Quantum Neural Machine Learning: Theory and Experiments DOI: http://dx.doi.org/10.5772/intechopen.84149

Table 1.

Now, the second step for the third brain region results from the activation of the

k¼1 ð Þ 1 � sk 1

k ⊕ rk � � � � � sk ⊕ s∗

> k ⊕ sk � � �

� ⊗ rk ⊕ s∗

2d

Since we have the Boolean equality p ⊕ ð Þ¼ q ⊕ p q, this means that the above density can be simplified, so that the neural field's probe and response dynamics for

> k¼1 s∗ k ⊕ rk �

The third brain region's computation takes advantage of the entangled dynamics between the first and second brain regions to learn the optimal action. For the final density, while the first and second brain regions exhibit an entangled probe and response dynamics, the third brain region is always projecting over the optimum. It is important to stress how QNRL takes advantage of quantum entanglement such that the neural field for the third brain region followed each alternative action and then the reward processing dynamics to find the optimum in all these alterna-

!

� � �

� � s∗

2d

^ þ skσ^1

� � (39)

k ⊕ sk � � ��

⊗ s∗ j i s∗ h j (41)

(40)

synaptic links with the second brain region, leading to the conditional unitary

⊗ j is h j s ⊗ d

k ⊕ sk � �

j ir h j s ⊗ d

tive paths, so that the optimal action is always followed by the agent.

device shown in Table 1, for 8192 repeated experiments.

As an example of the above problem, let us consider the case where we the reward set is Ω ¼ �f g 1; 1 , and that there are two possible actions 0 and 1 leading, respectively, to the classical probability measures P0 and P1, with P0½ �¼ w ¼ 1 0:4 and P1½ �¼ w ¼ 1 0:6; then, we get the probabilities of selection for each gamble and

As expected, the QASM simulator always selects the action 1, which is the best performing action by the conditional expected payoff criterion. The Tenerife device selects the correct action with a proportion of 0.778, while the Melbourne device selects the correct action with a proportion of 0.647. If, instead of the above gamble profile, we had P0½ �¼ w ¼ 1 0:6 and P1½ �¼ w ¼ 1 0:4, the optimal choice would be the action 0; in this case, as shown in Table 2, the QASM simulator, again, selects the correct action each time. The Tenerife device, in turn, selects the correct action

In Figure 5, we show the Melbourne device's results4 when we have four actions

P01½ �¼ w ¼ 1 0:4, P10½ �¼ w ¼ 1 0:8, and P11½ �¼ w ¼ 1 0:9, still setting the rewards to

In this case, if we run the experiment on the QASM backend, with 8192 shots, we get the action encoded by the string 11 with relative frequency equal to 1, which

4 We can only use the Melbourne device since the Tenerife device does not have the required capacity in

with a 0.857 frequency and the Melbourne device with a 0.814 frequency.

for the same rewards profile, and the probabilities are P00½ �¼ w ¼ 1 0:6,

U^ 4 ¼ ∑ s∈ Ad 2 ^1 ⊗ d

k¼1 s∗

k ⊕ rk �

the third brain region projects over the optimal action:

¼ ∑ r, s∈ Ad 2

� � s∗

Under this operator, we get the final density:

Artificial Intelligence - Applications in Medicine and Biology

operator:

ρ^4 ¼ U^ 4ρ^3U^ 4

¼ ∑ r, s∈ Ad 2

Ω ¼ �f g 1; 1 .

112

terms of number of quantum registers.

† ¼

ρ^4 ¼ U^ 4ρ^3U^ 4

†

j ir h j s ⊗ d

Results for two alternative actions using the QASM simulator, the Tenerife device (ibmqx4) and the Melbourne device (ibmq\_16\_melbourne); in each case, 8192 shots were used, with P0½ �¼ w ¼ 1 0:4 and P1½ �¼ w ¼ 1 0:6.

Table 2.

Results for two actions using the QASM simulator, the Tenerife device (ibmqx4) and the Melbourne device (ibmq\_16\_melbourne); in each case, 8192 shots were used, with P0½ �¼ w ¼ 1 0:6 and P1½ �¼ w ¼ 1 0:4.

