Integrative analysis of yeast colony growth.

Yeast colonies are routinely grown on agar plates in everyday experimental settings to understand basic molecular processes, produce novel drugs, improve health, and so on. Standardized conditions ensure these colonies grow in a reproducible fashion, while in nature microbes are under a constantly changing environment. Here we combine the power of computational simulations and laboratory experiments to investigate the impact of non-standard environmental factors on colony growth. We present the developement and parameterization of a quantitative agent-based model for yeast colony growth to reproduce measurements on colony size and cell number in a colony at non-standard environmental conditions. Specifically, we establish experimental conditions that mimic the effects of humidity changes and nutrient gradients. Our results show how colony growth is affected by moisture changes, nutrient availability, and initial colony inoculation conditions. We show that initial colony spread, not initial cell number have higher impact on the final size and cell number of colonies. Parameters of the model were identified by fitting these experiments and the fitted model gives guidance to establish conditions which enable unlimited growth of yeast colonies.

Wastewater-based modeling, reconstruction, and prediction for COVID-19 outbreaks in Hungary caused by highly immune evasive variants.

(MOTIVATION): Wastewater-based epidemiology (WBE) has emerged as a promising approach for monitoring the COVID-19 pandemic, since the measurement process is cost-effective and is exposed to fewer potential errors compared to other indicators like hospitalization data or the number of detected cases. Consequently, WBE was gradually becoming a key tool for epidemic surveillance and often the most reliable data source, as the intensity of clinical testing for COVID-19 drastically decreased by the third year of the pandemic. Recent results suggests that the model-based fusion of wastewater measurements with clinical data and other indicators is essential in future epidemic surveillance. (METHOD): In this work, we developed a wastewater-based compartmental epidemic model with a two-phase vaccination dynamics and immune evasion. We proposed a multi-step optimization-based data assimilation method for epidemic state reconstruction, parameter estimation, and prediction. The computations make use of the measured viral load in wastewater, the available clinical data (hospital occupancy, delivered vaccine doses, and deaths), the stringency index of the official social distancing rules, and other measures. The current state assessment and the estimation of the current transmission rate and immunity loss allow a plausible prediction of the future progression of the pandemic. (RESULTS): Qualitative and quantitative evaluations revealed that the contribution of wastewater data in our computational epidemiological framework makes predictions more reliable. Predictions suggest that at least half of the Hungarian population has lost immunity during the epidemic outbreak caused by the BA.1 and BA.2 subvariants of Omicron in the first half of 2022. We obtained a similar result for the outbreaks caused by the subvariant BA.5 in the second half of 2022. (APPLICABILITY): The proposed approach has been used to support COVID management in Hungary and could be customized for other countries as well.

Systematic analysis of negative and positive feedback loops for robustness and temperature compensation in circadian rhythms.

Temperature compensation and robustness to biological noise are two key characteristics of the circadian clock. These features allow the circadian pacemaker to maintain a steady oscillation in a wide range of environmental conditions. The presence of a time-delayed negative feedback loop in the regulatory network generates autonomous circadian oscillations in eukaryotic systems. In comparison, the circadian clock of cyanobacteria is controlled by a strong positive feedback loop. Positive feedback loops with substrate depletion can also generate oscillations, inspiring other circadian clock models. What makes a circadian oscillatory network robust to extrinsic noise is unclear. We investigated four basic circadian oscillators with negative, positive, and combinations of positive and negative feedback loops to explore network features necessary for circadian clock resilience. We discovered that the negative feedback loop system performs the best in compensating temperature changes. We also show that a positive feedback loop can reduce extrinsic noise in periods of circadian oscillators, while intrinsic noise is reduced by negative feedback loops.

Diversity of synaptic protein complexes as a function of the abundance of their constituent proteins: A modeling approach.

