Introduction to Systems Biology Coursera Quiz Answers 2022 [💯Correct Answer]

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About Introduction to Systems Biology Course

The student will learn about modern Systems in this class. Biology was mostly about the parts of mammalian cells and how they work. Molecular biology is giving way to modular biology.

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Introduction to Systems Biology Quiz Answers

Week 01: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Systems Level Reasoning | Molecules to Pathways

Q1. What is true about network models?

  • Graph theory analysis of network models was developed in response to the systems biological approach of understanding cellular systems.
  • Network models do not allow directed interactions.
  • Protein-protein interaction models have nodes as proteins, which are connected by edges, only if the proteins show functional redundancy.
  • Gene co-expression networks have genes as nodes, which are connected by edges, if their expression correlate under different conditions.
  • There are no computational tools for the analysis of networks online.

Q2. Top down modeling…

  • uses partial or ordinary differential equations to predict cellular behavior with respect to time or to time and place.
  • is used for hypothesis generation under complex situations.
  • is based on the analysis of a few outputs of a cellular system (e.g. MAPK phosphorylation).
  • focuses only on the description of an entire organism (e.g. the human body) never on single cells.
  • has been applied to generate the Hodgkin–Huxley model.

Q3. Bioinformatics…

  • integrates statistics, computational science and biology to get an understanding of large-scale datasets.
  • is an upgraded version of Matlab.
  • is the design and construction of new biological systems resembling electronic circuits as the basis for the development of computers made from biological components.
  • is becoming less important in today’s molecular cell biology.
  • deals with the automatic generation of short summaries about the content of online biology articles.

Q4. Which of the following statements about the usage of bioinformatics databases is correct?

  • The Ensembl database does not include information about transcripts.
  • Swiss-Prot contains information about protein-coding DNA sequences.
  • The OMIM database can be used to look for genes involved in a certain disease, e.g. rheumatoid arthritis.
  • The FDA Adverse Event Reporting System database summarizes unexpected events observed after knocking out genes in mice.
  • Experimental data is superior to information obtained from bioinformatic databases, so that any prior knowledge from bioinformatic databases can be ignored, once experimental data has been obtained.

Q5. What are the key characteristics of systems biology?

  • Systems biology describes how the human or animal phenotypes change due to disease, mutation and environment (for example, as outlined for gene mutations in the Mouse Genome Informatics Phenotypes, Alleles & Disease Model database).
  • Systems biology is a subtype of botany.
  • Systems biology focuses on development of mathematical descriptions of organ function such as the calculation of the glomerular filtration rate (GFR) for kidney function.
  • Systems biology describes the interface between brains and machines.
  • Systems biology integrates gene products, other molecules and their interactions into quantitative descriptions of physiological functions.

Q6. Different types of radiation increase the cellular protein p53 to produce variable responses to cell proliferation. What can you conclude from the schematic below?

  • Gamma radiation would be healthier than UV light if p53 dynamics are regulated properly.
  • The response of a cellular system to a given input only depends on the activated pathway.
  • Although different inputs activate the same protein, different activation dynamics could explain different cellular responses.
  • p53 expression is proportional to the wavelengths of the light.
  • p53 oscillations are detrimental for cell function under UV light.

Q7. Given the below reaction scheme, which statement about enzyme kinetics is correct?

  • cAMP production rate increases with increasing k-1.
  • cAMP production rate increases with increasing k2.
  • cAMP production rate increases with decreasing ATP concentration.
  • cAMP production rate decreases with increasing k2.
  • cAMP production rate increases with increasing k1.

Q8. The value of the Hill coefficient indicates…

  • the difference between cooperative binding and competitive binding of different substrates to an enzyme.
  • the number of reactions required for non-linear enzymatic processes.
  • the number of intermediates which are generated but not released during the enzymatic processing of a substrate to its final product.
  • the quotient of the activation energy (energy barrier) for an enzymatic reaction divided by the activation energy of the same reaction without enzymatic catalysis.
  • the influence of ligand binding to the first binding site on the affinity for the second binding site.

Q9. Which statement is true for partial agonist?

  • A partial agonist acts as a competitive antagonist to a full agonist.
  • A partial agonist is a chemical that is defined by a partial structural overlap with the agonist.
  • Addition of a partial agonist requires less agonist for receptor activation.
  • A partial agonist does not activate the receptor.
  • Partial agonists only activate extracellular domains of the receptor.

Q10. A competitive inhibitor competes with the substrate for the catalytic site. An uncompetitive inhibitor binds to a separate site, which reduces substrate conversion to the product. In the reactions below, which interaction depicts competitive inhibition only?

  • B
  • A
  • E
  • C
  • D

Q11. What is kcat?

  • kcat is the Michaelis constant (Km) divided by the total enzyme concentration.
  • kcat is the maximum reaction velocity (Vmax) divided by the total enzyme concentration.
  • kcat is the Michaelis constant divided by the free enzyme concentration.
  • kcat is the reaction velocity divided by the free enzyme concentration.
  • kcat is the Michaelis constant divided by the reaction velocity.

Week 02: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Pathways to Networks | Physical Forces and Electrical Activity in Cell Biology

Q1. What is true about compartmental models?

  • A compartmental model considers different spatial locations as different sets of ordinary differential equations, and allows the exchange of selected components between these sets.
  • A cellular model that distinguishes between cytosol and mitochondria is a 3-compartment model.
  • A compartmental model describes chemical reaction rates at different sites of a protein complex.
  • A compartmental model depicts the simulation of a paracrine hormone driven interactions of different cell types spatially separated from each other.
  • A compartmental model uses partial differential equations to depict the movement of components between different spatial locations.

Q2. Scaffold proteins…

  • bring together multiple proteins or enzymes.
  • catalyze reactions when enzyme levels are low.
  • play no active role in signaling.
  • are critical for receptor desensitization.
  • have a kinase domain that is not critical in signaling.

Q3. Cross pathway specificity…

  • involves the ability of signaling proteins to react with components of multiple pathways.
  • defines the process when one signaling pathway crosses the other without activating it.
  • does not contribute to cell biological network generation.
  • triggers differentiation of embryonic stem cells.
  • triggers proliferation of malignant tumor cells.

Q4. What is a bowtie configuration?

  • It is the binding motif of cAMP to PKA.
  • It is a unique dynamic stimulus for receptor activation that leads to a robust input-output relationship.
  • It is a gene regulatory pattern that creates a single output for multiple inputs.
  • It is a network pattern where a single protein or signaling component is connected to multiple inputs and multiple outputs.
  • It is a commonly occurring protein folding pattern.

Q5. There is multipathway connectivity in cellular systems because…

  • individual signaling pathways cannot create complex input-output dynamics.
  • cellular systems have to deal with large and small molecules.
  • multipathway connectivity is the only way to interact with multiple ligands at the same time.
  • multipathway connectivity makes a system more unstable against loss of individual components.
  • multipathway connectivity ensures coordinated responses to activation of different receptors.

Q6. Drug resistance of cancer is a clinical consequence of multipathway connectivity…

  • because receptors with multiple downstream targets are difficult to target with a single ligand.
  • because multiple pathways create redundancies allowing the tumor cells to bypass the inhibition.
  • because the dose of the drug needs to be increased with increased number of interacting proteins with a given receptor.
  • because tumor cells have no multipathway connectivity.
  • because chemotherapy drugs can only interact with highly connective targets called hub proteins.

Q7. Which signaling components enable networking?

  • Genes that disable transcription enable networking.
  • Proteins with bidirectional specificity enable networking.
  • Proteins that have crosspathway specificity enable networking.
  • Proteins that phosphorylate other proteins enable networking.
  • Genes that alter phenotypes enable networking.

Q8. Hereditary elliptocytosis is an inherited anemia characterized by erythrocytes that are large and elliptical instead of having a biconcave disc shape. Which molecular defect can explain this phenotype mechanistically?

  • Decreased glucose-6-phosphatedehyrogenase activity lowers the production of reduced glutathion, causing impairment in the removal of reactive oxygen species denaturing hemoglobin.
  • A defect in actin interacting proteins destabilizes the cortical actin cytoskeleton of erythrocytes.
  • A defect in collagen hydroxylation causes the production of instable collagen.
  • Defects in integrins prevent erythrocytes from forward and backward transmigration through small holes in the vascular wall, which normally transforms elliptic erythrocytes into disc shaped ones.
  • A mutation of the protein fibrillin reduces its capacity for intracellular transforming growth factor beta sequestration, so that it can stimulate apoptotic pathways.

Q9. Mechanotransduction explicitly refers to…

  • effect of physical forces on eukaryotic cells only.
  • the effect cells have on one another during tissue degeneration after infection by clostridium perfringens, a flesh eating bacteria.
  • conversion of signals from physical forces into biochemical signals.
  • biochemical signals being converted to mechanical forces.
  • the effects of transport of MAPK kinases into the nucleus.

Q10. Analytical models of cellular movements can be used to explain…

  • how signaling pathways connect to one another to form networks.
  • the basis of muscle degeneration.
  • how cells and extracellular matrix come together to form tissues.
  • how actin filaments push against the plasma membrane.
  • how reagents can be created to study the effect of actin on G-proteins.

