Tissue growth and remodeling.

Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR

Long term efficacy of tissue replacements or regenerative therapies rely on the critical processes of cell proliferation and differentiation, the production of organized matrix, and concurrent tissue remodeling or growth. Recent studies have shown that mechanical and chemical factors modulate cell function which has profound implications on tissue growth and remodeling. As such, creating engineered tissue replacement options requires a detailed command of the complex, dynamic, and reciprocal interactions which occur at the cell-ECM interface. To gain a better understanding of the coupled tissue-cellular deformation response, we propose the use of cell-microintegrated elastomeric scaffolds which provide a unique platform to investigate cellular deformations within a three dimensional fibrous scaffold. Scaffold specimens micro-integrated with vascular smooth muscle cells (VSMC) were subjected to controlled biaxial stretch with 3D cellular deformations and local fiber micro-architecture simultaneously quantified. Interestingly, local cellular deformations exhibited a non-linear deformation response with scaffold strain which was attributed to unique microarchitectural morphologies. Local scaffold microstructural changes induced by macro-level applied strain dominated cellular deformations, so that monotonic increases in scaffold strain do not necessitate similar levels of cellular deformation. This result has fundamental implications when attempting to elucidate the events of de-novo tissue development and remodeling in engineeredtissues.KeywordsFiber OrientationElectrospun FiberDeformation ResponseElectrospun ScaffoldFibrous ScaffoldThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

There are many types of cell death, each involving multiple and complex molecular events. Cell death can occur accidentally when exposed to extreme physical, chemical, or mechanical conditions, or it can also be regulated, which involves a genetically coded complex machinery to carry out the process. Apoptosis is an example of the latter. Apoptotic cell death can be triggered through different intracellular signalling pathways that lead to morphological changes and eventually cell death. This is a normal and biological process carried out during maturation, remodelling, growth, and development in tissues. To maintain tissue homeostasis, regulatory, and inhibitory mechanisms must control apoptosis. Paradoxically, these same pathways are utilized during infection by distinct intracellular microorganisms to evade recognition by the immune system and therefore survive, reproduce and develop. In cancer, neoplastic cells inhibit apoptosis, thus allowing their survival and increasing their capability to invade different tissues and organs. The purpose of this work is to review the generalities of the molecular mechanisms and signalling pathways involved in apoptosis induction and inhibition. Additionally, we compile the current evidence of apoptosis modulation during cancer and Leishmania infection as a model of apoptosis regulation by an intracellular microorganism.

For urban growth modeling, assessment metrics derived from cell-by-cell comparisons are mainly related to the size of the study area and the urban growth rate. Non-urban areas always occupy an important part of the city to which cellular automata (CA) models do not contribute much, so the simulation accuracy is often exaggerated when this part is included. To enable comparing simulation results across models, regions, and time, we developed an improved equivalent area-based assessment (EQASS) method using cell-by-cell comparison metrics. As against existing assessment methods, EQASS is computed by including the same area of urban and suburban areas (i.e., equivalent areas). EQASS was tested in three Chinese coastal cities using a heuristic CA model and two spatial statistical CA models to simulate urban growth. The results show that EQASS can exclude correct rejections that are not attributable to CA models; these correct rejections have a significant impact on the model assessment. The improved assessment can better evaluate the performance of CA models across regions and over time than the conventional assessment method that accounts for the full study area. This study extends the simulation assessment method and provides a good solution for selecting the best CA model from many candidate models.

