Flux balance analysis (FBA) is a key method for the constraint-based analysis of metabolic networks. A technical problem may occur in FBA when known  fluxes of certain reactions are integrated into an FBA scenario rendering the underlying linear program (LP) infeasible, for example, due to inconsistencies between some of the measured fluxes causing a violation of the steady-state or other constraints. Here, we present and compare two methods, one based on an LP and one on a quadratic program (QP), to find minimal corrections for the given flux values so that the FBA problem becomes feasible. We provide a general guide on how to treat infeasible FBA systems in practice and discuss relevant examples of potentially infeasible scenarios in core and genome-scale metabolic models. Finally, we also highlight and clarify the relationships to classical metabolic flux analysis, where solely algebraic approaches are used to compute unknown metabolic rates from measured fluxes and to balance infeasible flux scenarios. Various mathematical modeling techniques have been developed to facilitate the computer-aided analysis and exploration of complex metabolic networks. Constraint-based modeling is a particular class of those methods that is based on the network structure (stoichiometry), mass balances of metabolite concentrations in a steady state and certain physicochemical  and biological  constraints . Within 13C-MFA, which uses measurements of isotopic labeling patterns from 13C-tracer experiments to resolve unknown metabolic rates [One of the oldest applications of constraint-based modeling is metabolic flux analysis (MFA), where measurements of certain reaction rates are used to derive unknown reaction rates in a given metabolic network ,6,7,8. MEscherichia coli. The steady-state condition together with the known reaction rates lead to a system of linear equations, which, in classical MFA, is analyzed and solved by methods from linear algebra ,9,10,11.We consider a metabolic network with , can be easily embedded in an FBA problem, for example, to incorporate an upper limit for the total amount of enzymes that are needed to catalyze the fluxes contained in the rate vector [Setting a reaction with index e vector ,13. The FBA problems constitute linear programs (LPs) for which a variety of solvers are available. We assume that the base LP (4) is feasible, i.e., there are no conflicting constraints in Equations (1)–(3) and at least one rate vector E. coli is inactive under anaerobic conditions and the PDH flux as well as oxygen uptake can thus be set to zero when simulating anaerobic conditions. With In the context of MFA, simple constraints are added to integrate values of known fluxes. The latter typically include measurements of exchange rates of substrates and products, but may also reflect environmental conditions or biological knowledge. For example, the pyruvate dehydrogenase (PDH) in We denote the number of known reaction rates with We first review the techniques used to analyze and resolve infeasibility in classical MFA, which is solely based on constraints (1) and (5). This will later help us to relate the terminology and methods of classical MFA to the more general case of balancing infeasible FBA scenarios.In this section, we initially assume that the metabolic network has no conservation relations, i.e., the stoichiometric matrix has full rank  and (5) and integrates them by splitting up the stoichiometric matrix and the vector of reaction rates into known and unknown parts:m equations  is redundancy. A system is redundant if it contains linear dependencies between some rows (metabolites) of As a sub-categorization, if the system is redundant, it is either consistent (feasible) or inconsistent (infeasible), whereas non-redundant systems are always consistent. Consistency or feasibility means that there is at least one vector atrix NU . ImportaUsing (10) and We can rewrite (11) asIf the MFA scenario is feasible, then (11) and (13) yield zero or, equivalently,In a non-redundant system, Equation ). Note tSince the absolute value of We illustrate the introduced terms with the example network shown in The second scenario, S2, is typical for realistic MFA applications: all (four) exchange rates were measured. In well-posed systems, this results in a redundant system, since knowing all exchange fluxes but one always allows, due to mass balances, the calculation of the unknown exchange flux . The system is still redundant, but now all rates are uniquely determined (the system is determined). A system that is redundant and determined is often called overdetermined. Scenario S4 is still infeasible, but the redundancies (and inconsistencies) are again only posed by reactions R1–R4, whereas reaction R6 adds no redundancy to the system. The steady-state condition is violated for all internal metabolites but, still, it is the best solution that can be found for Equations (7) and (8) as it minimizes the (squared) error in (11). Finally, scenario S5 is identical to S4, except that we assume that we had not measured the rate of reaction R2. This system is not redundant, but determined and all rates (including for R2) can be uniquely calculated. Only in such a non-redundant and determined system does the Moore–Penrose pseudoinverse become a square matrix coinciding with the (then existing) inverse of In the considerations above, we assumed that there are no conservation relations (CRs) in the metabolic network, which refer to linearly dependent rows (metabolites) in the stoichiometric matrix Everything else remains unaffected, in particular, the redundancy matrix and the redundant rates can be determined as before.Scenarios S2 and S4 in  follows . We aim For the weights Although both Equation (18) as well as the solution based on the pseudoinverse in Equation (10) are least-squares solutions, they are fundamentally different: The former minimizes the deviation from a steady state under the consideration of the given reaction rates, whereas the second minimizes the (weighted) changes in the measured rates necessary to obtain a feasible system fulfilling the steady-state condition. This is illustrated by scenario S6 in Classical MFA cannot consider any other constraints than the steady-state condition (1) and the measured reaction rates (5). Even when considering only simple reversibility constraints (by using We therefore consider in the following an FBA scenario consisting of the FBA base constraints (1)–(3), the flux measurements (5), and the given FBA objective function from (4): As previously stated, we assume that the base constraints (1)–(3) form a feasible system, a property that is fulfilled in well-posed network models  that make the system feasible. We know that such a correction exists, as we assume that the base system (1)–(3) is feasible. We can follow a similar strategy as we did for the  least-squares approach (Equations (16) and (17)) of classical MFA. We again add a correction term (or slack variable) This approach mimics the weighted least-squares approach of classical MFA (17), but the difference here is that this optimization is subject to the full set of constraints (1)–(3) and (20), for which no analytical solution as in Equation (18) exists in general. In this setting, (21) becomes a quadratic program (QP), which is an LP extension that allows for quadratic terms in the objective function, while the constraint must still be linear. In general, the objective function  to be zero to set high penalties for corrections in those reaction rates. In case one is certain that a rate is zero, the corresponding reaction can also be removed from the network prior to the calculation or a weight of infinity could be used.We note that the corrections is  can be The QP-based approach for resolving inconsistencies goes beyond classical LP problems considered in FBA and requires a suitable QP solver. Furthermore, although the complexity of solving convex QP problems is polynomial , the comAs the objective function, we minimize the weighted sum of the slacks needed to make the system feasibleEffectively, this optimization problem minimizes the weighted sum of absolute changes and the use of two non-negative slack variables in (23) and (24) allows for the linear representation. Furthermore, note that in any optimal solution obtained with (24), at least one of the two correction terms It should be noted that, in contrast to the QP method, the LP-based correction approach 24) may have non-unique solutions for the slack variables. Consider a simple inconsistent scenario in the network in 4 may havtributed . HoweverThus far, we assumed that the fixed (measured/known) fluxes in (5) caused the inconsistencies and searched for suitable  corrections of these fluxes to make the system feasible. However, one may also consider the other constraints (1)–(3) as possible sources of the inconsistencies, and thus as targets for corrections. The procedure is analogous as for correcting the fixed fluxes. One first introduces slack variables on the right hand-side of the respective constraints. For each of the Normally, the (exclusive) correction of fixed/measured fluxes is most relevant for realistic MFA/FBA applications. Nevertheless, there might be cases in which the correction of other constraints is of interest, for example, (a) to identify reactions that are constrained to be irreversible but are in reality reversible, or (b) to resolve inconsistencies related to complex enzyme capacity constraints ,13 that As a summary, in the following, we provide a step-by-step procedure for how to analyze and resolve infeasibility in FBA scenarios:(1)Detect (in)feasibility: infeasibility of a metabolic flux  scenario can or will be detected by an associated error message of the LP solver when performing an FBA optimization or an FVA. If this infeasibility is not a consequence of fixing some reaction rates, i.e., if the base system (1)–(3) is already infeasible, use the methods explained in the previous section for resolving inconsistencies in these constraints.