Q11. ATP hydrolysis by motor proteins is used for…

  • generation of force.
  • the formation of actin filament branches.
  • connecting actin filaments to microtubules.
  • generation of inorganic phosphate that can be used to increase bone density in patients with osteoporosis.
  • reducing the concentration of ATP and thus inhibiting protein kinases.

Q12. The Nernst potential…

  • contains a factor describing tissue specific differences for ions.
  • negatively correlates with temperature (in Kelvin).
  • is used to calculate the ion-specific current flowing across the membrane.
  • considers the time and voltage dependant membrane conductance for the investigated ion.
  • is based on the intra- and extracellular concentration of free- and protein-bound ions.

Q13. Action potential…

  • is observed in neurons and liver cells.
  • is a slow intracellular signaling response in neurons.
  • is a phenomenon observed in certain cells where membrane potential is rapidly depolarized in response to electrical stimulus.
  • refers to the coupling between mechanical and biochemical signals in bacterial cells.
  • refers to the potential of a cell to generate actions in response to signals from adrenaline.

Q14. At the resting potential…

  • the opening of potassium channels depolarizes the cell membrane.
  • the opening of potassium channels repolarizes the cell membrane.
  • the opening of potassium channels hyperpolarizes the cell membrane.
  • the opening of potassium channels is followed by the opening of sodium channels.
  • the opening of potassium channels causes the refractory period.

Q15. The Hodgkin-Huxley model describes…

  • designer instruments to study electrical activity in cells.
  • integrated circuits within the nucleus of cells.
  • the propagation of an action potential along an axon.
  • how neurons can divide.
  • a contemporary model from the last decade of electrical activities in cells.

Q16. Integration of electrical and biochemical activities during long-term potentiation includes:

  • a series of hyperpolarizations in the presynaptic neuron.
  • an increase of the number potassium channels in the postsynaptic membrane.
  • activation of NMDA-channels to block Ca2+-flow across the membrane.
  • glutamate triggered depolarization of the postsynaptic membrane.
  • the activation of intracellular kinases by decreased Na+-levels.

Q17. Myelin sheath forms during development, enveloping the axons of neurons in the central nervous system. It is critical in electrical signal transmission, especially for neurons with longer axons, because it increases the amount of electrical charge the neuron can hold during depolarization by acting like additional layers of plasma membrane. Knowing below relationship, what can you say about myelin sheath’s effect on electrical properties of neurons?

  • It decreases CM
  • It increases EL, increases CM
  • It increases EL, increases EK
  • It increases RL, decreases RNa
  • It increases ELRL

Q18. Which statements are true about the cytoskeleton?

I. Microtubules and actins assemble from monomers in a vectorial manner.

II. Actin filaments are critical in generation of intracellular forces via myosin motors.

III. Intermediate filaments form the intracellular network for vesicular transport.

IV. In vivo actin polymerization can be mechanosensitive through its upstream signaling network.

V. GTPase control of microtubule filaments pushes the cell membrane during cell spreading.

  • I, III, IV
  • I, II, IV
  • I, II, III, IV
  • II, IV, V
  • I, II, III

Week 03: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Mathematical Representations of Cell Biological Systems | Simulations of Cell Biological Systems

Q1. We studied the recent cell spreading model developed by Rangamani et al. that used a hybrid approach. What was the “hybrid” approach employed by the investigators?

  • The study focused on the hybridoma technology.
  • It used deterministic signaling and stochastic filament dynamics models.
  • It was a hybrid of electrical and mechanical stimulations.
  • It used deterministic mechanics and stochastic signaling models.
  • It was a hybrid of electrical and mechanical signals.

Q2. Multicompartmental models…

  • ignore component exchange between the different compartments independent of their size.
  • consist of different sets of ordinary differential equations which are always solved analytically to predict the progression of concentration in every component.
  • assume high diffusion rates of all involved molecules and components through the boundaries separating the compartments.
  • always separate the cell into different signaling pathways, each of which is simulated independently.
  • assume the immediate availability of each newly produced component to all reactants within the same compartment.

Q3. Assume a receptor on the plasma membrane that is evenly distributed through all segments of a neuron, and activation of this receptor triggers the generation of a signaling component, such as cAMP, on the cytosolic site of the receptor. Differences in cell shape can lead to concentration variations of this membrane generated signaling component in the different subcellular parts, e.g. its concentration can be high in the dendrite and low in the cell body. Which of the following statements about the resulting concentration gradient is FALSE?

  • Changes in the dendrite length can influence the concentration gradient.
  • A low affinity of the receptor for its ligand would not prevent generation of a gradient.
  • Changes in the diffusion rate of the signaling component can influence the concentration gradient.
  • A high concentration of the signaling component in the dendrite and a low concentration in the cell body might allow the activation of dendritic components such as membrane channels without activating cell body components such as transcription factors.
  • If the receptor activity is only localized at the dendrite plasma membrane, the concentration gradient will disappear.

Q4. Norepinephrine is an extracellular ligand that activates multiple pathways in an intracellular signaling network. What effect of norepinephrine within the cell would be difficult to answer without mathematical modeling?

  • How many separate receptors are activated by norepinephrine with similar affinities?
  • What is the role of the positive feedback loop to act as a switch that turns on persistent MAPK activity?
  • Are Gq and Gi/o pathways both stimulated by norepinephrine?
  • Is dissociation of G protein subunits that interact with different multiple effectors required for MAPK activation?
  • Can a negative feedback loop within one epinephrine stimulated pathway lead to persistent MAPK activity?

Q5. Which one of the below statements about ordinary differential equations-based models is FALSE?

  • There is little or no time course data to constrain ODE models properly
  • There are number of software platforms that can be used to compute system of ODEs.
  • Multiple compartments, such as nucleus and golgi, need to be implemented when well-stirred assumption is inadequate.
  • ODE models cannot account for the effects of different cell morphologies.
  • ODE models assume that all reactants have equal access to their partners.

Q6. The Bateman function below describes the plasma levels of a given drug over time after administration of the drug into another compartment (e.g., skeletal muscle) from which it is slowly released into the blood. In addition, the drug is slowly cleared from the blood (e.g., by the liver). What can be said about the Bateman function?

y(t)= a ∙ k_1/(k_1-k_2 ) ∙(e^(-k_2 t)- e^(-k_1 t) )

I: compartment drug is administrated to (e.g. skeletal muscle)

II: blood

y(t): drug blood level at timepoint t

a: drug dose (divided by its apparent volume of distribution)

k1: rate of entry into blood

k2: rate of exit from blood

  • The Bateman function describes a system that cannot be solved analytically.
  • Parameters in Bateman function can be determined by fitting the blood concentrations of the drug as a function of time.
  • Bateman function is not an identifiable model since it describes the change in concentration of a drug within a biological compartment.
  • The parameters, k1 and k2, in the Bateman function do not depend on the applied drug.
  • Clearance defects (e.g., kidney malfunction) do not affect blood levels, thus they are ignored.

Q7. Each deterministic model of a cell biological system includes many parameters such as enzymatic reaction rates. Known enzymatic reaction rates were often determined….

  • by measuring substrate blood levels after application of an enzyme inhibiting drug.
  • based on experiments with intact tissues.
  • based on experiments with purified or recombinant proteins.
  • based on experiments with purified cellular organelles.
  • based on experiments with the intact cell.

Q8. Solving partial differential equations often requires the use of numerical approximations. What is true about such numerical analysis?

  • Numerical approximations need initial and final values to predict further progression.
  • Numerical approximation ends with descriptive equations for each component of the simulated system, describing its progression over time.
  • The Runge-Kutta methods are historically important but lack accuracy, thus are not used today.
  • One method for solving a system of partial differential equations involves division of continuous domain into smaller parts for which numerical solvers can be used.
  • The accuracy of forward Euler integration method does not depend on the size of time steps.

Q9. Numerical integration methods can be used to divide a time-dependent continuous function into small time-steps, and to approximate the area under the curve. Which one of the options about numerical integration is FALSE?

  • Increase in the size of time-steps leads to increased error.
  • Time-steps represent the resolution of approximation: smaller they are the more accurate will be the representation.
  • The result of the real integration is obtained when infinitesimally small time-steps are taken.
  • The approximation results do not depend on the time-step size.
  • Time-step size is inversely correlated with the computational cost of the operation.

Q10. Which one of the below statements about numerical simulations is FALSE?

  • One needs to include adequate biological details in a mathematical model to enable simulations that can provide non-intuitive predictions about the dynamical behavior of the system that can then be experimentally tested.
  • When a numerical simulation does not match experimentally observed biological behavior, the only way to fix the model is to tweak biochemical parameters.
  • Virtual Cell is a free academic software platform that can use real microscope images for partial differential equations-based models.
  • There are several Runge-Kutta solvers in Matlab, which can be used to solve systems of ordinary differential equations that define signaling networks.
  • Some of the parameters can be estimated using biochemical or cell biological experiments in the literature.

Week 04: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Experimental Technologies | Network Building and Analysis

Q1. Chromatin immunoprecipitation followed by sequencing of the immunoprecipitated DNA…

  • gives functional information about associations between transcription factor and target gene, i.e. if the transcription factor influences the target gene’s expression.
  • gives sequence information about the region of the gene where the transcription factor binds.
  • is experimentally challenging because genomic DNA needs to be preserved in one piece.
  • involves precipitation of chromatin fragments by artificial DNA-coupled beads that bind complementary genome regions of interest (e.g., the binding region of a certain transcription factor)
  • cannot be used to analyze the methylation status of genes.