#### Figure 5.

Results for four actions using the Melbourne device ("ibmq\_16\_melbourne"), with 8192 shots used, and probability profiles given by: P00½ �¼ w ¼ 1 0:6, P01½ �¼ w ¼ 1 0:4, P10½ �¼ w ¼ 1 0:8 and P11½ �¼ w ¼ 1 0:9.

is the optimal action. If we run the experiment with the same number of shots on the Melbourne device, then, as shown in Figure 5, the output 11 is still the dominant action, however, with a proportion of 0.370, the second dominant action being nonresidual and with a value of 0.309 occurs for the output 10.

Therefore, the first qubit tends to be measured with the right pattern with a proportion of 0.679 (0.309 + 0.370); the probability of the second qubit being correct given that the first is correct is only about 0.54492 (0.370/0.679). This suggests that the deviation may be due to the entanglement with the environment significantly deviating the second qubit from the correct pattern.

The above algorithm was implemented using Qiskit and Python's Object Oriented Programming (OOP); the code, shown in the appendix, exemplifies how OOP can be integrated with quantum computation for implementing quantum AI on any terminal, due to the cloud access to IBM's quantum resources, constituting an example of Quantum Object Oriented Programming (QOOP) using Qiskit.

The code defines the class "Agent" with an attribute that is the quantum neural network; in this case, the attribute will be assigned a quantum circuit with the required quantum and classical registers.

measurement for the decision module, executing the algorithm on the backend (taking advantage of the cloud access) and plotting the histogram to extract the main experimental relative frequencies obtained from the repeated experiments

Cloud-based access to quantum computers opens up a major point: the empirical testing of algorithms and the implementation of computer programs in a quantum

The IBM Q Experience constitutes an example of how a programmer can use Python programming language and IBM's Python Qiskit package for building programs that use quantum computation, limited only by the specific device resources,

For quantum AI and machine learning, this provides a way to effectively bring the algorithms from the theoretical level to the test level, allowing one to test drive different quantum AI frameworks on actual quantum computers. The work developed in the previous sections allowed us to provide several examples of such an

• We showed how one can address IBM's superconducting transmon devices as examples of QUANNs, since, just as in a QUANN, the devices can only implement the conditional gates depending on the network topology and the directions of the links, which only allow for specific conditional gates to be implemented; as an example, the Tenerife device is a bowtie feedforward network, we cannot turn it into a recurrent network so that the gates have to be implemented following specific directions of the links (this limit can be experienced by any user that accesses the online resources and tries to visually

• We exemplified how basic Boolean functions' representation, in this case the XOR function, can be implemented on a (physical) quantum computer using the cloud access to Tenerife and Melbourne devices and compared the experimental results with the theoretical derivation; a relevant point of this is that we only needed three quantum registers and no hidden layer to solve the XOR problem, a point already raised about this function and generalized to other functions in [4], regarding the theoretical efficiency of QUANNs.

• We addressed how a form of quantum adaptive computation, incorporating a reward-seeking behavior and a variant of QNRL, can be implemented, in the

The three main points above help strengthen two core arguments: the first is that quantum machine learning can now be tested on actual quantum computers, making it feasible to empirically test the algorithms; the second is that, in the near future, with further advancements in quantum computation and quantum hardware, quantum adaptive computation may be implemented on actual robots with a quantum cognitive architecture that is based on cloud access to a quantum computer.

The present work addresses both core arguments by exemplifying how a form of QNRL can be employed to implement quantum adaptive computation on a physical QUANN with cloud-based access, employing QOOP and addressing, experimen-

context of quantum robotics and AI, on different quantum devices.

(the number of shots).

computational substrate has become feasible.

namely the number of quantum registers available.

Quantum Neural Machine Learning: Theory and Experiments

DOI: http://dx.doi.org/10.5772/intechopen.84149

implementation, with a few main points standing out:

build circuits in IBM Q Experience homepage).

tally, a decision under risk problem.

115

4. Conclusions

There are two methods that any instance of the class Agent must be able to implement: the first method manages the cloud access to IBM's resources, the second method implements the action selection and the quantum algorithm.

The inputs for the first method are the accounts to be loaded, for the classical computer to be able to access quantum computer via the cloud service, and the backend code, which, by default, is set to the QASM simulator but can be changed to any of the devices. The method returns the backend to be used.

The second method, for the action selection, has a structure that is specific to the problem in question; that is, the agent is offered a set of rewards and probabilities associated with each alternative action, and must choose the action that maximizes the conditional expected reward.