The postsynaptic density (PSD) is a dense protein network playing a key role in information processing during learning and memory, and is also indicated in a number of neurological disorders. Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins (NMDAR, AMPAR, PSD-95, SynGAP, GKAP, Shank3, Homer1) based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients (altogether 524 different simulations). Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse.

Microsimulation based quantitative analysis of COVID-19 management strategies.

Pandemic management requires reliable and efficient dynamical simulation to predict and control disease spreading. The COVID-19 (SARS-CoV-2) pandemic is mitigated by several non-pharmaceutical interventions, but it is hard to predict which of these are the most effective for a given population. We developed the computationally effective and scalable, agent-based microsimulation framework PanSim, allowing us to test control measures in multiple infection waves caused by the spread of a new virus variant in a city-sized societal environment using a unified framework fitted to realistic data. We show that vaccination strategies prioritising occupational risk groups minimise the number of infections but allow higher mortality while prioritising vulnerable groups minimises mortality but implies an increased infection rate. We also found that intensive vaccination along with non-pharmaceutical interventions can substantially suppress the spread of the virus, while low levels of vaccination, premature reopening may easily revert the epidemic to an uncontrolled state. Our analysis highlights that while vaccination protects the elderly from COVID-19, a large percentage of children will contract the virus, and we also show the benefits and limitations of various quarantine and testing scenarios. The uniquely detailed spatio-temporal resolution of PanSim allows the design and testing of complex, specifically targeted interventions with a large number of agents under dynamically changing conditions.

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops.

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures.

Evolution of opposing regulatory interactions underlies the emergence of eukaryotic cell cycle checkpoints.

In eukaryotes the entry into mitosis is initiated by activation of cyclin-dependent kinases (CDKs), which in turn activate a large number of protein kinases to induce all mitotic processes. The general view is that kinases are active in mitosis and phosphatases turn them off in interphase. Kinases activate each other by cross- and self-phosphorylation, while phosphatases remove these phosphate groups to inactivate kinases. Crucial exceptions to this general rule are the interphase kinase Wee1 and the mitotic phosphatase Cdc25. Together they directly control CDK in an opposite way of the general rule of mitotic phosphorylation and interphase dephosphorylation. Here we investigate why this opposite system emerged and got fixed in almost all eukaryotes. Our results show that this reversed action of a kinase-phosphatase pair, Wee1 and Cdc25, on CDK is particularly suited to establish a stable G2 phase and to add checkpoints to the cell cycle. We show that all these regulators appeared together in LECA (Last Eukaryote Common Ancestor) and co-evolved in eukaryotes, suggesting that this twist in kinase-phosphatase regulation was a crucial step happening at the emergence of eukaryotes.

Interpretation of morphogen gradients by a synthetic bistable circuit.

During development, cells gain positional information through the interpretation of dynamic morphogen gradients. A proposed mechanism for interpreting opposing morphogen gradients is mutual inhibition of downstream transcription factors, but isolating the role of this specific motif within a natural network remains a challenge. Here, we engineer a synthetic morphogen-induced mutual inhibition circuit in E. coli populations and show that mutual inhibition alone is sufficient to produce stable domains of gene expression in response to dynamic morphogen gradients, provided the spatial average of the morphogens falls within the region of bistability at the single cell level. When we add sender devices, the resulting patterning circuit produces theoretically predicted self-organised gene expression domains in response to a single gradient. We develop computational models of our synthetic circuits parameterised to timecourse fluorescence data, providing both a theoretical and experimental framework for engineering morphogen-induced spatial patterning in cell populations.

Unifying the mechanism of mitotic exit control in a spatiotemporal logical model.