Q2. mRNA sequencing (Illumina)…

  • involves the removal of contaminating RNA that is characterized by poly A-tails.
  • can be used to identify new gene transcripts.
  • needs a different instrument for sequencing compared to genomic sequencing, because it has to sequence RNA instead of DNA.
  • always identifies all gene transcripts that are expressed in a cell independent of their expression levels.
  • can be used to determine the relative expression of one protein to another.

Q3. Proteomics…

  • is a useful technique to identify proteins that are part of one complex, if combined with immunoprecipitation.
  • is a single-step high-speed technique to identify protein structures.
  • studies have shown that protein and mRNA levels always match closely in health and disease.
  • can be used to identify all the proteins with high accuracy regardless of their size or quantity.
  • involves deep sequencing of nucleic acid fragments using mass-spectroscopy.

Q4. mRNA-sequencing creates tens of millions of sequencing reads, i.e. sequences consisting of about 30 to 150 bases. They have to be aligned to the reference genome to identify from which genes they come from. Afterwards transcript and gene expression levels can be determined and be compared between two different conditions (e.g. two different cell lines). What is true about the mRNA-sequencing data analysis?

  • Neither single- nor paired-end-sequencing (i.e. the sequencing of a cDNA-fragment from one or both ends) allow an estimation of the length of the fragment.
  • If not an experimental artifact, each cDNA sequence (that was generated by reverse transcription of the mRNA) can be aligned to a continuous part of the reference genome.
  • The units ‘fragments per kilo base pairs per million reads’ (FPKMs) and ‘reads per kilobase pairs per million reads’ (RPKMs) are gene expression units that do neither consider the length of the transcript nor the total number of sequenced reads in the sample.
  • The length of the gene’s mRNA (in base pairs) does not influence the number of sequencing reads associated with this gene.
  • Before differentially expressed genes between two samples can be identified, the gene expression values in each condition need to be normalized with regard to the total number of sequenced reads in that condition (or by a similar approach).

Q5. One method to identify processes for which genes are enriched within a gene list (e.g. a list of differentially expressed genes) is Gene-Set Enrichment Analysis. What can be said about this method?

  • Gene-set enrichment analysis is a special case of the Mann-Whitney test.
  • After the generation of a ranked gene list, gene-set enrichment analysis focuses on the top (e.g. top 5%) and the bottom genes for process enrichment by using a hypergeometric test.
  • Gene-set enrichment analysis is only allowed, if the values that are used for the ranking are positive.
  • Gene-set enrichment analysis investigates the distribution of genes related to a specific biological process among the ranked list of genes.
  • Gene-set enrichment analysis can only be applied to gene lists that have been generated by the analysis of gene expression values.

Q6. Ontologies such as ‘Gene Ontology’ associate genes or gene products with biological terms. What is true about the organization of the ‘Gene Ontology’ network?

  • The Gene Ontology namespaces ‘Biological Processes’, ‘Molecular Function’ and ‘Cellular Component’ share genes and terms.
  • The Gene Ontology database only deals with mouse and human genes.
  • ‘Gene Ontology’ edges represent different relationships between terms, e.g. ‘is_a’ (A is a B) and ‘part_of’ (A is part of B).
  • As a directed network the ‘Gene Ontology’ network contains loops.
  • The ‘Gene Ontology’ network is a tree in which more specific childterms are derived from less specific parent terms. The term tree states that each children process can only have one parent process.

Q7. Top-down approaches in systems biology produce huge amounts of data. One common way to analyze the data is to use graph theory. What can we say about biological networks?

  • Sign-specific directed networks are good starting points to build dynamical models of the system of interest.
  • In a physical interaction network, proteins are nodes that are connected to each other, if one protein activates the other.
  • In a gene regulatory network, genes are nodes that are connected to each other, if they are located in close proximity of the same chromosome.
  • Conditional relationships between the nodes of a directed network CANNOT be modeled as a Bayesian network.
  • The problem of crossing the seven bridges of Königsberg without passing one bridge more than once can be modeled as a sign-specific network.

Q8. Below is a list of different experimental approaches that are used to generate various networks. What is true about the experimental approach and the matching network generated with it?

  • After immunoprecipitation of a protein, other proteins that are part of the immunoprecipitated protein complex are direct interaction partners of the protein, such that they can become direct neighbors of the protein in a network describing physical interactions.
  • A graph describing functional relationships between transcription factors and target genes (i.e. transcription factors are connceted to target genes, if they (directly or indirectly) regulate the expression of the target genes), can be generated based on experiments where transcription factors are knocked down by siRNA.
  • The screening of PubMed research articles for keywords that are named together in the same article does not allow the generation of a network.
  • The calculation of gene expression correlations over a variety of different conditions can be used to generate a physical interaction network.
  • Since yeast-two-hybrid screening determines whether protein A interacts with protein B, this method is sufficient to build directed networks.

Q9. The term ‘bipartite’ describes networks that are composed of two not overlapping sets of nodes in which nodes of one set interact only with nodes from the other set. Which of the following networks allows overlapping sets of nodes and therefore cannot be a bipartite network in general?

  • A graph that describes (co-)authors interacting with their publications.
  • A graph that describes small molecule inhibitors interacting with the components of the target signaling pathway.
  • A graph that describes transcription-factor genes interacting with their target genes.
  • A graph that describes drugs interacting with adverse events they cause.
  • A graph that describes biological processes interacting with the genes that are associated with them.

Q10. After the identification of differentially expressed genes or proteins for condition-A (e.g. control condition) vs. condition-B (e.g. drug treatment), the data can be further analyzed for putative regulatory mechanisms such as transcription factor regulating target genes. For such analyses which of the following statements is correct?

  • The statistical procedure of enrichment analysis ignores the overlap between the experimental input gene list and the gene list associated with the transcription factor in question.
  • Such analyses provide useful hypotheses for new regulatory events that need to be experimentally verified.
  • A transcription factor gene target database generated by the analysis of chromatin immunoprecipitation experiments followed by sequencing or microarray analysis (‘ChIP-X’ = ‘Chip-Seq’ or ‘ChIP-CHIP’) describes cell line independent binding interactions between the transcription factors and gene targets.
  • Enrichment analyses using programs such as Chip-X Enrichment Analysis (ChEA) proves that certain transcription factors are involved in the regulation of the differentially expressed genes.
  • Databases generated by screening of the DNA for known transcription factor binding sequences or by screening protein sequences for known kinase phosphorylation sites are not experimentally verified, and thus do not have predictive power.

Q11. Protein-protein interaction network building programs such as ‘Genes2Networks’ can be used to identify proteins that serve as intermediate nodes to connect the input proteins (i.e. seed nodes) with each other. Let’s assume that all seed nodes and each node that is connected to at least two seed nodes will become part of a sub-network. What is true about the sub-network generated in this manner?

  • The sub-network generated as described in the question is larger than the sub-network that is generated by including all direct neighbors of the seed nodes.
  • If the seed nodes are transcription factors, the intermediate nodes cannot be transcription factors.
  • The size of the generated sub-network is not influenced by the number of direct interaction partners of the seed nodes.
  • The probability to identify a node as an intermediate node by chance increases with the number of direct interaction partners of the node and with the number of seed nodes.
  • The analysis of the identified network for an enrichment of transcription factors that have the same kinases among their gene targets allows the prediction of regulatory kinases of this network.

Q12. There are numerous computational tools for analysis and visualization of biological networks. What can be said about these computer programs?

  • Pajek is a computer program that does not permit network visualization.
  • Nodes in a network produced by Genes2Networks are connected, if the corresponding proteins share kinase phosphorylation sites.
  • A statistical method that can be used for the analysis of a list of genes for enrichment of gene targets of a transcription factor cannot be applied to the analysis of a list of proteins for enrichment of protein kinase targets.
  • Cytoscape is a computer program whose main task is to calculate enrichment of genes among certain categories such as biological processes or cellular components.
  • Apart from the generation of protein-protein interaction networks, the generation of gene co-expression networks is another option that is available in many computer programs (such as ‘Lists2Networks’).

Week 5: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Midterm Exam

Q1. Essay

Cells respond to external cues using a limited number of signaling pathways that are activated by plasma membrane receptors, such as G protein-coupled receptors (GPCRs) and receptor tyrosine kinases (RTKs). These pathways do not simply transmit but also process, encode and integrate internal and external signals. Thus, distinct activation profiles that vary with space and time for the same repertoire of signaling proteins result in different gene-expression patterns, and they can lead to diverse physiological responses. The resulting cellular responses occur through complex biochemical circuits of protein-protein interactions and protein phosphorylation cascades.

