Noise in cellular signaling pathways: causes and effects

The substrate quality of CK2 target sites has a determinant role on their function and evolution

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

Functional selection in SH3-mediated activation of the PI3 kinase

The phosphoinositide-3 kinase (PI3K), a heterodimeric enzyme, plays a pivotal role in cellular metabolism and survival. Its deregulation is associated with major human diseases, particularly cancer. The p85 regulatory subunit of PI3K binds to the catalytic p110 subunit via its C-terminal domains, stabilising it in an inhibited state. Certain Src homology 3 (SH3) domains can activate p110 by binding to the proline-rich (PR) 1 motif located at the N-terminus of p85. However, the mechanism by which this N-terminal interaction activates the C-terminally bound p110 remains elusive. Moreover, the intrinsically poor ligand selectivity of SH3 domains raises the question of how they can control PI3K. Combining structural, biophysical, and functional methods, we demonstrate that the answers to both these unknown issues are linked: PI3K-activating SH3 domains engage in additional "tertiary" interactions with the C-terminal domains of p85, thereby relieving their inhibition of p110. SH3 domains lacking these tertiary interactions may still bind to p85 but cannot activate PI3K. Thus, p85 uses a functional selection mechanism that precludes nonspecific activation rather than nonspecific binding. This separation of binding and activation may provide a general mechanism for how biological activities can be controlled by promiscuous protein-protein interaction domains.

Timing neurogenesis: a clock or an algorithm?

Emerging evidence supports the existence of dedicated molecular mechanisms under evolutionary selection to control time during neurogenesis. Here, we briefly review these mechanisms and discuss a potentially useful conceptual framework inspired by computer science to think about how these biological mechanisms operate during brain development and evolution.

Cellular heterogeneity in TNF/TNFR1 signalling: live cell imaging of cell fate decisions in single cells

Cellular responses to TNF are inherently heterogeneous within an isogenic cell population and across different cell types. TNF promotes cell survival by activating pro-inflammatory NF-κB and MAPK signalling pathways but may also trigger apoptosis and necroptosis. Following TNF stimulation, the fate of individual cells is governed by the balance of pro-survival and pro-apoptotic signalling pathways. To elucidate the molecular mechanisms driving heterogenous responses to TNF, quantifying TNF/TNFR1 signalling at the single-cell level is crucial. Fluorescence live-cell imaging techniques offer real-time, dynamic insights into molecular processes in single cells, allowing for detection of rapid and transient changes, as well as identification of subpopulations, that are likely to be missed with traditional endpoint assays. Whilst fluorescence live-cell imaging has been employed extensively to investigate TNF-induced inflammation and TNF-induced cell death, it has been underutilised in studying the role of TNF/TNFR1 signalling pathway crosstalk in guiding cell-fate decisions in single cells. Here, we outline the various opportunities for pathway crosstalk during TNF/TNFR1 signalling and how these interactions may govern heterogenous responses to TNF. We also advocate for the use of live-cell imaging techniques to elucidate the molecular processes driving cell-to-cell variability in single cells. Understanding and overcoming cellular heterogeneity in response to TNF and modulators of the TNF/TNFR1 signalling pathway could lead to the development of targeted therapies for various diseases associated with aberrant TNF/TNFR1 signalling, such as rheumatoid arthritis, metabolic syndrome, and cancer.

Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules

A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions.

Actomyosin pulsing rescues embryonic tissue folding from disruption by myosin fluctuations

During early development, myosin II mechanically reshapes and folds embryo tissue. A muchstudied example is ventral furrow formation in Drosophila, marking the onset of gastrulation. Furrowing is driven by contraction of actomyosin networks on apical cell surfaces, but how the myosin patterning encodes tissue shape is unclear, and elastic models failed to reproduce essential features of experimental cell contraction profiles. The myosin patterning exhibits substantial cell-to-cell fluctuations with pulsatile time-dependence, a striking but unexplained feature of morphogenesis in many organisms. Here, using biophysical modeling we find viscous forces offer the principal resistance to actomyosin-driven apical constriction. In consequence, tissue shape is encoded in the direction-dependent curvature of the myosin patterning which orients an anterior-posterior furrow. Tissue contraction is highly sensitive to cell-to-cell myosin fluctuations, explaining furrowing failure in genetically perturbed embryos whose fluctuations are temporally persistent. In wild-type embryos this disastrous outcome is averted by pulsatile myosin time-dependence, which rescues furrowing by eliminating high frequencies in the fluctuation power spectrum. This low pass filter mechanism may underlie the usage of actomyosin pulsing in diverse morphogenetic processes across many organisms.

Intrinsically disordered proteins and conformational noise: The hypothesis a decade later

Phenotypic plasticity is the ability of individual genotypes to produce different phenotypes in response to environmental perturbations. We previously postulated how conformational noise emanating from conformational dynamics of intrinsically disordered proteins (IDPs) which is distinct from transcriptional noise, can contribute to phenotypic switching by rewiring the cellular protein interaction network. Since most transcription factors are IDPs, we posited that conformational noise is an integral component of transcriptional noise implying that IDPs may amplify total noise in the system either stochastically or in response to environmental changes. Here, we review progress in elucidating the details of the hypothesis. We highlight empirical evidence supporting the hypothesis, discuss conceptual advances that underscore its fundamental importance and implications, and identify areas for future investigations.