Thus, the probabilities are known to the agent and form part of the gamble that is offered to it; therefore, we are dealing with a decision problem under risk, and wish to address how the agent's QUANN can exhibit an adaptive computation with respect to this problem.

While, in the above equations, the adaptive nature of the quantum neural circuit was introduced in the unitary operator's parameters themselves, the Python code for the method must use the gamble's inputs to make the quantum circuit adaptive; that is, the method must be such that the agent designs its own cognitive architecture (updating its qnnet attribute) and quantum circuit using the inputs to the method, and, then, the agent must implement the cloud-based access to run, in IBM's quantum computers, the corresponding quantum algorithm.

The inputs for the method are, then, given by a list of probability distributions, where each line corresponds to a different probability gamble profile associated with each action, for instance, in the case of Table 1, the distributions are given by ([0.6, 0.4],[0.4, 0.6]). In the case of Tables 1 and 2 and Figure 5, the rewards list is [1, 1].

The other two inputs for the method are the backend used which allows the agent that is instantiated in a classical computer to access via cloud the quantum computer, using the backend code (backend\_used) and repeatedly running the algorithm on the respective device for a number of shots (num\_shots).

The choose\_action method's step zero is the extraction of the expected values and of the corresponding parameters for the adaptive gates, namely, the expected values array associated with each action is extracted by the agent using the Python library NumPy's dot product applied to the distributions and rewards lists.

The number of actions and dimension d that determines the network size are extracted from the length of the expected values array; then, the parameters for the adaptive gates are extracted by applying NumPy's argmax function on the expected values array and then converting the resulting index in binary format (using NumPy's binary\_repr). Since the indexes match the lexicographic order of binary strings, the agent, thus, effectively extracts the parameters for the adaptive unitary gates.

Now, the next step is to set up the QUANN, including the three modules, the classical registers for the measurement of the final actions to be chosen and updating the agent's qnnet attribute, assigning it the corresponding Qiskit's quantum circuit object.

The last step implements the QNRL algorithm, following the inter-module links as per the main equations introduced in this section, and defines the quantum

measurement for the decision module, executing the algorithm on the backend (taking advantage of the cloud access) and plotting the histogram to extract the main experimental relative frequencies obtained from the repeated experiments (the number of shots).


**5. The regulation of iron homeostasis is tightly connected to central metabolic pathways**

As mentioned previously, iron deficiency is one of the major causes of stress in cyanobacterial communities. Due to the occurrence of iron in most electron transport proteins conforming photosynthetic, respiratory and nitrogenase pathways, the adaptive strategies developed by the cyanobacteria are tightly related to the rearrangement and modulation of these processes. Furthermore, many of the different responses triggered by iron deprivation are aimed to prevent and alleviate oxidative stress and to the modulation of central metabolism.

Further transcriptomic studies evaluating the cyanobacterial response to iron deficiency unveiled that as a general trend, photosynthesis genes were repressed under low-iron conditions and induced upon the re-addition of iron. Many of those genes belonged to the *psa* and *psb* families, components of the phycobilisomes and genes involved in the synthesis of chlorophyll are also direct targets of FurA [14, 24, 141]. Furthermore, Fur is involved in the control of genes involved in carboxysome formation and Calvin cycle. Notably, a close relationship between light availability and iron requirements can be inferred from different studies, such as the differentially expressed genes in [142], the regulation of *furA* and the *alpha-furA* antisense RNA by light [143], or the need of an active photosynthetic electron transport chain for the expression of the *mcy* operon in *M. aeruginosa*, that in turn is controlled by FurA [124, 143, 144]. As *furA* from *M. aeruginosa*, the expression of the *Anabaena* sp. PCC7120 ortholog is controlled by an antisense RNA whose inactivation produces iron-deficient cells and severe structural disorders in the photosynthetic apparatus of *Anabaena*. Furthermore, disruption of the dicistronic message encoding the *alr1690-alpha-furA* tandem leads to lower photosynthetic performance indexes, unveiling that its expression is required for maintenance of a proper thylakoid arrangement, efficient regulation of iron uptake and optimal yield of the photosynthetic machinery [123, 145]. In addition, FurA modulates the transcription of the LexA regulator in *Anabaena* PCC7120. This regulator is critical to the survival of cyanobacterial cells facing inorganic carbon starvation, since most of the LexA-responsive genes were known to

The Challenge of Iron Stress in Cyanobacteria http://dx.doi.org/10.5772/intechopen.76720 121

be involved in carbon assimilation or controlled by carbon availability [146].