(2)If not yet performed, identify all reactions with a fixed rate .(3)It is recommended to check via Equation (15) whether there are algebraic redundancies in the defined scenario (solely related to the steady-state constraint) and, if so, to find out which of the fixed reaction rates induce this redundancy (via the redundancy matrix (12)). This can be very helpful to find possible sources of mutually inconsistent rates (but it does not provide a (complete) explanation if constraints (2) and (3) are also involved in the inconsistency).(4)Decide on which optimization approach (QP (21) or LP (24)) and which of the three weighting schemes, W1–W3, are to be applied. Recommendation: if the measurements variances (5)Compute the corrections with the respective optimization approach and analyze them to identify the given rates that were assigned the largest changes and have thus caused the largest inconsistencies.(6)Apply the corrections and compute with the balanced (now feasible) system the solutions for the original FBA/FVA problem. In particular, FVA can be used to identify unknown rates that are uniquely determined from the measured rates . Generally, when performing FBA or FVA in the corrected system, one may face numerical problems: the precision of the calculated (and applied) corrections might not be sufficient enough for the solver used in the subsequent FBA/FVA optimizations, again resulting in an infeasibility message of the solver. If this happens to be the case, it is advised to use a slightly lower precision for the subsequent optimizations. In the example applications described in http://www2.mpi-magdeburg.mpg.de/projects/cna/cna.html (accessed on 22 June 2020). Previous versions of the MATLAB toolbox CellNetAnalyzer (CNA) ,19 alreahttps://github.com/cnapy-org/CNApy (accessed on 22 June 2020).Likewise, these methods have been implemented in the latest release of CNApy, a recently published Python toolbox . CNApy hEscherichia coli core model 2 (ECC2comp), a model of the central metabolism of E. coli comprising 82 reactions [The methods introduced herein are especially useful to treat infeasibility in FBA systems arising from measured or known fluxes. How likely is it in practice that a network model becomes infeasible if flux measurements are added to the network constraints? Clearly, this depends on (i) network size and structure, (ii) the set of measured fluxes, and (iii) the type of included constraints in (3). Fluxes that can often be determined for microorganisms in dedicated experiments are the exchange rates of (carbon) substrates, oxygen, carbon dioxide, hydrogen and of carbon (by)products, as well as the growth rate. Fixing these fluxes in the (compressed) eactions , alreadyThen, one may apply FVA to investigate whether uniquely determined rates result from the given scenario. However, even in core models and even if further constraints are added (such as non-growth associated maintenance (NGAM) demand of ATP, enzyme capacity constraints, etc.), typically only few of the unknown internal rates become uniquely determined. Here, FBA can be used to focus on optimal flux distributions. If the growth rate has been measured and added as a constraint, the classical FBA objective function of growth maximization would not be meaningful anymore. A reasonable assumption is then to assume that, under the given constraints, the cell has optimized the remaining fluxes towards ATP production, i.e., the NGAM reaction  is maximized. This approach was also used in  after reE. coli model iJO1366 [2), while the others can be assumed to be zero. Again, the resulting redundancy and the magnitudes of the computed  corrections needed to make the system feasible can then deliver important insights on the overall carbon balance and point to possible modeling (or measurement) errors. Although less likely, infeasibilities can also arise in genome-scale models and, thus, methods to analyze and treat them become relevant in these large models as well. In particular, redundancies will appear in any well-posed stoichiometric metabolic model if all exchange fluxes relevant for the balance of a certain element  or for charge balance have been measured or fixed. For example, fixing the fluxes of all exchange reactions that transport carbon metabolites in the genome-scale  iJO1366  indeed lE. coli with enzyme constraints [Redundancies related to carbon balance are the most valuable, as the latter integrates a large number of fluxes in the metabolism; however, redundancies from other elemental or charge balances can be relevant and useful as well. In contrast to simple algebraic models of elemental balances ,9, FBA mstraints  and provE. coli. In both networks, we considered three different infeasibility problems (P1–P3) with increasing complexity: P1—two strictly coupled reactions have inconsistent fluxes; P2—growth rate and all carbon exchange fluxes fixed ; P3—one reaction rate within a given complete flux distribution (FBA solution for maximization of growth) is changed to an inconsistent value. For each problem in each network, we considered two weighting schemes (W2 and W3) and calculated the LP-based as well as the QP-based solution. We first used CPLEX’s (cplexlp for the LP-based and cplexqp for the QP-based approach) and then MATLAB’s solver (linprog for LP and quadprog for QP) with standard tolerances and parameters. The calculation via the LP-based approach could be successfully finished within one second (standard PC) for all tested problems in the core, as well as in the genome-scale model with both tested LP solvers. Regarding the QP-based solutions, all problems in both networks could again be solved within one second when using CPLEX’s QP solver with the “simplex” method. In contrast, while MATLAB’s QP solver succeeded the calculation of all example scenarios in the core network , only for one (out of six) tested genome-scale problems a QP-based solution could be found. In the other cases, the solver stopped with the message that it had reached the maximum number of iterations or that it encountered numerical problems . These results indicate that the LP-based solution may in some cases be more practical in large-scale models. We also tested the LP- and QP-based methods implemented in CellNetAnalyzer to resolve typical examples of infeasible scenarios in the compressed core (ECC2comp) and in the genome-scale (iJO1366) model of In this work, we presented and discussed methods useful to analyze and resolve infeasible FBA scenarios. We focused on the most relevant situation in which inconsistencies arise due to predefined fluxes, but also outlined the more general case where inconsistencies are caused by other linear constraints of an FBA scenario. We are not aware of any previous work that provides a systematic approach to treat and resolve inconsistent FBA scenarios with fixed fluxes and how this links to classical MFA. Abbate et al.  used adaAnother small methodological overlap exists with FBA methods that seek to identify stoichiometric inconsistencies  in metabolic models . HoweverWe presented and discussed two major approaches for treating infeasibility: the QP-based approach minimizing the (weighted) squares of changes, and the LP approach minimizing the sum of (weighted) changes. In the field of linear programming, a variety of further methods has been developed to deal with infeasibility in systems of linear equalities and inequalities . For exaAnother possible extension is to analyze in more detail the structural sources of the detected infeasibility, for example, by identifying smallest (irreducibly) inconsistent subsets of constraints (IIS ). To mak


Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC. Diagnosing chemoresistance and predicting the patient’s outcome is a major challenge in the clinical management of ovarian carcinoma (OvC) . Most afTumor progression and response to treatment are influenced by the complex cell-cell, cell-extracellular matrix (ECM) and cell-soluble factor interactions (including metabolites) that compose the tumor microenvironment (TME) ,7,8. In The molecular and cellular heterogeneity of the TME further complicates the establishment of predictive models of patient response from indirect data, such as blood-based liquid biopsies, demanding especially large patient cohorts due to the low signal-to-noise ratio from the specimen background ,23. PatiHerein, we took advantage of an OvC ex vivo model developed by our team. We previously showed that patient-derived OvC tissue can be cultured as explants (OvC-PDE) that sustain the tumor architecture and the TME cellular components for several weeks and are suitable for cyclic drug exposure assays using LDH release as a readout ,29. HereConsent was obtained for the use of fresh, surgically removed tumors from nine patients with OvC who underwent surgery at Instituto Português de Oncologia de Lisboa Francisco Gentil (IPOLFG) from 2018 to 2020. Tumor samples  were alsv/v) Fetal Bovine Serum (Gibco) and 1% (v/v) PenStrep (Gibco), from the surgery room to the laboratory and processed under 4 h. Samples were weighed and then mechanically dissociated into fragments of approximately 1 mm3, as previously described by our team − or [M + Cl]− ionic species in negative ionization datasets. To filter the number of candidates obtained in the accurate MS search, logP was considered as described in the methods section. Putative identifications with different levels of annotation confidence were achieved for 33 features, annotated according to Schrimpe–Rutledge et al. [m/z matched to a metabolite database (confidence level 3), and 42 remained unknown (confidence level 5). However, by performing adduct grouping, the 33 identified features were translated into 10 compounds, which