Phase separation enhances probability of receptor signalling and drug targeting

The probability of a given receptor tyrosine kinase (RTK) triggering a defined cellular outcome is low because of the promiscuous nature of signalling, the randomness of molecular diffusion through the cell, and the ongoing nonfunctional submembrane signalling activity or noise. Signal transduction is therefore a 'numbers game', where enough cell surface receptors and effector proteins must initially be engaged to guarantee formation of a functional signalling complex against a background of redundant events. The presence of intracellular liquid-liquid phase separation (LLPS) at the plasma membrane provides a mechanism through which the probabilistic nature of signalling can be weighted in favour of the required, discrete cellular outcome and mutual exclusivity in signal initiation.

Subcellular location defines GPCR signal transduction

Intracellular G protein-coupled receptors (GPCRs) can be activated by permeant ligands, which contributes to agonist selectivity. Opioid receptors (ORs) provide a notable example, where opioid drugs rapidly activate ORs in the Golgi apparatus. Our knowledge on intracellular GPCR function remains incomplete, and it is unknown whether OR signaling in plasma membrane (PM) and Golgi apparatus differs. Here, we assess the recruitment of signal transducers to mu- and delta-ORs in both compartments. We find that Golgi ORs couple to Gαi/o probes and are phosphorylated but, unlike PM receptors, do not recruit β-arrestin or a specific Gα probe. Molecular dynamics simulations with OR-transducer complexes in bilayers mimicking PM or Golgi composition reveal that the lipid environment promotes the location-selective coupling. We then show that delta-ORs in PM and Golgi have distinct effects on transcription and protein phosphorylation. The study reveals that the subcellular location defines the signaling effects of opioid drugs.

Actomyosin pulsing rescues embryonic tissue folding from disruption by myosin fluctuations

During early development, myosin II mechanically reshapes and folds embryo tissue. A much-studied example is ventral furrow formation in Drosophila , marking the onset of gastrulation. Furrowing is driven by contraction of actomyosin networks on apical cell surfaces, but how the myosin patterning encodes tissue shape is unclear, and elastic models failed to reproduce essential features of experimental cell contraction profiles. The myosin patterning exhibits substantial cell-to-cell fluctuations with pulsatile time-dependence, a striking but unexplained feature of morphogenesis in many organisms. Here, using biophysical modeling we find viscous forces offer the principle resistance to actomyosin-driven apical constriction. In consequence, tissue shape is encoded in the direction-dependent curvature of the myosin patterning which orients an anterior-posterior furrow. Tissue contraction is highly sensitive to cell-to-cell myosin fluctuations, explaining furrowing failure in genetically perturbed embryos whose fluctuations are temporally persistent. In wild-type embryos, this catastrophic outcome is averted by pulsatile myosin time-dependence, a time-averaging effect that rescues furrowing. This low pass filter mechanism may underlie the usage of actomyosin pulsing in diverse morphogenetic processes across many organisms.

Coupling cell shape and velocity leads to oscillation and circling in keratocyte galvanotaxis

During wound healing, fish keratocyte cells undergo galvanotaxis where they follow a wound-induced electric field. In addition to their stereotypical persistent motion, keratocytes can develop circular motion without a field or oscillate while crawling in the field direction. We developed a coarse-grained phenomenological model that captures these keratocyte behaviors. We fit this model to experimental data on keratocyte response to an electric field being turned on. A critical element of our model is a tendency for cells to turn toward their long axis, arising from a coupling between cell shape and velocity, which gives rise to oscillatory and circular motion. Galvanotaxis is influenced not only by the field-dependent responses, but also cell speed and cell shape relaxation rate. When the cell reacts to an electric field being turned on, our model predicts that stiff, slow cells react slowly but follow the signal reliably. Cells that polarize and align to the field at a faster rate react more quickly and follow the signal more reliably. When cells are exposed to a field that switches direction rapidly, cells follow the average of field directions, while if the field is switched more slowly, cells follow a "staircase" pattern. Our study indicated that a simple phenomenological model coupling cell speed and shape is sufficient to reproduce a broad variety of different keratocyte behaviors, ranging from circling to oscillation to galvanotactic response, by only varying a few parameters.

Understanding noise in cell signalling in the prospect of drug-targets

The introduction of noise to signals can alter central regulatory switches of cellular processes leading to diseases. Noise is inherently present in the cellular signalling system and plays a decisive role in the input-output (I/O) relation. The current study aims to understand the noise tolerance of motif structures in the cell signalling processes. The vulnerability of a node to noise could be a significant factor in causing signalling error and need to be controlled. We developed stochastic differential equation (SDE) based mathematical models for different network motifs with two nodes and studied the association between motif structure and signal-noise relation. A two-dimensional parameter space analysis on motif sensitivity with noise and input signal variation was performed to classify and rank the motifs. Identifying sensitive motifs and their high druggability infers their significance in screening potential drug-target candidates. Finally, we proposed a theoretical framework to identify nodes from a network as potential drug targets. We applied this mathematical formalism to three cancer networks to identify drug-targets and validated them with existing databases.

