Systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering

Abstract

Systems biology aims at achieving a system-level understanding of living organisms and applying this knowledge to various fields such as synthetic biology, metabolic engineering, and medicine. System-level understanding of living organisms can be derived from insight into: (i) system structure and the mechanism of biological networks such as gene regulation, protein interactions, signaling, and metabolic pathways; (ii) system dynamics of biological networks, which provides an understanding of stability, robustness, and transduction ability through system identification, and through system analysis methods; (iii) system control methods at different levels of biological networks, which provide an understanding of systematic mechanisms to robustly control system states, minimize malfunctions, and provide potential therapeutic targets in disease treatment; (iv) systematic design methods for the modification and construction of biological networks with desired behaviors, which provide system design principles and system simulations for synthetic biology designs and systems metabolic engineering. This review describes current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers. We also discuss challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Figure 1

The role of systems biology as an integrated platform in modern biological research Systems biology integrates information on genetics, proteins, DNA-protein binding, and metabolism with system dynamics modeling and system identification technology to develop models and mechanisms for the interpretation of phenotypes or behaviors of cellular physiology. Since large-scale data sets need to be mined, powerful computational tools are necessary. Based on system models and mechanisms in systems biology, synthetic genetic circuits are designed to investigate specific desired cellular behaviors of cellular physiology. Discrepancies between real and desired cellular behaviors are used as feedback to adjust system models and mechanisms. Systems biology is thus positioned to play the role of integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.

Figure 2

Schematic diagram of the integrated cellular network. The integrated cellular network consists of two subnetworks. The signaling regulatory pathway (green) contains protein–protein interaction (PPIs). The gene regulatory network (yellow) contains transcription regulations. The transcription factors serve as the interface between the two subnetworks.

Figure 3

The S. cerevisiae integrated cellular network under hyperosmotic stress. By following the schematic diagram of an integrated genetic regulatory network (GRN) and PPI cellular network in Figure S1, the integrated cellular network of signaling regulatory pathway and GRN for hyperosmotic stress in S. cerevisiae is identified by dynamic modeling and data mining. Receptor proteins in the plasma membrane, signal regulatory pathways in the cytoplasm, and transcription factors and GRNs in the nucleus are used to construct an integrated cellular network for S. cerevisiae under hyperosmotic stress.

Figure 4

Multiple loops of a gene regulatory network associated with aging-related pathophyiological phenotypes. This aging-related gene regulatory network includes 16 genes: FOXO_s, NF-kB, P53, SIRT1, HIC1, Mdm2, Arf1, PTEN, P13K, Akt, JNK, IKK, IkB, BTG3, E3F1, and ATM. Dashed red lines and black arrows indicate negative and positive parameters of regulated interaction, respectively.

Figure 5

The constructed network-based biomarker. (A) Cancer protein association network (CPAN) obtained from C in (2.18) by maximum likelihood estimation, Akaike’s information criterion (AIC) selection, and Student’s t-test. (B) Non-cancer protein association network (NPAN) obtained from N in Equation (2.18) using the same criteria.

Figure 6

The difference between CPAN and NPAN obtained from Equation (2.20) for network-based biomarkers for lung cancer. The significance of proteins (indicated by circle size) to the network-based marker is dependent on their CRVs in Equation (2.21), which are listed in Table S1.

Figure 7

The linear n genes GRN with interaction N_ij, intrinsic fluctuation ΔN_ij, gene expression x_i(t), and extrinsic fluctuation υ_i(t).

Figure 8

A simple two-gene cross-inhibition network. The network’s regulation functions are given in Equations (3.1) and (3.2).

Figure 9

Block diagram of the optimal tracking scheme for synthetic biological circuit design using an evolutionary systems biology algorithm. Based on a network algorithm mimicking natural selection in an evolutionary process, the design parameters k of a synthetic biological circuit are tuned to minimize the tracking error between the desired logic circuit and the stochastic synthetic biological circuit, and to achieve the desired behavior tracking.

Figure 10

Single schematic diagram of the synthetic promoter-regulation gene circuit. The existing TetR-regulated promoter library contains the minimum and maximum values of fluorescence [y_imin, y_imax] corresponding to with and without TetR (repressor) binding. Based on the promoter regulation function (3.21) and these values, the promoter library is redefined for the design of synthetic gene networks (Table 2).

Figure 11

Synthetic gene circuit topology: simple toggle switch. The regulatory protein TetR, which is induced by ATc, inhibits the transcription of lacI by binding promoter c₂. TetR also inhibits transcription of yegfp by binding promoter c₃ to repress the expression of the fluorescent protein yEGFP. The protein LacI, which is induced by the inducer IPTG, inhibits the transcription of tetR by binding promoter c₁. The gene circuit has two distinct stable states, and can reversibly switch between them by changing the inducers ATc and IPTG. If an adequate promoter set c = [c₁, c₂, c₃] is selected from corresponding promoter libraries, then yEGFP can be used to track the desired behaviors generated by a reference model. In the reference model, c₁ is selected from the LacI-regulated promoter library, and c₂ and c₃ are selected from the TetR-regulated promoter library in Table 2 (i.e., c₁ ∈ Lib_LacI, c₂, c₃ ∈ Lib_TetR).

Figure 12

Simulation of toggle switch. By solving the LMI-constrained optimization problem of the H₂/H_∞ design objective Equations (3.23) and (3.24) for the synthetic gene network in Figure 11 through the library searching method, an adequate promoter set c = [c₁, c₂, c₃] = [L₉, T₂, L₈] is selected from the corresponding promoter libraries. The inducer ATc is added to the synthetic gene network at 80–160 hours to induce the gene network, and then the inducer IPTG is added at 160–240 hours. The output y(c, t) clearly produces a robust track with the desired reference output y_r(t).

Figure 13

Linear metabolic network of n molecules with intrinsic parameter fluctuation Δa_ij and extrinsic noise ν_i f_ij denotes the biochemical circuit design from x_j to x_i to improve network robustness stability and noise-filtering ability.

Figure 14

Engineered synthetic metabolic pathway for isobutanol production in E. coli. (A) Schematic representation of engineered isobutanol production pathway. (B) Engineered synthetic genetic circuit to generate the enzymes necessary for pathway in (A) for isobutanol production in E. coli.