**5.3. Iron-responsive genes involved in cyanobacterial respiratory pathways**

wild-type strain has affected its respiratory activity [145].

**5.4. Cross-talk between iron and nitrogen metabolism**

In addition to the photosynthetic electron transport chains, cyanobacterial thylakoids contain multiple respiratory electron transport complexes [147]. Thus, photosynthesis and respiration are tightly related in cyanobacteria since both pathways share several components, such as a quinone/quinol pool [148], plastoquinone, cytochrome b6f and plastocyanin/cytochrome [148, 149]. Furthermore, the cyanobacteria contain a second complete respiratory chain present in the cell membrane that also uses the same mobile quinone pool mediating electrons in the photosynthetic and thylakoidal respiratory processes. Several studies evidence the relationship between the iron pool and the respiratory activity. The major oxidase in cyanobacteria, COX, is encoded by the *cox* operon (*coxBAC*) and FurA regulated though the modulation of *cox*B [15]. Similarly, the transcription of *alr0869* (*ndhF*) and the subunit 5 of NADH dehydrogenase encoded by *all1127* are regulated by FurA as response to iron availability [15]. Furthermore, iron starvation in *S. elongatus* causes upregulation of several cytochrome oxidases and the increase of respiratory electron transport [22, 150], while an *Anabaena* mutant lacking of the *alr1690-alpha-furA* message that exhibits a reduced iron pool with respect to the

The electron carriers involved in nitrogen metabolism are also rich in iron, especially the proteins involved in nitrogen fixation. Nitrogenase and nitrogenase reductase complex harbor around 40 atoms of Fe2+ distributed between the iron-molybdenum cofactor (FeMo-co) and the [8Fe-7S] P-cluster present in NifDK nitrogenase, and the [4Fe-4S] cubane in the NifH dinitrogenase reductase. In addition, most of the proteins involved in the assembly

## **5.1. Iron availability and the oxidative stress response**

Oxidative stress is one of the many consequences of iron imbalance in cyanobacteria. Thus, the control of iron homeostasis is intimately linked to the regulation of many genes involved in the response to oxidative stress [4, 14, 24, 94]. Moreover, the master regulators involved in such processes in cyanobacteria, namely FurA and PerR/FurC, display a set of common targets [14, 136]. Furthermore, PerR/FurC is able to modulate *in vitro* FurA-DNA binding activity [117]. Transcriptomic analyses and differential proteomics focused on the definition of the FurA regulon in *Anabaena* PCC 7120 unveiled that around 13% of FurA targets with a known function were involved in detoxification of ROS [14]. Those FurA-regulated genes belong to different subcategories, such as electron transport proteins dedicated to restore oxidized thiols (*trxA*, *trxB*, the glutaredoxin-related protein *alr0799* and the glutathione S-transferases *alr3195* and *alr7354*, among others); detoxification of hydrogen peroxide (the Mn-catalase *katB* and the peroxiredoxins *all1541* and *alr4641*) or the protection of DNA (*dpsA*) [14, 106, 119, 137]. FurA also controls the expression of flavodoxin that is strongly induced under iron deficiency [13, 138]. Initially described as a substitute for ferredoxin I (Fd) in the photosynthetic electron transport to NADP+ [45, 138] (reviewed in Sections 2.1.1 and 5.2), flavodoxin is also a powerful scavenger of ROS. Interestingly, the expression of flavodoxin in chloroplasts of tobacco unveiled that this flavoprotein is able to effectively interact with several Fd-dependent oxidoreductive pathways, including thioredoxin reduction [139]. The expression of flavodoxin in plastids protected target enzymes of central metabolic pathways from oxidative inactivation, such as the Calvin cycle components fructose-1,6-bisphosphatase (FBPase) and phosphoribulokinase (PRK). Therefore, the expression of flavodoxin triggered by iron deficiency relieves the oxidative stress in the cyanobacteria and contributes to the reconstitution of the electron transport chains rich in iron-containing proteins whose iron-sulfur clusters are immediate targets of free radicals, minimizing the effect of the oxidative damage on the photosynthetic rates and the nitrogen metabolism, among other metabolic pathways [139].

