurcation structure of a mass-action model to the properties of a bipartite graph with nodes representing chemical species and reactions. We provide several examples of the application of these theorems.

Systems biology by the rules: hybrid intelligent systems for pathway modeling and discovery

BACKGROUND

Expert knowledge in journal articles is an important source of data for reconstructing biological pathways and creating new hypotheses. An important need for medical research is to integrate this data with high throughput sources to build useful models that span several scales. Researchers traditionally use mental models of pathways to integrate information and development new hypotheses. Unfortunately, the amount of information is often overwhelming and these are inadequate for predicting the dynamic response of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative dynamics are useful systems biology tools for theoreticians, experimentalists and clinicians and may provide a means for cross-communication.

RESULTS

A novel approach for biological pathway modeling based on hybrid intelligent systems or soft computing technologies is presented here. Intelligent hybrid systems, which refers to several related computing methods such as fuzzy logic, neural nets, genetic algorithms, and statistical analysis, has become ubiquitous in engineering applications for complex control system modeling and design. Biological pathways may be considered to be complex control systems, which medicine tries to manipulate to achieve desired results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological system dynamics and computational exploration of new drug targets. A new modeling approach based on these methods is presented in the context of hedgehog regulation of the cell cycle in granule cells. Code and input files can be found at the Bionet website: www.chip.ord/~wbosl/Software/Bionet.

CONCLUSION

This paper presents the algorithmic methods needed for modeling complicated biochemical dynamics using rule-based models to represent expert knowledge in the context of cell cycle regulation and tumor growth. A notable feature of this modeling approach is that it allows biologists to build complex models from their knowledge base without the need to translate that knowledge into mathematical form. Dynamics on several levels, from molecular pathways to tissue growth, are seamlessly integrated. A number of common network motifs are examined and used to build a model of hedgehog regulation of the cell cycle in cerebellar neurons, which is believed to play a key role in the etiology of medulloblastoma, a devastating childhood brain cancer.

Liénard systems and potential-Hamiltonian decomposition: applications in biology

In separated notes, we described the mathematical aspects of the potential-Hamiltonian (PH) decomposition, in particular, for n-switches and Liénard systems [J. Demongeot, N. Glade, L. Forest, Liénard systems and potential-Hamiltonian decomposition - I. Methodology, II. Algorithm and III. Applications, C. R. Acad. Sci., Paris, Ser. I, in press]. In the present note, we give some examples of biological regulatory systems susceptible to be decomposed. We show that they can be modelled in terms of 2D ordinary differential equations belonging to n-switches and Liénard system families [O. Cinquin, J. Demongeot, High-dimensional switches and the modeling of cellular differentiation, J. Theor. Biol. 233 (2005) 391-411]. Although simplified, these models can be decomposed into a set of equations combining a potential and a Hamiltonian part. We discuss about the advantage of such a PH-decomposition for understanding the mechanisms involved in their regulatory abilities. We suggest a generalized algorithm to deal with differential systems having a second part of rational-fraction type (frequently used in metabolic systems). Finally, we comment what can be interpreted as a precise signification in biological systems from the dynamical behaviours of both the potential and Hamiltonian parts.

Quantitative analysis of signaling networks

The response of biological cells to environmental change is coordinated by protein-based signaling networks. These networks are to be found in both prokaryotes and eukaryotes. In eukaryotes, the signaling networks can be highly complex, some networks comprising of 60 or more proteins. The fundamental motif that has been found in all signaling networks is the protein phosphorylation/dephosphorylation cycle--the cascade cycle. At this time, the computational function of many of the signaling networks is poorly understood. However, it is clear that it is possible to construct a huge variety of control and computational circuits, both analog and digital from combinations of the cascade cycle. In this review, we will summarize the great versatility of the simple cascade cycle as a computational unit and towards the end give two examples, one prokaryotic chemotaxis circuit and the other, the eukaryotic MAPK cascade.

Bio-array images processing and genetic networks modelling

The new tools available for gene expression studies are essentially the bio-array methods using a large variety of physical detectors (isotopes, fluorescent markers, ultrasounds...). Here we present first rapidly an image-processing method independent of the detector type, dealing with the noise and with the peaks overlapping, the peaks revealing the detector activity (isotopic in the presented example), correlated with the gene expression. After this primary step of bio-array image processing, we can extract information about causal influence (activation or inhibition) a gene can exert on other genes, leading to clusters of genes co-expression in which we extract an interaction matrix M and an associated interaction graph G explaining the genetic regulatory dynamics correlated to the studied tissue function. We give two examples of such interaction matrices and graphs (the flowering genetic regulatory network of Arabidopsis thaliana and the lytic/lysogenic operon of the phage Mu) and after some theoretical rigorous results recently obtained concerning the asymptotic states generated by the genetic networks having a given interaction matrix and reciprocally concerning the minimal (in the sense of having a minimal number of non-zero coefficients) matrices having given stationary stable states.

Genetic regulation networks: circuits, regulons and attractors

We deal in this paper with the concept of genetic regulation network. The genes expression observed through the bio-array imaging allows the geneticist to obtain the intergenic interaction matrix W of the network. The interaction graph G associated to W presents in general interesting features like connected components, gardens of Eden, positive and negative circuits (or loops), and minimal components having 1 positive and 1 negative loop called regulons. Depending on parameters values like the connectivity coefficient K(W) and the mean inhibition weight I(W), the genetic regulation network can present several dynamical behaviours (fixed configuration, limit cycle of configurations) called attractors, when the observation time increases. We give some examples of such genetic regulation networks and analyse their dynamical properties and their biological consequences.