Probabilistic edge weights fine-tune Boolean network dynamics

Biological systems are noisy by nature. This aspect is reflected in our experimental measurements and should be reflected in the models we build to better understand these systems. Noise can be especially consequential when trying to interpret specific regulatory interactions, i.e. regulatory network edges. In this paper, we propose a method to explicitly encode edge-noise in Boolean dynamical systems by probabilistic edge-weight (PEW) operators. PEW operators have two important features: first, they introduce a form of edge-weight into Boolean models through the noise, second, the noise is dependent on the dynamical state of the system, which enables more biologically meaningful modeling choices. Moreover, we offer a simple-to-use implementation in the already well-established BooleanNet framework. In two application cases, we show how the introduction of just a few PEW operators in Boolean models can fine-tune the emergent dynamics and increase the accuracy of qualitative predictions. This includes fine-tuning interactions which cause non-biological behaviors when switching between asynchronous and synchronous update schemes in dynamical simulations. Moreover, PEW operators also open the way to encode more exotic cellular dynamics, such as cellular learning, and to implementing edge-weights for regulatory networks inferred from omics data.

Stochastic dynamics of Type-I interferon responses

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

We investigate the impact of intrinsic and extrinsic noise on the reliability of interferon signaling. Information must be transduced robustly despite existing biochemical variability and at the same time the system has to allow for cellular variability to tune it against changing environments. Getting insights into stochasticity in signaling networks is crucial to understand cellular dynamics and decision-making processes. To this end, we developed a detailed stochastic computational model based on single cell data. We are able to show that reliability is achieved despite high noise at the receptor level.

Plant volatiles as regulators of hormone homeostasis

Some canonical plant hormones such as auxins and gibberellins have precursors that are biogenic volatiles (indole, indole acetonitrile, phenylacetaldoxime and ent-kaurene). Cytokinins, abscisic acid and strigolactones are hormones comprising chemical moieties that have distinct volatile analogues, and are synthesised alongside constitutively emitted volatiles (isoprene, sesquiterpenes, lactones, benzenoids and apocarotenoid volatiles). Nonvolatile hormone analogues and biogenic volatile organic compounds (BVOCs) evolved in tandem as growth and behavioural regulators in unicellular organisms. In plants, however, nonvolatile hormones evolved as regulators of growth, development and differentiation, while endogenous BVOCs (often synthesised lifelong) became subtle regulators of hormone synthesis, availability, activity and turnover, all supported by functionally redundant components of hormone metabolism. Reciprocal changes in the abundance and activity of hormones, nitric oxide, and constitutive plant volatiles constantly bridge retrograde and anterograde signalling to maintain hormone equilibria even in unstressed plants. This is distinct from transient interference in hormone signalling by stress-induced and exogenously received volatiles.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Protein conformational dynamics and phenotypic switching

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure but exist as conformational ensembles. Because of their structural plasticity, they can interact with multiple partners. The protein interactions between IDPs and their partners form scale-free protein interaction networks (PINs) that facilitate information flow in the cell. Because of their plasticity, IDPs typically occupy hub positions in cellular PINs. Furthermore, their conformational dynamics and propensity for post-translational modifications contribute to "conformational" noise which is distinct from the well-recognized transcriptional noise. Therefore, upregulation of IDPs in response to a specific input, such as stress, contributes to increased noise and, hence, an increase in stochastic, "promiscuous" interactions. These interactions lead to activation of latent pathways or can induce "rewiring" of the PIN to yield an optimal output underscoring the critical role of IDPs in regulating information flow. We have used PAGE4, a highly intrinsically disordered stress-response protein as a paradigm. Employing a variety of experimental and computational techniques, we have elucidated the role of PAGE4 in phenotypic switching of prostate cancer cells at a systems level. These cumulative studies over the past decade provide a conceptual framework to better understand how IDP conformational dynamics and conformational noise might facilitate cellular decision-making.

Pleiotropy enables specific and accurate signaling in the presence of ligand cross talk

Living cells sense their environment through the binding of extracellular molecular ligands to cell surface receptors. Puzzlingly, vast numbers of signaling pathways exhibit a high degree of cross talk between different signals whereby different ligands act through the same receptor or shared components downstream. It remains unclear how a cell can accurately process information from the environment in such cross-wired pathways. We show that a feature which commonly accompanies cross talk-signaling pleiotropy (the ability of a receptor to produce multiple outputs)-offers a solution to the cross-talk problem. In a minimal model we show that a single pleiotropic receptor can simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture. We calculate the fundamental limits of the signaling specificity and accuracy of such signaling schemes. The model serves as an elementary "building block" toward understanding more complex cross-wired receptor-ligand signaling networks.

Bioelectric signaling as a unique regulator of development and regeneration

It is well known that electrical signals are deeply associated with living entities. Much of our understanding of excitable tissues is derived from studies of specialized cells of neurons or myocytes. However, electric potential is present in all cell types and results from the differential partitioning of ions across membranes. This electrical potential correlates with cell behavior and tissue organization. In recent years, there has been exciting, and broadly unexpected, evidence linking the regulation of development to bioelectric signals. However, experimental modulation of electrical potential can have multifaceted and pleiotropic effects, which makes dissecting the role of electrical signals in development difficult. Here, I review evidence that bioelectric cues play defined instructional roles in orchestrating development and regeneration, and further outline key areas in which to refine our understanding of this signaling mechanism.