  • Partial differential equation models along with experimental verification could provide insights into the spatial and temporal relationships between stimuli and cellular responses. They further reveal the mechanisms that are responsible for properties such as signal amplification, noise reduction and generation of discontinuous bistable dynamics or oscillations in a time- and location-dependent manner. Thus, under certain conditions in neurons ERK activation regulates both channels and transcription factors while under other conditions ERK only regulates channels.
  • To understand information processing through these biochemical circuits, it is necessary to develop dynamical models using differential equations. Such models describe how the system changes with respect to space and time when the cell receives an input signal.
  • Experimental studies using high throughput gene expression profile experiments from whole cells that focus on emergent properties of such biochemical interactions reveal numerous mechanistic insights on how spatial and temporal characteristics of signaling networks can govern cell physiology. Numerous disease processes, such as cancer, have been linked to abnormal circuit behaviors due to mutations in signaling proteins.
  • Computational studies have shown that when distinct stimulations to different receptors couple to the same signaling pathways, only some of the signaling components are activated. Such selective activation within a highly connected network provides the specificity that lead to different physiological responses. Experiments have verified these observations. For example, ERK is transiently activated when cells are stimulated with EGF, while only MEK is activated during FGF stimulation of its receptor. More studies, both experimental and computational are needed to decode the upstream signaling mechanisms that lead to such emergent behavior.

Q2. The essay below contains three sequential sentences (one each in Q2, Q3, and Q4) that connect the top (Introduction in Q2) and bottom (Conclusion in Q4) sections. For each of the questions Q2, Q3 and Q4, choose one sentence; sentences may be fully correct, partially correct or incorrect. Please, make sure that the final text of the essay is coherent and accurate. The fully correct answers have the appropriate level of detail while the partially correct answers, although conceptually accurate, are missing relevant details. Hint: It may be very helpful to copy and paste your choices into a separate text editor, so as to sequentially assemble the whole essay before submitting your final choices for questions Q2, Q3 and Q4.

One of the choices here is the sentence that would follow the Introduction given below in italics. The options are either correct, partially correct or wrong. You can choose only one answer.


Most diseases have complex pathogenesis involving the interplay of genetic and environmental factors. Diagnostic and treatment decisions are typically based on phenomenological observations such as histology, serologic markers, and clinical manifestations of the disease. With the advent of next-generation DNA sequencing technologies and computational methods to identify molecular species and interactions at the genome-wide scale, it is theoretically feasible to generate a global picture of cellular functions from a molecular perspective, and to link phenotype to molecular networks that govern pathophysiological changes.

  • Since genomics, transcriptomics, proteomics, and other genome-wide-scale experimental methods referred to as “omics” technologies are capable of measuring changes in large numbers of components and interactions, they can provide an overview of different conditions: healthy versus diseased state, before versus after drug treatment, and one cell type versus another.
  • Genomics, transcriptomics, proteomics, and other genome-wide-scale experimental methods referred to as “omics” technologies are capable of measuring quantities of large number of components and interactions, providing a systems-level understanding of disease processes.
  • Genomics, transcriptomics, proteomics, and other genome-wide-scale experimental methods referred to as “omics” technologies can be used as standalone tools to build specific linear pathways with increased accuracy and precision, providing mechanistic insight into how these pathways operate under different conditions: healthy versus diseased state, before versus after drug treatment, and one cell type versus another.

Q3. Q3: Continuing from the sentence you have selected in Q2, add one more sentence from the choices below. You have a correct and an incorrect choice.

  • This unique characteristic of high-throughput experimental approaches used in systems biology, which provides the capacity to measure multiple entities simultaneously, enables building of network models that capture numerous relationships simultaneously.
  • This unique characteristic of high-throughput experimental approaches used in systems biology is the ability to identify multiple functions of single proteins, such as an enzymatic and scaffolding functions with improved accuracy allowing for the development of more precise networks.

Q4. Continuing from the sentence you have selected in Q3, add one more sentence from the choices below. Adding this sentence will allow you to merge the essay you have from Q3 with the Conclusion given below in italics. You have correct, partially correct and incorrect choices; you can choose only one. At this stage you will have the complete essay. Conclusion (This will follow the sentence you choose.)

  • Many studies show that genome sequencing-based data alone do not provide sufficient insights into regulatory mechanisms of function. Combined analysis of transcriptomic and proteomic data to understand the relationship between mRNA and protein levels, in the configuration of functional protein interaction networks may be the first step in obtaining such insights.
  • To obtain predictive capability from these network models, it is also important to consider the regulatory mechanisms, such as epigenetic regulation, microRNAs, post-translational modifications and protein degradation when the high throughput data is limited to genome sequencing.
  • To obtain predictive capability from these network models, it is important to consider the differences observed between protein and mRNA levels, if only genome sequencing and mRNA expression profiling are used.
  • To obtain predictive capability from these network models, it is also critical that we separately identify nodes that connect individual pathways to form networks that are critical in post-translational modifications before drawing any conclusions from high-throughput data experiments.
  • To obtain predictive capability from these network models, it is also important to realize that interactions between linear pathways, such as feed-forward and feedback loops, will not create discrepancies between transcriptomic data and functional outputs.

Q5. The system of equations describes…

  • rate of change of A is a function of rate of change of B only.
  • rate of change from A to B and B to A, as a function of time.
  • two independent relationships that require a third function to reconcile.
  • rate of change of two chemicals as a function of space.
  • two separate enzymatic reactions occurring at two different spatial points.

Q6. Which statement below best describes the first equation (top) in words



pM 8MkdeEeW8dA7aNEIYLw 258d1075e1d175cebc5374cfaa5a178f Screen Shot 2015 08 20 at 10.12.19 AM
37r BkdeEeW6pwoi9vIVSw a62e59af662fd5d515e1691a9f24a9ea Screen Shot 2015 08 20 at 10.13.54 AM
jKg19kdeEeWqSwoADz3juQ 7d866dd2c3946d52a138d4651c761615 Screen Shot 2015 08 20 at 10.11.37 AM
xl6IBEdeEeWW4hKNiIFfBQ 38a4b0383bf7b4568e37ececcf246466 Screen Shot 2015 08 20 at 10.13.11 AM

Q8. There are two figures above that represent the results of two separate computational studies (simulation A and simulation B) looking at normalized concentration of phosphorylated Src (i.e. active Src) upon platelet-derived growth factor stimulation. Given the capabilities of different types of models, which of the below statements about the two simulations are FALSE.

  • I, III, V, VII
  • II, V, VIII
  • II, III, IV, V, VIII
  • I, III, IV, V, VI, VII

Week 6: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Analysis of Networks | Topology to Function

Q1. The clustering coefficient is a property that determines the density of connectivity within a network. What can be said about the clustering coefficient and its calculation?

  • If a node’s neighbors form a clique the local clustering coefficient is exactly 1.
  • The total number of possible edges between the k direct neighbors of a node of interest in an undirected or directed network is calculated by k(k-1).
  • Local clustering coefficient can be used as an indicator of loops around the node formed by the node’s neighbors.
  • In contrast to the global clustering coefficient the local clustering coefficient describes overall network properties.
  • Average clustering coefficient can be calculated by multiplying all local clustering coefficients with each other and dividing by the number of nodes.

Q2. In graph theory several centrality measures have been described. One common centrality measure is betweenness centrality. What is true about this centrality measure?

  • If an odd number of network nodes are only connected by a single line, the betweenness centrality is the highest for the node in the middle.
  • To calculate the betweenness centrality of a node, it is sufficient to consider first and second direct neighbors of the node (i.e., those nodes which are within a distance of two edges from the node).
  • The betweenness centrality can only be calculated for undirected networks.
  • Betweenness centrality denotes the fraction of longest paths from each node to all others that pass through the node of interest.
  • Nodes that are at the constriction point in a bowtie topology within a network are likely to have low betweenness centralities.

Q3. Hub nodes are nodes that have high degrees (i.e., have many direct neighbors) compared to non-hub nodes. Accordingly, what can be concluded about this attribute?

  • Duplication of non-hub nodes will make the network more fragile.
  • The removal of hub nodes as compared to random removal of nodes from the network is associated with a higher likelihood to break the network into separate pieces.
  • Degrees of hub nodes cannot be defined in bipartite graphs.
  • If nodes are picked randomly from the network, hub proteins have a higher probability to be drawn.
  • If all hub proteins are connected in a network by the addition of extra edges, the network diameter will increase.

Q4. What is true about the organization of biological networks such as protein-protein interaction networks?

  • Biological networks are defined by the ability of a walker to visit all nodes without walking across an edge more than once.
  • In biological networks, numbers of edges that are connected to nodes follow a Poisson-distribution.
  • Biological networks are small-world networks meaning that they are composed of many ‘small-world’ subnetworks that are connected with each other by a few bridging nodes.
  • Many low degree nodes and a few high degree nodes increase the probability of a network breakup when random nodes are removed.
  • Biological networks are scale-free and are characterized by a few nodes with high degrees and many nodes with low degrees.

Q5. Apart from protein-protein interaction networks other commonly studied networks include gene co-expression networks. In this network type, nodes are genes that are connected by edges if their expression levels correlate under different conditions (e.g., different phases of the cell cycle). What can be said about gene co-expression networks?

  • In comparison to protein-protein interaction networks, gene co-expression networks are more likely to capture functional instead of direct interaction relationships between genes.
  • Gene co-expression networks can be generated using gene expression profiles of single cells, but not gene expression profiles of whole tissues.
  • Generation of gene co-expression networks involves enrichment analysis for the co-expressed genes of transcription factor gene targets.
  • Since the correlation coefficient between two genes is a qualitative measure, edges in gene co-expression networks cannot be weighted.
  • Gene co-expression networks overlap with protein-protein interaction networks.

