Method for analysing switching and regulation processes

Abstract

The present invention relates to a method for analysing switching and regulation processes, the method comprising the following steps: a) providing at least one reaction system; b) measuring and selecting at least one parameter of at least one reaction system from step a); c) repeating step b) at least once; d) creating at least one time series by means of at least one parameter selected in step b); e) discretising the measured parameter values from step b) in the time series from step d) in order to generate a sequence of micro-states; f) generating a directed graph of macro-states from the at least one discretised time series in step e); g) performing a structural analysis of the directed graph from step f) for combined determination of the number of temporally correlated changes to the micro-states in a subset of randomly selected system components.

Description

[0001] Methods for analyzing switching and regulation processes

[0002] Technical field

[0003] The present invention relates to a method for analyzing switching and regulation processes in at least one reaction system.

[0004] State of the art

[0005] The global dynamics of complex gene regulatory networks have been metaphorically illustrated by the Waddington landscape (Huang et al., 2009; Waddington, 1957). A biophysical interpretation of the Waddington landscape as a quasi-potential landscape views the gene regulatory network as a dynamic system that determines the probabilities of gene expression states and transitions that can occur between these states (Graf and Enver, 2009; Huang, 2011; Macarthur et al., 2009; Moris et al., 2016; Wu et al., 2017; Zhou and Huang, 2011). The Waddington landscape implies that the stable states of gene expression that produce the different cell types can be achieved in individual cells via qualitatively alternative pathways. Alternative pathways from one differentiation state to another have been experimentally demonstrated in Physarum polycephalum for the development of amoebae to plasmodia at the morphological level (Solnica-Krezel et al., 1991 ) and demonstrated at the molecular level in clonal populations of mammalian cells (Bargaje et al., 2017; Huang et al., 2007; Zhou et al., 2016).

[0006] The biological relevance of Waddington's paradigm is supported by experimental evidence (Huang et al., 2009; Wu et al., 2017) and has been confirmed by extensive theoretical studies using approaches from nonlinear dynamics (Ferrell and Machleder, 1998; Ferrell Jr, 2012) or statistical physics (Bornholdt and Kauffman, 2019;

[0007] Huang, 2011) confirms this. Single-cell sequencing data from animal cells have been interpreted accordingly in light of the Waddington landscape, whereby cells are arranged along reconstructed developmental trajectories based on the similarity of their gene expression patterns (Saelens et al., 2019). The explanatory power of the pseudo-time series thus obtained is limited for fundamental reasons, and their interpretability with respect to the Waddington landscape is correspondingly questionable (Sparta et al., 2023; Weinreb et al., 2018). Against the background of this problem, methods have recently been described that allow sequential sampling from single mammalian cells (Chen et al., 2022; Marcuccio, 2023), which expand the relevance and potential scope of application of the method according to the invention.

[0008] Summary of the invention

[0009] The object of the present invention is therefore to measure real time series of the states of interacting components in evolving reaction systems and to investigate the consecutive changes in the states of the components that accompany the interaction.

[0010] The object of the invention is achieved by providing a method for analyzing switching and regulation processes, comprising the following steps: a) providing at least one reaction system, b) measuring and selecting at least one parameter of the at least one reaction system from step a), c) repeating step b) at least once, d) creating at least one time series using at least one parameter selected in step b), e) discretizing the measured parameter values ​​from step b) in the time series from step d) to generate a sequence of microstates, f) generating a directed graph of macrostates from the at least one discretized time series in step e), g) structural analysis of the directed graph from step f) to combinatorially determine the number of time-correlated changes of the microstates in a subset of randomly selected system components.A reaction system describes one or more cells, a cell population, an in vitro system, a subsystem within a cell, or an extracellular system.

[0011] Parameters refer to various measurable quantities or properties used to characterize the structure, dynamics, or function of reaction systems within a network. A parameter defines a microstate.