This review highlights the multifunctional properties of perlecan (HSPG2) and its potential roles in repair biology. Perlecan is ubiquitous, occurring in vascular, cartilaginous, adipose, lymphoreticular, bone and bone marrow stroma and in neural tissues. Perlecan has roles in angiogenesis, tissue development and extracellular matrix stabilization in mature weight bearing and tensional tissues. Perlecan contributes to mechanosensory properties in cartilage through pericellular interactions with fibrillin-1, type IV, V, VI and XI collagen and elastin. Perlecan domain I - FGF, PDGF, VEGF and BMP interactions promote embryonic cellular proliferation, differentiation, and tissue development. Perlecan domain II, an LDLR-like domain interacts with lipids, Wnt and Hedgehog morphogens. Perlecan domain III binds FGF-7 and 18 and has roles in the secretion of perlecan. Perlecan domain IV, an immunoglobulin repeat domain, has cell attachment and matrix stabilizing properties. Perlecan domain V promotes tissue repair through interactions with VEGF, VEGF-R2 and α2β1 integrin. Perlecan domain-V LG1-LG2 and LG3 fragments antagonize these interactions. Perlecan domain V promotes reconstitution of the blood brain barrier damaged by ischemic stroke and is neurogenic and neuroprotective. Perlecan-VEGF-VEGFR2, perlecan-FGF-2 and perlecan-PDGF interactions promote angiogenesis and wound healing. Perlecan domain I, III and V interactions with platelet factor-4 and megakaryocyte and platelet inhibitory receptor promote adhesion of cells to implants and scaffolds in vascular repair. Perlecan localizes acetylcholinesterase in the neuromuscular junction and is of functional significance in neuromuscular control. Perlecan mutation leads to Schwartz-Jampel Syndrome, functional impairment of the biomechanical properties of the intervertebral disc, variable levels of chondroplasia and myotonia. A greater understanding of the functional working of the neuromuscular junction may be insightful in therapeutic approaches in the treatment of neuromuscular disorders. Tissue engineering of salivary glands has been undertaken using bioactive peptides (TWSKV) derived from perlecan domain IV. Perlecan TWSKV peptide induces differentiation of salivary gland cells into self-assembling acini-like structures that express salivary gland biomarkers and secrete α-amylase. Perlecan also promotes chondroprogenitor stem cell maturation and development of pluripotent migratory stem cell lineages, which participate in diarthrodial joint formation, and early cartilage development. Recent studies have also shown that perlecan is prominently expressed during repair of adult human articular cartilage. Perlecan also has roles in endochondral ossification and bone development. Perlecan domain I hydrogels been used in tissue engineering to establish heparin binding growth factor gradients that promote cell migration and cartilage repair. Perlecan domain I collagen I fibril scaffolds have also been used as an FGF-2 delivery system for tissue repair. With the availability of recombinant perlecan domains, the development of other tissue repair strategies should emerge in the near future. Perlecan co-localization with vascular elastin in the intima, acts as a blood shear-flow endothelial sensor that regulates blood volume and pressure and has a similar role to perlecan in canalicular fluid, regulating bone development and remodeling. This complements perlecan’s roles in growth plate cartilage and in endochondral ossification to form the appendicular and axial skeleton. Perlecan is thus a ubiquitous, multifunctional, and pleomorphic molecule of considerable biological importance. A greater understanding of its diverse biological roles and functional repertoires during tissue development, growth and disease will yield valuable insights into how this impressive proteoglycan could be utilized successfully in repair biology.

In this study, we address the computational modeling of biological soft tissue growth, focusing on the development of epithelial tissues. The formulation of the corresponding constitutive growth law using non-linear continuum mechanics and its implementation within a total-Lagrangian-type finite element method are described. In describing the growth law, we use multiplicative decomposition of the deformation gradient into a growth part and an elastic part. We propose two surface growth deformation gradients; isotropic surface growth and anisotropic surface growth with relative shrinkage in the principal direction of maximum stress at the initial state. We first apply our laws to a hollow thick-walled hemiellipsoid that idealizes a structure generally observed in the early development of epithelial tissues. Our simulation shows the following: (i) under isotropic surface growth, the hemiellipsoid becomes sphere-like, i.e., the ratio between the longest and shortest axial length tends to 1; (ii) in contrast, under anisotropic surface growth, the tissue elongates and flattens, i.e., the ratio between the longest and shortest axial length is enhanced. These results motivate us to apply the latter growth law to the realistic example of a vertebrate limb bud, which shows similar elongation and flattening during development. As expected, our numerical simulation succeeded in reproducing the essential aspects of morphological change in the limb bud, providing a new hypothesis for the vertebrate limb development.