Q6. What is true about networks?

  • Number of edges in a network connected as a ring is the highest possible number of edges.
  • A directed acyclic graph is always a directed tree.
  • A directed tree has to be directed acyclic graph.
  • A directed network cannot contain loops.
  • A bipartite network is characterized by the fact that the neighbors for any two nodes of the same set do not overlap.

Q7. The following motifs show proteins that stimulate each other when they are connected by arrows, and inhibit each other when they are connected by plungers. Blue edges symbolize actions that start immediately and finish within a brief period, while green connections symbolize edges that start action with a delay, but the action lasts for longer period. Which one of the following motifs is the best configuration to obtain a transient (short peak) activation of the red node?

  • C
  • D
  • E
  • B
  • A

Q8. What is true about scaffolding proteins in a directed signed graph?

  • The distribution of the degrees among all network nodes, as described by the scale free topology, excludes scaffolding nodes.
  • Neighbors of scaffolding nodes do not interact with each other.
  • Many scaffolding proteins only have one interaction neighbor.
  • Scaffolding nodes have at least two or three interaction partners (edges).
  • Scaffolding proteins do not exist.

Q9. Bistability describes a system property that allows two possible stable steady-states. What is true about bistability?

  • In contrast to negative feedback loops, positive feedback loops, such as A activates B and B activates A, cannot produce bistability.
  • Bistability cannot be used as a cellular switch.
  • Between the two stable steady states, there is a metastable threshold state.
  • Certain network motifs (i.e. active A inhibits the activation of B, active B inhibits the activation of A) always lead to bistability regardless of kinetic parameters.
  • Bistability is a very rare phenomenon in biology.

Q10. Boolean networks allow for a simpler way of modeling dynamics as compared to ODE models. Each node (e.g. representing a gene or a protein) is either active or inactive. The activity state of a node depends on the number of inputs it receives from other nodes. What is true about Boolean networks?

  • Bistability in cell biological systems preclude the use of Boolean networks as a modeling approach.
  • Boolean networks allow the prediction of an ordered sequence of activation patterns, but not of the duration of each pattern.
  • The attractor cycle describes a sequence of nodes but not states (e.g., genes that activate each other in a circular manner).
  • Prior knowledge does not help for the construction of Boolean networks.
  • All network states that are modeled by Boolean networks have precursor and descendent states.

Q11. To study the contribution of individual reactions of genome-scale metabolic networks to a desired output such as cellular growth in bacteria, Flux Balance Analysis (FBA) is a valuable tool. What underlying principle is FBA based on?

  • Flux Balance Analysis always depends on the steady state assumption.
  • Gene knockouts of involved enzymes can be simulated by forcing a minimum flux for the involved reaction rate.
  • Flux Balance Analysis of metabolic networks is based on the construction of a matrix representing Michaelis Constants (Km) and maximal reaction velocities (vmax).
  • Flux Balance Analysis allows us to predict metabolite concentration changes over time as it is based on a large-scale approach.
  • The predictions made by Flux Balance Analysis are accurate and do not need to be experimentally validated.

Q12. Which of the following network motifs is NOT a coherent feedforward loop?

  • E
  • D
  • C
  • B
  • A

Q13. Which of the following network motifs is a bifan?

  • B
  • E
  • A
  • D
  • C

Week 7: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Strengths and Limitations of Different Types of Models | Identifying Emergent Properties

Q1. Variations within the locus of a gene can be statistically associated with variations in the gene’s expression and variations in the trait (phenotype) of the organism (e.g. body weight). What can be said about possible probabilistic relationships?

  • A causal relationship describes locus variation that affects the gene expression and the phenotype separately.
  • A causal relationship describes a trait (phenotype) that is altered by a change in gene expression, which in turn was altered by the variation within a locus.
  • A relationship can either be causal or independent.
  • If the locus variation neither affects the gene expression nor the trait, the relationship is called independent.
  • If the locus variation affects the gene expression, which in turn affects the trait, the relationship is called independent.

Q2. Which one of the statements below correctly identifies a LIMITATION of statistical models?

  • Statistical models are incapable of capturing probabilistic relationships between phenotypes and genes.
  • Statistical models capture component interactions such as those between proteins and microRNAs.
  • Mechanistic details can be inferred from co-relations when large enough samples sizes are utilized.
  • Statistical models can determine changes in parameters such as copy number variation to characterize network behavior.
  • Statistical models often overlook spatiotemporal changes such as those that can change during development.

Q3. What EXCLUSIVE advantages do partial differential equation (PDE) models have in comparison to ordinary differential equation (ODE) or Boolean models?

  • PDE models are the only models that allow the prediction of a substrate concentration at each timepoint for the whole cell.
  • PDE models enable an accurate representation of cell shape when modeling biochemical reactions within the cell.
  • PDE models allow the separation of reactions into few reaction sets that account for spatial separation of the reactions into multiple compartments.
  • PDE models allow the implementation of network motifs such as positive and negative feedback loops.
  • PDE models need less parameters, e.g. reaction constants.

Q4. Biological networks are a good basis for the development of dynamical models. In the choices below different networks are presented in a certain order. Which order follows the rule: least suited network for the development of dynamical models on the left –> more suited –> most suited network on the right?

  • sign-specified directed network –> undirected network –> directed network
  • undirected network –> sign-specified directed network –> directed network
  • sign-specified directed network –> directed network –> undirected network
  • undirected network –> directed network –> sign-specified directed network
  • directed network –> undirected network –> sign-specified directed network

Q5. Bhalla and Iyengar used a computational model to describe the bistable behavior of a well-studied small signaling network. Which one of the statements about this study is FALSE?

  • System behavior was independent of input duration or amplitude.
  • Amplification of signals through a positive feedback is responsible for the bistable switch.
  • The metastable state defines the threshold activity levels of key enzymes that need to be achieved in order to switch states.
  • Simulations showed that the signaling topology led to two stable states.
  • Extending input stimulation time beyond a threshold caused transient response to switch to a sustained response.

Q6. How did the concept of bistability in signaling networks affect one of the central dogmas in cell biology?

  • Persistence of signaling after withdrawal of stimulus was thought to be an effect of mutations such as those in oncogenes.
  • Emergent behaviors of signaling networks, such as oscillations, were not described before.
  • There were no robust signaling networks.
  • It was known that signaling molecules could only be activated transiently at all times.
  • Interaction of multiple signaling pathways was not studied before.

Q7. Consider a negative feedback loop that interacts with a bistable system (positive feedback loop). How is the activity of this the negative feedback loop related to robustness of the bistable behavior?

  • Negative feedback loops are the only way to achieve transient responses.
  • Negative feedback is never alone in biological systems. Feedback loops are always coupled to coherent feed-forward motifs that amplify signals, which create system robustness.
  • Positive feedback requires that only an inhibitor that has an effect of sufficient amplitude and duration of signal suppression will deactivate the system.
  • Negative feedback always ensures system stability at low enzyme concentrations by internalizing activated receptors.
  • Feedback loops are not related to system robustness.

Q8. Ordinary differential equation and partial differential equation based models allow the discovery of a non-intuitive behavior of a cell biological system with well known network structure and parameters. Similar to any other computational prediction, they need to be verified experimentally. Which statement is true about the experimental verification approaches?

  • The variation in the Western blot analysis of a protein phosphorylation based on a whole cell extract (which was made from thousands of cells) between different experiments can be used as a measure of cell-to-cell variation of protein phosphorylation over within the same experiment.
  • PDE model predictions such as spatial gradients of a signaling component like cAMP can be verified via Western blot of whole cell lysates.
  • Microscopic imaging does not allow the investigation of individual cell behavior in response to a stimulus.
  • High-throughput gene expression data can be used to verify protein levels that have been predicted by a one-compartment ODE-model.
  • Live cell imaging can be used verify the prediction of the change in concentration of an activated component of a signaling pathway made by either an ODE or PDE model.

Q9. The figure below shows a model for the time-course of signal transduction events, which are involved in Long Term Depression (LTD). What can be said about the described pathway?

  • The pathway shows a negative feedback loop that causes low PKC phosphorylation as a prerequisite for AMPA-type glutamate receptors (AMPAR) internalization via clathrin-mediated endocytosis.
  • Highly abundant Ca2+-binding proteins enable long-lasting increase in free Ca2+.
  • The arrow between phospholipase A2 (PLA2) and protein kinase C (PKC) indicates a direct lipolytic activity of PLA2 on PKC.
  • The feedforward loop shown here functions as a bistable switch.
  • The shown mechanism demonstrates that LTD depends on long-lasting elevations of Ca2+ concentration in the post-synaptic neuron.

Q10. Yang et al. described a neuronal circuit regulating hunger and saturation states based on hormone levels. This system has a bistable switch in the presynaptic neuron that regulates the AGRP neuron. The presence of such a switch confers signal processing capability to this system of three neurons. What is true about their system?