Physical tuning of galectin-3 signaling

Galectin-3 (Gal3) exhibits dynamic oligomerization and promiscuous binding, which can lead to concomitant activation of synergistic, antagonistic, or noncooperative signaling pathways that alter cell behavior. Conferring signaling pathway selectivity through mutations in the Gal3-glycan binding interface is challenged by the abundance of common carbohydrate types found on many membrane glycoproteins. Here, employing alpha-helical coiled-coils as scaffolds to create synthetic Gal3 constructs with defined valency, we demonstrate that oligomerization can physically regulate extracellular signaling activity of Gal3. Constructs with 2 to 6 Gal3 subunits ("Dimer," "Trimer," "Tetramer," "Pentamer," "Hexamer") demonstrated glycan-binding properties and cell death-inducing potency that scaled with valency. Dimer was the minimum functional valency. Unlike wild-type Gal3, which signals apoptosis and mediates agglutination, synthetic Gal3 constructs induced cell death without agglutination. In the presence of CD45, Hexamer was distributed on the cell membrane, whereas it clustered in absence of CD45 via membrane glycans other than those found on CD7. Wild-type Gal3, Pentamer, and Hexamer required CD45 and CD7 to signal apoptosis, and the involvement of caspases in apoptogenic signaling was increased in absence of CD45. However, wild-type Gal3 depended on caspases to signal apoptosis to a greater extent than Hexamer, which had greater caspase dependence than Pentamer. Diminished caspase activation downstream of Hexamer signaling led to decreased pannexin-1 hemichannel opening and interleukin-2 secretion, events facilitated by the increased caspase activation downstream of wild-type Gal3 signaling. Thus, synthetic fixation of Gal3 multivalency can impart physical control of its outside-in signaling activity by governing membrane glycoprotein engagement and, in turn, intracellular pathway activation.

Synergy and allostery in ligand binding by HIV-1 Nef

The Nef protein of human and simian immunodeficiency viruses boosts viral pathogenicity through its interactions with host cell proteins. By combining the polyvalency of its large unstructured regions with the binding selectivity and strength of its folded core domain, Nef can associate with many different host cell proteins, thereby disrupting their functions. For example, the combination of a linear proline-rich motif and hydrophobic core domain surface allows Nef to bind tightly and specifically to SH3 domains of Src family kinases. We investigated whether the interplay between Nef's flexible regions and its core domain could allosterically influence ligand selection. We found that the flexible regions can associate with the core domain in different ways, producing distinct conformational states that alter the way in which Nef selects for SH3 domains and exposes some of its binding motifs. The ensuing crosstalk between ligands might promote functionally coherent Nef-bound protein ensembles by synergizing certain subsets of ligands while excluding others. We also combined proteomic and bioinformatics analyses to identify human proteins that select SH3 domains in the same way as Nef. We found that only 3% of clones from a whole-human fetal library displayed Nef-like SH3 selectivity. However, in most cases, this selectivity appears to be achieved by a canonical linear interaction rather than by a Nef-like ‘tertiary' interaction. Our analysis supports the contention that Nef's mode of hijacking SH3 domains is a virus-specific adaptation with no or very few cellular counterparts. Thus, the Nef tertiary binding surface is a promising virus-specific drug target.

Lung mediated auditory contrast enhancement improves the Signal-to-noise ratio for communication in frogs

Environmental noise is a major source of selection on animal sensory and communication systems. The acoustic signals of other animals represent particularly potent sources of noise for chorusing insects, frogs, and birds, which contend with a multi-species analog of the human "cocktail party problem" (i.e., our difficulty following speech in crowds). However, current knowledge of the diverse adaptations that function to solve noise problems in nonhuman animals remains limited. Here, we show that a lung-to-ear sound transmission pathway in frogs serves a heretofore unknown noise-control function in vertebrate hearing and sound communication. Inflated lungs improve the signal-to-noise ratio for communication by enhancing the spectral contrast in received vocalizations in ways analogous to signal processing algorithms used in hearing aids and cochlear implants. Laser vibrometry revealed that the resonance of inflated lungs selectively reduces the tympanum's sensitivity to frequencies between the two spectral peaks present in conspecific mating calls. Social network analysis of continent-scale citizen science data on frog calling behavior revealed that the calls of other frog species in multi-species choruses can be a prominent source of environmental noise attenuated by the lungs. Physiological modeling of peripheral frequency tuning indicated that inflated lungs could reduce both auditory masking and suppression of neural responses to mating calls by environmental noise. Together, these data suggest an ancient adaptation for detecting sound via the lungs has been evolutionarily co-opted to create auditory contrast enhancement that contributes to solving a multi-species cocktail party problem.

CRYPTOCHROMES confer robustness, not rhythmicity, to circadian timekeeping

Circadian rhythms are a pervasive property of mammalian cells, tissues and behaviour, ensuring physiological adaptation to solar time. Models of cellular timekeeping revolve around transcriptional feedback repression, whereby CLOCK and BMAL1 activate the expression of PERIOD (PER) and CRYPTOCHROME (CRY), which in turn repress CLOCK/BMAL1 activity. CRY proteins are therefore considered essential components of the cellular clock mechanism, supported by behavioural arrhythmicity of CRY-deficient (CKO) mice under constant conditions. Challenging this interpretation, we find locomotor rhythms in adult CKO mice under specific environmental conditions and circadian rhythms in cellular PER2 levels when CRY is absent. CRY-less oscillations are variable in their expression and have shorter periods than wild-type controls. Importantly, we find classic circadian hallmarks such as temperature compensation and period determination by CK1δ/ε activity to be maintained. In the absence of CRY-mediated feedback repression and rhythmic Per2 transcription, PER2 protein rhythms are sustained for several cycles, accompanied by circadian variation in protein stability. We suggest that, whereas circadian transcriptional feedback imparts robustness and functionality onto biological clocks, the core timekeeping mechanism is post-translational.