[0012] The measurement in step b) to obtain a measured value can be carried out either on a sample taken from the at least one reaction system or physically, for example in the form of fluorescence.

[0013] A microstate refers to the detailed state of a parameter in the at least one reaction system at the molecular level. Thus, a microstate can be the concentration of a biomolecule or the specific arrangement of biomolecules relative to one another. A macrostate is the combination of microstates that describes the aggregated or global state of the at least one reaction system at a higher level. System components in step f) are defined as biomolecules, e.g., mRNAs, proteins, metabolites including their covalent modifications, or as complexes (aggregates) of biomolecules.

[0014] In a preferred embodiment of the present invention, a method is provided in which the at least one reaction system is contained in or consists of eukaryotic or prokaryotic cells.

[0015] A further preferred embodiment of the present invention is one in which a method is provided in which, after step a), a step a1) is carried out in which perturbation takes place on the at least one reaction system.

[0016] Perturbation means that a change in the activity of a component in a reaction system is induced. This change occurs through stimulation, pharmacological substances or other agents such as antibodies or aptamers, etc., or, for example, by means of optogenetic methods, genetic or genetic engineering manipulation, or by fusion of cells with different genetic or physiological constitutions, or by mixing corresponding reaction systems.

[0017] Furthermore, a method according to the present invention is preferred in which perturbation is achieved by induction of a cell through stimulation. The stimulation is effected by a light stimulus.

[0018] A method according to the present invention is also preferred, in which in step d) a starting point is determined from the measurement data in step b) for the creation of the time series.

[0019] A method according to the present invention is particularly preferred in which, in step b), the at least one parameter is the cellular concentration of biomolecules, their covalent modifications, or the concentration of existing complexes (aggregates) of biomolecules. Biomolecules are, for example, mRNAs, proteins, or metabolites, including their covalent modifications, as well as the relative arrangement of such biomolecules forming complexes (aggregates).

[0020] Additionally preferred is a method according to the present invention in which a logarithm of the concentration of each parameter is plotted against a defined time interval and graphically represented.

[0021] A further preferred method is one according to the present invention in which the at least one repetition in step c) takes place in a time-defined interval.

[0022] Furthermore, a method according to the present invention is preferred in which the macrostates are brought about by changing at least one parameter of the microstates in step e).

[0023] A preferred method is also one according to the present invention in which the change is an increase or a decrease, or an oscillation of a parameter, or no change at all. A particularly preferred method according to the present invention is one in which the sequence of macrostates is represented in the form of a directed graph, wherein the directed graph is a finite automaton. A finite automaton is a model of behavior consisting of macrostates, state transitions, and actions.

[0024] A method according to the present invention is particularly preferred in which the finite automaton is represented as a Petri net. A Petri net is a mathematical structure capable of formally representing reaction networks, including graphically. It is a directed bipartite graph consisting of places and transitions connected by edges. Places can represent components, such as macrostates of a reaction system, and the transitions represent the transitions between macrostates. In the graphical representation, places are represented as circles and transitions as rectangles or squares.

[0025] Furthermore, a method according to the present invention is preferred in which, in step g), different macrostates are determined which end in a common macrostate.

[0026] Furthermore, a method according to the present invention is preferred in which, in step g), the number of identical macro state changes that end in a common macro state is determined.

[0027] A preferred method is also a method according to the present invention in which a change in the microstate of a parameter is identified, which occurs against the background of other parameters whose microstate is not yet or no longer changing at the respective time.

[0028] Furthermore, a method according to the present invention is preferred in which, in step g), all initial and final macro states and their number are determined.

