Experimental and computational approach to establish fit-for-purpose cell viability assays.

Aim: Cell viability assays are critical for cell-based products. Here, we demonstrate a combined experimental and computational approach to identify fit-for-purpose cell assays that can predict changes in cell proliferation, a critical biological response in cell expansion. Materials & methods: Jurkat cells were systematically injured using heat (45 ± 1°C). Cell viability was measured at 0 h and 24 h after treatment using assays for membrane integrity, metabolic function and apoptosis. Proliferation kinetics for longer term cultures were modeled using the Gompertz distribution to establish predictive models between cell viability results and proliferation. Results & conclusion: We demonstrate an approach for ranking these assays as predictors of cell proliferation and for setting cell viability specifications when a particular proliferation response is required.

In recent years, there has been a surge in the amount of cellular therapy products which have been engineered to treat patients with severe diseases. These cellular products use living cells to treat the disease, and the quality of these cell products is critical for ensuring product safety and effectiveness. Throughout the process of engineering and manufacturing these cell products, many cells can die or be in the process of dying, and the amount of dead cells in the product can impact product yield and quality. In any given cell product at any given time during the manufacturing process, cells are exposed to stresses, and these stresses can injure the cells through several mechanisms, leading to a range of cell death events that can follow different timelines. There are many existing assays which evaluate the health of the cells, known as cell viability assays, and these assays can be based on many different cell features that indicate a cell has been injured (i.e., cell membrane permeability, changes in cell metabolism, molecular markers for cell death). These cell viability assays provide different insights into the state of cell health/injury based on what cell features are being evaluated and the timing at which the viability measurements are taken, and some viability assays may be more appropriate than others for specific applications. Therefore, a method is needed to appropriately select cell viability assays that are designed to evaluate injuries to cells that occur in specific bioprocess. In this series of studies, we used a range of analytical methods to study the number of living and dead cells in a series of cell populations that we treated to induce damage to the cells, reducing their ability to grow. We then used mathematical models to determine the relationship between cell viability measurements and cell growth over time, and used the results to determine the sensitivity of the viability assays to changes in cell growth. We used a specific cell line in this example, but this technique can be applied to any cell line or cell sample population and different types of injuries can be applied to the cells. This approach can be used by manufacturers of cell-based products and therapies to identify cell viability assays that are meaningful for monitoring the production of cells and characterizing product quality.

Nearly maximal information gain due to time integration in central dogma reactions.

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).

Single-cell measurement quality in bits.

Single-cell measurements have revolutionized our understanding of heterogeneity in cellular response. However, there is no universally comparable way to assess single-cell measurement quality. Here, we show how information theory can be used to assess and compare single-cell measurement quality in bits, which provides a universally comparable metric for information content. We anticipate that the experimental and theoretical approaches we show here will generally enable comparisons of quality between any single-cell measurement methods.

Information Thermodynamics and Reducibility of Large Gene Networks.

Gene regulatory networks (GRNs) control biological processes like pluripotency, differentiation, and apoptosis. Omics methods can identify a large number of putative network components (on the order of hundreds or thousands) but it is possible that in many cases a small subset of genes control the state of GRNs. Here, we explore how the topology of the interactions between network components may indicate whether the effective state of a GRN can be represented by a small subset of genes. We use methods from information theory to model the regulatory interactions in GRNs as cascading and superposing information channels. We propose an information loss function that enables identification of the conditions by which a small set of genes can represent the state of all the other genes in the network. This information-theoretic analysis extends to a measure of free energy change due to communication within the network, which provides a new perspective on the reducibility of GRNs. Both the information loss and relative free energy depend on the density of interactions and edge communication error in a network. Therefore, this work indicates that a loss in mutual information between genes in a GRN is directly coupled to a thermodynamic cost, i.e., a reduction of relative free energy, of the system.

Sparse estimation of mutual information landscapes quantifies information transmission through cellular biochemical reaction networks.

Measuring information transmission from stimulus to response is useful for evaluating the signaling fidelity of biochemical reaction networks (BRNs) in cells. Quantification of information transmission can reveal the optimal input stimuli environment for a BRN and the rate at which the signaling fidelity decreases for non-optimal input probability distributions. Here we present sparse estimation of mutual information landscapes (SEMIL), a method to quantify information transmission through cellular BRNs using commonly available data for single-cell gene expression output, across a design space of possible input distributions. We validate SEMIL and use it to analyze several engineered cellular sensing systems to demonstrate the impact of reaction pathways and rate constants on mutual information landscapes.

The role of fluctuations in determining cellular network thermodynamics.

The steady state distributions of phenotypic responses within an isogenic population of cells result from both deterministic and stochastic characteristics of biochemical networks. A biochemical network can be characterized by a multidimensional potential landscape based on the distribution of responses and a diffusion matrix of the correlated dynamic fluctuations between N-numbers of intracellular network variables. In this work, we develop a thermodynamic description of biological networks at the level of microscopic interactions between network variables. The Boltzmann H-function defines the rate of free energy dissipation of a network system and provides a framework for determining the heat associated with the nonequilibrium steady state and its network components. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible. We describe the use of Fokker-Planck dynamics to calculate housekeeping heat from the experimental data by a method that we refer to as Thermo-FP. The method provides insight into the composition of the network and the relative thermodynamic contributions from network components. We surmise that these thermodynamic quantities allow determination of the relative importance of network components to overall network control. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system that is associated with system size or modularity, and we show that the dissipative heat has a lower bound.

Quantifying the sensitivity of the network structure and properties from simultaneous measurements during photopolymerization.

We present a method that combines experimental and computational approaches to assess a comprehensive set of structural and functional evolution during a network formation process via photopolymerization. Our work uses the simultaneous measurement of the degree of conversion, polymerization stress, the change in reaction temperature, and shrinkage strain in situ. These measurements are combined with the theory of viscoelastic materials to deduce the relaxation time and frequency-dependent modulus of the polymerizing network. The relaxation time and degree of conversion are used to demonstrate the effect of processing parameters (e.g. curing protocol adjusted by the light intensity) in creating different network structures for the same initial resin. We describe experimental trends using effective medium calculations on a cross-linked polymer network model. In particular, we show that the effect of curing conditions on the spatial heterogeneity in crosslink density can be quantified using multiparametric measurements and modeling. Collectively, the present method is a way to examine holistically the complex structural and functional evolution of the network formation process.