An important class of peptides which inhibit serine proteases are collectively known as serpins. These inhibitors are widely distributed in tissues and are involved in several processes including fibrinolysis, blood coagulation and complement activation 1,2. Members of the serpin family include plasminogen activators (PAI), protease nexins, α1-antichymotrypsin, C1-inhibitor, and α2-antiplasmin. PAI-1 is one of two inhibitors of the highly specific serine proteases, urokinase-type plasminogen activator (u-PA) and tissue-type plasminogen activator (t-PA). Balance between plasminogen activators and activator inhibitors ultimately regulates levels of activated plasmin, itself a broad spectrum serine protease. Activated plasmin, formed by u-PA or t-PA cleavage of the inactive proenzyme plasminogen, proteolytically degrades fibrin and a variety of extracellular matrix and basement membrane proteins, and may be involved in biological processes reliant upon breakdown of extracellular matrix and basement membranes, such as cell migration, invasion, tissue remodeling, and tumor metastasis2. Thus, balance between u-PA, t-PA and their inhibitors PAI-1 and PAI-2, may be critical to normal cellular and tissue growth and development, whereas their imbalance may contribute to the pathogenesis of certain tissue invasive diseases.KeywordsPlasminogen ActivatorNeurite OutgrowthThymidine IncorporationSerine Protease InhibitorBrain AstrocyteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Some of the key factors that regulate growth and remodeling of tissues are fundamentally mechanical. However, it is important to take into account the role of bioavailability together with the stresses and strains in the processes of normal or pathological growth. In this sense, the model presented in this work is oriented to describe the growth of soft biological tissue under "stress driven growth" and depending on the biological availability of the organism. The general theoretical framework is given by a kinematic formulation in large strain combined with the thermodynamic basis of open systems. The formulation uses a multiplicative decomposition of deformation gradient, splitting it in a growth part and visco-elastic part. The strains due to growth are incompatible and are controlled by an unbalanced stresses related to a homeostatic state. Growth implies a volume change with an increase of mass maintaining constant the density. One of the most interesting features of the proposed model is the generation of new tissue taking into account the contribution of mass to the system controlled through biological availability. Because soft biological tissues in general have a hierarchical structure with several components (usually a soft matrix reinforced with collagen fibers), the developed growth model is suitable for the characterization of the growth of each component. This allows considering a different behavior for each of them in the context of a generalized theory of mixtures. Finally, we illustrate the response of the model in case of growth and atrophy with an application example.

Cellular automaton simulation of dynamic recrystallization behavior in V-10Cr-5Ti alloy under hot deformation conditions