The transition from mitosis into the first gap phase of the cell cycle in budding yeast is controlled by the Mitotic Exit Network (MEN). The network interprets spatiotemporal cues about the progression of mitosis and ensures that release of Cdc14 phosphatase occurs only after completion of key mitotic events. The MEN has been studied intensively; however, a unified understanding of how localisation and protein activity function together as a system is lacking. In this paper, we present a compartmental, logical model of the MEN that is capable of representing spatial aspects of regulation in parallel to control of enzymatic activity. We show that our model is capable of correctly predicting the phenotype of the majority of mutants we tested, including mutants that cause proteins to mislocalise. We use a continuous time implementation of the model to demonstrate that Cdc14 Early Anaphase Release (FEAR) ensures robust timing of anaphase, and we verify our findings in living cells. Furthermore, we show that our model can represent measured cell-cell variation in Spindle Position Checkpoint (SPoC) mutants. This work suggests a general approach to incorporate spatial effects into logical models. We anticipate that the model itself will be an important resource to experimental researchers, providing a rigorous platform to test hypotheses about regulation of mitotic exit.

Comparative analysis of the intracellular responses to disease-related aggregation-prone proteins.

Aggregation-prone proteins (APPs) have been implicated in numerous human diseases but the underlying mechanisms are incompletely understood. Here we comparatively analysed cellular responses to different APPs. Our study is based on a systematic proteomic and phosphoproteomic analysis of a set of yeast proteotoxicity models expressing different human disease-related APPs, which accumulate intracellular APP inclusions and exhibit impaired growth. Clustering and functional enrichment analyses of quantitative proteome-level data reveal that the cellular response to APP expression, including the chaperone response, is specific to the APP, and largely differs from the response to a more generalized proteotoxic insult such as heat shock. We further observe an intriguing association between the subcellular location of inclusions and the location of the cellular response, and provide a rich dataset for future mechanistic studies. Our data suggest that care should be taken when designing research models to study intracellular aggregation, since the cellular response depends markedly on the specific APP and the location of inclusions. Further, therapeutic approaches aimed at boosting protein quality control in protein aggregation diseases should be tailored to the subcellular location affected by inclusion formation. SIGNIFICANCE: We have examined the global cellular response, in terms of protein abundance and phosphorylation changes, to the expression of five human neurodegeneration-associated, aggregation-prone proteins (APPs) in a set of isogenic yeast models. Our results show that the cellular response to each APP is unique to that protein, is different from the response to thermal stress, and is associated with processes at the subcellular location of APP inclusion formation. These results further our understanding of how cells, in a model organism, respond to expression of APPs implicated in neurodegenerative diseases like Parkinson's, Alzheimer's, and ALS. They have implications for mechanisms of toxicity as well as of protective responses in the cell. The specificity of the response to each APP means that research models of these diseases should be tailored to the APP in question. The subcellular localization of the response suggest that therapeutic interventions should also be targeted within the cell.

Combining data integration and molecular dynamics for target identification in α-Synuclein-aggregating neurodegenerative diseases: Structural insights on Synaptojanin-1 (Synj1).

Parkinson's disease (PD), Alzheimer's disease (AD) and Amyotrophic lateral sclerosis (ALS) are neurodegenerative diseases hallmarked by the formation of toxic protein aggregates. However, targeting these aggregates therapeutically have thus far shown no success. The treatment of AD has remained particularly problematic since no new drugs have been approved in the last 15 years. Therefore, novel therapeutic targets need to be identified and explored. Here, through the integration of genomic and proteomic data, a set of proteins with strong links to α-synuclein-aggregating neurodegenerative diseases was identified. We propose 17 protein targets that are likely implicated in neurodegeneration and could serve as potential targets. The human phosphatidylinositol 5-phosphatase synaptojanin-1, which has already been independently confirmed to be implicated in Parkinson's and Alzheimer's disease, was among those identified. Despite its involvement in PD and AD, structural aspects are currently missing at the molecular level. We present the first atomistic model of the 5-phosphatase domain of synaptojanin-1 and its binding to its substrate phosphatidylinositol 4,5-bisphosphate (PIP2). We determine structural information on the active site including membrane-embedded molecular dynamics simulations. Deficiency of charge within the active site of the protein is observed, which suggests that a second divalent cation is required to complete dephosphorylation of the substrate. The findings in this work shed light on the protein's binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and give additional insight for future targeting of the protein active site, which might be of interest in neurodegenerative diseases where synaptojanin-1 is overexpressed.