### **5.2. Influence of iron availability in the control of photosynthetic genes**

As it has been shown previously, iron limitation has important consequences in the composition and performance of cyanobacterial photosystems. Several photosynthetic cyanobacterial specific genes induced under iron deficiency contribute to modify their photosynthetic machineries such as *isiA*, *isiB* (flavodoxin), *idiA*, *idiB* and *idiC* proteins (reviewed in Section 2.1.1). Fur controls the expression of *isiA* and isiB [13], whose transcription is induced by multiple stresses such as treatment with hydrogen peroxide or high salt [56, 136, 140].

Further transcriptomic studies evaluating the cyanobacterial response to iron deficiency unveiled that as a general trend, photosynthesis genes were repressed under low-iron conditions and induced upon the re-addition of iron. Many of those genes belonged to the *psa* and *psb* families, components of the phycobilisomes and genes involved in the synthesis of chlorophyll are also direct targets of FurA [14, 24, 141]. Furthermore, Fur is involved in the control of genes involved in carboxysome formation and Calvin cycle. Notably, a close relationship between light availability and iron requirements can be inferred from different studies, such as the differentially expressed genes in [142], the regulation of *furA* and the *alpha-furA* antisense RNA by light [143], or the need of an active photosynthetic electron transport chain for the expression of the *mcy* operon in *M. aeruginosa*, that in turn is controlled by FurA [124, 143, 144]. As *furA* from *M. aeruginosa*, the expression of the *Anabaena* sp. PCC7120 ortholog is controlled by an antisense RNA whose inactivation produces iron-deficient cells and severe structural disorders in the photosynthetic apparatus of *Anabaena*. Furthermore, disruption of the dicistronic message encoding the *alr1690-alpha-furA* tandem leads to lower photosynthetic performance indexes, unveiling that its expression is required for maintenance of a proper thylakoid arrangement, efficient regulation of iron uptake and optimal yield of the photosynthetic machinery [123, 145]. In addition, FurA modulates the transcription of the LexA regulator in *Anabaena* PCC7120. This regulator is critical to the survival of cyanobacterial cells facing inorganic carbon starvation, since most of the LexA-responsive genes were known to be involved in carbon assimilation or controlled by carbon availability [146].

## **5.3. Iron-responsive genes involved in cyanobacterial respiratory pathways**

**5. The regulation of iron homeostasis is tightly connected to central**

vent and alleviate oxidative stress and to the modulation of central metabolism.

**5.1. Iron availability and the oxidative stress response**

As mentioned previously, iron deficiency is one of the major causes of stress in cyanobacterial communities. Due to the occurrence of iron in most electron transport proteins conforming photosynthetic, respiratory and nitrogenase pathways, the adaptive strategies developed by the cyanobacteria are tightly related to the rearrangement and modulation of these processes. Furthermore, many of the different responses triggered by iron deprivation are aimed to pre-

Oxidative stress is one of the many consequences of iron imbalance in cyanobacteria. Thus, the control of iron homeostasis is intimately linked to the regulation of many genes involved in the response to oxidative stress [4, 14, 24, 94]. Moreover, the master regulators involved in such processes in cyanobacteria, namely FurA and PerR/FurC, display a set of common targets [14, 136]. Furthermore, PerR/FurC is able to modulate *in vitro* FurA-DNA binding activity [117]. Transcriptomic analyses and differential proteomics focused on the definition of the FurA regulon in *Anabaena* PCC 7120 unveiled that around 13% of FurA targets with a known function were involved in detoxification of ROS [14]. Those FurA-regulated genes belong to different subcategories, such as electron transport proteins dedicated to restore oxidized thiols (*trxA*, *trxB*, the glutaredoxin-related protein *alr0799* and the glutathione S-transferases *alr3195* and *alr7354*, among others); detoxification of hydrogen peroxide (the Mn-catalase *katB* and the peroxiredoxins *all1541* and *alr4641*) or the protection of DNA (*dpsA*) [14, 106, 119, 137]. FurA also controls the expression of flavodoxin that is strongly induced under iron deficiency [13, 138]. Initially described as a substitute for ferredoxin I (Fd) in the photosynthetic electron

ful scavenger of ROS. Interestingly, the expression of flavodoxin in chloroplasts of tobacco unveiled that this flavoprotein is able to effectively interact with several Fd-dependent oxidoreductive pathways, including thioredoxin reduction [139]. The expression of flavodoxin in plastids protected target enzymes of central metabolic pathways from oxidative inactivation, such as the Calvin cycle components fructose-1,6-bisphosphatase (FBPase) and phosphoribulokinase (PRK). Therefore, the expression of flavodoxin triggered by iron deficiency relieves the oxidative stress in the cyanobacteria and contributes to the reconstitution of the electron transport chains rich in iron-containing proteins whose iron-sulfur clusters are immediate targets of free radicals, minimizing the effect of the oxidative damage on the photosynthetic