How to Explain the Differences Between Renin Angiotensin System Modulators

The bone remodeling cycle is intimately involved in the metabolic homeostasis of mineral balance.1 Bone formation and the remodeling cycle are essential in maintaining the structural integrity of skeletal tissue in response to the mechanical loading to which it is subjected.2, 3 It is likely that remodeling also provides the means of repairing bone tissue damaged as a result of mechanical fatigue.4 Thus, the skeletal cells (osteoblasts and osteoclasts) that mediate the remodeling process almost certainly are regulated by their mechanical environment. In the article by Terai et al., "Role of osteopontin in bone remodeling caused by mechanical stress"5 published in this issue of JBMR, data are presented that begin to elucidate the molecular events that regulate the remodeling cycle in response to mechanical stimulation. The authors show that induction of osteopontin (OPN) expression by osteoblasts in response to mechanical stimulation provides one of the mechanisms by which osteoclast function is coupled to a response that is regulated by the mechanical loading of the bone tissue. A brief review is presented in this perspective on the state of knowledge concerning the function of OPN in bone and other tissues. This perspective also presents the emerging picture that OPN in particular, as well as other molecules of the extracellular matrix (ECM), play essential roles in skeletal tissue structure and also mediate crucial autocrine and paracrine functions in the regulation of tissue formation and remodeling. OPN was initially characterized as one of the predominant noncollagenous proteins that accumulate in the ECM of bone tissue.6-10 Both its levels of expression relative to other noncollagenous proteins,11, 12 and its quantities of accumulation6-10, 13, 14 in the bone demonstrate that it's one of the most prevalent noncollagenous proteins synthesized by osteoblasts. The protein is ∼300 amino acids in length and shows a variable size on SDS-PAGE (∼70–45 kDa) due primarily to its differing degrees of post-translational modifications. Sequence comparisons among various species of OPN15 have identified the conserved protein domains within the mature molecule. These domains include a number of N- and O-linked glycosylation sites, a poly-aspartic acid sequence of 7–10 consecutive aspartic residues, an RGD integrin recognition site, and a number of casein kinase II phosphorylation sites.16, 17 The OPN gene is ubiquitously expressed in all skeletal tissues during embryogenesis. However, it is not exclusively restricted to skeletal tissues since it is expressed at moderately high levels within developing kidneys.18 During the in vitro growth of primary osteoblast cultures, OPN expression shows a very early transient peak of expression, after these cells initially attach to their substrate, followed by a continued rise in expression that precedes that of osteocalcin and the mineralization of the ECM.19-22 Maximal levels of OPN expression are observed when osteoblasts reach full differentiation, and regulatory elements appear to control the promoter activity in both a skeletal tissue-specific manner and in response to AP-1–related activation.15 During skeletal tissue development in vivo, OPN expression reaches maximal levels of expression during postembryological bone development.23, 24 There is now considerable evidence that osteoclasts also express25 and synthesize OPN during bone remodeling,26 suggesting that this protein plays a more important role in postnatal skeletal tissue homeostasis than in initial skeletal tissue development. Extensive ultrastructural studies of OPN distribution in skeletal tissue ECMs illustrate that the protein is distributed throughout the mineralized areas of the bone and hypertrophic cartilage ECM. It is primarily associated with electron-dense areas of matrix-containing aggregates of other noncollagenous proteins that appear between collagen fibrils in the interfibrillar spaces of the ECM.27-29 One of the most unique aspects of OPN structural organization is its very specific association with boundary surfaces of skeletal tissues. These include the cement lines that demarcate matrix–matrix interfaces, such as that surrounding osteocytes and their canalicular processes and between lining cells and the underlying bone matrix.30 Other ultrastructural studies indicate that OPN localizes adjacent to the clear zones of resorptive osteoclasts.31 It has been proposed from these structural and immunocytochemical studies that OPN may have a functional role in providing tissue cohesion within the mineralized portions of the ECM where it is ubiquitously distributed and, likewise, might promote tissue and cellular adhesion at boundary surfaces such as at cement lines, the lamatan limitantes, and sites of matrix resorption.32 Numerous functional studies have focused on defining the role of OPN in bone metabolism. It was theorized that, due to its highly anionic and phosphorylated nature and its ability to tightly bind Ca2+ and hydroxyapatite,33, 34 phosphorylated proteins in general, and osteopontin in particular, played a role in the initiation and/or regulation of the mineralization process.33-37 A number of in vitro studies, however, indicate that OPN acts primarily as an inhibitor of mineral nucleation and growth,38, 39 raising the