belong to amino acids (2), peptides (2), carnitines and acylcarnitines (1), fatty acids and conjugates (2) and vitamins (1) metabolite classes. Carboplatin, one of the drugs used in this study, was also identified in both HILIC datasets ((+) and (−)). In addition, seven metabolites  known to be present in DMEM culture media were identified   .As metabolic footprinting revealed differences in response to drug treatment in PDE cultures, a semi-targeted metabolomics approach was then employed, analyzing the metabolite classes of the most relevant features identified in the untargeted metabolomics experiment, using a semi-polar LC-MS/MS method C. FurtheWe analyzed CM duplicates of all experimental conditions of each OvC-PDE culture . In addiThe PCA based on CM samples from HGSC-derived cultures  identified different drug response groups based on the ex vivo drug-induced cell death  of the drug-induced cell death as a continuous variable  between the PDE metabolic footprint and the patient chemotherapy regimen  using the drug combination dataset allowed a fine discrimination  fatty acids metabolism, specifically in beta-oxidation of very-long-chain fatty acids and fatty acid biosynthesis; (ii) amino acids metabolism, namely valine, leucine and isoleucine degradation, glycine and serine, arginine and proline, histidine, methionine, and betaine metabolism; and (iii) pyrimidine metabolism . For theSupervised learning uncovered metabolic signatures that differentiated HR from LR, that were consistent using linear and nonlinear/ensemble algorithms. Thus, potential multi-biomarker panels were explored using multivariate exploratory receiver operating characteristics (ROC) analysis, generated using PLS-DA as a classification algorithm and feature ranking method. This methodology has been widely applied for assessing the discriminant performance for biomarkers .From the binary comparison between HR and LR in the untreated control dataset , multivaOn the other hand, multivariate PLS-DA ROC curve analysis based on a 20-feature model  showed a predictive accuracy of 90%  for the The prognostic of OvC is still poor, mainly due to the late diagnosis, lack of efficient targeted therapies and high frequency of chemoresistance and tumor relapse, urging the identification of novel and more cancer-specific biomarkers. Our study contributes to the definition of the metabolic footprint underlying chemosensitivity ex vivo, in OvC-PDE cultures. Overall, we established a workflow combining ex vivo models with metabolomics frameworks that paves the way to the identification of systemic prognostic and therapeutic monitoring biomarkers for OvC.Indirect data, such as with blood-based liquid biopsies, demand especially large patient cohorts due to the low signal-to-noise ratio from the specimen background. Therefore, we hypothesized that metabolomics applied directly to PDE cultures can uncover complex metabolic signatures of treatment response and could be extremely valuable to understand and predict treatment efficacy. We employed an ex vivo model based on the OvC-PDE culture method previously established and characterized by our team. The PDE cultures retain features of the original TME  and are As a proof-of-concept of the power of metabolic footprinting to depict metabolic modulation by chemotherapy agents, we performed untargeted metabolomics on two OvC-PDE cultures. Differences between drug-challenged conditions  and untreated controls supported the hypothesis that complex metabolic signatures correlate with OvC-PDE drug response levels and treatment response. Although many efforts have been made towards automated and assisted feature annotation in untargeted analysis, it is still a challenge to translate features into metabolite identities, even with the MS/MS data available, which results in difficult interpretation. Most of the time, few (under 20) metabolites are identified ,41. On tSemi-targeted metabolic footprinting of OvC-PDE cultures derived from eight HGSC allowed the identification of distinct metabolic signatures of HR vs. LR (based on the ex vivo drug combination-induced cell death). We demonstrate that metabolites identified using a high-throughput method (LC-MS) perform well in predicting HR vs. LR. Notably, a panel of metabolites is required for this prediction, which reflects the complexity of the underlying molecular mechanisms. This result could only be captured by the application of multivariate statistical approaches, such as PCA and linear/nonlinear/ensemble machine learning algorithms.2 and Q2 parameters in PLS-DA, all > 0.8; an error rate ≤ 12.5% in sPLS-DA; and an OOB error < 0.4 for RF. Additional LGSC PDE cultures would be required to assess the performance of similar predictive models for LGSC.Moreover, our prediction models were based only on eight HGSC PDE cultures, and distinct groups of HR and LR are already observed. Due to the limited amount of tumor samples from which PDE cultures were derived, we used three classification algorithms  to assess the consistency of top metabolic features. We opted for these algorithms since they are feature selection machine learning models widely applied to clinical prediction modeling . FeatureWe also observed that metabolic profiles hold considerably more information than pathway analysis, which may result from the different scopes of these variables. Top metabolite features identified via machine learning models suggest that more pathways might be affected. Similarly, the classes and related pathways of discriminant metabolites here identified have been previously implicated in OvC pathophysiology and chemoresistance ,44. We i2 was lower than 0.5. Non-responder patients revealed a higher level of succinate and 3-hydroxybutyrate, whereas the responder patients showed higher levels of glutamate, glutamine, aspartate, and creatine [Using statistical methods such as ROC exploratory curves enabled us to turn metabolic signatures into biomarker panels. Biomarker studies using metabolomics for prognosis and correlation with clinical follow-up are scarce in the literature. Chen et al.  performecreatine . Our PLScreatine ; trimethcreatine ; and fincreatine ,60, as wcreatine . In factOn the other hand, high levels of N-acetyl-ornithine (upregulated in LR) were associated with primary cervical cancer patients and relapse ; 3-methySome other metabolites identified in this study were reported to contribute to chemoresistance within the context of metabolic alterations by the TME. For instance, 2′-deoxycytidine (upregulated in LR) increases in the plasma of cancer patients with poor prognoses; upon chemotherapy, such as cyclophosphamide, methotrexate, and 5-fluorouracil . IwazakiIn line with the pathway analysis, once again we identified amino acids (glycine—upregulated in HR) and their degradation products , which indicates a rapid amino acid catabolism . Hatae e2 FC compared to the HSA.The platform proposed herein has great potential, as discussed above, but still, it has some limitations. The workflow adopted here does not allow capturing the metabolite dynamics in terms of the rate of change of metabolites and flux direction , even though the experimental platform is compatible with such studies. Because OvC-PDEs are composed of tumor epithelial cells and fibroblasts as the major TME compartment, we cannot distinguish metabolic alterations driven by each compartment. Despite these limitations, we demonstrated that the metabolic changes identified were already pointed out as relevant in metabolic plasticity, drug resistance, and proliferation. Interestingly, despite needing further retrospective studies for approach validation, in the cases where patients received neoadjuvant chemotherapy , clinical evaluation matched the combination effect evaluated by the logThus, this platform can be a powerful tool to uncover metabolism-based biomarkers of therapeutic response, patients’ stratification, and adjustments in the clinical management of OvC patients. Ultimately, the panel of markers identified herein should be considered in a follow-up study as systemic prognostic and therapeutic monitoring biomarkers for OvC.This study establishes a platform to probe metabolic features of the TME associated with response to treatment. We present a proof-of-concept of the power of this platform by demonstrating that metabolites identified by means of exometabolomic studies in ex vivo patient-derived models, using a high-throughput method (LC-MS), perform well in predicting high or low-responder tissue cultures, identified by the ex vivo drug-induced cell death. Notably, a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the metabolic network underlying molecular mechanisms. Our studies can be a first step toward the identification of biomarkers of chemosensitivity and resistance using patient-derived models. Subsequent prospective studies with a larger patient dataset and control of patient subgroups will allow determining the capability of the tissue cultures to predict clinical outcomes and validate a biomarker panel for clinical use. In fact, most of the biomarkers here identified were reported to be involved in OvC pathophysiology, and response to therapy, as well as in other cancer types. Further characterization of these distinct profiles can provide mechanistic insights into the metabolic crosstalk between cancer and TME cells, in this case, epithelial tumor cells and fibroblasts. Biomarkers identified in the TME exometabolome can potentially be released into the bloodstream and be easily accessible for patient stratification as a serum biomarker, improving clinical management ,75. Due