  • Under starvation leptin levels get elevated and drive the described system to signals hunger to the brain.
  • The shown Set and Reset (SR) flip-flop memory state contains an “As-long-as loop”, which says that state A is active (e.g. the hunger state) as long as input 1 (e.g. ghrelin) exists.
  • Bistable systems such as the one described by Yang et al. allow only a single stable state.
  • Elevation of ghrelin-blood levels increases synaptic glutamate release from Agouti-related protein (AGRP) neurons and thus causes the activation of Pro-opiomelanocortin (POMC) neurons.
  • The neuronal circuit described by Yang et al. acts like a switch.

Week 8: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Emergent Properties: Ultrasensitivity and Robustness | Case Studies

Q1. Zero-order ultrasensitivity was first described by Goldbeter and Koshland 1981 for the system shown below. Zero-order kinetics mean that the enzymes are completely saturated with substrate — or in terms of the Michaelis-Menton equation, the concentration of the substrate, W or W*, is much higher than the Km of the corresponding enzyme, E1 or E2. Why does this system produce ultrasensitivity (under the assumption that WT is much higher than E1T and E2T)?

v1 = maximal velocity of the first reaction = k1*WE1 = k1 * E1T

v2 = maximal velocity of the first reaction = k2*WE2 = k2 * E2T

E1T and E2T = total enzyme concentrations

v’1 = velocity of the first reaction as calculated by Michaelis-Menton kinetics

v’2 = velocity of the second reaction as calculated by Michaelis-Menton kinetics

WT = W + W* + WE1 + WE2

  • The stimulation of E2 by W and of E1 by W* generates a classical bistable system that consists of two feed-forward loops.
  • The absolute difference between the Michaelis-Menton constants of both reactions is the greatest, if the enzymes are fully saturated.
  • As long as the enzymes are fully saturated, changes in substrate levels of W and W* do not influence the difference between the velocities v’1 and v’2 that is responsible for further changes in W and W*.
  • The difference between the maximal velocities v1 and v2 is the greatest for every ratio of the maximal velocities, if the enzymes are fully saturated.
  • f both enzymes are fully saturated, the difference between velocities v’1 and v’2 is greater than it would be if only half of both enzymes were bound by their substrates.

Q2. Goldbeter and Koshland described zero-order ultrasensitivity by showing that a simple system, as shown in (i), is ultrasensitive, if the involved enzymes E1 and E2 operate under saturating conditions. In the two-cycle system, as shown in (ii), W* catalyzes the modification of Z into Z*. What did the authors show as an effect of coupling two systems?

  • The coupling of the two cycles decreases the ultrasensitivity of the final output of the system.
  • The coupling of the two cycles generates a metastable state.
  • The coupling of the two cycles increases the ultrasensitivity of the final output of the system.
  • The coupling of the two cycles is a theoretical problem without biological relevance.
  • The coupling of the two cycles has no effect on the ultrasensitivity of the final output of the system.

Q3. Name a systems level advantage of ultrasensitivity.

  • The system can develop a switch-like emergent behavior without loops.
  • The system can respond to high levels of stimuli.
  • Ultrasensitivity leads to sustained oscillations instead of transient ones.
  • Ultrasensitivity is a kinase-specific property that increases their sensitivity.
  • The proportional (i.e. operational) range of the input for a given system increases with ultrasensitivity.

Q4. Chandra et al. shows that system robustness can be achieved with negative feedback loops. What is true about the proposed outcome for this emergent systems-level property?

  • Complexity in biological systems reduces both stability and fragility.
  • Oscillations are the primary information content in the system.
  • Stable systems are more efficient than unstable systems.
  • Feedback loops provide robustness in exchange of efficiency with oscillations as a side effect.
  • A single feedback loop is adequate to compensate for instabilities in kinetic parameters.

Q5. Good et al. make a circuit board analogy for scaffolding proteins because they increase precision of information processing in cell biological systems. Which properties of scaffolding proteins are critical for this attribute?

  • While regulatory modification of scaffolds is important, their contribution to spatial information content is negligible.
  • Regulatory modifications of scaffolding proteins do not affect their capacity to bind target proteins.
  • Scaffold proteins control spatial organization of components always through phosphorylation.
  • Scaffold proteins can be functionally controlled through regulatory modifications while being spatial modulators of information flow.
  • Scaffold proteins phosphorylate substrates better than kinases, an attribute that provides an evolutionary advantage.

Q6. We learned about the computational model developed by Rust et al. to study the oscillatory behavior of bacterial protein KaiC. Which one of the below is NOT an assumption in their model?

  • The bacterial cytoplasm is a well-stirred compartment.
  • Serine 431 phosphorylated KaiC monomers bind KaiB to inactive KaiA dimers at a one-to-one ratio.
  • Concentrations of three species are the only slow dynamical variables.
  • Phosphorylation and dephosphorylation events are governed by first-order kinetics
  • Double phosphorylation probability distribution function governs whether KaiC is active or not.

Q7. What was the main objective of Neves et al. in their study of microdomain dynamics?

  • To understand the role of signaling pathways in transmitting spatial information.
  • To develop a comprehensive model of cAMP signaling and discover new components.
  • To understand the role of kinetic parameters in beta-adrenergic receptor activity.
  • To develop a neuron-specific spatial model of neurotransmitter release.
  • To form in vitro microdomains, which are normally observed only in vivo.

Q8. Neves et al. used sets of kymographs to visualize the effect of multiple parameters such as dendrite diameter, distance and time, on cAMP concentration. In order to achieve these outputs, they utilized “ball-and-chain toy models”. Which one of the statements below is true about the process with which they developed these graphs?

  • Well-stirred reaction chamber assumption was made throughout the modeling process. The goal was to determine if change in diffusion coefficients played a significant role in formation of concentration gradients.
  • Ordinary differential equation models were converted into partial differential equation models by mapping into simplified geometries. The goal was to understand if the diameter of the dendrite had a role in microdomain formation.
  • Results of partial differential equation models were plotted with respect to experimental observations that used a loss-of-FRET probe. The goal was to identify novel cAMP-driven mechanisms in neuronal function.
  • Ordinary differential equation models were used to determine the effects of surface area-to-volume ratio phenomenon in the dendrite. The goal was to quantify the role of dendrite diameter in formation of cAMP gradients.
  • Realistic geometries were used in conjunction with finite volume methods to match experimentally observed cAMP profiles. The goal was to determine the kinetic parameters of the cAMP pathway.

Q9. Keren et al. developed a computational model to study cell shape and whole-cell motility using the mechanics of actin cytoskeleton interaction with membranes in fish keratocytes. The study is a good example of a hybrid approach where statistical modeling techniques (e.g. correlations) are combined with our physical understanding of the system (e.g. actin biophysics) producing mechanistic insights. What was one of the main conclusions of this paper?

  • Model shows that myosin contractility is critical in determination of keratocyte shape.
  • Model shows that there is no explicit or implicit relationship between cellular shape and motility.
  • Certain characteristics of keratocyte cell shape and locomotion can be explained by the coupling of actin filament dynamics and cell membrane tension.
  • Shape of the growing edge is governed mainly by contractile activity while rear boundary is mostly driven by actin treadmilling.
  • The specific aspect ratio and transitioning of keratocyte shape stems from spatial heterogeneity of membrane tension that resists actin polymerization.

Q10. “Models of whole-cell phenotypes always require that we model all the details within the cell.” This statement is false because…

  • Keren et al. were able to use a simple model of actin filament pushing against the membrane to accurately describe whole-cell movement.
  • Breast cancer cells have to be characterized for their genomic, epigenomic and proteomic properties to understand cell proliferation.
  • Karr et al. used a whole-cell model to predict kinetic parameters of enzymes that have not been previously measured in their model organism.
  • Neves et al. show that PDE models are not required for comparisons with live-cell imaging experiments.
  • Boolean networks do not give us the ability to distinguish between different network topologies.

Week 9: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Case Studies | Systems Biomedicine | Systems Pharmacology and Therapeutics | Perspective

Q1. We looked at two different modeling studies by Neves et al. and Saez-Rodriguez et al. that investigates signaling network dynamics from distinct perspectives. Which one of the statements below does NOT highlight a difference in the approach and methodologies used in these two studies?

  • Both used literature-based, sign-specific, directed networks; however, Saez-Rodriguez et al. extended this by training the network against biochemical data.
  • Saez-Rodriguez et al. used a logic-based approached while Neves et al. used a deterministic kinetic model.
  • Neves et al.’s ordinary differential equations and Saez-Rodriguez et al.’s Boolean logic-based models use different mathematical foundations to come to same conclusions.
  • Neves et al. focused on spatial dynamics while Saez-Rodriguez et al. used a whole-cell approach.
  • Neves et al. showed that same topology could have different outcomes depending on cell shape whereas Saez-Rodriguez et al. showed that topologies are different for primary and cancer cell lines.

Q2. Which one of the statements below is FALSE about the signaling network used by Saez-Rodriguez et al.?

  • The network is formed by improving an initial connectivity map with experimental data.
  • The network is based on prior knowledge.
  • Boolean logic was used to model experimental behavior.
  • Each ligand has its own unique network.
  • There were ~1038 different circuits which were individually compared against data.