[0029] Brief description of the characters

[0030] The present invention is explained in more detail with reference to the accompanying drawings. Figure 1A shows an exemplary graphical representation of discretized gene expression states according to the method of the invention;

[0031] Fig. 1 B is an exemplary graphical representation of the process of a discretization according to Figure 1 A;

[0032] Fig. 1 C is an example of a gene expression status table according to the method according to the invention;

[0033] Fig. 1 D a Petri net which was constructed according to the inventive method using the gene expression state table from Figure 1 C;

[0034] Fig. 1 E shows a second example of a Petri net constructed using the method according to the invention;

[0035] Fig. 2A a Petri net constructed using the method according to the invention for the gene anxA;

[0036] Fig. 2B a Petri net constructed using the method according to the invention for the gene bzpJ;

[0037] Fig. 3 a graphical representation of a hub (A) and a rhombus (B) as structural elements of a Petri net structure;

[0038] Fig. 4 graphical representations of possible arrangements of hubs and rhombuses as directly connected parts of a Petri net structure;

[0039] Fig. 5 shows an excerpt from a list of hubs;

[0040] Fig. 6 shows the temporal frequency distribution of hubs as a function of a minimum number of cells that contributed to a hub. Detailed description of the invention

[0041] The method according to the invention can be used, for example, to reconstruct gene regulatory networks for the analysis of switching and regulatory processes, in particular to

[0042] (1) to distinguish concurrent from sequential processes,

[0043] (2) to quantify the degree of concurrency of processes,

[0044] (3) to determine the temporal sequence of switching operations,

[0045] (4) to measure the strength of the correlation (coupling) of switching operations and

[0046] (5) to determine the probability with which certain intermediate or final states are reached and thus, for example, the strength of attractors of a dynamic system at the level of biomolecules in the sense described above.

[0047] The interactions of genes and proteins represent an essential part of a machinery (gene regulatory network) such as that underlying the development of the human body from a fertilized egg cell, giving rise to all bodily functions.

[0048] So-called gene expression rates, which form a gene expression pattern, provide a description or representation of a gene regulatory network or its current state. Thus, the gene expression pattern of a cell represents the current state of that cell's gene regulatory network.

[0049] In Boolean models of gene regulatory networks, each gene can assume two states: "on" or "off," depending on whether it is transcriptionally active or not. When a gene is switched on, the concentration of its mRNA increases until the gene is switched off again or until mRNA synthesis and degradation occur at equal rates and the mRNA concentration reaches a consistently high level after a steady state is established. Besides transcription, other factors influencing mRNA concentration include the regulation of mRNA stability and thus degradation. The concentration of mRNA increases as long as the rate of biosynthesis is higher than the rate of degradation, and it decreases when the rate of degradation is higher than the rate of biosynthesis.The temporal profile of mRNA concentration can thus provide insights into the differential regulation of a gene, although a distinction between the regulation of transcription and the regulation of mRNA stability is not possible without further criteria. If the logarithm of the mRNA concentration is plotted against time, the slope of the kinetic curve is proportional to the x-fold change in mRNA concentration per time interval. Visual analysis of many semi-logarithmic kinetics reveals that larger (significant) changes in mRNA concentration are often accompanied by discontinuities, manifested as sharp kinks in the kinetic curves. Between these kinks, the curve in the semi-logarithmic plot is often largely linear, suggesting an exponential decrease in mRNA concentration in the "off" state and an exponential increase with autocatalytic kinetics of mRNA synthesis in the "on" state.By means of the method according to the invention, it is possible, by defining five expression states, to analyze the change in the expression state of a gene, especially in correlation with other genes, even more precisely than is possible with Boolean models.

[0050] At constant expression levels, a further distinction was made between high, medium, and low. In the high and medium states, the gene is transcriptionally active (i.e., essentially switched on), but the synthesis and degradation rates are balanced.

[0051] Description of the exemplary implementations

[0052] The following examples explain the method according to the invention in more detail, without limiting the scope of the invention.