Finally, approaches to investigate the relatively ambiguous electron transfer mechanisms in such photosystems are discussed through the presentation of spectroscopic techniques, eventually leading to what this will mean for the future of microbial hybrid photosystems.This review discusses the progress in the assembly of photosynthetic biohybrid systems using enzymes and microbes as the biocatalysts which are capable of utilising light to reduce carbon dioxide to solar fuels. We begin by outlining natural photosynthesis, an inspired biomachinery to develop artificial photosystems, and the rationale and motivation to advance and introduce biological substrates to create more novel, and efficient, photosystems. The case studies of various approaches to the development of CO Interfacing photocatalysts with microbes to produce solar fuels and chemicals from carbon dioxide and sunlight. Hydrogen (H2) gas, a traditional energy carrier most commonly used in the production of fertilisers and refinement of oil, is a green alternative to reduce carbon emissions.1–3 It has a high energy density and a variety of applications but, along with carbon monoxide (CO), it is normally generated through the combustion of fossil fuels. Moreover, the biofuel is limited to H2i.e., more complex biofuels such as ethanol and acetate cannot be made. Other current renewable technologies, such as solar and wind power, suffer from low efficiencies due to the fluctuations in the energy source input and this has created a large disparity between the supply and demand.4–6Initiated by the industrial revolution in the eighteenth century, our world has made major strides in several industries such as transport, research and development, and agriculture. However, coupled with the increase in human population, there is now a greater energy demand, consequently generating the current global warming crisis from rising carbon dioxide  electrode.14 Upon exposure to light, if the photon energy is equal to or greater than the bandgap of the material, electron–hole pairs are generated, and they can either recombine to generate heat or take part in photoreactions. Although PEC cells are theoretically able to achieve fairly high solar-to-chemical (STC) efficiencies, in the case of water oxidation, four electrons and four protons are lost to generate a O–O double bond (498.36 ± 0.17 kJ mol−1).15 Thus, solar water splitting can be very energetically demanding with high kinetic and thermodynamic barriers. Nevertheless, to achieve high solar water-splitting efficiency, it is essential to develop a proton reduction catalyst capable of catalysing the production of H2via the conduction band edge potential of the semiconductor.Artificial photosystems have been commonly used to convert solar energy and feedstock chemicals such as COto fuels . For exa2 is more thermodynamically stable due to the presence of the strong C PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.dbd" format="PBM" resolution="300" x-size="8" y-size="10" xml:space="preserve">00000000000000000000000000000000111111110000000011111111000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>O double bond (532.2 ± 0.4 kJ mol−1).15 Hence, more amount of energy will be required to overcome the activation barrier. Similar to water splitting, upon light absorption, an electron–hole pair is generated and to favourably reduce CO2, the choice of photocatalyst is extremely important. The photocatalyst should contain a suitable band structure where the conduction band edge must be more negative than the redox potential of CO2  and the valence band edge more positive than water oxidation.16,17 If not, surplus photon energy will be lost as heat. Currently, there are limited materials with the suitable alignment of the energy band positions. Different strategies to alter the band gap have been employed including doping and surface modifications. Compared to plain bismuth oxide (Bi2O3), α-Bi2O3 doped with selenium had a smaller bandgap and increased photocatalytic activity.18 In a more novel approach, tuning the stoichiometric amount of formic acid added to a dicyandiamide polymerisation reaction led to the tailoring of the O- and N- link chains in the polymer which determined the band position shifts.19In comparison, CO20 H2, CO, and methane (CH4) were all able to be selectively retrieved in the photoreduction of CO2 by tuning the size of the cadmium selenium (CdSe) crystals that had been doped onto nickel oxide.21 This in turn allowed for the control of the electron transfer kinetics. In another example, a zinc/cobalt-based ZIF was used to coat caesium lead bromide (CsPbBr3) QDs to greatly enhance the photocatalytic reduction of CO2 into CO and CH4.22 Compared to bare CsPbBr3, the electron consumption rate of CsPbBr3-ZIF67 and CsPbBr3-ZIF8 was 2.66 and 1.39 greater, respectively. In situ photoluminescence measurements on cobalt-containing ZIF revealed its role in promoting electron to cadmium sulfide (CdS).23 In other studies, the potential of ZIFs in the activation of CO2 has been demonstrated through enhanced activity.24–27The use of quantum dots (QDs) has also attracted lots of attention due to the quantum confinement effect giving rise to its distinct electronic properties, particularly its size-dependent bandgap.2 reduction catalysts but the reported efficiencies have been low.28,29 By introducing palladium as a co-catalyst, a carbon nitride and LDH assembly proceeded to show an increase in reduction efficiency.30 This was due to the distortion of CO2 upon interaction with the photocatalyst surface, thus allowing for high reactivity of the photocatalyst for CO2 conversion. A similar improvement was also seen in the reduction of CO2 to CH4 through the use of sodium niobate (NaNbO3) nanowires in which higher photocatalytic activity was observed compared to its bulk counterpart.31 However, the reason for the increase in photocatalytic activity in this system is most likely due to the surface-to-volume ratio and crystallinity.Layered double hydroxides (LDH) have previously been investigated for the use of CO6,32–34 Over the last few years, there has been a shift in focus towards the development of biological-based photosystems, i.e., photobiohybrid systems. Their intrinsic metabolic pathways and numerous enzyme cascades enable the efficient conversion of simple feedstock into complex solar fuels and chemicals with greater product selectivity and stability. Photobiohybrid systems can be classified as enzymatic or microbial according to the biocatalyst.Whilst artificial photosystems have greater solar to fuel efficiency compared to natural photosystems, there are still several drawbacks including high costs, toxicity, relatively low efficiencies, and inability to produce higher fuels – all of which are bottlenecks for commercialisation.2 transformations one can carry out. Therefore, although high rates and yields are achieved, sourcing the most suitable enzyme may result in a rise in costs. Redox enzymes play an important role in industrially relevant bioreactions and to carry out their jobs, reducing equivalents are generally required. Typically, nicotinamide adenine dinucleotide (NADH) and NADPH are generated by a secondary enzyme within the system. However, as mentioned above, photocatalysts can also provide reducing equivalents or directly transfer photoelectrons to active redox reactions. Photohybrid systems utilising enzymes have shown success in not only CO2 reduction processes but also nitrogen-fixation and the synthesis of value-added products.35In a typical enzymatic photosystem, to which artificial photosystems are inspired, photocatalysts use solar energy to convert and generate photoexcited electrons which are then used to convert reducing equivalents or are directly supplied to the enzyme . The sel2 evolution due to their remarkable catalytic activity. In some cases, turnover frequencies of greater than 1000 s−1 have been reported.36–38 [FeFe]-based hydrogenase was one of the first reported to be able to receive electrons from photocatalysts such as complexes involving rare metals and carbon nanotubes.39,40 Taking [FeFe]-hydrogenase from Clostridium acetobutylicum, Brown et al. integrated the enzyme with cadmium telluride (CdTe) nanocrystals but the random nature of absorption throughout the complex limited the efficiency of electron transfer complexes. This led to limited H2 production.41Hydrogenases, a type of metalloenzyme, are often used in the study of H2 is through the use of formate dehydrogenase (FDH) as a biocatalyst. Using FDH derived from Saccharomyces cerevisiae and zinc tetrakis(4-methylpyridyl)porphyrin as the light harvester, 62 μM formic acid was produced after 3 hours of irradiation.42 This was carried out in the presence of methyl viologen (MV) as the electron mediator. On the other hand, Noji et al. used tri(bipyridine)ruthenium(ii) (Ru(bpy)32+) as the photosensitiser to produce formic acid from CO2.43 The use of a porous glass plate increased the conversion efficiencies of Ru(bpy)32+/MV2+/FDH from 1% to 22%. The nanopores within the porous glass plates allowed for the faster accumulation of MV+˙ than in the solution, leading to greater interactions between FDH and MV+˙ due to the higher density. This is also previously seen when the rate of photoreduction was 16 times higher in a nanopore than in solution when photosystem II was employed as the photocatalyst.44A common approach to producing formic acid from CO2 mixture as the semiconductor, a ruthenium bipyridyl photosensitiser, and the enzyme carbon monoxide dehydrogenase, CO2 was reduced to CO.45 However, a SED was required to quench the hole left by the ruthenium complex and to regenerate the photosensitiser. Moreover, the average turnover rate with P25 TiO2 was only 0.14 s−1 at 20 °C which was lower than with a MV radical (100 s−1). Even the addition of the electron donor ethylenediaminetetraacetic acid (EDTA) did not show any noticeable improvement, suggesting inefficient photosensitiser regeneration. A range of surface linkers was also tested to study the effects of enzyme binding to the TiO2 surface with regard to electron transfer efficiency. However, those