As it has been shown previously, iron limitation has important consequences in the composition and performance of cyanobacterial photosystems. Several photosynthetic cyanobacterial specific genes induced under iron deficiency contribute to modify their photosynthetic machineries such as *isiA*, *isiB* (flavodoxin), *idiA*, *idiB* and *idiC* proteins (reviewed in Section 2.1.1). Fur controls the expression of *isiA* and isiB [13], whose transcription is induced by mul-

rates and the nitrogen metabolism, among other metabolic pathways [139].

**5.2. Influence of iron availability in the control of photosynthetic genes**

tiple stresses such as treatment with hydrogen peroxide or high salt [56, 136, 140].

[45, 138] (reviewed in Sections 2.1.1 and 5.2), flavodoxin is also a power-

**metabolic pathways**

120 Cyanobacteria

transport to NADP+

In addition to the photosynthetic electron transport chains, cyanobacterial thylakoids contain multiple respiratory electron transport complexes [147]. Thus, photosynthesis and respiration are tightly related in cyanobacteria since both pathways share several components, such as a quinone/quinol pool [148], plastoquinone, cytochrome b6f and plastocyanin/cytochrome [148, 149]. Furthermore, the cyanobacteria contain a second complete respiratory chain present in the cell membrane that also uses the same mobile quinone pool mediating electrons in the photosynthetic and thylakoidal respiratory processes. Several studies evidence the relationship between the iron pool and the respiratory activity. The major oxidase in cyanobacteria, COX, is encoded by the *cox* operon (*coxBAC*) and FurA regulated though the modulation of *cox*B [15]. Similarly, the transcription of *alr0869* (*ndhF*) and the subunit 5 of NADH dehydrogenase encoded by *all1127* are regulated by FurA as response to iron availability [15]. Furthermore, iron starvation in *S. elongatus* causes upregulation of several cytochrome oxidases and the increase of respiratory electron transport [22, 150], while an *Anabaena* mutant lacking of the *alr1690-alpha-furA* message that exhibits a reduced iron pool with respect to the wild-type strain has affected its respiratory activity [145].

#### **5.4. Cross-talk between iron and nitrogen metabolism**

The electron carriers involved in nitrogen metabolism are also rich in iron, especially the proteins involved in nitrogen fixation. Nitrogenase and nitrogenase reductase complex harbor around 40 atoms of Fe2+ distributed between the iron-molybdenum cofactor (FeMo-co) and the [8Fe-7S] P-cluster present in NifDK nitrogenase, and the [4Fe-4S] cubane in the NifH dinitrogenase reductase. In addition, most of the proteins involved in the assembly of the metalloclusters embedded within the NifDK protein also contain diverse [Fe-S] centers [151, 152]. Thus, growing under nitrogen fixation conditions adds an additional iron stress to the cell. Therefore, optimal cyanobacterial performance requires a tight and coordinated regulation of iron and nitrogen metabolisms [137]. Nitrogen metabolism in cyanobacteria is controlled by the master regulator NtcA [153] that usually senses the C/N balance through the intracellular 2-oxoglutarate levels [154]. NtcA controls a wide regulon of genes involved in different functional categories [155, 156]. Among them, NtcA controls most steps required for nitrogen fixation in cyanobacteria, starting from heterocyst differentiation and development until *nif* genes expression. NtcA also controls key genes in nitrogen assimilation pathways in cyanobacteria [157]. Different studies evidence a tight relationship between iron and nitrogen metabolism. Interestingly, transcription of the *nif-HDK* operon and excision of the 11 kb DNA fragment required for heterocyst differentiation was observed in iron-starved *Anabaena*, even though cells grew in the presence of combined nitrogen [138]. Further studies showed that the expression of FurA is highly induced in the heterocyst [137]. FurA participates in the regulation of *nif* genes, and the levels of this regulator are critical for the modulation of heterocyst differentiation by controlling the expression of NtcA and vice versa [14, 16]. Thus, several iron-responsive genes in cyanobacteria, such as *nblA, petH*, *pkn41*, *pkn42*, among others, are also modulated by NtcA [137, 158–161]. Conversely, in *Synechocystis sp.* PCC 6803, the NtcA-regulated genes *bgtB, glnA* and *urtB* are highly upregulated under iron limitation [162]. Different studies focused on the identification of the FurA and NtcA regulons in different cyanobacterial strains support that FurA and NtcA are interactive regulators and corroborate that both transcription factors share an important number of targets mainly related to photosynthesis and respiration, iron uptake and incorporation, oxidative stress response and nitrogen metabolism [137]. However, given that both FurA and NtcA are global regulators, it is not surprising that the nitrogen starvation response involves a large number of genes not only related to iron metabolism but also to heavy metal and oxidative stress adaptation, reinforcing the interrelationship of those processes [162].