Intrinsically Disordered Proteins: Insights from Poincaré, Waddington, and Lamarck

The past quarter-century may justly be referred to as a period analogous to the “Cambrian explosion” in the history of proteins. This period is marked by the appearance of the intrinsically disordered proteins (IDPs) on the scene since their discovery in the mid-1990s. Here, I first reflect on how we accidentally stumbled on these fascinating molecules. Next, I describe our research on the IDPs over the past decade and identify six areas as important for future research in this field. In addition, I draw on discoveries others in the field have made to present a more comprehensive essay. More specifically, I discuss the role of IDPs in two fundamental aspects of life: in phenotypic switching, and in multicellularity that marks one of the major evolutionary transitions. I highlight how serendipity, imagination, and an interdisciplinary approach embodying empirical evidence and theoretical insights from the works of Poincaré, Waddington, and Lamarck, shaped our thinking, and how this led us to propose the MRK hypothesis, a conceptual framework addressing phenotypic switching, the emergence of new traits, and adaptive evolution via nongenetic and IDP conformation-based mechanisms. Finally, I present a perspective on the evolutionary link between phenotypic switching and the origin of multicellularity.

Modeling the intracellular dynamics of the dengue viral infection and the innate immune response

The interplay between the dengue virus and the innate immune response is not fully understood. Here, we use deterministic and stochastic approaches to investigate the dynamics of the interaction between the interferon-mediated innate immune response and the dengue virus. We aim to develop a quantitative representation of these complex interactions and predict their system-level dynamics. Our simulation results predict bimodal and bistable dynamics that represent viral clearance and virus-producing states. Under normal conditions, we determined that the viral infection outcome is modulated by the innate immune response and the positive-strand viral RNA concentration. Additionally, we tested system perturbations by external stimulation, such as the direct induction of the innate immune response by interferon, and a therapeutic intervention consisting of the direct application of mRNA encoding for several interferon-stimulated genes. Our simulation results suggest optimal regimes for the studied intervention approaches.

Information Theory in Computational Biology: Where We Stand Today

"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

An integrative method to predict signalling perturbations for cellular transitions

Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.

SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies

SUMMARY

Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing.

AVAILABILITY AND IMPLEMENTATION

SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

On Information Extraction and Decoding Mechanisms Improved by Noisy Amplification in Signaling Pathways

The cells need to process information about extracellular stimuli. They encode, transmit and decode the information to elicit an appropriate response. Studies aimed at understanding how such information is decoded in the signaling pathways to generate a specific cellular response have become essential. Eukaryotic cells decode information through two different mechanisms: the feed-forward loop and the promoter affinity. Here, we investigate how these two mechanisms improve information transmission. A detailed comparison is made between the stochastic model of the MAPK/ERK pathway and a stochastic minimal decoding model. The maximal amount of transmittable information was computed. The results suggest that the decoding mechanism of the MAPK/ERK pathway improve the channel capacity because it behaves as a noisy amplifier. We show a positive dependence between the noisy amplification and the amount of information extracted. Additionally, we show that the extrinsic noise can be tuned to improve information transmission. This investigation has revealed that the feed-forward loop and the promoter affinity motifs extract information thanks to processes of amplification and noise addition. Moreover, the channel capacity is enhanced when both decoding mechanisms are coupled. Altogether, these findings suggest novel characteristics in how decoding mechanisms improve information transmission.

Intrinsically disordered proteins and phenotypic switching: Implications in cancer

It is now well established that intrinsically disordered proteins (IDPs) that constitute a large part of the proteome across the three kingdoms, play critical roles in several biological processes including phenotypic switching. However, dysregulated expression of IDPs that engage in promiscuous interactions can lead to pathological states. In this chapter, using cancer as a paradigm, we discuss how IDP conformational dynamics and the resultant conformational noise can modulate phenotypic switching. Thus, contrary to the prevailing wisdom that phenotypic switching is highly deterministic (has a genetic underpinning) in cancer, emerging evidence suggests that non-genetic mechanisms, at least in part due to the conformational noise, may also be a confounding factor in phenotypic switching.

The analysis of a time-course transcriptome profile by systems biology approaches reveals key molecular processes in acute kidney injury

Background:

Acute kidney injury is a common debilitating disease with no curative treatment. The recent development of big biological data is expected to expand our understanding of the disorder if appropriately analyzed to generate translational knowledge. We have here re-analyzed a time-course microarray data on mRNA expression of rat kidneys exposed to ischemia-reperfusion to identify key underlying biological processes.

Materials and Methods:

The dataset was quality controlled by principal component analysis and hierarchical clustering. Using limma R package, differentially expressed (DE) genes were detected which were then clustered according to their expression trajectories. The biological processes related to each cluster were harvested using gene ontology enrichment analysis. In addition, the interaction map of proteins encoded by the DE genes was constructed, and the functions related to network central genes were determined. Furthermore, signaling pathways related to the DE genes were harvested using pathway enrichment analysis.