tested, including polymyxin B sulfate and glutamic acid (which had been previously described to strongly coordinate with TiO2)46 showed very little change in activity and stability. In one case, the addition of o-phosphorylethanol amine even resulted in a lower enzyme uptake and thus, decreased the rate of catalysis.In a hybrid system containing anatase/rutile TiO2 fixation. In this approach, CO2 is reduced to acetyl-coenzyme A and eventually converted to acetic acid which can be further used to make more complex products.47–51 By utilising the countless metabolic pathways present within the microbe and combining it with the high solar-to-energy efficiencies achieved by artificial semiconductors, products more complex and selective than what enzymes are capable of producing can be generated.Enzymatic inorganic hybrid systems appear to overcome the challenges presented by artificial photosystems. However, as described by the above studies, they suffer from several disadvantages including instability issues, poor durability, complicated experimental conditions, and high costs . Out of 2 reduction. Nanoparticles, in particular, are especially attractive and have drawn a lot of interest due to their quantum confinement effect. Essentially, if the size of the particle is comparable to that of the Bohr radius, then the optical and electronic properties become size-dependent.52,53 By altering the valence and conduction bands of the material, photocatalytic activity can be improved and in turn, greater solar-to-fuel efficiencies can be achieved.In the following discussion, we summarise the recent achievements of artificial microbial photohybrid systems utilising solar energy and waste carbon to produce solar fuels and chemicals. Microbial photohybrid systems typically rely on the photosensitisation from organic or inorganic light absorbers to provide the reducing equivalents that allow them to carry out COet al. found that most of the samples had thin iron and manganese oxide coatings.54 Presently, researchers often use Mn- and Fe- oxide in the study of photochemical systems due to their efficiency as photocatalysts and their intrinsic properties. As expected, it was found that photocurrent was only detectable in the Fe- and Mn- region on the coating and, upon exposure to solar energy, generated approximately 2.23 × 1016 photoelectrons. This is very meaningful as those photoelectrons could act as a major energy source to fuel the growth of photoelectronic bacteria which has been reported numerous times.55,56 The study conducted by Li et al. is an important precursor to the research into artificial microbial systems as it reinforces the current research on the stimulation of bacterial growth obtained through phototrophic experiments using semiconductors as photocatalysts.57Semiconductors are a vital aspect when striving towards an efficient photosystem capable of converting solar energy to biofuels. When discussing semiconductors, it is natural to immediately think about all those made in the laboratory. Decades of science and technology have made probing into light-harvesting capabilities of countless photosystems possible. However, long before we had the technology to investigate these systems, and before man-made semiconductors came about, nature had its inherent semiconductor found, remarkably, in rocks. In rock samples taken from China, Li et al., in the study of Moorella thermoacetica with CdS QDs.47 From Tauc plots, a method of estimating bandgap energies, and absorption spectra, the quantum confinement effect was considered to have come into play when the direct bandgap was reported to be 2.51 ± 0.05 eV, as opposed to the expected lower value of 2.42 eV. The electrons generated by CdS when irradiated under visible light generated H+, a reducing equivalent, which was then used in the Wood–Ljungdahl pathway to produce acetic acid from CO2 with a maximum yield of 90%. Later, TiO2-manganese phthalocyanine (MnPc) was added to form a tandem system and cysteine/cystine (CySS/Cys) was chosen as the redox couple.58 In general, this expanded hybrid system demonstrated greater acetic acid production. The system containing bare TiO2 and M. thermoacetica–CdS had greater acetic acid production than M. thermoacetica–CdS but less than that of TiO2–MnPc. These results demonstrate the superior efficiency of MnPc as a catalyst for CySS reduction. However, a recent study has highlighted pitfalls in the usage of organic chemicals such as cysteine as the SED.59 The study revealed that bacteria can convert cysteine to acetate under dark condition which was largely neglected in previous studies due to poor control experiments. Hence, extreme care is needed in the selection of SED as bacteria can metabolize various substrates. Overall, this milestone, along with many other microbial photobiohybrid systems including the use of Clostridium ljungdahlii with CdS, Escherichia coli with Eosin Y, and Sporomusa ovata with silicon nanowires have led to the innovation of many new and improved microbial hybrid photosystems H/NAD(P)+ increased when paired with H2. NAD(P)H/NAD(P)+ play an important role in various biological processes, including biomass and biofuel production and often prevent efficient biotransformation processes. Therefore, by enhancing the production of NAD(P)H/NAD(P)+, increased biomass generation will take place. However, microbial systems often contain various intricate biological networks and physiological systems and can self-organise their metabolic system, such that even with bioengineering, manipulation of the NAD(P)H/NAD(P)+ production pathway will be met with unexpected barriers.61–63 Future research into strategies to increase NADH/NAD+ or NADPH/NAD+ production will be required to understand and optimise conditions for NADH/NAD+ and NADPH/NAD+ production for various biotransformation processes. The CdS–C. autoethanogenum system demonstrates the viability of coupling microbes with semiconductors, and although further improvements are to be made in terms of electron capture for NADH and NADPH production, the system shows great potential for increased CO2-fixing efficiency in nanoparticle-based hybrid photosystems through electron transfer processes.In a typical colloidal photosystem, the semiconductor is present as a suspension. In some cases, they may be deposited onto the surface of the microbe. Such is the case for the microbial photohybrid system consisting of CdS nanoparticles attached to the surface of M. barkeri which resulted in a CH4 production rate of 0.19 μmol h−1 with 0.34% quantum efficiency.64 This system was further improved when doped with nickel, as well as CdS, which increased the photoelectron transfer within the system and led to an approximate rate of 0.24 μmol h−1 CH4.65 Investigation into the proteomics of the system revealed that the addition of nickel caused a higher expression of proteins related to electron transfer, energy conversion and CO2 fixation which contributed to the increased photoelectron transfer rate. Increasing protein expression is one method to increase electron transfer rates. Another viable method is through pi conjugation. Using the organic semiconductor poly(fluorene-co-phenylene) and perylene diimide derivative to photosensitise M. thermoacetica, the production of acetic acid from CO2 was successful.66 Efficient electron transfer was enabled through the pi conjugation present in the semiconductor which has been suggested to exhibit higher biocompatibility. Moreover, the quantum efficiency of the perylene diimide derivative was found to be comparable to inorganic semiconductors.The use of CdS nanoparticles has also been implemented in a hybrid system with via CO2 reduction was also realised through the use of gold nanoclusters with M. thermoacetica.67 In a previous study carried out by Jiang et al., the microbe viability before and after the photocatalytic reaction was measured through cell density and the lack of change indicated good viability of the cells.68 In this study, the gold nanoclusters quenched reactive oxygen species (ROS) as well as acted as a light absorber. This enabled M. thermoacetica to ensure high bacterium viability over 6 days of continuous CO2-fixation, showcasing the system's promising potential as a sustainable system.The production of acetic acid R. eutropha H16 were used to increase polyhydroxybutyrate (PHB) production from fructose 1.4-fold with triethanolamine (TEOA) as an SED.69 Further works by the group include the use of CdS nanorods to produce PHB from CO2 and fructose by C. necator.70 Optimisation of CdS nanorods resulted in high photoactivity and achieved a yield of 28 mg over 48 hours PHB from CO2. On the other hand, Ding et al. employed different core–shell QDs to bind to specific enzyme binding sites in bacteria to improve product selectivity in photosynthetic biohybrid systems.71 Through this, not only was gram scale production of PHB achieved but also multi-carbon compounds. The production of PHB successfully increased using only a bioreactor with LED panels, illustrating the ease of scaling up beyond laboratory conditions and implementation on a commercial scale.Semi-artificial microbial systems can also be used to make more complex biofuels. Proof-of-concept was demonstrated in the works of Xu and coworkers where graphitic nitride and et al. consists of CdS nanoparticles biologically precipitated onto the surface of Thiobacillus thioparus and is able to reduce CO2 to multicarbon glutamate synthase without being restricted by the need for an anaerobic environment.72 Interestingly, while chemically synthesised CdS predictably led to cell death in the later stages of cultivation, the biologically synthesised CdS showed a higher promoting effect for the growth of T. thioparus. For the system to sustain cell growth in the presence of biologically synthesised CdS, not only is the electron transfer