[0053] In all examples, multinucleated giant cells (plasmodia) of P. polycephalum were used, whose protoplasm is constantly mixed due to strong flow, thus forming a homogeneous protoplasmic reaction volume. Each time series was obtained by repeated sampling of a single plasmodium cell, with one sample taken before and the other 10 samples after a far-red light pulse that triggered sporulation in each of the analyzed plasmodium cells. Two protoplasmic samples were taken from each plasmodium cell at each time point, and total RNA was isolated from each sample. The concentration of each analyzed mRNA was determined twice in each RNA preparation using GeXP RT-PCR in a manner known to those skilled in the art, to obtain two technical replicates of each of the two biological replicates, corresponding to four expression values ​​for each analyzed mRNA at each time point in each plasmodium cell.A total of 122 gene transcripts were obtained (Table A, Appendix).

[0054] Example 1

[0055] The normalized, averaged expression values ​​of each analyzed gene were plotted against time for each cell separately. To illustrate the x-fold changes in gene expression, the logarithm of the concentration of each mRNA is plotted against time in a semilogarithmic diagram.

[0056] To enable a qualitative comparison of the expression kinetics of a gene between different cells, the temporal changes in mRNA concentration are discretized to derive states.

[0057] To discretize the time-dependent changes in the concentration of a gene-specific mRNA within a defined time period in a time series, five states of gene expression are defined, depending on whether the mRNA concentration increases (on), decreases (off), or remains constant over a specific period. A constant level can be low (Io), medium (in), or high (hi).

[0058] Figure 1A schematically depicts the time course of the logarithmic abundance of a gene-specific mRNA, which has been discretized to the gene expression states according to the inventive method. The graphical representation shows that the gene activity, and thus the gene expression state, changes over time.

[0059] Technically, discretization according to the inventive method is performed by evaluating temporally successive data points of each semi-logarithmic diagram by calculating the angle enclosed by each double arrow connecting three temporally successive data points. Figure 1B shows an exemplary graphical representation of the discretization process according to the inventive method. Discretization is performed by scanning successive time points of a single-cell time series of the concentration of a specific mRNA (logfmRNA against time) using a double-arrow algorithm to detect sudden changes in expression kinetics. The algorithm evaluates the angle enclosed by two arrows, one pointing into the past and the other into the future, relative to the current time point. Sudden changes (discontinuities or peaks) are detected by the algorithm.Kinks in the curve result in a small angle β between the two arrows. The algorithm's output, along with the x-fold change in mRNA concentration between successive data points, is translated into discrete gene expression states as shown in Figure 1A.

[0060] For the automatic construction of the Petri nets, the results of the double-arrow algorithm are entered into a table listing the states derived from the temporal evolution of the transcript concentration of each gene. Figure 1C shows an example of such a state table, generated when the method according to the invention is carried out. The table represents the states for three genes and two cells. The state table is used to construct a corresponding state machine in the form of a Petri net, shown in Figure 1D, which represents the behavior of all cells under consideration with respect to the selected genes. For graphical representation, the Petri nets were encoded in ANDL format (Abstract Net Description Language; (Heiner et al., 2013)) and exported as text using an R script.Each Petri net was generated in two versions: one in which the transitions of each cell were represented as individual, potentially parallel transitions, and another in which the parallel transitions connecting the sites were combined into a single transition. Each ANDL file was then imported into Snoopy to obtain the graphical layout of an executable Petri net.

[0061] To represent similarities and differences between individual cells, each transition between successive states of a cell is represented by a cell-specific transition named after the cell ID number and the time of the transition. For graphical representation and illustration, the transitions are color-coded either according to the cell ID number, as shown in Figure 1D, and / or according to the time at which the transition between the states occurred.

[0062] The transitions representing the changes within the same cell can be highlighted in the same color. In the present example, both cells begin in the same initial state (gray circle labeled lo_lo_hi, Initial State), but then take different paths (i.e., they pass through different intermediate states) and finally end up in the same final state (gray circle labeled lo_hi_off), which is assigned to them at the end of the experiment.