doubt that this molecule plays an important role in the initial deposition of the mineral in the matrix. The identification of the RGD sequence in the OPN molecule and its unique ultrastructural localization at resorption and boundary surfaces30 within the ECM of mineralized tissues led to other studies focusing on the function of the protein in mediating cell–matrix interactions through its RGD binding with specific integrin receptors.31, 40 Initial in vitro studies demonstrated that OPN promoted cellular adherence of a wide variety of cells including osteoblasts,13, 14 fibroblasts,41-43 macrophages, T-cells,44 and osteoclasts.45 The initial data, indicating that OPN specifically interacted with the αvβ3 integrin isotype40 and identification of αvβ3 integrin as one of the predominant cell adhesion receptors on the surface of osteoclasts.45-47 subsequently led to studies that have documented that osteopontin interaction with this receptor potentiates resorption,48, 49 while antagonism of this receptor interaction with OPN specifically inhibits bone resorption.50, 51 Finally, other data also suggest that both OPN, as well as other RGD-containing extracellular matrix proteins such as fibronectin and bone sialoprotein, may play important roles in directing cellular migration during skeletal growth or in defining the spatial boundaries of mineral deposition within the extracellular matrix.13, 52 Thus, the preponderance of functional and structural data to date suggest that the function of OPN in skeletal tissue is more associated with tissue remodeling and providing cells with positional information within the ECM and less of an inductive role in the mineralization process. Independent research efforts in both cancer biology and immunology identified that OPN plays an important role in aspects of metastasis and immunological response. OPN is an early, immediate-response gene during both T-cell53, 54 and macrophage activation.44 In addition, it is an AP-1–inducible gene following phorbol ester tumor promoter treatment in both epithelial55 and skeletal cells.56 Finally, it is identified independently as both a serum marker of malignant cell growth57 and as a component of metastatic tumors from a wide variety of primary tissue origins in mice and humans.58, 59 Numerous studies have shown OPN expression at varying levels in many tissues in conjunction with inflammatory processes induced either in response to infection or tissue injury.59 Considerable data have been accumulated which show that OPN is associated with a wide variety of pathologies including solid tumors, atherosclerotic plaques,60 granuloma tissue formation,61 as well as exogenous pathogen infection.62 The identification of an OPN interaction with the CD44 receptor also suggests that this molecule may use an alternate cell surface receptor to discriminate varying functions in these differing immunological processes in which it is involved.63 Common functions that have been ascribed to OPN in these differing pathologies have been its chemotaxis activities and its role in promoting adhesion of monocytes, macrophages, T-cells, and B-cells.59, 62 It is interesting to note the many common aspects of the remodeling cycle of bone in comparison with the biological events associated with inflammation and tissue repair in which OPN may be involved. Previous studies from several research groups have now shown that opn expression is responsive to mechanical stimulation within osteoblasts both in vitro64-67 and in vivo.5, 68 The current study by Terai et al.5 provides yet another study demonstrating that OPN expression is induced by physiologically relevant mechanical strains in vivo. OPN induction by mechanical stimulation also occurs in vascular smooth muscle cells69 and in the egg laying organ of chickens (M. Pines, personal communication). The induction of this gene therefore appears to be a common response to mechanical stimulation in many tissues, and its induction may serve a common function in tissue ECM remodeling that is needed in conjunction with the structural demands of the mechanical environment. The intracellular mechanisms that transduce mechanical stimuli are still poorly understood. However, a number of different signal transduction mechanisms including Ca2+ transients,70 cAMP/PKA stimulation,71 alterations in the cytoskeleton,67, 72, 73 and integrin mediated74, 75 second signal cascades are all activated by mechanical stimulation of osteoblasts. Examination of the mechanisms of mechano-signal transduction that induced opn expression demonstrated that the signal transduction process was a primary response through the activation of pre-existing transcription factors, dependent on the activation of a tyrosine kinase(s) and protein kinase A (PKA) or a PKA-like kinase, and dependent on microfilament integrity. These studies also demonstrated that mechanical stimuli activated focal adhesion kinase pp125FAK,67 which specifically transduces signals from integrin receptors. Subsequent studies demonstrated that the cell adhesion to fibronectin but not tissue culture plastic alone mimics the mechano-transduction of opn mRNA expression. Furthermore, integrin receptor(s) are involved in mediating the signal transduction processes of both cell attachment and mechanical stimulation, since incubation of osteoblasts with the integrin antagonists such as RGDS partially blocked