between the semiconductor-microbe interface efficient but also CO2 fixation. Once CO2 has been fixed by NADPH, the resulting precursors which undergo chemical transformations within T. thioparus become biochemical components that support cell growth and replication. Hence, enabling the hybrid system to undergo self-replication. In not being limited by strict anaerobic conditions and its ability to sustain microbial growth with only simple, economical components, this work shows potential for the reduction of CO2 in terms of design and sustainability.Many of the hybrid systems mentioned are limited by anaerobic conditions which may impede the assimilation of microbial hybrid photosystems in industry. The hybrid system presented by Liu et al. involves both R. eutropha and H2O2-degrading catalyse for bioplastic production and water-splitting, respectively.73 Although using the light absorber, graphitic nitride, and R. eutropha alone can successfully produce PHB, the yield was lower than with the addition of H2O2-degrading catalyse. Possible explanations for the increased yield include increased H2 for R. eutropha to maintain its activity and the use of O2 produced from water-splitting as an additional electron acceptor. The increased PHB production resulting from coupling water-splitting reactions and biochemical cascades within R. eutropha showcases a promising platform for efficient PHB production. However, compromises may have to be made with regards to performance and design due to the expense and temperament of enzymes.While enzymes and microbes have individually been interfaced with light absorbers, the hybrid system established by Tremblay 2 is becoming an increasingly popular energy carrier, as evident by the global initiatives to invest in the hydrogen economy, such as the UK's Hydrogen Strategy,74 USA's Hydrogen Program Plan,75 Canada's Hydrogen Strategy,76 Japan's Basic Hydrogen Strategy,77 and the European Clean Hydrogen Alliance which consists of industries and authorities from various countries.78 At present, the production of H2 is still mainly from fossil fuels but in recent years, research into the electrolysis of water from renewable electricity has garnered a lot of attention. The downfall of this method, however, is that rare and precious metals are used in the synthesis of the catalysts involved, meaning an increase in production costs and limited sustainability and scale-up potential.HClostridium butyricum and Escherichia coli, along with semiconductors, were initially used to explore photocatalytic H2 production.79–84 To this day, the pool of microorganisms used to explore H2 production has expanded to include Shewanella oneidensis and more.85–89 In the works of Martins and coworkers, Desulfovibrio desulfuricans, Citrobacter freundii, and S. oneidensis were used in conjunction with CdS nanoparticles to investigate H2 production.84D. desulfuricans was found to be the most efficient, owing to the high biological activity and/or efficient electron transfer. This was accompanied by the fact that it showed the highest hydrogenase activity compared to S. oneidensis, E. coli, and C. freundii . High levels of the enzyme hydrogenase correlate with greater H2 production as they catalyse H2 formation from protons or the oxidation to protons. The high levels of hydrogenase in D. desulfuricans were found to be from the [NiFe] and [FeFe] families. These enzymes tend to be located in the periplasm of the microorganism,90,91 leading to a more efficient transfer of electrons from the nanoparticle to the enzyme than those found in the intracellular compartment. Cyanide was used to inhibit the hydrogenases within D. desulfuricans, almost completely preventing H2 formation. This ultimately highlights the importance of hydrogenase in the pursuit of efficient H2 formation. H2 evolution was also possible through the use of a tandem involving silver indium sulfide/indium sulfide and E. coli.68 In this system, the quantum efficiency of 3.3% was achieved and showed very little change in cell viability after the photocatalytic reaction, demonstrating the stability of the hybrid system.As previously stated, current inorganic systems involving photovoltaic technology and semiconductors have surpassed natural biotransformation efficiencies. However, reaching similar conversion efficiencies is still a challenge yet to be achieved. 2 production, reduced graphene oxide was chosen to be integrated with S. oneidensis MR-1.92 The reduced graphene oxide acted as a solid support surface and was able to efficiently collect electrons for the semiconductor, cuprous oxide (Cu2O). In addition, the reduced graphene oxide operated as a pathway for electrons to travel between. All the above allowed for efficient electron transfer from the Cu2O nanoparticles to S. oneidensis MR-1, thus contributing to efficient H2 production, as evident in the increased photocatalytic H2 production when compared to pristine Cu2O and Cu2O coupled with reduced graphene oxide (322.0 μmol g2OCu−1vs. 7.0 μmol g2OCu−1 and 4.0 μmol g2OCu−1 within 4 hours respectively).A major bottleneck of microbial hybrid photosystems is the photoelectron transfer from the inorganic component of the system to the biological cell. In a study to increase H2 evolution within a hybrid system. Upon deletion of H2ase, it was clear that within S. oneidensis, H2ase was the active site for H2 evolution as there was no H2 production.92 Deletion of the proteins MtrC/OmcA was found to have inhibited the production of H2, thus establishing the fact that the cytochromes, including MtrA and MtrB, are involved in the electron transfer between the nanoparticle and S. oneidensis. While the work by Shen and coworkers concerns H2 production, the understanding of the electron transfer pathway in the photocatalytic conversion extends to the production of all biofuels, not just H2. In resolving and understanding the electron transfer pathway within a system, the overall design of hybrid systems can then be further improved, yielding greater electron transfer efficiency and ultimately, greater yields.It is important to elucidate the electron transfer pathway to understand the mechanism behind HD. sulfuricans involved hydrogenases found in the periplasm of the microbe. Similarly, S. oneidensis also expresses periplasmic hydrogenases.93 This was exploited by Shi and coworkers, in which copper indium sulfide/zinc sulfide (CuInS2/ZnS) QDs were translocated into S. oneidensis, resulting in an increase in H2 production due to the decrease in electron transfer distance and energy loss.94 Through the deletion of certain genes, and the introduction of MV as a redox mediator, direct electron transfer from the QDs to S. oneidensis was inferred. As expected, single and double deletion of the periplasmic hydrogenase, [FeFe] and [NiFe], caused a decrease in the production of H2, which suggests the two coenzymes play a crucial role in H2 production. Deletion of the electron transport proteins had little to no effect on the production of H2, nor did the introduction of MV as a redox mediator.The kinetics of the electron transfer is extremely important in the conversion of solar energy to solar fuels. The previously mentioned work by Martin and coworkers on E. coli with precipitated CdS, [NiFe]-hydrogenase HyaABCDEF plasmid was transformed into the bacteria to catalyse H2 formation.95 Compared to non-HydA-induced hybrids, the resulting output per cell was much greater. In the case involving CdS and E. coli, genetic engineering was used to emphasise the expression of [FeFe]-hydrogenase gene, consequently yielding a photoactive E. coli hybrid system capable of converting solar energy into H2 within a single cell.96 Although the yield was less than previously reported studies involving TiO2 and E. coli,97 the genetically engineered hybrid is a viable method for enhancing photocatalytic H2 production.Manipulation of the genetic material within the microbe is becoming an increasingly popular method of increasing the production of biofuels. Using genetically engineered 98–100 In response to this, a silicon nanowire array was interfaced with S. oneidensis MR-1.101 Through this model, it was discovered that not only attachment locations were influenced by the topography, but also the swimming patterns of the microbes. In a previous study conducted by Goto et al., the tendency of single flagellated bacteria to travel in a straight trajectory was revealed.102 However, S. oneidensis was shown to have circled the nanowire despite being a singly flagellated bacterium. Moreover, the reversible bacterial attachment was demonstrated after some of the bacteria were found to separate from the Si array following a short period. The swimming behaviour exhibited by S. oneidensis is also observed by E. coli K-12 strains.103 By highlighting the important role of topography and its interaction with bacteria, knowledge that will serve to aid in the design of future microbial-nanowire interfaces was realised.Along with colloidal systems, microbes have also been interfaced with nanowires and PEC systems. Due to the large surface area, nanowires are extraordinary light harvesters. Initial studies on mammalian cells have demonstrated the ability to recognise and respond to nanoscale topographies.et al., the use of a nanowire array proved to be more advantageous than that of a single nanowire.101 The use of arrays has been widely implemented in several research studies. Liu et al. cultured Sporomusa ovata with a silicon nanowire array passivated by TiO2 protection layer, leading to the production of acetic acid from CO2 after the incubation period.57 Using dual light-absorbers allow the system to fully absorb solar energy, thus increasing photovoltage for the system. This is demonstrated by the high faradaic efficiency (90%). This system was taken further and extended by allowing genetically engineered E. coli