Information Cascades and the Collapse of Cooperation.

In various types of structured communities newcomers choose their interaction partners by selecting a role-model and copying their social networks. Participants in these networks may be cooperators who contribute to the prosperity of the community, or cheaters who do not and simply exploit the cooperators. For newcomers it is beneficial to interact with cooperators but detrimental to interact with cheaters. However, cheaters and cooperators usually cannot be identified unambiguously and newcomers' decisions are often based on a combination of private and public information. We use evolutionary game theory and dynamical networks to demonstrate how the specificity and sensitivity of those decisions can dramatically affect the resilience of cooperation in the community. We show that promiscuous decisions (high sensitivity, low specificity) are advantageous for cooperation when the strength of competition is weak; however, if competition is strong then the best decisions for cooperation are risk-adverse (low sensitivity, high specificity). Opportune decisions based on private and public information can still support cooperation but suffer of the presence of information cascades that damage cooperation, especially in the case of strong competition. Our research sheds light on the way the interplay of specificity and sensitivity in individual decision-making affects the resilience of cooperation in dynamical structured communities.

Coupled membrane transporters reduce noise.

Molecular systems are inherently probabilistic and operate in a noisy environment, yet, despite all these uncertainties, molecular functions are surprisingly reliable and robust. The principles used by natural systems to deal with noise are still not well understood, especially in a nonhomogeneous environment where molecules can diffuse across different compartments. In this paper we show that membrane transport mechanisms have very effective properties of noise reduction. In particular, we show that active transport mechanisms (those that can transport against a gradient of concentration by using energy or by means of the concentration gradient of other substances), such as symporters and antiporters, have surprising efficiency in noise reduction, which outperforms passive diffusion mechanisms and are well below Poisson levels. We link our results to the coupled transport of potassium, sodium, and glucose to show that the noise in internal glucose level can be greatly reduced. Our results show that compartmentalization can be a highly effective mechanism of noise reduction and suggests that membrane transport could give this extra benefit, contributing to the emergence of complex compartmentalization in eukaryotes.

Proteostasis collapse, a hallmark of aging, hinders the chaperone-Start network and arrests cells in G1.

Loss of proteostasis and cellular senescence are key hallmarks of aging, but direct cause-effect relationships are not well understood. We show that most yeast cells arrest in G1 before death with low nuclear levels of Cln3, a key G1 cyclin extremely sensitive to chaperone status. Chaperone availability is seriously compromised in aged cells, and the G1 arrest coincides with massive aggregation of a metastable chaperone-activity reporter. Moreover, G1-cyclin overexpression increases lifespan in a chaperone-dependent manner. As a key prediction of a model integrating autocatalytic protein aggregation and a minimal Start network, enforced protein aggregation causes a severe reduction in lifespan, an effect that is greatly alleviated by increased expression of specific chaperones or cyclin Cln3. Overall, our data show that proteostasis breakdown, by compromising chaperone activity and G1-cyclin function, causes an irreversible arrest in G1, configuring a molecular pathway postulating proteostasis decay as a key contributing effector of cell senescence.

Synthetic Physical Interactions with the Yeast Centrosome.

The yeast centrosome or Spindle Pole Body (SPB) is an organelle situated in the nuclear membrane, where it nucleates spindle microtubules and acts as a signaling hub. Various studies have explored the effects of forcing individual proteins to interact with the yeast SPB, however no systematic study has been performed. We used synthetic physical interactions to detect proteins that inhibit growth when forced to associate with the SPB. We found the SPB to be especially sensitive to relocalization, necessitating a novel data analysis approach. This novel analysis of SPI screening data shows that regions of the cell are locally more sensitive to forced relocalization than previously thought. Furthermore, we found a set of associations that result in elevated SPB number and, in some cases, multi-polar spindles. Since hyper-proliferation of centrosomes is a hallmark of cancer cells, these associations point the way for the use of yeast models in the study of spindle formation and chromosome segregation in cancer.