Results:

We found 8139 DE genes that drive critical processes such as the control of blood circulation, reactive species metabolism, mitochondrial respiration, apoptosis, cell proliferation, as well as inflammatory and immunological reactions. The role of less recognized pathways such as olfactory signaling in acute kidney injury is also proposed that remains to be investigated in future studies.

Conclusion:

Using systems biology top-down approach, we have suggested novel potential genes and pathways to be intervened toward kidney regeneration.

Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy

The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.

Ligand Density and Nanoparticle Clustering Cooperate in the Multivalent Amplification of Epidermal Growth Factor Receptor Activation

Multivalent presentation of ligands on nanoparticles (NPs) is considered a general strategy for enhancing receptor binding and activation through amplification of ligand-receptor interactions within the footprint of the individual NPs. The spatial clustering of ligand-functionalized NPs represents an additional, less well understood mechanism for increasing local ligand-receptor interactions, especially for receptors that form higher-order assemblies, such as the epidermal growth factor (EGF) receptor (EGFR). To shed light on the interplay between ligand density ( i.e., multivalency) and NP clustering in signal amplification, we apply EGF-functionalized 72 ± 1 nm gold nanoparticles (NP-EGF) with known ligand loading (10-200 EGF/NP) as quantifiable and experimentally tractable units of EGFR activation and characterize the NP-mediated amplification of EGFR phosphorylation as a function of both EGF surface density and NP-EGF clustering for two cancer cell lines (HeLa and MDA-MB-468). The measurements confirm a strong multivalent amplification of EGFR phosphorylation through NP-EGF on the cellular level that results in EGF-loading-dependent maximum EGFR phosphorylation levels. A microscopic analysis of NP-EGF-induced EGFR phosphorylation reveals a heterogeneous spatial distribution of EGFR activation across the cell surface. Clustering of multivalent NP-EGF on sub-diffraction-limited length scales is found to result in a local enhancement of EGFR phosphorylation in signaling "hot spots" from where the signal can spread laterally in an EGF-independent fashion. Increasing EGF loadings of the NP enhances NP-EGF clustering and intensifies EGFR phosphorylation. These observations suggest that NP-EGF clustering and the associated local enhancement of ligand-receptor interactions are intrinsic components of the multivalent amplification of phosphorylation for the heterogeneously distributed EGFR through NP-EGF.

Multisite phosphorylation provides a reliable mechanism for making decisions in noisy environments

The ability to make decisions at the cellular level is absolutely critical for the survival of organisms. Eukaryotic cells are constantly making binary decisions in response to internal and environmental signals. Among the most notable transducers of information are protein kinases. The regulation of these signaling proteins often relies on the activity of other protein kinases located upstream in the signaling cascade. However, these signaling systems are by their own nature an important source of molecular noise. Herein, we have assessed the role of multisite phosphorylation in detecting signals in the face of molecular noise. To address this issue, we have conceptually envisioned the biochemical transduction machinery as a classifier model that can lead to four possible outputs: true positives and negatives, and false positives and negatives. In this probabilistic framework, we show that multisite phosphorylation represents a mechanism to filter noise during the decision-making process. We present results showing that nonessential phosphorylation sites contribute to increase the rate of true positives while, at the same time, they can lessen the rate of false positives. This simultaneous increase in sensitivity and specificity, makes multisite phosphorylation a valuable and easily implemented mechanism to reliably transduce information in noisy contexts.

Sporadic cases of lumpy skin disease among cattle in Sharkia province, Egypt: Genetic characterization of lumpy skin disease virus isolates and pathological findings

BACKGROUND AND AIM

Lumpy skin disease (LSD) is a highly infectious viral disease upsetting cattle, caused by LSD virus (LSDV) within the family Poxviridae. Sporadic cases of LSD have been observed in cattle previously vaccinated with the Romanian sheep poxvirus (SPPV) vaccine during the summer of 2016 in Sharkia province, Egypt. The present study was undertaken to perform molecular characterization of LSDV strains which circulated in this period as well as investigate their phylogenetic relatedness with published reference capripoxvirus genome sequences.

MATERIALS AND METHODS

A total of 82 skin nodules, as well as 5 lymph nodes, were collected from suspect LSD cases, and the virus was isolated in embryonated chicken eggs (ECEs). LSD was confirmed by polymerase chain reactions amplification of the partial and full-length sequences of the attachment and G-protein-coupled chemokine receptor (GPCR) genes, respectively, as well as a histopathological examination of the lesions. Molecular characterization of the LSDV isolates was conducted by sequencing the GPCR gene.

RESULTS

Characteristic skin nodules that covered the whole intact skin, as well as lymphadenopathy, were significant clinical signs in all suspected cases. LSDV isolation in ECEs revealed the characteristic focal white pock lesions dispersed on the chorioallantoic membranes. Histopathologic examination showed characteristic eosinophilic intracytoplasmic inclusion bodies within inflammatory cell infiltration. Phylogenetic analysis revealed that the LSDV isolates were clustered together with other African and European LSDV strains. In addition, the LSDV isolates have a unique signature of LSDVs (A11, T12, T34, S99, and P199).

CONCLUSION

LSDV infections have been detected in cattle previously vaccinated with Romanian SPPV vaccine during the summer of 2016 and making the evaluation of vaccine efficacy under field conditions necessary.