**6.1. Iron and blooms occurrence**

**6.2. Iron and cyanotoxin production**

Iron availability and biolimitation by iron of the phytoplankton are important subjects discussed for many years. After IronExII [2], it was definitively established that iron availability limits rates of cell division, as well as abundance and production of phytoplankton of the equatorial Pacific and likely in other "high nutrient, low chlorophyll regions" [55]. There is broad agreement that nutrient over-enrichment of freshwater and marine ecosystems promote cyanobacterial blooms. Phosphorus and nitrogen have traditionally been considered the key nutrients limiting primary productivity and algal biomass. But based on such accessibility (and light and temperature suitable for cyanobacterial growth), iron availability could be suggested to be the switch that triggers a bloom. Cyanobacteria compete very efficiently with other phytoplankton species for iron resources and often end up dominating the population. In addition to all, the adaptive strategies previously mentioned, in some cases, their competi-

The Challenge of Iron Stress in Cyanobacteria http://dx.doi.org/10.5772/intechopen.76720 123

Cyanotoxins are a heterogenous group of molecules that include hepatotoxins, neurotoxins, dermatotoxins and cytotoxins, with diverse chemical nature such as cyclic peptides: cyclic peptides, alkaloids, non-proteic amino acids. The synthesis of most toxins is inducible, and the genes involved in its biosynthesis have been identified during these last years [165]. The genes conforming biosynthetic pathways, its regulation and the molecular mechanisms involved in toxicity are in each case different. However, NRPS are present in all the described toxic operons, involved in cyanotoxin synthesis. Many NRPS present in many bacteria are iron regulated [166, 167]. A substantial variety of siderophore structures, toxins and antimicrobial molecules with toxic effects are produced from similar NRPS assembly lines [167], and a large number of

Among cyanotoxins, microcystins are the most ubiquitous toxins causing several environmental and health problems. They are a family of cyclic heptapeptides, synthesized by a mixed PKS-NRPS system called microcystin synthetase encoded in *mcy* operon [168]. The role of microcystins in cyanobacteria is still unclear, but there are evidences that could confer to the toxic strains advantages for survival in iron-limited conditions. The microcystin synthesis has been linked to iron metabolism for many years. Lyck and colleagues [169] showed that during iron depletion, toxic strains of *Microcystis* maintained cell vitality much longer than the nontoxic strains. Moreover, Utkilen and Gjolme [170] found that toxic strains exhibited higher rates of iron uptake than nontoxic strains. They proposed that microcystin could be an intracellular chelator of Fe+2, as well as predicted that the synthesis of the toxin would be controlled by the amount of free iron present in the cells. Structural similarities between microcystin and bacterial siderophores [167] led also to propose a putative role as an extracellular iron-scavenging molecule. Recently, it was shown that while the microcystin producing strain *M. aeruginosa* PCC 7806 and its close strain, the non-producing *M. aeruginosa* PCC 7005 grew similarly in BG11 in the presence of 17 μM iron, under severe iron deficient conditions (0.05 μM), the toxigenic strain grew slightly less than in iron-replete conditions, while the non-producing microcystin strain was not able to grow [171]. Taking together all these data suggest that microcystin production

tive advantage is based on its ability to vertical migration [164].

secondary metabolites are also synthesized as response to iron starvation.