[0063] To obtain an initial label on the Petri net and define the specific initial state of a network, all sites representing initial states are connected to the init site (the precursor site) by an immediate transition (a transition that switches instantly, represented as a black rectangle). This transition moves a label to one of the sites representing the possible initial states of the reaction network. All sites that represent neither initial nor final states always have exactly as many incoming as outgoing edges. To directly read the activity of individual genes and generate simulation curves, a Petri net can optionally be equipped with additional sites whose labels represent the activity of the individual genes, as shown in Figure 1E.

[0064] Example 2

[0065] Figure 2A shows a Petri net used to analyze the anxA gene using the method according to the invention. Samples were taken from a total of 16 Plasmodium cells for the experiment.

[0066] In most cells (14 / 16), the expression of anxA mRNA was high at the beginning of the experiment and decreased over the course of one hour after the FR light stimulus. Subsequently, the mRNA concentration remained temporarily constant in ten cells before the gene was switched off again and the mRNA concentration decreased accordingly. Four cells went directly from "off" to the final state "Io" without going through the intermediate "in" state. The different shades of gray of the transitions show that the switching between the states occurred in the same temporal sequence in most cells, but at individually different times.

[0067] A similar behavior regarding intermediate "in" states is observed for other genes, such as the bzpJ gene. The corresponding Petri net is shown in Figure 2B.

[0068] Using the method according to the invention, it is possible to examine the temporal regulatory patterns of individual analyzed genes as well as the variability of these patterns. Both the single-gene Petri net for the anxA gene in Figure 2A and for the bzpJ gene in Figure 2B show essentially only one or a few main trajectories of gene expression states followed by the cells. While these main trajectories referred to the temporal sequence of changes between the expression states of a specific single gene, there were significant differences in the chronological times at which these transitions occurred in individual cells. Example 3

[0069] Using the method according to the invention, genes that change their expression status in a correlated manner can be identified with the aid of hubs and diamonds in the Petri net. Diamonds also indicate changes in the expression state of genes that occur against the background of other genes whose expression state is not yet or no longer changing at the respective time point, thus revealing the temporal partial order of differential regulation.

[0070] Figure 3 shows possible structural representations of hubs (A) and rhombuses (B) within a Petri net structure. Furthermore, hubs and rhombuses can be structural motifs of a Petri net and can also be directly connected to each other within a Petri net, as shown in Figure 4.

[0071] The inventive method was used for all combinations of 72 highly differentially regulated genes in the Plasmodium cells to search for hubs in the constructed Petri nets, defined by a specific minimum number of incoming edges. Each hub was characterized by the combination of the names of the genes for which the Petri net was constructed, the expression status of these genes as represented by the hub site, the ID number of the cells in which the change in expression status, as represented by the hub site, occurred, the times at which the transitions occurred in each cell, and the number and identity of the antecedent sites of the transitions, i.e., the sites that lead directly into the hub. Figure 5 shows an exemplary extract from a list of hubs.

[0072] Thus, the inventive method makes it possible to perform a quantitative investigation of genes by comparing their expression states over time. These comparisons allow an assessment of whether a change in the expression states of several genes is due to coordinated or random changes. As shown in Figure 6, the differences between random and coordinated changes become clear when the temporal distribution of hubs is considered as a function of the minimum number of cells that contributed to a hub. The number of hubs was plotted against the minimum, median, and maximum time of their occurrence, as a function of the minimum number of cells that formed a hub.The distributions for a minimum of one or two cells were rather random, with a peak around the "point of no return" (4-5 h), where the expression of many genes likely changed. For a minimum of six or more cells, the distributions were qualitatively different compared to those for a minimum of only one cell. There was a high number of hubs occurring during the first hour, i.e., immediately after the far-red inductive light pulse. Considering the median or maximum time of a hub transition, there is a substantial number of changes occurring throughout the entire observation period, at least for 7-8 hours after the light pulse, suggesting that correlated changes in gene expression took place throughout this entire timeframe.If all genes for which multiple combinations were found were strictly correlated in their expression, all transitions leading to the corresponding sites of such a hub would have to originate from the same group of cells. Thus, gene pairs that contribute more frequently to the occurrence of a hub than others can also be identified using the method according to the invention, which then indicates potential co-regulation.