Q3. The Cancer Genome Atlas Network used various high-throughput methods to analyze the molecular patterns of breast cancer and its main subclasses. One of the used methods was “whole-exome-sequencing” where only the coding regions of the genome are sequenced. Coding regions of the genome are those regions that code for protein sequence: After DNA is transcribed into RNA in the cellular nucleus, the RNA gets further modified. One modification is the removal of RNA-pieces that do not code for the protein (the removed parts are named introns, the kept ones exons). The final RNA (that is called messenger RNA (mRNA)) gets exported from the nucleus and is the ‘blue print’ for protein synthesis (i.e. translation). During experimental sample preparation (i.e. library preparation) for “whole-exom-sequencing” all non-coding regions of the DNA are removed from the sample. In contrast, “whole-genome-sequencing” sequences the complete genome including the exons, introns and all the non-coding regions between the genes (about 98% of the human genome is non-coding). The library preparation for “whole-genome-sequencing” does not include such a removal step. What is true about “whole-exom-sequencing” in comparison with “whole-genome-sequencing”?

  • In contrast to whole genome sequencing, whole exome sequencing only focuses on the transcribed gene region.
  • If the total amount of DNA that is finally sequenced (i.e. the size of the library) is the same, coding DNA is more concentrated during whole exome-sequencing than during whole genome-sequencing.
  • In contrast to whole genome sequencing, whole exome sequencing does not allow the identification of genomic mutations.
  • Differences between whole-exome-sequencing and whole genome-sequencing only refer to the computational data analysis of the sequencing results.
  • In contrast to whole-exome-sequencing, whole-genome-sequencing does not allow the identification of genomic mutations.

Q4. The Cancer Genome Atlas (TCGA) focuses on …

  • … the description of drug-caused genomic and other variations.
  • … the identification of genomic and other variations causing mental disorders.
  • … the identification of genomic and other variations triggering autoimmune disorder.
  • … the identification of genomic and other variations related to neoplasm.
  • … the description of genomic and other variations associated with hereditary diseases.

Q5. Statistical correlations can provide an overview of a complex biological process. For this we need…

  • protein-protein interaction networks only.
  • to start with gene regulatory networks.
  • at least two measured entities (e.g., expression levels of two protein) under different conditions.
  • prior knowledge of all the mutations in the genome.
  • one type of parameter (e.g. levels of one microRNA) under different conditions.

Q6. “Computational models are not useful if they cannot make predictions”.

  • This statement is true because models that do not predict are erroneous due to lack of appropriate details in model setup.
  • This statement is false because as the study by Rust et al. shows, a computational model can be used to establish that there are no hidden variables that account for experimental observations.
  • This statement is false because for very large, complex systems such as metabolic networks, models that account for each and every reactant, reaction and product are useful in systematically describing the system.
  • This statement is true because differential equation models are better than Boolean models.
  • This statement is true because,

Week 10: Introduction to Systems Biology Coursera Quiz Answers

Quiz 1: Final Exam

Q1. Below is an essay on emergent properties that is divided into five sections in Q1 to Q4. Sections need to be connected with four transitional statements that are composed of 1-2 sentences. For each of the transitional statement, you are given four options. The correct statement is not only factually accurate and complete with the appropriate amount of detail, but it also ensures continuity between segments. The partially correct statement is factually accurate but lacks detail.

Q1, Q2 and Q3 contain only top sections to which transitional statements need to be added. Q4 contains top and bottom parts. Once you have chosen a transitional statement for Q1 add it to the bottom of the paragraph, which should then merge with the paragraph in Q2. This type of assembly continues till you reach Q4 where your choice connects the essay to the concluding para. It may be helpful to assemble the entire essay in a word processing program and read it carefully prior to making the final choices for the transitional statements.

Q1 A primary focus of systems biology studies is the identification of emergent properties of systems. Here, a system is defined as a set of interacting entities. In cell biology, the entities are often genes, proteins or other cellular components such as lipids or complex sugars. Interactions can range from direct interactions between proteins representing covalent or non-covalent chemical reactions to indirect regulatory relationships, such as those between genes in gene regulatory networks. Emergent properties are defined as properties possessed by a system as a whole but cannot be attributed to any individual component or interaction.

However, they are never observed in gene regulatory networks.

The development of these properties can often be traced to specific attributes of individual components such as scaffold proteins that interact with multiple nodes to form hubs.

In other words, emergent properties are inherent to the system.

Put another way each component (node) and interaction (edge) of a system is necessary but not sufficient for the appearance of an emergent property.

Q2 Three emergent properties of systems have been well characterized. These are ultrasensitivity in coupled enzymatic pathways, bistability of positive feedback loops, and robustness of systems to internal and external perturbations. Ultrasensitivity arises from coupled enzymatic reactions, and it can be considered an elemental emergent property since it can occur in a simple system of two enzymes that share a substrate whose activity state they drive in opposite directions.

  • Ultrasensitivity is a very important property in cell biological systems because it is manifested in the ubiquitous MAP-kinase signaling pathway, which controls proliferation. This emergent property plays a role in the switch-like responses of the cell in entering cell cycle in response to activation of growth factor receptors.
  • Ultrasensitivity is also critical for nuclear translocation of proteins making it important for transcriptional regulation. It is strictly a eukaryotic phenomenon, rarely observed in lower organisms.
  • Ultrasensitivity is very important in another emergent property called bistability, which requires at least two of its components to be ultrasensitive.
  • Ultrasensitivity is ubiquitously observed in cell biology; its inherent switch-like characteristic sometimes plays a role in MAP-kinase pathway regulation of effectors.

Q3 In contrast, bistability of positive feedback loops is not as widely distributed, although it has been observed in important brain systems such as those involved in learning, memory and hunger control. Bistability produces a switch-like response in a system whereby an external stimulus can activate the system, and the system stays active even after the stimulus has ended.

  • Bistability requires not only the components of the system to be appropriately coupled (so as to produce a positive feedback loop), but also that the activity of each component to be of sufficient magnitude and duration so that each component reaches an activity above a certain threshold; this threshold represents a metastable third state.
  • Bistable systems never produce transient responses regardless of the input. They are either in on or off state. This property is specifically important in gene regulatory networks.
  • Bistable systems are almost always coupled to negative feedback that controls the input signal; thus upstream negative feedback is critical for the operation of any switching operation. These negative feedback systems are often based on phosphatases that specifically target activated receptor tyrosine kinases.
  • Bistable systems have on and off states that are distinguished by the system according to the amplitude of the input signal.

Q4 Once a stable active state is reached, a bistable system can only be deactivated when the deactivating signal is of a sufficient magnitude and duration. Otherwise, even when one of components is deactivated, reaction flow within the loop is such that the system can recover and go back to the activated stable state even after removal of the deactivating signal. This ability to bounce back can also be considered a systems property called robustness. Thus, even a simple system like a positive feedback loop, can possess more than one emergent property. Robustness endows the system with the ability to maintain function at a preset level in response to a range of disruptions.

Conclusion (This will follow the sentence you choose)

Robustness is a property found in many systems, not just cell biological systems. It is found in electronic control systems as well as financial markets. Robustness is often observed in financial markets where certain trading firms are able to produce relatively constant financial yields even when the market as a whole is fluctuating. Similarly, bistability can also be found in a variety of systems such as electrical switches (used to turn lights on and off) and in ballpoint pens (where a spring is used to push the ballpoint in or out of the casing). Hence, emergent properties are found in a many types of systems, naturally occurring and human-designed systems.

  • System robustness plays a critical role in biological networks especially when stability of the output is important.
  • Disruptions in system components, such as activating mutations in GTPases, do not affect system robustness. Robust systems are impervious to mutations; most oncogenes are those that are part of fragile networks.
  • Most robust biological control systems comprise positive feedback loops, e.g. G-protein coupled receptor internalization mechanisms that utilize clathrin-coated vesicles. Robustness in biological systems is akin to efficiency in electrical control systems.
  • Such disruptions may be internal, i.e. within the system whereby the level or activity of one or more components change without affecting the output of the system. Alternatively, robustness could represent the ability of a system to produce a constant output in response to varying (i.e. noisy) input signals.

Q5. There are numerous emergent behaviors associated with network motifs. Below is a list of motifs that are common in cell signaling networks along with emergent behaviors we have studied throughout this course. Please identify the behavior that best matches with each motif. Each question is worth 1 point.

  • Transient or limited activation of downstream node(s)
  • Bistability or switching behavior
  • Synchronized activation of downstream node(s)
  • Oscillations in response to stimulation of the upstream node(s)


  • Oscillations in response to stimulation of the upstream node(s)
  • Synchronized activation of downstream node(s)
  • Bistability or switching behavior
  • Transient or limited activation of downstream node(s)


  • Oscillations in response to stimulation of the upstream node(s)
  • Synchronized activation of downstream node(s)
  • Bistability or switching behavior
  • Transient or limited activation of downstream node(s)


  • Synchronized activation of downstream node(s)
  • Bistability or switching behavior
  • Transient or limited activation of downstream node(s)

Oscillations in response to stimulation of the upstream node(s)

Q9. describe a high throughput experiment and computational analyses for prediction of upstream pathway. Each question tests your knowledge about one part of the pathway. Each question has only one right answer and is worth 1 point.