to activate acetate into acetyl-CoA, ultimately partaking in the biosynthesis of various complex molecules such as cadinene, PHB, and n-butanol. The study is especially important as the system itself generated a local anaerobic environment at the bottom of the nanowire array, encouraging the reduction of CO2 under aerobic (21% O2) conditions. Su et al. later used highly doped p+ silicon nanowire arrays and platinum wire as the counter electrode to replace the TiO2 photoanode previously used.104 By adjusting the electrolyte pH and increasing the buffering capacity, the group successfully achieved the formation of closed-pack silicon nanowires. The resulting close-packed biohybrid system obtained a solar-to-acetate efficiency of 3.8% and a current density of 0.65 ± 0.11 mA cm−2, as opposed to the energy conversion of 0.38% for acetic acid production and 0.35 mA cm−2 photocurrent by Liu and colleagues.57As expected from the above study by Jeong 2 to CH4 with a faradaic efficiency of up to 96% with only solar energy as the only energy input was achieved.105 Solar-to-fuel conversion efficiency was found to be at 0.1%, approximately half of that of natural photosynthesis. The greatest limitation in the system is expected to be from the bandgap of the TiO2 photoanode employed. TiO2 is often used in designing photosystems, but its downfall is that it can only absorb a small range of wavelengths of solar energy. Thus, in expanding the absorption range of materials used in photosystems, the efficiency will likely increase. A combination of TiO2/CdS was also used as a photoanode in the reduction of CO2 by methanogens.106106 Cu2ZnSn4 was later employed to sensitise the photoanode. Due to the lower charge separation resistance, the photogenerated electron–hole separation was much more favoured and this led to greater CH4 production. Suspending microorganisms in solution is also a viable method to catalyse redox reactions fuelled by solar energy. By interfacing the archaea Methanosarcina barkeri with a platinum electrode, Nichols et al. achieved a faradaic efficiency of up to 86% for electrochemical CO2 to CH4 conversion.107 Replacing the platinum electrode with synthetic α-nickel sulfide H2 evolution reaction catalyst resulted in similar faradaic efficiencies and CH4 yields. Furthermore, by using an indium phosphide photocathode and a TiO2 photoanode, the system proceeded to result in unassisted light-driven CH4 production from CO2 and this system yielded much greater CH4 than that with the platinum electrode and α-NiS catalyst.107 This proof-of-concept of bridging semiconductors with microbes proves efficient solar conversion to carbon-based fuels is attainable.Using methanogens in a PEC cell setup, a one-step transformation from COAlthough microbial hybrid systems have proven to be a promising approach to producing biofuels, achieving yields that can compete with current energy outputs from fossil fuels are still yet to be realised. 108–111 ATR-IR spectroscopy is an attractive method for being not only suitable for solid samples but also for biological liquid samples. In the works of Freitag and colleagues, an acoustic trap was combined with ATR-IR spectroscopy to enable the entrapment of enzyme-labelled beads without the need for mechanical retention components, such as optical fibres, which are normally found in typical bead injection systems.112 The set-up allowed for the conversion of p-nitrophenylphosphate into p-nitrophenol and phosphate under varying loadings of enzymes to be monitored, showcasing its great potential for enzymatic kinetic studies.Attenuated total reflection infrared (ATR-IR)  has beenet al. was used to investigate the separation and transfer of photogenerated charge carriers in the system.92 In comparison with the absence of the Cu2O nanoparticle, the steady-state photoluminescence intensity of Cu2O/reduced graphene oxide decreased. This indicates that the separation of the photogenerated electron–hole pair was sufficient for a pathway between the Cu2O nanoparticle and S. oneidensis cells for electron transfer. Similar observations were made with CuInS2/ZnS QDs interfaced with genetically engineered S. oneidensis.94Steady-state photoluminescence  on the p113–115 For example, TRIR spectroscopy, coupled with transient absorption (TA) spectroscopy . TRIR is often used to study the kinetics, formation of intermediate products, and intramolecular charge transport of both biological and chemical molecular species.troscopy , was useM. thermoacetica–CdS. It was reported that M. thermoacetica–CdS incubated with H2 : CO2 displayed faster decay kinetics compared to that incubated with glucose.47,116 The faster decay kinetics was attributed to the increased H2ase activity and hence, the greater e− transfer rates and/or the greater quantity of e− acceptors in the H2 incubated system. Unsurprisingly, multi-exponential decay in the timescale of picoseconds was found to be expressed in the hybrid system. Likewise, in the previously mentioned works of Luo et al., when CuInS2/ZnS QDs were integrated with S. oneidensis MR-1 through co-incubation, transient fluorescence and transient absorption lifetimes were shorter than that with CuInS2/ZnS than with the hybrid system, indicating enzymes are efficient acceptors of photogenerated charge.94 Decay kinetics in the same timescale as the M. thermoacetica–CdS hybrid was reported in a hybrid metal–semiconductor nanoparticle system consisting of CdSe–Au.117 However, lifetimes reported in the range of nanoseconds were found in Fe–Fe H2ase–CdS systems,118 possibly owing to hydrogen bonding, solvent effects, and charge transfer barriers. From the evidence gathered via TA and TRIR spectroscopy, two proposed pathways for charge and energy transfer were proposed: the non-H2ase-mediated pathway in glucose incubated cells and the membrane-bound H2ase mediated pathways in H2 incubated cells. Overall, the existence of a charge-transfer pathway was supported with TA and TRIR spectroscopy.In the same model, TA was used to aid in the elucidation of the decay kinetics of 119–121 The previously mentioned system consisting of cadmium selenium–gold (CdSe–Au) had undergone photoexcitation of the local surface plasmon resonance of the metal. By using ultrafast spectroscopy, along with high temporal resolution and near-infrared excitation, the plasmon-induced charge transfer (PICT) pathway was resolved, and it was discovered that hot-electron transfer to CdSe occurred in the timescale of fewer than 30 femtoseconds, whereas back transfer from the semiconductor to the metal was found to be within 210 fs.117 To compete with the extremely fast back-transfer to the metal, extraction of the electrons needs to be greater than the rate of back-transfer. This issue needs to be realised when designing hybrid systems to avoid inefficient and undesired transfer pathways. With suitable band alignments, such issues may be mitigated.Due to the extremely fast nature of the charge separation and transfer, (from milliseconds to picoseconds), TA is also capable of providing information on parameters such as triplet excitons, exciton energy, and decay kinetics which can be used to speculate charge transfer processes.The kinetic study of this hybrid construct using high time resolution TA, along with the aforementioned studies, has highlighted the importance of spectroscopic techniques in the pursuit of elucidating electron transfer kinetics in photoactive systems. In establishing the requirements for resolving ultrafast photophysics of hybrid systems, there will be a greater understanding of the kinetic pathways within a system, resulting in a more intelligent design of hybrid systems which will allow for more diverse photocatalytic applications.M. thermoacetica–CdS hybrid system developed by Sakimoto and coworkers, Zhang et al. studied the energy conservation processes within the system using untargeted and targeted protein and metabolite quantification processes.122 Through this approach, it was discovered that the energy-metabolism-associated glycolysis and tricarboxylic acid cycle were active alongside the well-known Wood–Ljungdahl pathway. M. thermoacetica was also implied to contain an increased expression of proteins capable of protecting M. thermoacetica from oxidative stress but the damage far outweighed the protection provided.The involvement of proteomic and metabolomic analysis in the study of hybrid systems has recently become more prominent. Whilst the above-mentioned spectroscopic techniques will help clarify kinetics and electron transfer processes, proteomics and metabolomics will enable a greater understanding of the biocatalytic mechanisms, energy conservation in microbes, and physiological state of the organism. Taking the model et al.,122 the aforementioned spectroscopic techniques, and more importantly, with technological advancements, the means to fabricate highly efficient, low cost, and industrially competitive biohybrid photosystems will become more tangible in the nearby future.Although the current biohybrid photosystems are still in their infancy, with the study presented by Zhang 2 to biofuels. However, there are still significant developments to be made. The commercialisation of hybrid photosystems continuously proves to be a challenge and several considerations including, but not limited to, cost, toxicity, environmental impact, and the transition from laboratory to industrial scale still need to be addressed further.To conclude, while artificial photosystems and enzymatic photosystems have many advantages, artificial microbial photosystems present themselves as a much more powerful tool for converting sunlight and CO2-utilisation technology sector. This will be further aided by the advancement of the synthetic biology sector, allowing for a new class of responsive, functionalised, and customisable hybrid systems to drive the future of green energy.With