[0073] The inventive process is summarized below in other words:

[0074] A method for analyzing switching and / or regulatory processes, comprising the following steps: a) Creating one or more time series, each by determining one or more parameters that represent successive states of the system under consideration. The beginning of the time series can be defined by a perturbation, such as the induction of a cell or a biochemical reaction system, or by another event. b) Discretization, that is, converting the time series into a sequence of defined (discrete) states. A state can, but need not, also be defined by the way in which a parameter changes. The change could, for example, be an increase, a decrease, or an oscillation within the time window to which the state is assigned.c) Combinatorial search for time-correlated changes of state in a subset of randomly selected system components (and / or individuals or systems) and determination of the frequency with which these state changes occur in a correlated manner. This search can and should be performed systematically, so that all possible combinations of the components of a subset are evaluated. In practice, this search can be carried out by constructing a finite automaton (or a corresponding mathematical structure), for example in the form of a Petri net, and by structurally analyzing the automaton or the mathematical structure.

[0075] The following writings are cited in this document:

[0076] Bargaje, R., Trachana, K., Shelton, M. N., McGinnis, C. S., Zhou, J. X., Chadick, C., Cook, S., Cavanaugh, C., Huang, S., Hood, L, 2017. Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells. Proc. Nat. Acad. Sei. USA 114, 2271-2276.

[0077] Bornholdt, S., Kauffman, S., 2019. Ensembles, dynamics, and cell types: Revisiting the statistical mechanics perspective on cellular regulation. Journal of Theoretical Biology 467, 15-22.

[0078] Ferrell, J. E. J., Machleder, E. M., 1998. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280, 895-898.

[0079] Ferrell Jr, J. E., 2012. Bistability, bifurcations, and Waddington’s epigenetic landscape. Current Biology 22, R458-R466.

[0080] Heiner, M., Schwarick, M., Wegener, J., 2015. Charlie — An Extensible Petri Net Analysis Tool. In: Devillers R., Valmari A. (eds) Application and Theory of Petri Nets and Concurrency. PETRI NETS 2015. Lecture Notes in Computer Science, vol 9115. Springer, Cham, pp. 200-211 .

[0081] Hopfensitz, M., Müssel, C., Maucher, M., Kestler, H. A., 2012. Attractors in Boolean networks: a tutorial. Computational Statistics 28, 19-36.

[0082] Huang, S., 2011 . The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post-Darwinian biology? BioEssays 34, 149-157.

[0083] Huang, S., Ernberg, I., Kauffman, S., 2009. Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin Cell Dev Biol 20, 869-876.

[0084] Huang, S., Guo, Y.-P., May, G., Enver, T., 2007. Bifurcation dynamics in lineagecommitment in bipotent progenitor cells. Developmental Biology 305, 695-713. Macarthur, B. D., Ma'ayan, A., Lemischka, I. R., 2009. Systems biology of stem cell fate and cellular reprogramming. Nat Rev MolCell Biol, 1 -10.

[0085] Machado, D., Costa, R. S., Rocha, M., Ferreira, E. C., Tidor, B., Rocha, I., 2011.

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[0088] Quantifying 35 transcripts in a single tube: model-based calibration of the GeXP multiplex RT-PCR assay. BMC Biotechnology 21 , 29.

[0089] Moris, N., Pina, C., Arias, A. M., 2016. Transition states and cell fate decisions in epigenetic landscapes. Nature Reviews Genetics 17, 693-703.