Q10-Q13 are based on Chen et al. Expression2Kinases: mRNA Profiling Linked to Multiple Upstream Regulatory Layers. Bioinformatics. (2012) 28 (1): 105-111

From Differentially Expressed Genes to Putative Regulatory Kinases

The figures below show the results of a study which investigated the effects of a drug on a certain cell line. The cells were incubated for 6h with or without (control) drug, followed by the determination of the levels of expression of 22,000 genes by microarrays. Differentially expressed genes between drug-treated and control cells were determined by a two tailed unpaired student’s t-test based on 4 independent experiments (i.e. biological replicates). A two tailed test allows the identification of both up- and down-regulated genes.

Q9 Identification of the differentially expressed genes

Figure (i) shows the differentially expressed genes. Each column shows the log2 fold changes of one individual experiment, which have been calculated by the following formula: log2 fold change = log2(Gene expression with drug / Gene expression without drug). Each little horizontal bar within a column represents the log2 fold change for an individual gene.

What is TRUE about this analysis?

  • If the control cells had been treated with a different drug instead of receiving no treatment at all, a different statistical analysis would have been needed to identify differentially expressed genes.
  • The consideration of 4 independent experiments allows us to account for variations that occur during the measurement of gene expression, but not for variations that occur during the drug treatment period or variations that occur during the growing of the cells used in the experiments.
  • The greater the variation of a gene’s expression in replicates for the same condition, the higher is the likelihood that we can identify the gene as being differentially expressed between the two conditions.
  • Down-regulated genes have a higher probability to be identified as differentially expressed as compared to up-regulated genes.
  • Visual inspection indicates “yellowness” at the top and “blueness” at the bottom of the heatmap, suggesting that the biological replicates are indeed reproducible. Further statistical tests are needed to establish this.

Q10. Prediction of regulatory transcription factors

The differentially expressed genes from the analysis above were used to predict putative transcription factors. For this, the researchers used the Transfac-database that associates transcription factors with a list of their target genes. Using a statistical test, the differentially expressed genes were analyzed for enrichment of target genes of different transcription factors. Figure (ii) shows the top 5 transcription factors with the lowest p-values (lower the p-value, stronger the association between differentially expressed genes and transcription factor of interest).

What is TRUE about this analysis?

  • The reliability of the transcription factor predictions decreases with the number of biological replicates.
  • Transcription factors that are highly ranked need to be part of a gene-regulatory network.
  • The experimental identification of these transcription factors in a nuclear cell lysate that bind to their promoter sequence cannot provide confirmation of this predicted list.
  • Only one statistical test (Fisher exact test) can be used to predict putative regulatory transcription factors.
  • The different transcription factors can belong to different signaling pathways and hence maybe regulated by different classes of receptors.

Q11. Prediction of regulatory transcription factor interaction subnetwork

To identify a putative transcription factor interaction network that could regulate the predicted transcription factors, the top 2 transcription factors from the previous question were used as seed nodes, and a sub network was constructed using the human protein-protein interaction network (human interactome). The network extension algorithm identifies all intermediate nodes that are part of a path that connects two seed nodes with each other with a maximum pathlength of two edges. Figure (iii) shows the subnetwork that was generated.

What is TRUE about the subnetwork shown above?

  • If random seed nodes were chosen from the human interactome, we would get the same network.
  • The algorithm excludes the identification of additional transcription factors that are not seed nodes.
  • The network does not contain interactions between intermediate nodes.
  • There is no interaction between the two seed nodes, because the algorithm does not identify interactions between seed nodes.
  • The algorithm identified all those nodes that directly interact with both of the transcription factors of interest.

Q12. Prediction of regulatory kinases

The next step focuses on the prediction of protein kinases that regulate the transcription factor interaction subnetwork identified in Q11. Using a statistical test all nodes of this network were analyzed for protein kinases whose targets are enriched within the transcription factor interaction network. The top 5 protein kinases with the lowest p-values are shown in figure (iv).

What is FALSE about the identification of protein kinases?

  • These protein kinases could also be differentially expressed.
  • The statistical test cannot identify protein kinases that are not in the protein kinases and their targets database.
  • These protein kinases are identified with high certainty and hence do not need to be experimentally verified.
  • Protein kinases that are nodes within pathways from receptors to transcription factor cannot be identified as putative regulators of the subnetwork.
  • Analysis of the networks upstream of the enriched protein kinases for gene ontology processes is feasible.

Q13. Limitations of the pathway prediction approach

This sequential computational building of pathways using multiple databases introduces uncertainties regarding the reliability and relevance of the predicted pathway in a cell type of interest.

  • Which of the following statements regarding the uncertainty of this multistep computational analysis is FALSE?
  • Not all protein-protein interactions in the human interactome would be relevant in the cell type of interest.

One level of uncertainty is at the level of transcription factor binding to promoter regions of differentially expressed genes as captured in the Transfac-database.

Since protein kinases can often have broad substrate specificity, the degree of uncertainty of a predicted pathway increases when protein kinases are identified through inferred transcription factors from differentially expressed genes.

The degree of uncertainty decreases during the analysis from steps (i) to (iv).

Limits of experimental accuracy contribute to the overall uncertainties of computational analyses.

Q14. are based on the paper by John Tyson in PNAS (88:7328-32). Each question is worth 2 points.

ODE model of the cell cycle

A dynamic model of frog egg development by John J. Tyson was one of the first mathematical models of cell division. The model is based on a simple set of ordinary differential equations, and it describes different stages of frog egg development. These stages include the developing egg before fertilization, the fertilized egg that undergoes rapid divisions, and the midblastular embryo where cellular division is growth-hormone controlled.

Q14 Formulation of the ODE model

The system shown in figure (i) is a kinetic model of the levels of maturation promoting factor (M) that controls the mitotic cycles in embryonic and somatic cells, and it peaks rapidly before cell division.

Which of the following equations does NOT describe a reaction of the model shown in (i)?

Q15. Characterization of parameter spaces influencing the system’s behavior

The parameter k4 is the maximum rate of M activation, whereas k6 is the dissociation rate of the M-complex. Figure (ii) shows the behavior of the system as a function of parameters k6 and k4 (all other parameters are fixed at pre-defined values). A and C are state spaces where M levels are stable. In contrast B is a state space where levels of M oscillate.

What can be concluded from figure (ii), the description above and reaction scheme in figure (i)?

  • The levels of pM are same in state spaces A and B.
  • Because in state space C k6 is relatively high and k4 is relatively low, M levels in state space C are lower than those in state space A.
  • The levels of M are same in state spaces A and C.
  • Because in state space B k4 and k6 are balanced, state space B represents a robust state where the levels of M and pM are constant.
  • Because in state space A k4 is always high, in state space A pM levels are constantly high.

Q16. Analysis of the oscillatory system behavior

In state space B of figure (ii) (Q15), M and YT levels oscillate. Figure (iii) shows the oscillatory behavior of M and total cyclin (YT). Both values have been normalized to total cdc2 levels (CT). Each time M level peaks, there is a cell division event.

What can be concluded about the system’s behavior from figure (iii) and figure (i)?

  • A positive feedback loop is responsible for the rapid increase in M, once the total cyclin (YT) level exceeds a certain threshold level.
  • Changing the total levels of cdc2 (CT) increases the distance between the peaks of the two oscillations.
  • The degradation rate of the M-complex is only modest.
  • The rapid dissociation of the M-complex is ensured by a positive feedforward loop, once the total M level exceeds a certain threshold level.
  • Cell division events occur about every 60 min.

Q17. Biological interpretation of the modeling results

Now let’s look at figure (ii) again; the line connecting the points 1-5 represents the developing egg as it moves through different developmental stages. At position 1, the unfertilized egg is arrested in metaphase, so that division events can take place after fertilization. After fertilization before position 2, the fertilized egg enters the oscillatory state (B) and undergoes rapid cellular divisions because of the oscillatory behavior of M levels as shown in figure (iii). Just like in state space A, no division events take place in state space C.

What is true about the positions 4 and 5?

  • Further division events can be triggered or prevented by controlling k4 and k6 such that the system can or cannot oscillate between high and low levels of M.
  • Position 5 is associated with cell cycle arrest because the levels of M are starting to increase.
  • Once in state space C, cell division events are never possible because of an irreversible decrease in the levels of cdc2.
  • The degradation rate, k6, cannot influence the system’s behavior.
  • Position 5 corresponds to an apoptotic cell state that leads to cell death.

More About This Course

The student will learn about modern Systems in this class. Biology was mostly about the parts of mammalian cells and how they work. Molecular biology is giving way to modular biology.

As we learn more about our genome and how genes are expressed and make lists of molecules (proteins, lipids, ions) that are involved in cellular processes, we need to figure out how these molecules work together to form modules that work as separate functional systems.

These systems are at the heart of important subcellular processes like signal transduction, transcription, movement, and the ability of cells to get electrically excited.

In turn, these processes work together to make cells do things like secrete substances, grow, and make action potentials. What are these subcellular and cellular systems made of? How do systems behave in ways that don’t come from their parts? What kinds of experiments help people think about systems? Why do we need to do calculations and simulations to understand these systems?

The course will help students come up with more than one way to answer the questions above. The design, execution, and interpretation of multivariable experiments that produce large data sets and quantitative reasoning, models, and simulations are two of the most important types of reasoning.

We will talk about examples to show “how” cell-level functions come about and “why” mechanistic knowledge lets us predict how cells behave in disease states and in response to drugs.


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