Competition in the chaperone-client network subordinates cell-cycle entry to growth and stress.

The precise coordination of growth and proliferation has a universal prevalence in cell homeostasis. As a prominent property, cell size is modulated by the coordination between these processes in bacterial, yeast, and mammalian cells, but the underlying molecular mechanisms are largely unknown. Here, we show that multifunctional chaperone systems play a concerted and limiting role in cell-cycle entry, specifically driving nuclear accumulation of the G1 Cdk-cyclin complex. Based on these findings, we establish and test a molecular competition model that recapitulates cell-cycle-entry dependence on growth rate. As key predictions at a single-cell level, we show that availability of the Ydj1 chaperone and nuclear accumulation of the G1 cyclin Cln3 are inversely dependent on growth rate and readily respond to changes in protein synthesis and stress conditions that alter protein folding requirements. Thus, chaperone workload would subordinate Start to the biosynthetic machinery and dynamically adjust proliferation to the growth potential of the cell.

Computing with biological switches and clocks.

The complex dynamics of biological systems is primarily driven by molecular interactions that underpin the regulatory networks of cells. These networks typically contain positive and negative feedback loops, which are responsible for switch-like and oscillatory dynamics, respectively. Many computing systems rely on switches and clocks as computational modules. While the combination of such modules in biological systems leads to a variety of dynamical behaviours, it is also driving development of new computing algorithms. Here we present a historical perspective on computation by biological systems, with a focus on switches and clocks, and discuss parallels between biology and computing. We also outline our vision for the future of biological computing.

Toward Large-Scale Computational Prediction of Protein Complexes.

Cellular functions are often performed by multiprotein structures called protein complexes. These complexes are dynamic structures that evolve during the cell cycle or in response to external and internal stimuli, and are tightly regulated by protein expression in different tissues resulting in quantitative and qualitative variation of protein complexes. Advances in high-throughput techniques, such as mass-spectrometry and yeast two-hybrid provided a large amount of data on protein-protein interactions. This sparked the development of computational methods able to predict protein complex formation under a variety of biological and clinical conditions. However, the challenges that need to be addressed for successful computational protein complex prediction are highly complex.The post-genomic era saw an emerging number of algorithms and software, which are able to predict protein complexes from protein-protein interaction networks and a variety of other sources. Despite the high capacity of these methods to qualitatively predict protein complexes, they could provide only limited or no quantitative information of the predicted complexes. Recently, a new large-scale simulation of protein complexes was able to achieve this task by simulating protein complex formation on the proteome scale.In this chapter, we review representative methods that can predict multiple protein complexes at different scales and discuss how these can be combined with emerging sources of data in order to improve protein complex characterization.

Computational Models of Cell Cycle Transitions.

The cell cycle is one of the best understood cellular processes in biology. Many of the key interactions occurring throughout the cell cycle have already been identified. This feature makes the system ideally suited for modelers who can use all the available interaction knowledge to build a systems level model of the underlying molecular regulatory network. This model can serve to identify gaps in our knowledge and to test theoretical assumptions or constrain the space of possible solutions. The cell cycle is a repetitive chain of events that goes through several checkpoints. Thus, the cell cycle can be studied under the perspective of an oscillator with checkpoints built into it, or as a series of switch-like transitions that goes from one state to another, converging on a closed loop. We shall discuss that latter position and present a framework for building and analyzing differential equation models of switch-like behavior. We shall then apply and review diverse models for each of the cell cycle transitions and discuss how multiple switches are combined in the cell cycle to create fast and robust transitions.

Context-dependent prediction of protein complexes by SiComPre.

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.