[0090] Pretschner, A., Pabel, S., Haas, M., Heiner, M., Marwan, W., 2021. Regulatory dynamics ofc ell differentiation revealed by true time series from multinucleate single cells. Front. Genet. 11 , 612256.

[0091] Rätzel, V., Marwan, W., 2015. Gene expression kinetics in individual plasmodial cells reveal alternative programs of differential regulation during commitment and differentiation. Develop. Growth Differ. 57, 408-420.

[0092] Saelens, W., Cannoodt, R., Todorov, H., Saeys, Y., 2019. A comparisonof singlecell trajectory inference methods. Nature Biotechnology 37, 547-554.

[0093] Sauer, H. W., Babcock, K. L., Rusch, H. P., 1969a. Sporulation in Physarum polycephalum. A model system for studies on differentiation. Exp. Cell Res. 57, 319- 327.

[0094] Solnica-Krezel, L., Burland, T. G., Dove, W. F., 1991 . Variable pathways for developmental changes of mitosis and cytokinesis in Physarum polycephalum. The Journal of Cell Biology 113, 591-604. Sparta, B., Hamilton, T., Hughes, S., Natesan, G., Deeds, E. J., 2023. A lack of distinct cell identities in single-cell measurements: revisiting Waddington’s landscape. bioRxiv, 2022.06.03.494765.

[0095] Waddington, C. H., 1957. The Strategy of the Genes; a Discussion of Some Aspects of Theoretical Biology. Allen & Unwin, London.

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[0101]

[0102] Gen Definition Organismus UniProt Länge E.Value Identi

[0103]

[0104] Gene Definition Organism UniProt Length E.Value Identi pptB Protein phosphatase 2C 43 sativa subsp. japonica _Probably Oryza sativa subsp. japonica Q7XUC5 388 IE-24 31 pumA Pumilio homolog 1 thaliana Arabidopsis thaliana Q9ZW07 968 ​​3E-133 36 rgsA Regulator of G-protein signaling 2 musculus Mus musculus 008849 211 0.014 41 spiA Rootletin musculus Mus musculus Q8CJ40 2009 0.0002 25 scaper S-phase cyclin A-associated protein in the endoplasmic reticulum Homo sapiens Q9BY12 1400 IE-57 25 uchA Secretory immunoglobulin A-binding protein EsiB coli Escherichia coli O6:H1 (strain CFT073 / A0A0H2VD490 2E-15 33 atm Serine protein kinase ATM musculus Mus musculus Q62388 3066 0 29 rps6KA5 Serine / threonine protein kinase fhkE discoideum -Probably .. Dictyostelium discoideum Q54VI1 712 3E-76 32 mrkC Serine / threonine protein kinase MARK-B discoideum _Probable Dictyostelium discoideum Q54MV2 715 6E-109 35 pakA Serine / threonine protein kinase pa kA discoideum Dictyostelium discoideumQ55D99 1197 3E-84 42 rocol Serine / Threonine protein kinase patsl discoideum - Probably Dictyostelium discoideum Q55E58 3184 IE-10 22 pksA Serine / Threonine protein kinase phg2 discoideum Dictyostelium discoideum Q54QQ1 1387 3E-87 37 skpl SKPl-like protein 21 thaliana Arabidopsis thaliana Q8LF97 351 3E-11 39 spdYC Speedy protein A norvegicus Rattus norvegicus Q8R496 312 3E-06 26 spoTA5 / J Spermogenesis-associated protein 5-like protein 1 sapier Homo sapiens Q9BVQ7 753 8E-133 40 tspA Spore wall protein 2 intestinalis Encephalitozoon intestinalis Q95WA4 1002 2E-27 24 cklA T-cell leukemia homeobox protein 3 gallus Gallus gallus 093367 297 5E-06 40 hcaD Testis-expressed protein 30 musculus Mus musculus Q3TUU5 225 3E-09 33 agdA Transcription factor SPT20 homologous discoideum Dictyostelium discoideum Q54VY3 1402 IE-34 39 pbrMl Transcription initiation factor TFIID subunit 1 discoideum Dictyostelium discoideum Q54DH8 2310 7E-27 24 mybQ transcription activator Myb gallus Gallusgallus P01103 641 3E-58 59 cudB Transcriptional regulator cudA discoideum _Putative ... Dictyostelium discoideum 000841 802 5E-64 60 altA Tubulin alpha-1A chain polycephalum Physarum polycephalum P50258 450 IE-136 77 gapA Uncharacterized protein C622.14 pombe (strain 972 / ATCSchizosaccharomyces pombe (strain 97094601 321 8E-21 37 cudA Uncharacterized protein DDB_G0286447 discoideum Dictyostelium discoideum Q54LR3 521 2E-50 50 psgA Uncharacterized transmembrane protein DDB_G0284159 dis Dictyostelium discoideum Q54Q42 86 0.069 100 weel Wheel-like protein kinase musculus Mus musculus P47810 646 8E-57 39 ncbP Zinc finger CCCH domain-containing protein 13 musculus Mus musculus E9Q784 1729 2E-34 36 znfA Zinc finger protein 732 sapiens Homo sapiens B4DXR9 585 5E-14 26