the induction of opn expression in response to both stimuli.75 The induction of the opn expression, which itself is an integrin ligand, through both integrin occupancy and/or activation by mechanical stimulation, leads to the possibility that the molecules containing integrin binding domains such as RGDS may themselves behave like cytokines. The studies showing that osteoclast recruitment is blocked by the apparent antagonism of OPN function in mechanically stimulated resorption during orthodontic tooth movement5 provides further evidence supporting the idea that this molecule behaves in many ways like a cytokine. It is interesting to note that fibronectin and bone sialoprotein are also uniquely and separately regulated by mechanical stimulation, while osteocalcin and collagen are not (Carvalho, Schaffer, and Gerstenfeld, unpublished data). Such data provide further evidence that other integrin ligands may have an autocrine or paracrine function in regulating cellular function in the ECM. Indeed, these data show that the composition of the ECM, as regulated by mechanical stimuli, may itself have a specific feedback effect on the osteoblast, thereby providing a self-regulating mechanism of controlling ECM composition. Although the structural roles of the ECM are easily appreciated, their role in regulatory functions is only now becoming fully realized with the identification of the complexity of cellular receptors that interact with the ECM.76-79 The cellular logic of using these molecules to carry out regulatory function is both implicit and explicit. The implicit informational content of these molecules resides in the context of their spatial and temporal assembly in the matrix. These molecules therefore provide "positional information." As an example, OPN is synthesized both during early assembly and remodeling steps and later during the period when osteoblasts are synthesizing and mineralizing their ECM after the resorption process has reversed. Thus, during the initial periods of remodeling, transient OPN production provides important regulatory information for the recruitment and migration of osteoclasts into an active area of resorption. The incorporated OPN in the matrix, which had been previously laid down at the end of the last remodeling cycle, subsequently provides the latent structural mechanisms by which osteoclast attachment to mineralized matrix and its activation may be facilitated during a new round of remodeling. Other positional information imparted by OPN includes its role in regulating osteoclast polarity. Explicit information contained in ECM molecules is related to their receptor specificity. The specific ECM protein interactions with unique subsets of cellular receptors provides the means by which specific second signal kinase(s) cascades are activated and cellular responses initiated. As an example, recent data indicate that cellular adhesion to αvβ3 ligands activates a specific member of the focal adhesion kinase family, PYK2, and this kinase specifically interacts with src.80 Downstream from the activation of specific signal transduction cascades are wide-ranging numbers of cellular response, including specific gene activation, stimulation of cellular replication, or induction of apoptosis.77 While this review has focused only on the function of one ECM protein, OPN, a brief description is provided of other data that demonstrate the central role of the ECM in providing regulatory signals to cells. It has been recognized for some time that collagenous matrix itself provides crucial signals during the temporal differentiation of osteoblasts and experimentally this may be observed since osteoblasts growing in the absence of ascorbate (an essential cofactor in the synthesis of collagen) fail both to develop a collagenous matrix and to progress in their differentiation.20, 81, 82 Recently, a number of studies have shown that integrin ligation of α2 receptors is necessary for the ascorbate acid mediated induction of the osteogenic phenotype in cultures MC3T3 E-1.83-85 More specifically, these studies have identified both focal adhesion kinase (FAK) and mitogen-activated protein kinases as mediating the ascorbic acid induction. In one study, the specific activation of pre-existent CBFA transcriptional factor has been shown to be the mechanism of action by which the matrix induces osteogenic progression.85 In conclusion, the data presented in this perspective present the convergence of several different lines of research that provide an emerging picture that the ECM contributes its own set regulatory signals crucial for mediation, cellular formation, remodeling, and repair. We acknowledge Drs. Marc McKee, Jeff Berman, Joel Barnes, and Paul Kostenuick for their discussions and useful insights during the course of writing this review. We thank Drs. Mark Pines and Renny Franceschi for sharing their results before publication. Preparation of this review and studies in this laboratory on the mechano-induction of osteopontin are supported by a grant from the Department of Defense Bone Health and Military Medical Readiness Program.

Mechanical models and numerical simulations in nanomechanics: A review across the scales

Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies.

2.6.4 - Micromechanical Design Criteria for Tissue-Engineering Biomaterials

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.