An optimization framework for network annotation

Motivation

A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well-studied biological pathways.

Results

Here we present a novel optimization framework for large-scale sign annotation that employs different plausible models of signaling and combines them in a rigorous manner. We apply our framework to two large-scale knockout datasets in yeast and evaluate its different components as well as the combined model to predict signs of different subsets of physical interactions. Overall, we obtain an accurate predictor that outperforms previous work by a considerable margin.

Availability and implementation

The code is publicly available at https://github.com/spatkar94/NetworkAnnotation.git.

Real Talk: The Inter-play Between the mTOR, AMPK, and Hexosamine Biosynthetic Pathways in Cell Signaling

O-linked N-acetylglucosamine, better known as O-GlcNAc, is a sugar post-translational modification participating in a diverse range of cell functions. Disruptions in the cycling of O-GlcNAc mediated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively, is a driving force for aberrant cell signaling in disease pathologies, such as diabetes, obesity, Alzheimer's disease, and cancer. Production of UDP-GlcNAc, the metabolic substrate for OGT, by the Hexosamine Biosynthetic Pathway (HBP) is controlled by the input of amino acids, fats, and nucleic acids, making O-GlcNAc a key nutrient-sensor for fluctuations in these macromolecules. The mammalian target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) pathways also participate in nutrient-sensing as a means of controlling cell activity and are significant factors in a variety of pathologies. Research into the individual nutrient-sensitivities of the HBP, AMPK, and mTOR pathways has revealed a complex regulatory dynamic, where their unique responses to macromolecule levels coordinate cell behavior. Importantly, cross-talk between these pathways fine-tunes the cellular response to nutrients. Strong evidence demonstrates that AMPK negatively regulates the mTOR pathway, but O-GlcNAcylation of AMPK lowers enzymatic activity and promotes growth. On the other hand, AMPK can phosphorylate OGT leading to changes in OGT function. Complex sets of interactions between the HBP, AMPK, and mTOR pathways integrate nutritional signals to respond to changes in the environment. In particular, examining these relationships using systems biology approaches might prove a useful method of exploring the complex nature of cell signaling. Overall, understanding the complex interactions of these nutrient pathways will provide novel mechanistic information into how nutrients influence health and disease.

Cell Polarity in Cerebral Cortex Development-Cellular Architecture Shaped by Biochemical Networks

The human cerebral cortex is the seat of our cognitive abilities and composed of an extraordinary number of neurons, organized in six distinct layers. The establishment of specific morphological and physiological features in individual neurons needs to be regulated with high precision. Impairments in the sequential developmental programs instructing corticogenesis lead to alterations in the cortical cytoarchitecture which is thought to represent the major underlying cause for several neurological disorders including neurodevelopmental and psychiatric diseases. In this review article we discuss the role of cell polarity at sequential stages during cortex development. We first provide an overview of morphological cell polarity features in cortical neural stem cells and newly-born postmitotic neurons. We then synthesize a conceptual molecular and biochemical framework how cell polarity is established at the cellular level through a break in symmetry in nascent cortical projection neurons. Lastly we provide a perspective how the molecular mechanisms applying to single cells could be probed and integrated in an in vivo and tissue-wide context.

SiGNet: A signaling network data simulator to enable signaling network inference

Network models are widely used to describe complex signaling systems. Cellular wiring varies in different cellular contexts and numerous inference techniques have been developed to infer the structure of a network from experimental data of the network's behavior. To objectively identify which inference strategy is best suited to a specific network, a gold standard network and dataset are required. However, suitable datasets for benchmarking are difficult to find. Numerous tools exist that can simulate data for transcriptional networks, but these are of limited use for the study of signaling networks. Here, we describe SiGNet (Signal Generator for Networks): a Cytoscape app that simulates experimental data for a signaling network of known structure. SiGNet has been developed and tested against published experimental data, incorporating information on network architecture, and the directionality and strength of interactions to create biological data in silico. SiGNet is the first tool to simulate biological signaling data, enabling an accurate and systematic assessment of inference strategies. SiGNet can also be used to produce preliminary models of key biological pathways following perturbation.

Noise-processing by signaling networks

Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways in which signaling networks process environmental noise are not well understood. Here we outline a mathematical framework that relates a network’s structure to its capacity to process noise, and use this framework to dissect the noise-processing ability of signaling networks. We find that complex networks that are dense in directed paths are poor noise processors, while those that are sparse and strongly directional process noise well. These results suggest that while cross-talk between signaling pathways may increase the ability of signaling networks to integrate multiple stimuli, too much cross-talk may compromise the ability of the network to distinguish signal from noise. To illustrate these general results we consider the structure of the signalling network that maintains pluripotency in mouse embryonic stem cells, and find an incoherent feedforward loop structure involving Stat3, Tfcp2l1, Esrrb, Klf2 and Klf4 is particularly important for noise-processing. Taken together these results suggest that noise-processing is an important function of signaling networks and they may be structured in part to optimize this task.