[0105] Reference symbol list

[0106] On rise Off fall

[0107] Low

[0108] In medium or inactive mode

[0109] Hi up

Claims

Patent claims 1. A method for analyzing switching and regulation processes, comprising the following steps: a) providing at least one reaction system, b) measuring and selecting at least one parameter of the at least one reaction system from step a), c) repeating step b) at least once, d) creating at least one time series using at least one parameter selected in step b), e) discretizing the measured parameter values ​​from step b) in the time series from step d) to generate a sequence of microstates, f) generating a directed graph of macrostates from the at least one discretized time series in step e), g) structural analysis of the directed graph from step f) to combinatorially determine the number of time-correlated changes of the microstates in a subset of randomly selected system components.

2. Method according to claim 1, characterized in that the at least one reaction system is contained in or consists of eukaryotic or prokaryotic cells.

3. Method according to claim 1 or 2, characterized in that after step a) a step a1 ) is carried out in which perturbation takes place on the at least one reaction system.

4. Method according to claim 3, characterized in that perturbation is carried out as induction of a cell by stimulation.

5. Method according to claim 1 or 2, characterized in that in step d) a starting point is determined from the measurement data in step b) for the creation of the time series.

6. Method according to at least one of the preceding claims, characterized in that in step b) the at least one parameter is the cellular concentration of biomolecules, of their covalent modifications or the concentration of existing complexes (aggregates) of biomolecules.

7. Method according to claim 6, characterized in that a logarithm of the concentration of each parameter is plotted against a defined time interval and graphically represented.

8. Method according to at least one of the preceding claims, characterized in that the at least one repetition in step c) takes place in a time-defined interval.

9. Method according to at least one of the preceding claims, characterized in that the macrostates are brought about by changing at least one parameter of the microstates in step e).

10. Method according to claim 9, characterized in that the change is an increase or a decrease or an oscillation of a parameter or no change.

11. Method according to at least one of the preceding claims, characterized in that the sequence of macrostates is represented in the form of a graph, wherein the directed graph is a finite automaton.

12. Method according to claim 11, characterized in that the finite automaton is represented as a Petri net.

13. Method according to at least one of the preceding claims, characterized in that in step g) different macrostates are determined which end in a common macrostate.

14. Method according to claim 13, characterized in that in step g) the number of identical macro state changes is determined which end in a common macro state.

15. Method according to claim 13, characterized in that a change in the microstate of a parameter is identified, which occurs against the background of other parameters whose microstate is not yet or no longer changing at the respective time.

16. Method according to at least one of the preceding claims, characterized in that in step g) all initial and final macro states and their number are determined.

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