emerging technologies able to elucidate the electron transfer mechanisms in microbial hybrid photosystems, our understanding of the interactions occurring within the system will deepen, allowing for maximisation of solar-to-chemical yields, more complex solar fuel production, and smarter system design which will pave the way towards a revolutionised CO2 to fuels is promising but the variants of CO2-fixation pathways all have different kinetic and ATP requirements. For example, the Calvin cycle is deemed inefficient due to its slow kinetics and high demand for ATP. For this reason, it is essential to design and develop pathways that can support greater carbon fixation. An example of this would be the pyruvate synthase-pyruvate carboxylate-glyoxylate pathway.123 Genome-resolved metagenomics has also been used to identify potential enzymes that will show greater efficiency once integrated into CO2-fixation pathways. Using metagenomics, Figueroa et al. identified genes responsible for phosphite oxidation and CO2 reduction within Candidatus Phosphitivorax anaerolimi strain Phox-21.124 It was discovered that while Ca. P anaerolimi contained the genes necessary for Wood–Ljungdahl pathway, the genes for other carbon fixation pathways were absent. Moreover, CO2 reduction via the Wood–Ljungdahl pathway was not feasible for Phox-21 as certain genes for particular carbonyl steps were missing. In light of these discoveries, Figeuroa et al. proposed an approach as to how Phox-21 could reduce CO2 to formate before proceeding to formate assimilation pathways. With the rapid development of analytical and biological techniques, carbon fixation may be enhanced to the standards of practical applications.The microbial conversion of COClostridium autoethanogenum has already led to its large-scale industrial application for the production of ethanol from syngas.125 Microorganisms lacking CO2-fixing pathways, such as E. coli and S. cerevisiae, may be transplanted into heterotrophs like S. oneidensis and G. sulfurreducens. Pyrococcus furiosus is also another host candidate and attempts have been made to incorporate CO2-fixation pathways into the archaea.126,127 The use of thermophile such as P. furiosus in hybrid microbial photosystems will be highly beneficial as solar transformations of CO2 to volatile biofuels at higher temperatures will become more feasible.Expanding the library of microbes may also lead to unfamiliar reaction pathways that can be exploited to give greater STC efficiencies and more energy-dense solar fuels. The metabolic engineering of 2 transformation, investigations into the energy modules should not be disregarded. As described at the beginning of the review, a lot of energy is required to convert CO2 to biofuels due to its high stability. To circumvent this, the study of energy modules has been undertaken regarding semiartificial microbial photosystems. Owing to stoichiometric modelling, and computational analyses we now have a comparative analysis of autotrophic growth and production based on different natural and synthetic CO2 fixation pathways and energy uptake.128,129 Advances in technology have now allowed for the multi-omics study of not only ATP production, but also electron transfer from semiconductor to microbe and CO2 reduction which was carried out by Zhang et al.122Whilst the study and discovery of new carbon fixation pathways within the microbe is essential for more meaningful COUnderstanding the electron transfer between the semiconductor and microbe will also be essential for the future development of hybrid photosystems. Optimisation of the charge transfer processes through genetic engineering can take place, ensuring superior interaction between the biological and semiconductor interface. This in turn will theoretically give us greater STC efficiencies as well as a deeper understanding of how more complex biofuels can be generated.130,131 MOFs have shown their capability through successfully harvesting water from desert air with only ambient sunlight as the energy input.132,133 For the solar conversion of CO2 to biofuels, when interfaced with anaerobic microbes, MOFs have the potential to offer protection from oxygen and ROS, ultimately prolonging the lifetime of the bacteria and ultimately allowing for continuous biofuel production.134 In using a zirconium nanocluster-based monolayer to wrap around M. thermoacetica, CO2 transformation to acetate was able to last approximately 2.5 days whereas, without the MOF protection, CO2 fixation was only able to last 1 day.135 This was owed to the zirconium nanoclusters present within the MOF as it decomposed the hydrogen peroxide formed on the cell membrane, preventing ROS accumulation. Hydrogels have also been used to shield photosensitised M. thermoacetica, extending the viability of the bacterium and increasing acetate production.136Metal–organic frameworks (MOF) are becoming an emerging contender for light harvesting. MOFs are composed of transition metal ions that are connected to organic linkers and have crystalline, porous, extended structures. They are structurally tuneable, allowing for the assembly of higher surface areas for the capture of solar energy and its self-assembly into crystalline structures enables the MOF to be computationally analysed.137 However, initiatives such as the Paris Agreement and investments into green energy will help further the advancement of microbial biohybrid photosystems, especially as world leaders initiate the phasing out of fossil fuel industries. Like hybrid photosystems, next-generation materials consisting of smart, functional properties are being heavily invested in. In particular, synthetic biological materials are attracting attention for their potential in the construction of smart living due to their environmental responsiveness, adaptive abilities, self-healing capabilities, and self-regeneration, just to name a few. Several successful engineering efforts have taken place to mimic these biological materials on a large scale. Buildings like the OME, an experimental biological house in the North-East of England which aims to test new, experimental biotechnologies for sustainable living138 will help bridge the transition from laboratory to industry, and eventually, to real, everyday life. By monitoring the biological materials' response to the environment, the integration of microbial photosystems into these ‘smart’ properties will become more feasible, alleviating the population's dependency on fossil fuels.Despite the benefits of microbial hybrid photosystems, commercialisation of large-scale production is still in the distant future. The major factors to consider are cost and efficiency, not to mention more research into novel artificial semiconductors that will bolster STC is needed. Hence, fossil fuels are still the most widely used source of fuel production.E. coli stimulated the synthetic biology sector and has now reached the fields of material science and engineering.139 Rather than relying on bioengineering, the genetic toggle switch is controlled via manipulation of the network architecture. Demonstrating that the principles of engineering may be exploited to revolutionize simple cells into living machines, manipulation of the network architecture will enable the control of cell function and maximise STC efficiency. Moreover, by interfacing with inorganic materials such as gold nanoparticles and QDs, numerous environmentally responsive and controllable functional composite materials were developed. As previously mentioned, analysis of these environmental responses and adaption of the cellular functions will be important if the integration of microbial photosystems in buildings is to take place.In the 2000s, the invention of the genetic toggle switch first implemented within the plasmid of et al. who reported the engineering of E. coli to produce biomass from only CO2.140 To do this, the glycolysis and pentose-phosphate pathways, were disrupted whilst key enzymes in the Calvin cycle were expressed. A vital step in this study was the introduction of NAD-dependent FDH which would later be used to generate NADH to serve as the reducing power to drive carbon fixation. The strain was left to optimise for CO2 fixation by allowing it to adapt and evolve for over 350 days. Genomic sequence analysis confirmed the presence of mutations that regulate gene expression and pathway regulations, overall achieving 100% biomass origin from CO2 fixation. Similarly, Gassler and coworkers rewrote the xylulose monophosphate cycle to introduce the Calvin cycle.141 Methanol assimilating genes within the peroxisomes of Pichia pastoris were deleted and the energy and reducing power were supplied by methanol oxidation. These modifications resulted in the fungi utilising CO2 as its sole carbon source, increasing its maximum growth rate on CO2 more than two-fold. These studies highlight the importance of not only carbon fixation pathways but also the energy and reducing power on cell growth.In recent years, the ability to transform waste carbon into biomass has become more and more important. Studies in this area include the works of Gleizer 2 only, especially in allowing for a more in-depth study of how synthetic CO2-fixation pathways can be assimilated into non-native hosts, as well as opening the likelihood of increasing the repertoire of microbes able to undergo solar reduction of CO2 where new metabolic pathways may be discovered. The accomplishments of Gleizer and more have stimulated the interdisciplinary field of synthetic biology and the various research currently in motion have demonstrated the great potential of smart materials in the application of sustainable production of biofuels in photohybrid systems.The works of Gleizer and co-workers have set an important milestone towards future works in the sustainable production of biofuels from COThere are no conflicts to declare.