Prostate-associated gene 4 (PAGE4), an intrinsically disordered cancer/testis antigen, is a novel therapeutic target for prostate cancer

Prostate-associated gene 4 (PAGE4) is a remarkably prostate-specific Cancer/Testis Antigen that is highly upregulated in the human fetal prostate and its diseased states but not in the adult normal gland. PAGE4 is an intrinsically disordered protein (IDP) that functions as a stress-response protein to suppress reactive oxygen species as well as prevent DNA damage. In addition, PAGE4 is also a transcriptional regulator that potentiates transactivation by the oncogene c-Jun. c-Jun forms the AP-1 complex by heterodimerizing with members of the Fos family and plays an important role in the development and pathology of the prostate gland, underscoring the importance of the PAGE4/c-Jun interaction. HIPK1, also a component of the stress-response pathway, phosphorylates PAGE4 at T51 which is critical for its transcriptional activity. Phosphorylation induces conformational and dynamic switching in the PAGE4 ensemble leading to a new cellular function. Finally, bioinformatics evidence suggests that the PAGE4 mRNA could be alternatively spliced resulting in four potential isoforms of the polypeptide alluding to the possibility of a range of conformational ensembles with latent functions. Considered together, the data suggest that PAGE4 may represent the first molecular link between stress and prostate cancer (PCa). Thus, pharmacologically targeting PAGE4 may be a novel opportunity for treating and managing patients with PCa, especially patients with low-risk disease.

Organelles - understanding noise and heterogeneity in cell biology at an intermediate scale

Many studies over the years have shown that non-genetic mechanisms for producing cell-to-cell variation can lead to highly variable behaviors across genetically identical populations of cells. Most work to date has focused on gene expression noise as the primary source of phenotypic heterogeneity, yet other sources may also contribute. In this Commentary, we explore organelle-level heterogeneity as a potential secondary source of cellular 'noise' that contributes to phenotypic heterogeneity. We explore mechanisms for generating organelle heterogeneity and present evidence of functional links between organelle morphology and cellular behavior. Given the many instances in which molecular-level heterogeneity has been linked to phenotypic heterogeneity, we posit that organelle heterogeneity may similarly contribute to overall phenotypic heterogeneity and underline the importance of studying organelle heterogeneity to develop a more comprehensive understanding of phenotypic heterogeneity. Finally, we conclude with a discussion of the medical challenges associated with phenotypic heterogeneity and outline how improved methods for characterizing and controlling this heterogeneity may lead to improved therapeutic strategies and outcomes for patients.

Spatiotemporal Patterns of Noise-Driven Confined Actin Waves in Living Cells

Cells utilize waves of polymerizing actin to reshape their morphologies, which is central to physiological and pathological processes alike. Here, we force dorsal actin waves to propagate on one-dimensional domains with periodic boundary conditions, which results in striking spatiotemporal patterns with a clear signature of noise-driven dynamics. We show that these patterns can be very closely reproduced with a noise-driven active medium at coherence resonance.

New insights into mammalian signaling pathways using microfluidic pulsatile inputs and mathematical modeling

Temporally modulated input mimics physiology. This chemical communication strategy filters the biochemical noise through entrainment and phase-locking. Under laboratory conditions, it also expands the observability space for downstream responses. A combined approach involving microfluidic pulsatile stimulation and mathematical modeling has led to deciphering of hidden/unknown temporal motifs in several mammalian signaling pathways and has provided mechanistic insights, including how these motifs combine to form distinct band-pass filters and govern fate regulation under dynamic microenvironment. This approach can be utilized to understand signaling circuit architectures and to gain mechanistic insights for several other signaling systems. Potential applications include synthetic biology and biotechnology, in developing pharmaceutical interventions, and in developing lab-on-chip models.

Distinct cellular states determine calcium signaling response

The heterogeneity in mammalian cells signaling response is largely a result of pre‐existing cell‐to‐cell variability. It is unknown whether cell‐to‐cell variability rises from biochemical stochastic fluctuations or distinct cellular states. Here, we utilize calcium response to adenosine trisphosphate as a model for investigating the structure of heterogeneity within a population of cells and analyze whether distinct cellular response states coexist. We use a functional definition of cellular state that is based on a mechanistic dynamical systems model of calcium signaling. Using Bayesian parameter inference, we obtain high confidence parameter value distributions for several hundred cells, each fitted individually. Clustering the inferred parameter distributions revealed three major distinct cellular states within the population. The existence of distinct cellular states raises the possibility that the observed variability in response is a result of structured heterogeneity between cells. The inferred parameter distribution predicts, and experiments confirm that variability in IP3R response explains the majority of calcium heterogeneity. Our work shows how mechanistic models and single‐cell parameter fitting can uncover hidden population structure and demonstrate the need for parameter inference at the single‐cell level.

Integration and global analysis of isothermal titration calorimetry data for studying macromolecular interactions

Isothermal titration calorimetry (ITC) is a powerful and widely used method to measure the energetics of macromolecular interactions by recording a thermogram of differential heating power during a titration. However, traditional ITC analysis is limited by stochastic thermogram noise and by the limited information content of a single titration experiment. Here we present a protocol for bias-free thermogram integration based on automated shape analysis of the injection peaks, followed by combination of isotherms from different calorimetric titration experiments into a global analysis, statistical analysis of binding parameters and graphical presentation of the results. This is performed using the integrated public-domain software packages NITPIC, SEDPHAT and GUSSI. The recently developed low-noise thermogram integration approach and global analysis allow for more precise parameter estimates and more reliable quantification of multisite and multicomponent cooperative and competitive interactions. Titration experiments typically take 1-2.5 h each, and global analysis usually takes 10-20 min.